For an in-depth analysis of Claude 3’s capabilities and its comparison to GPT-4, refer to Anthropic’s official documentation and news announcements.

Understanding Claude 2, Claude 3, and GPT-4

All 3 of these large language models represent significant advancements in AI, but they each have unique characteristics that define their applications. Claude 2, shines in specific areas such as math, reasoning, and coding, offering robust capabilities in these domains. Its affordability and focus on producing safer and more legally compliant outputs give it an edge in certain scenarios​​​​. 

Claude 3 builds on this legacy, introducing multimodal capabilities, superior reasoning, and enhanced safety features. It competes with and surpasses GPT-4 in specific benchmarks, offering a nuanced understanding of context and refined interaction capabilities. 

GPT-4, developed by OpenAI, is renowned for its extensive language support and adaptability across various tasks. It excels in tasks like email generation, code debugging, and accessing internet data, making it a versatile tool in the AI toolkit​​.

Comparison in Key Areas:

• Training Methods: GPT-4’s training involved a massive dataset encompassing a wide array of internet text, making it incredibly versatile. Claude 2, while also trained on diverse data, puts a stronger emphasis on ethical guidelines and safety, setting it apart in applications where these factors are critical​​​​. Claude 3 has advanced training on diverse datasets, emphasizing its improved contextual understanding and ethical considerations. A common concern of enterprise customers is data security as it relates to the way LLMs are trained. Because data provided by users to the free version of these chatbots is then used to train the greater model, corporations fear the sharing of their private data on the world wide web. But it is possible to download and utilize a private instance of the LLM to ensure data security for any customer, no matter the industry or sensitivity of their information.
• Computation Requirements: The larger model size of GPT-4 demands more computational resources, which might be a consideration for projects with limited infrastructure. Conversely, Claude 2’s smaller model size makes it more accessible for such projects​​. Claude 3 is efficient and balances computational demands with advanced capabilities.
• Fine-tuning Capabilities: Both Claude 2 and GPT 4 models offer fine-tuning capabilities, but GPT-4’s larger model size and extensive training data provide a broader scope for customization, making it suitable for a wider range of applications​​.  Claude 3 offers nuanced customization options, adapting to a wider range of tasks with high efficiency.
• Performance: GPT-4 is known for its higher overall performance, especially in coding and language generation across 200+ languages. Claude 2, while having fewer capabilities, offers more affordable pricing and generates safer outputs​​. Claude 3 is a model that bridges Claude 2’s ethical focus with GPT-4’s extensive adaptability, offering breakthroughs in multimodal understanding and interaction.
• Context Length and File Formats: Claude 2 can process documents with up to 100,000 tokens, while GPT-4 has a variable token limit depending on the model version. Claude 3 extends the token limits, accommodating more extensive data analysis and supporting a broader range of file formats, including images. GPT-4’s ability to handle diverse file formats like PDFs, CSVs, and even images gives it an edge in adaptability​​.
• Ethical Considerations and Safety: Claude 2’s design adheres to ethical guidelines and is less likely to produce harmful responses, making it a safer choice in sensitive applications. Claude 3 has improved safety measures and ethical design, reducing the rate of incorrect refusals for harmless prompts. GPT-4, while powerful, lacks explicit safeguards against problematic content generation​​. 

Incorporating LLMs into Your Business Strategy

The advancement from Claude 2 to Claude 3, alongside GPT-4, represents a significant evolution in AI capabilities. Understanding the strengths and limitations of each LLM is crucial for businesses to make informed decisions. Whether it’s Claude 2’s ethical focus and safety or GPT-4’s versatility and language capabilities, each model offers unique benefits that can be harnessed for specific business needs.

Use Cases and Specialization

Both models are suitable for text and code generation. Claude 3’s extensive training makes it ideal for tasks requiring a deep understanding of context and nuance, such as advanced chatbots, language translation services, and content creation. Claude 2, with its focus on ethical AI and safety, is well-suited for applications where these factors are paramount​​​​. 

Below are some specific use cases for Claude 2, Claude 3, and GPT 4.

• Content Generation: Claude 3 excels in generating complex content such as detailed blog posts, product descriptions, and other website content, while GPT-4’s prowess lies in generating high-quality content across various industries including news, marketing, and academic writing​​​​.
• Chatbots and Virtual Assistants: GPT-4 is ideal for creating advanced chatbots and virtual assistants due to its sophisticated, context-aware interactions. Claude 2 can also be employed for chatbots, especially where safety and ethical considerations are paramount​​​​. Claude 3 enhances this capability with fewer erroneous refusals and a broader understanding of user intents, especially useful in settings requiring high ethical standards.
• Language Translation Services: GPT-4’s deep multilingual capabilities make it a strong candidate for language translation services. Claude 3 improves on Claude 2’s basic translation services by processing larger context windows and supporting more languages efficiently, making it a stronger contender for complex translation tasks.

Potential Industry Applications

• Healthcare: Like GPT-4, Claude 3’s advanced capabilities in analyzing and summarizing complex material can significantly benefit healthcare and pharmaceutical research. Its improved accuracy and multimodal features allow it to process medical research papers and visual data more effectively, providing richer insights.
• Education: Building on Claude 2’s ability to explain reasoning, Claude 3 enhances educational tools by offering deeper contextual understanding and handling a broader range of educational content, including visual materials, making it even more beneficial in learning environments.
• Legal and Compliance: While Claude 2 is noted for producing ethically aligned and safe outputs, Claude 3 extends these capabilities with better context comprehension and fewer incorrect refusals, ensuring reliable outputs in sensitive legal and compliance applications. This makes it a more robust tool for managing complex legal documents and compliance requirements.

These enhancements make Claude 3 a valuable addition across various sectors, improving upon the foundations laid by Claude 2 and competing models like GPT-4.

How AnswerRocket Leverages LLMs for Data Analysis

Claude 2, Claude 3, and GPT-4 are also revolutionizing the field of data analytics. Their transformative capabilities are effectively harnessed in AnswerRocket’s suite of tools, notably Max and Skill Studio, to deliver cutting-edge analytical solutions.

GPT-4’s Role in Advanced Data Analysis

• Comprehensive Data Processing: GPT-4’s vast training data and sophisticated model architecture enable it to handle complex analytical tasks. Its ability to process and interpret vast amounts of data is unparalleled, making it an invaluable asset in deriving deep and nuanced insights.
• Integration in AnswerRocket’s Max: Max incorporates GPT-4’s capabilities to enhance its analytical power. This integration allows Max to not only understand and process large datasets but also to interpret user queries and provide contextually relevant insights. The result is a more intuitive, conversational interface that simplifies complex data analysis.

Claude 2’s Specialized Analytical Applications

• Focused Data Analysis: While Claude 2 and Claude 3 might not match GPT-4’s breadth, they excel in delivering powerful AI analytics in specialized areas. Their structured approach to data interpretation makes it particularly effective in scenarios that require detailed and specific analytical tasks.
• Utilization in Skill Studio: AnswerRocket’s Skill Studio can leverage Claude 2 or Claude 3’s strengths to create customized analytical models. These models, or ‘Skills,’ are tailored to specific business needs, embedding unique analytical methods directly into the LLM’s capabilities. This customization ensures that businesses can utilize Claude 2 or Claude 3’s analytical power in a way that aligns perfectly with their operational goals.

Real-World Applications of Max and Skill Studio:

• Enhanced Business Intelligence: Max, with its integration of GPT-4, can transform raw business data into actionable insights. This capability enables businesses to make data-driven decisions quickly and accurately.
• Custom AI Solutions with Skill Studio: Skill Studio allows businesses to build custom AI solutions that are closely aligned with their specific analytical needs. Whether it’s predicting market trends or analyzing consumer behavior, Skill Studio equips businesses with the tools to harness the power of LLMs for their unique challenges.

Future of AI Analytics with LLMs at AnswerRocket:

• As we continue to evolve and enhance our offerings, the potential applications of LLMs in data analysis will expand. Our commitment to leveraging the latest advancements in AI ensures that AnswerRocket remains at the forefront of analytical technology.

The integration of LLMs like Claude 2, Claude 3, and GPT-4 into AnswerRocket’s Max and Skill Studio tools exemplifies the cutting-edge possibilities in modern data analytics. These technologies not only simplify complex data processing but also open doors to customized, highly effective business intelligence solutions.

Conclusion

Whether you’re a data scientist, a member of an insights team, or a CTO, understanding these LLMs is key to unlocking their potential for your business.

Let’s explore the possibilities and harness the power of LLMs like Claude 2, Claude 3, and GPT-4 to transform your data analysis and business intelligence strategies. Contact our team today to learn more about how we can tailor these advanced AI tools to your unique business needs.

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ATLANTA—Sept. 19, 2023—AnswerRocket, an innovator in GenAI-powered analytics, today announced new features of its Max solution to help enterprises tackle a variety of data analysis use cases with purpose-built GenAI analysts.

Max offers a user-friendly chat experience for data analysis that integrates AnswerRocket’s augmented analytics with OpenAI’s GPT large language model, making sophisticated data analysis more accessible than ever. With Max, users can ask questions about their key business metrics, identify performance drivers, and investigate critical issues within seconds. The solution is compatible with all major cloud platforms, leveraging OpenAI and Azure OpenAI APIs to provide enterprise-grade security, scalability, and compliance. 

“AI has been reshaping what we thought was possible in the market research industry for the past two decades. Combined with high-quality and responsibly sourced data, we can now break barriers to yield transformative insights for innovation and growth for our clients,” said Ted Prince, Group Chief Product Officer, Kantar. “Technologies like AnswerRocket’s Max combined with Kantar data testify to the power of the latest technology and unrivaled data to shape your brand future.”

Following its March launch, AnswerRocket has been working with some of the largest enterprises in the world to solve critical data analysis challenges using Max. Highlighted applications of the GenAI analytics assistant include:

• Automating over a dozen analytics workflows for a Fortune 500 global beverage leader, reducing time to insights by 80% and empowering decision-makers to respond quickly to market share and brand equity changes with data-driven action plans.
• Helping a Fortune 500 pharmaceutical company generate groundbreaking insights revealing the direct impact of sales activities on market share.
• Empowering a global consumer packaged goods leader to quickly respond to macro market trends by generating insights from unstructured market research alongside structured company performance analysis.

“Today’s enterprises demand instant insights, and the traditional methods are no longer sufficient on their own,” said Alon Goren, CEO, AnswerRocket. “Max is enabling several of the world’s most recognizable brands to understand better what’s driving shifts in their business performance, effectively turning their vast data lakes and knowledge bases into a treasure trove of business insights.”

Max’s advanced capabilities solidify its position as the first GenAI assistant for data analysis built for the enterprise. Enhancements to the solution include:

• Customizable Analyses: Out-of-the-box Skills used by Max for business performance analysis, including search, metric drivers, metric trend, competitor performance, and more. AnswerRocket also offers support for custom Skills using enterprises’ own models. Skills can be configured to reflect unique business rules, processes, language, outputs, etc. 
• Structured and Unstructured Data Support: Max supports both tabular and text-based data analysis, allowing companies to glean insights from vast enterprise data, documents, and multiple data sources seamlessly in a single conversation.
• Automation of Routine Analysis Workflows:  Max can execute multi-step analytics processes to free up analyst time for more strategic projects while giving business stakeholders timely analysis and self-service answers to ad hoc follow-up questions.
• Integration with Third-party Tools: Embed the Max chat experience into tools like Power BI, Slack, Teams, and CRMs, enabling users to analyze their data in tools they’re already using.

“Max brings forward a seismic shift in how companies can transform their data into actionable intelligence with unprecedented speed,” continued Goren. “With Max, everyone within the enterprise can have immediate access to an AI analyst, providing them with prescriptive recommended actions and helping to guide them towards data-driven decisions.” 

AnswerRocket is a Platinum Sponsor of Big Data LDN, taking place on September 20-21, 2023 at Olympia in London. They will be showcasing their revolutionary GenAI analytics assistant, Max, alongside early adopters of the technology in three sessions:

• Wednesday, September 20 from 4:40 – 5:10 p.m. – How CPW Scaled Data-Driven Decisions with Augmented Analytics & Gen AI (Chris Potter, Global Applied Analytics, Cereal Partners Worldwide; Joey Gaspierik, Enterprise Accounts, AnswerRocket)
• Thursday, September 21 from 2:40 – 3:10 p.m. – How Anheuser-Busch InBev Unlocked Insights on Tap with a Gen AI Assistant (Elizabeth Davies, Senior Insights Manager, Budweiser – Europe, Anheuser-Busch InBev; Joey Gaspierik, Enterprise Accounts, AnswerRocket)
• Thursday, September 21 from 4:00  – 4:30 p.m. –  Maximizing Data Investments with Automated GenAI Insights (Ted Prince, Group Chief Product Officer, Kantar; Alon Goren, CEO, AnswerRocket)

For more information on AnswerRocket’s industry-leading solutions, please visit: https://answerrocket.com/max.

About AnswerRocket

Founded in 2013, AnswerRocket is a generative AI analytics platform for data exploration, analysis, and insights discovery. It allows enterprises to monitor key metrics, identify performance drivers, and detect critical issues within seconds. Users can chat with Max–an AI assistant for data analysis–to get narrative answers, insights, and visualizations on their proprietary data. Additionally, AnswerRocket empowers data science teams to operationalize their models throughout the enterprise. Companies like Anheuser-Busch InBev, Cereal Partners Worldwide, Beam Suntory, Coty, EMC Insurance, Hi-Rez Studios, and National Beverage Corporation depend on AnswerRocket to increase their speed to insights. To learn more, visit www.answerrocket.com.

Contacts

Vivian Kim
Director of Marketing
vivian.kim@answerrocket.com
(404) 913-0212

The post AnswerRocket’s GenAI Assistant Revolutionizes Enterprise Data Analysis first appeared on AnswerRocket.

Watch the video below or read the transcript of the interview to learn more.

How is AnswerRocket making these emerging technologies enterprise-ready?

Mike: I would start simply by saying that the idea of keeping data secure and providing answers that are of high integrity is table stakes for an enterprise provider. Making sure that users who should not have access to data don’t have access to it and that the data is never leaked out, right? That’s table six for any software at the enterprise. And so it doesn’t change with the advent of the AI technology. So AnswerRocket is very focused on ensuring that data flowing from the database to the models, whether it’s the OpenAI models or other models of our own, does not result in anything being trained or saved. So that it could be used by some third party, so that it could be leaked out, so that it could be taken advantage of in any way other than its intended purpose. So that’s a core part of what we offer. 

The flip side of that is, as you mentioned, many of these models are sort of famous at this point for producing hallucinations, where when you under specify what you ask the model, you don’t give it enough information, it fills in the blanks. It’s what it does, it’s generative, right? The G in generative is what makes it want to fill in these blanks. AnswerRocket takes two steps to ensure that doesn’t happen. First of all, when we pose a question to the language model, we ensure that the facts supporting that question are all present. It doesn’t need to hallucinate any facts because we’re only giving it questions that we have the factual level answers for so that it can make a conversational reply. The second thing we do is when we get that conversational reply, like a good teacher, we’re grading it. We’re going through checking every number, what is the source of that number, is that one of the numbers that was provided? 

Is it used in the correct way? And if so, we allow that to flow through and if not, we never show that to the user, so they never see it. A demonstration is not value creation, right? A lot of companies that just kind of learned about this tech are out and are out there demonstrating some cool stuff. Well, it’s really easy to make amazing demonstrations out of these language models. What’s really hard is to make enterprise solutions that are of high integrity that meet all of the regulatory compliance requirements that provide value by building on what your knowledge workers are doing and making them do a better job still. And so that’s very much in the DNA of AnswerRocket. And it’s 100% throughout all the work that we do with language models. 

How can enterprises avoid data leakage and hallucinations when leveraging GPT?

Pete: A lot of the fear that you hear people saying, oh, I’m going to have leaking data and so on, a lot of that’s just coming from ChatGPT. And if you go and read the terms and conditions of ChatGPT, it says, hey, we’re going to use your information, we’re going to use it to train the model. And it’s out there. That’s where you’re seeing a lot of companies really lock down ChatGPT based on the terms and conditions that makes sense. But when you look at the terms and conditions of, say, the OpenAI API, it is not using your data to train the model. It is not widely available to even anybody inside of the company, it’s removed in 30 days and so on. So those are much more restrictive and much more along the lines of what I think a large enterprise is going to expect.

You can go to another level. And I think a lot of our, what we’re seeing is a lot of our customers, they do a lot of business with, say, Microsoft. Microsoft also can host that model inside the same environment that you’re hosting all your other corporate data and so on. So it really has that same level of security that if you trust, say, Microsoft, for example, to host your corporate enterprise data, well then really trusting them to sort of host the OpenAI model is really on that same level. And what we’re seeing is large enterprises are getting comfortable with that. And in terms of hallucinations, as Mike said, it’s really just important how we use it. We analyze the data, we produce facts, and there are settings in these large language models to tell it how creative to get or not. And you say, don’t get creative, I just want the facts, but give me a good business story about what is happening. 

And then we also provide information to the user that tells them exactly where that information came from, traceable all the way down to the database based, all the way down to the SQL query so that it’s completely audible in terms of where the data came from and being able to trust. 

In Conclusion

Analytics and Insights leaders who wish to utilize ChatGPT technology in their organizations have to balance the possible rewards with the risks. We’re committed to providing a truly enterprise-ready solution that leverages the power of ChatGPT with our augmented analytics platform to securely get accurate insights from your data. By providing AI models with complete and correct supporting facts, we can eliminate the possibility for hallucinations and maintains full control over the generated responses in the platform. Furthermore, we use astringent grading process to validate the AI-generated insights before presenting them to the users. 

The post Preventing Data Leaks and Hallucinations: How to Make GPT Enterprise-Ready first appeared on AnswerRocket.

Watch the video below or read the transcript to learn more.

Ryan: My name is Ryan Goodpaster. I’m one of the sales guys here at AnswerRocket. Been with the company for six years. And what we do is we help our customers get insights out of their enterprise data in seconds, using techniques like natural language processing, natural language generation, AI, and machine learning, model integration, and now GPT. 

How does AnswerRocket help CPG’s accelerate data analysis?

Ryan: We’re fairly industry agnostic, but we have a pretty heavy focus in consumer goods. We help them with a lot of their internal data, their third party data, like Nielsen and IRI and Kantar. This data is really important to a lot of departments, so they see a lot of value across lots of different business areas in their companies. 

How does AnswerRocket uncover insights from syndicated market data?

Ryan: Every month, the Nielsen data updates. And for the most part, it takes a company maybe a week or two to get through a comprehensive analysis of what’s going on with their business. With AnswerRocket, you can do a full deep dive in seconds, and you don’t have to wait a week or two. So you get those insights much faster and you can really figure out what to do, what actions to take with those insights, and get that to their customers even quicker. 

Why are customers excited about Max, our AI assistant for analytics?

Ryan: Most everybody that I’ve talked to is really excited about Max. They want Max yesterday. So we are working very hard to deliver that to them, especially our current customers. I don’t see very many people being scared of it, because it’s one of those things where you have to get on board with it or you’ll get left behind, right? Everybody and every company that I’m talking to is trying to figure out how do we leverage GPT, not only for analytics, but across the entire organization? How do we make ourselves more efficient in these current times? 

Conclusion: AnswerRocket’s industry-agnostic approach is particularly beneficial for consumer goods companies, where they provide valuable insights by leveraging both internal and third-party data sources like  Nielsen and IRI. Our solution enables CPGs to accelerate data analysis, allowing them to dive deep into syndicated market data within seconds, facilitating quicker decision-making and actions based on the obtained insights. Customers are excited about Max, which is powered by GPT, as it promises enhanced efficiency and competitiveness across organizations. 

The post Transform CPG Analytics with AnswerRocket: Max, the AI Assistant for Accelerated Insights first appeared on AnswerRocket.

Watch the video below or read the transcript to learn more.

Ryan: My name is Ryan Goodpaster. I’m one of the sales guys here at AnswerRocket. Been with the company six years. And what we do is we help our customers get insights out of their enterprise data in seconds, using techniques like natural language processing, natural language generation, AI and machine learning, model integration, and now GPT. 

What problem does AnswerRocket help solve?

Ryan: Most of our customers, when they first come to AnswerRocket, they’re leveraging something like a dashboard to track their business performance. And when they see something has changed, a number has gone up or down, their next question is typically, why? And it takes a long time to answer that. It’s a lot of manual analysis. And so we leverage AI and machine learning and now GPT to take a natural language question like, why are my sales down? And help them get an answer to that in seconds so that they can spend more time doing what they were hired to do. And that’s being human and coming up with solutions and strategy, not digging through data. 

How does AnswerRocket improve enterprise data analysis?

Ryan: The solution to the problem of “it takes too much time to get through data” is I need to hire more analysts. But what I’m sure everybody has seen in the marketplace and in the talent pool is there’s just not enough analysts to hire. Right? And it’s hard to retain them. So by giving them things that make them more efficient and get them better answers to serve up to their customers, you have happier analysts. They’re more efficient, and you don’t have to hire as many. You get more production out of all of the talent that you’ve already got with all of the business knowledge that they’ve learned over the years. 

Why are customers excited about Max, our AI assistant for analysis?

Ryan: Most everybody that I’ve talked to is really excited about Max. They want Max yesterday. So we are working very hard to deliver that to them, especially our current customers. I don’t see very many people being scared of it, because it’s one of those things where you have to get on board with it or you’ll get left behind, right? Everybody and every company that I’m talking to is trying to figure out, how do we leverage GPT, not only for analytics, but across the entire organization? How do we make ourselves more efficient in these current times? 

Conclusion: AnswerRocket’s analytics AI assistant, Max, powered by GPT, excites customers as it promises to enhance efficiency and effectiveness across organizations. Organizations recognize that embracing AI and GPT is crucial for staying competitive in today’s dynamic landscape. By leveraging GPT and AI technologies, companies can stay competitive and optimize their operations while making data-driven decisions swiftly and effortlessly. 

The post Discover AnswerRocket: Unlocking the Power of AI for Your Enterprise Data Analysis first appeared on AnswerRocket.

Watch the video below or read the transcript to learn more.

How has GPT enabled AnswerRocket to create an AI assistant for data analysis?

Pete: Look, we started with a pretty lofty vision that we wanted to be able to provide this thing that’s an intelligent agent to help business users with data analysis. And we started the company in 2013 with basically natural language search. We moved into automation of analysis, incorporating data science and machine learning. We moved into generating stories to go along with that, to help business users understand what was going on under the covers. And so what GPT has done for us is a few things. One is it’s made it even easier to understand what it is that the user is asking for and how we can quickly solve the problem for them. It’s making the narrative that we build even richer, more meaningful and understandable by the business user. What I would say is it’s opened up the door to really, first of all, bring us much closer to that vision of this intelligent agent number one, because of the user experience. But number two, what it’s done is it’s really opened our eyes to oh, this is how we’re going to unlock. 20% of the data that companies have is in these structured databases, 80% are in these unstructured sources. And so what large language models are really good at is understanding these unstructured text oriented documents, emails, PDFs and so on. And so what that does for us is it opens up our ability to provide a much more robust analysis to the users much more broadly than we can do just out of what data happens to be cleansed in a structured database. And so it significantly enhances our ability to provide a much more meaningful insight that business can act on to drive their business forward. 

How does GPT serve as a bridge between users and their data?

Mike: AnswerRocket has the ability to gather all of the right data to produce an answer and the business user has in mind a question that they want answered. GPT bridges the gap between those two. It allows the understanding of the question that the user has in mind and it allows the retrieval of the information that AnswerRocket can provide. So it’s that perfect bridge and it serves both to help AnswerRocket understand that business user, but then it also helps the business user to in turn understand AnswerRocket back. That combination is really what makes something that might have been a dashboard retrieval in the past become a conversation in the future. It’s what really transforms, let’s call it an extraction with analysis and maybe a report into an engagement with an analyst, with an agent that’s working on your behalf. Ultimate vision for AI assistance. So fundamentally we would like for the AI to feel like a collaborator, like somebody else on the team who can be sent away to do a task, research, summarize, make conclusions, build a presentation and return that back for evaluation. So essentially every human employee becomes an executive and that executive is managing a resource which is a set of AI agents that’s the vision where we think it’s going, then that happens. Of course, as the models get larger. GPT-4 is an example. It’s able to take in more data, simultaneously begin to more closely approximate the decisions people would make. Also, as the models get trained on deeper concepts in business, they become more able to provide a level of expertise that they don’t have. Now, let’s face it, these models have expertise around crafting language, around understanding history, the topics that they would find if they were searching through the training data that were provided, which is off the Internet and contracts and a few other things. As these models get trained more specifically on problems within businesses, we’ll see them go from passing the SAT to passing, let’s say, a very sophisticated test that a business might give to an executive who is a category manager or who is somebody who manages pricing, somebody who’s in charge of purchasing, right? So these agents can become much more like a partner to those people and less like a simple tool that’s used to refer to facts. 

Are we finally getting our own J.A.R.V.I.S.?

Pete: If you remember the Tony Stark movies, he has this assistant, J.A.R.V.I.S., that helps him do all sorts of things, right? If you asked me a year ago how far away from having that, I would have said probably something like 20 years. And I don’t know where it is now, but I can tell you it feels a whole lot closer than it did at that time. And that’s really what I think at the end, we sort of aspire to is that level of capability, that level of intelligent agent that’s helping people at whatever level they are in the company, whether they’re just managing Google Ads or whether they’re running entire sets of operational components of the business that they have that level of assistance that’s really knowledgeable about their business can help them map out scenarios and can help them really start to think about making recommendations about what should be done. I think we’re much closer to that than ever before.

Conclusion:

Through the incorporation of GPT and advanced language models, AnswerRocket has embarked on a transformative journey, creating an intelligent agent that understands users’ needs and effortlessly retrieves essential information. With an eye towards the future, AnswerRocket envisions a world where AI is not just a tool but a collaborative AI assistant for every business executive, providing comprehensive analyses, strategic recommendations, and expert-level insights. 

To learn more about what AnswerRocket is doing in the analytics space with generative AI, visit AnswerRocket.com/Max.

The post Unleashing the Power of GPT: From AI Assistants for Analytics to our own J.A.R.V.I.S.? first appeared on AnswerRocket.

Watch the video below or read the transcript to learn more.

Is Max dependent on GPT or can other LLMs be used?

Mike: So it’s 100% flexible to use lots of different versions of GPT, or lots of different locations where the language models are stored, or lots of different language models. So we look at the language model very much like a database, something that over time will become faster and cheaper and more commoditized and we want to be able to swap in and out whatever those models are over time, so that we’re not dependent on it, on any one. We do use every capability that’s available to us from the language models, things like completions and embeddings, these are technical terms of the capabilities of the models and we will look for those same capabilities as we expand into additional models. But it’s not a dependency for our solution. And in fact there is a mode where AnswerRocket can run, in fact has run, until about six months ago when these language models were introduced. 

That does not rely on external language models at all, right? It relies instead on the semantics of the database, on the ontology that’s defined by a business and how they like to use their terms. And so it does not rely on having to have a GPT source. But when there is a language model in the mix, you get a more conversational flow to the analysis which makes it feel a lot more comfortable to the user. It’s clear that from a foundation model perspective, the providers of the core algorithms behind these models, there will be models that are specific to medical, that are specific to consumers, that are specific to different industries and different spaces. And so we very much expect to be able to multiplex across those models as appropriate for the use case and again treat them like any other component of infrastructure, whether that’s storage or database or compute. 

These models just become one more asset that’s available to enterprise applications that are really putting together productivity suites for the end user. 

Conclusion: AnswerRocket is not solely dependent on GPT; in fact, it initially operated without relying on external language models, using database semantics and business-defined ontologies. However, when language models are incorporated, it enhances the user experience, enabling a more conversational flow in data analysis. The focus is on leveraging the diverse capabilities of language models while treating them as components of infrastructure alongside storage, databases, and compute resources. Analytics and Insights experts like Mike foresee a future with specialized language models catering to various industries. The aim is to provide enterprise applications with enhanced productivity suites for end-users by multiplexing across different models as needed for various use cases.

The post The Future of Language Models in the Enterprise: A Multi-Model World first appeared on AnswerRocket.

Will One Large Language Model Rule Them All?

While single-model language systems have been groundbreaking, they have limitations such as biases, inflexibility in handling different tasks, and the risk of overfitting. The multi-model approach offers a solution by combining the strengths of multiple models. By using different models for specific tasks, businesses can enhance their data analytics capabilities and overcome the limitations of relying solely on a single dominant model.

The Evolving Landscape of Large Language Models

In the near term, it is unlikely that a single large language model will emerge as the dominant player. Instead, we can expect a range of models tailored for specific tasks and excelling in their respective domains. Looking further into the future is challenging, but ongoing development will continue to improve both domain-specific and general models. Companies will invest in building robust large language models that cover a wide array of knowledge and information. Instead of narrowing their focus, these models will incorporate multiple domains and subjects, creating a diverse ecosystem of models.

A Nuanced Approach: Leveraging Multiple Models

A multi-model approach recognizes the limitations of a single dominant model and embraces the diverse capabilities offered by different models. By combining domain-specific and general models, businesses can achieve more accurate and contextually relevant language understanding and generation. This approach allows for the utilization of smaller, specialized models that excel in specific areas, providing more relevant insights.

The Benefits of Multiple LLMs in a Multi-Model Language System

Multi-model language systems integrate multiple Large Language Models (LLMs), each specializing in different areas. This approach offers several advantages:

– Businesses can leverage the unique strengths and expertise of each model. For example, one model may excel in generating creative content for a digital marketing agency, while another might be proficient in assessing market trends for a consumer goods manufacturer. This blending of models enables businesses to achieve comprehensive language processing capabilities.

– The potential applications of multi-model language systems are vast. In the pharma sector, integrating LLMs trained on medical literature, patient treatment therapies, and clinical trial data could facilitate drug research, development, and improved patient outcomes. Likewise, in insurance, combining models trained on claims data, policies, and regulatory documents could enable accurate predictions, effective risk management, and regulatory compliance.

The Future of Multi-Model Systems

The future of multi-model systems is incredibly promising. Ongoing research and development will likely lead to even more advanced capabilities and increased efficiency. Businesses are progressively adopting multi-model approaches to leverage their data analytics endeavors.

However, it’s crucial to acknowledge the challenges and limitations. Developing and refining multiple Language Learning Models (LLMs) require significant resources and expertise. Ensuring seamless integration and addressing biases inherent in individual models are complex tasks.

Multi-model language systems are reshaping the world of large language models. By combining the strengths of multiple LLMs, businesses can unlock new levels of language understanding and generation. The advantages of a multi-model approach over a single-model system are clear: enhanced capabilities, broader applicability, and improved performance. Executives in the data analytics space should recognize the potential of multi-model language systems and incorporate them into their AI strategies. Continued research and development will be vital in harnessing the power of a multi-model world and shaping the future of data analytics. Embrace the potential, explore the possibilities, and embark on the journey towards a multi-model future.

The post The Future of LLMs: Embracing a Multi-Model World first appeared on AnswerRocket.

You can listen to Mike’s episode of Inside Analysis on Apple Podcasts or Spotify. You can also watch the video below on YouTube.

On this episode of the podcast, Eric sits down to talk with our very own Mike Finley about all things chatbots, large language models, and generative AI. 

AnswerRocket has long been using natural language to help businesses have conversations with their data. With the emergence of OpenAI and ChatGPT, this capability has taken a huge leap forward. This is because LLMs are really good at:

1. Understanding what humans mean.
2. Understanding what they are trying to achieve.

How is this different from how we’ve traditionally gleaned insights from our data?

For years, we’ve made humans learn how to talk like computers, and speak in computer code. We can now finally ask a question in our language and get an answer back in our language. That is the true benefit of large language models and generative AI.

Mike makes a couple of great points about working with LLMs: 

You have to to “treat it like a human coworker…you have to train them on your business, make sure they are an expert in that area that you’re talking about, and you would fact check their results…”

LLMs like GPT are great because it knows a lot about general things, but it doesn’t know anything specific about your business and your data. 

This is where Max comes in. Max is a co-pilot for your business that helps with AnswerRocket’s BI initiatives.

AnswerRocket’s Max can dive in and understand your business and your data while GPT can communicate those insights in plain English with end users. 

While organizations may have concerns about sharing their data with the most used chatbot in the world, Mike assures us that the Max solution and its connection with OpenAI is built for enterprises.

→ When we connect to OpenAI on behalf of customers, we use a private instance purchased just for the customer, it’s just as private and secure as anything else you might be using in the cloud.

→ Max comes with all the features of an enterprise level BI tool such as role management, security, role-level data isolation, etc. 

The implications of this type of technology are huge for businesses looking to get valuable insights quickly. As Mike points out, large language models or “intelligence on tap” is the ultimate power tool for business. 

Visit answerrocket.com/max/ to learn more exploring and analyzing your data 10x faster with AI. 

The post Mike Talks Max on Inside Analysis Podcast first appeared on AnswerRocket.

Fast forward to the launch of ChatGPT in November 2022. The AI landscape has evolved leaps and bounds in just a few short months and presented a unique opportunity to organizations of all industries to consider how they would take advantage of the technology. 

We sat down with Alon to get his insights on ChatGPT, large language models, and the evolution of data analysis. He shares how AnswerRocket has layered in ChatGPT with AnswerRocket’s augmented analytics software to create a conversational analytics AI assistant for our customers.

Read the transcript of that interview below.

Question: Why was AnswerRocket started?

Alon Goren: We started AnswerRocket with the idea that anybody should be able to get easy answers from their data and it should be as easy as interacting with a personal assistant. That whole idea came from the frustration of sitting in many meetings where a discussion was had around some critical thing being presented, whether it was a board meeting or a management meeting. A PowerPoint was presented with “here’s the reason why we should do X.” 

Inevitably there were follow up questions that couldn’t be answered by the PowerPoint. There were requirements to say can we go out and do analysis? And those would take days or weeks. And that felt very wasteful. It felt like the data is there, why can’t we just go out, ask the question of the data and get back the response? We wanted that experience to be something that was available to everybody in the enterprise. 

Question: What does the current AnswerRocket offering include?

Alon Goren: The current AnswerRocket offering is kind of a full pipeline that starts with connecting to data sources and then the end product is some kind of an automatic visualization narrative in response to a user question. 

So along the way, the technology we have to build is certainly connecting to a wide range of data sources, including all the major data cloud providers. We built a pipeline that starts with a natural language question that the user is posing, breaks that down to an understanding of how to query the underlying source. Sometimes the analysis requires us to do further things than just querying the database. It requires us to do a forecast or some kind of a machine learning based algorithm to answer the user’s ultimate question. Then the presentation of that answer is in the form of a chart, a narrative, a combination of both those things. The technology to achieve all those are part of the kind of the AnswerRocket modules. Now, when we get into enterprise deployments, which is our core market, there’s lots of surrounding stuff that you have to do so around security and authentication and robustness for enterprise deployment. There’s lots of infrastructure that comes along for the ride. The differentiated modules are kind of at the heart around deep analysis of underlying data and presenting sophisticated answers, but in an easy to read way. 

Question: How has the data and analytics space evolved in the last decade?

Alon Goren: AnswerRocket was founded almost ten years ago and since then a lot has happened in the space, a lot has happened with technology in general. I kind of pointed out, I guess, several things. One is the number of data sources that are accessible to enterprise users have grown tremendously. It used to be the case that maybe there was a corporate data warehouse with some critical ERP kind of information in it, maybe basic sales information, but over the years it’s grown to the point where any interaction that happens in the enterprise, most of those interactions are captured digitally, and those interactions can be made into data. 

So whether you think about website interactions or HR interaction, customer experience interactions, any of those things usually leave a trail of data behind them. There are more and more digital products or applications that are used by the enterprise, the number of applications for enterprise has probably doubled in those ten years. What we see is just a diversity of kinds of data sources that are accessible, and the need therefore, to accommodate all of those. 

The second thing that’s really interesting is the pressure to get answers out of your data in a self service mode has probably increased over time. As the data sets grow, as the kind of questions that could be asked have grown, it puts more and more pressure on the data science team or the data analytics team to field those requests by business users. Because of that pressure, it’s impossible to keep up with that demand. 

And so, self-service in theory is the way to solve that problem, where users can ask their own questions and get their own answer. That started with a movement to visualize data with dashboards. Over the years, what’s happened is the proliferation of dashboards has really made it hard for users to find what they’re looking for because they have to understand, “Well, which of the hundred dashboards that I have accessible is the answer actually in?” That evolution of everyone essentially is their own analyst to some degree is a change in the space that technologies have to keep up with. Most significantly, the recent inflection point in large language models has created an opportunity to start dealing with users’ questions in the most natural possible way in terms of language and the response to those questions. I would say the natural language technology stack has really hit that part of the growth curve where everything now appears. 

There’s going to be massive disruption and massive changes in the ability to answer users underlying questions. 

Question: Why is ChatGPT a revolutionary technology for knowledge workers?

Alon Goren: Technology like ChatGPT is going to have a huge impact on technology, broadly on knowledge workers, probably broadly in many ways for us at AnswerRocket, because we started this journey ten years ago looking for a way to essentially make a solution that feels more like an assistant than a software tool. We’ve been in this mode of trying to understand how we can harness language models and other aspects of natural language processing to achieve that mission. What we see now is that, as is evidenced by the growth of ChatGPT users, that there is a huge appetite for interaction, kind of this natural language level. Right? Before, I would say before the launch of ChatGPT, it was more of an interesting, maybe in academia circles, like the idea of how well is natural language evolving? What problems can it tackle? 

Once ChatGPT hit the public web and a million users had access to it within the first week, and something on the order of magnitude of 100 million users have accessed it over time, it has changed the way, I think, the perception of what natural language can achieve. Not just in the sense of “can a machine tease apart what the sentence means, but can a machine carry on a conversation to some productive end?”

Which I think is the biggest kind of revelation with a chat-style interface is that it’s not just about the initial question, it’s about the context of that question phrase and the follow up opportunities to explain what’s in the answer and refine it. So that technology is tremendous. I think it’s going to have a broad impact, not just in analytics, but in any knowledge worker type of tasks where if your interactions to accomplish a job is with a computer, you have to ask the question, well what could that computer be doing for me in a way that doesn’t require me to understand where the buttons and the menu options are in order to achieve whatever I’m trying to do? 

Question: How does AnswerRocket use ChatGPT’s large language model?

Alon Goren: We span so many different data sources that a user can connect to and so many different systems that the kinds of questions they can ask are very broad. Our ability to then tackle those questions through the usage of a large language model where we’re not just confined to, “oh, the underlying data that to your question says that the right answer is the number x”, but rather it’s a story that explains what’s going on in the data. 

So, for instance, asking a question like, let’s say you work in a consumer goods company and you’re a multinational and you want to know about what’s going on in Southern Europe, how well are we doing versus the competition, that kind of a broad statement implies that there’s an understanding of this competition. 

• What’s “my” brands? 
• What’s “their” brands? 
• How do I measure performance? 
• Is it in currency, is it in share, is it in share of volume? 

Those are all variables or interesting kinds of KPIs that you can’t answer that question. We use a language model, it lets us back off the idea of saying all the information that’s in the data has to be queried very specifically and narrowly. The final number is X to more of an assessment that says, “oh, we understand in this data set that we have here’s how your business is presented and here’s how the competition presented.”

We’ve gone through that data set and in fact, looked at all things that are of interest to you based on a process where you tell us what you care about. Now we can pull from that information and weave together a story that combines information from any of those kinds of analytics. Not only that, but we actually combine that information with any other information that you have potentially connected us to. For example, if you have PowerPoints or PDFs or other documents or websites that incorporate interesting information that relate to the ultimate problem that you’re trying to solve, those are now accessible. Not just accessible, but summarizable in the same process of looking at your underlying data sources. 

You get a much richer story about how things happen and you can have that experience of asking and receiving that story and refining what you’re looking for in a natural language kind of way. 

Question: What are the challenges with GPT and other large language models?

Alon Goren: There are many challenges with large language models. They are moving targets though. The kinds of things that we see as challenges today, the techniques by which we solve them will evolve over time. 

Kind of a snapshot today would be core issues are:

Hallucinations, which is the idea that the model essentially gives you information that is what you would consider fictional, right? The language models; what they understand is whatever they’ve read and they’ve read a lot of fiction and nonfiction in the course of essentially reading the entire web. The model doesn’t distinguish between those two things per se as far as it’s concerned, you’re asking it to tell a story and it’s going to tell a story and sometimes it’s a fiction writer and sometimes it’s a nonfiction writer just depending on the best resources that it found to answer the question. 

In that, our challenge is to make sure because people are asking for factual information from us, right, they want to know what’s going on in the real world, not in some fiction, so we make sure we put the right kind of pre- and post-processing to the natural language model. That means when we ask the question, we provide context to say here is relevant information that you should use in answering your question. In post-processing, meaning we look at the answers that it provides and examine it for truthfulness in terms of does it connect back to the facts. So that is a core challenge. Now, outside of how effective the language model is doing that work, there are things like price and performance that will continue to improve. 

There are, let’s say, other technological aspects to it that are a moving target in terms of the kind of information that the model has access to and how to connect to it. For instance, in this recent week actually OpenAI introduced the concept of plugins, which is the idea that you can take a chat experience and extend it, almost like if you think of an app store that lets you download things to your phone or browsers to let you connect plugins. The language model itself serves as a basis for having a conversation across a lot of information that it has. For instance, it doesn’t know real time stock prices, it doesn’t know how to place an order online. Those are things that can be achieved through usage of plugins, meaning that the model has to be taught that if the user is asking to book an appointment somewhere, what tool do I need to achieve that result? 

The extension of these models is a very critical area that’s I would say fairly nascent at the moment. We expect that area to grow by a lot in terms of the sophistication and the kinds of things that the models can achieve. AnswerRocket sits in this interesting position where we want to use the model as the basis for the conversation, but we want to augment it both in the sense of providing the tools to answer questions and those tools can appear in the form of a real time interaction with AnswerRocket APIs. Another mechanism is to actually retrieve information and use that as the context. These techniques are called tool augmentation and retrieval augmentation. There are ways of extending what a model can do given that the model is trained on some generic but very broad set of data. The kind of challenges that we face today are engineering challenges by and large of wrestling the existing language models into doing our bidding. 

It takes energy to make it suitable for enterprises and our enterprise customers in terms of the end results they get. It doesn’t feel like they’re having a conversation that’s partially fiction, partially nonfiction.

Question: Where will AI and data analysis be in 5 years?

Alon Goren: The pace of the technology and the change in technology for language models and chat experiences is such that there’ll be huge pressure to create very narrow answers or narrow solutions that are really deep for certain fields, right? It’s easy to imagine a world where instead of having one large language model or several large language models that are very broad, those then get operationalized or customized for various use cases. Having an assistant that helps you deal with data analytics could be one of ten assistants that you talk to. They all maybe share some common interface where it’s a team that’s helping as opposed to an individual, but it’s all accessible, let’s say, with the same kind of chat paradigm. 

Those deep models, you can imagine each model becoming better and better at serving its users. In our space, we would imagine that if you’re an ecommerce company and you’re trying to do analysis on promotions right. That is probably powered by a bot that’s learned a lot about the ecommerce space and learned a lot about promotional activities and customer behavior, which could be very different from, let’s say, the kind of bot that you’re talking to if you’re trying to do planning for a wedding. Both those scenarios are equally valid in terms of can you have an assistant that helps you do tasks, basically? Anywhere where there is a computer centric task. You have to ask the question, what would a really smart assistant who had access to all the information that it needed to make recommendations me? What could it do for me? And then the possibilities are somewhat endless. 

Now, how fast can we realize that vision? It appears that right now, based on the improvements, so if you look at the technology side of it, the capabilities, both in terms of the kinds of information that’s available through a chat bot and the speed at which it operates, those are growing kind of a Moore’s Law or better kinds of numbers. We’re talking about doubling every year or so. That’s because both the hardware and the software are improving in this case, right? Both the algorithms are getting more efficient and the hardware that they’re running on, GPUs, is becoming faster. You get this kind of effect that multiplies those two improvements and that unleashes at the moment. When we look at large language models increasing the size of the parameters that they use, increasing the amount of data that they see, increasing the amount of time they get to train on that data, all those have not been tapped out. 

All those seem to continue to add capabilities. Those emergent capabilities create a future where you say, okay, what questions shouldn’t it be able to answer, right? If it’s given access to all the information it needs, what are the emergent things that we will find? Because it was a total surprise that suddenly language models can write poems in any number of styles, the creative side of doing tasks was not the thing that AI was supposed to automate. It was supposed to automate routine things, not things that we consider creative tasks. It’s been very surprising to see that actually, as it learns more and more about language and content, that language or the world through text, but the capabilities have increased tremendously. If you put it back down to the concept of five years from now, I feel like this kind of conversation will probably be one or more assistants on this call, and they’ll participate in some ways that help you sharpen your answer, help you better understand the content. 

It feels like there’ll be a world where whatever you write to communicate, an assistant will help you. Whoever’s reading might say, well, just give me a summary across all of the information I got. It feels like we’ll have a system of both the receiving and the sending side of the conversation in various ways.

Question: What unique value does Max deliver?

Alon Goren: The way we approach building Max and what we think we could achieve is unique and very valuable. 

Probably first and foremost revolves around the idea of getting deep within the customer base that we choose to serve. We’re not trying to necessarily go across all industries and all use cases. We’re trying to be much more targeted because we believe that by being targeted you can get much deeper. You could better build a deep understanding of what users want and how to deliver it. Especially in the case of having conversational type interactions. 

It’s challenging or currently impossible to teach a bot to know everything about all kinds of questions. If we focus on something really interesting, like we spend a lot of time with consumer goods companies, we spend time with the finance companies and healthcare more broadly, in each of those areas, there are needs to understand their domain, understand their particular, not just the vocabulary, but what drives the business. 

• How do they measure performance of a business?
• How do they set up the objectives for those businesses?
• How do they go about their work, of planning out what to do? 
• Where are the opportunities, where are the threats? 

The unique thing that we can bring to the table is working closely with those customers to ensure that the domain that we build, the knowledge that we build into the system to work alongside them, is second to none. 

That’s in contrast to probably the broadest solutions that are out there, that are designed more to be platforms to serve all use cases, right? Where they have to be agnostic about the kind of data and the kind of questions that they answer, which I think will work great for a lot of broad use cases, but won’t be the best choice for those companies who have a need for deeper analysis and the desire to automate more of the work that they’re doing.

In Conclusion

Looking ahead, the advancements in language models and chat experiences hold tremendous potential for the field of AI and data analysis. The future may see the emergence of specialized AI assistants customized for specific domains, capable of providing deep and tailored insights. These assistants, powered by advanced large language models, could transform various industries by offering efficient and personalized assistance. As technology continues to improve, with hardware and software enhancements complementing each other, the possibilities for AI and data analysis are expanding rapidly. With ongoing developments, the vision of having smart assistants with access to vast amounts of information and the ability to provide valuable recommendations is within reach. The trajectory of progress suggests that the limitations of what these assistants can achieve will continue to be pushed, unlocking new and unforeseen possibilities in the near future.

To learn more about the Data Superpowers within your reach, watch our YouTube playlist here. 

The post Conversational Analytics with Max first appeared on AnswerRocket.

Driver analysis is a powerful type of analysis that can help you identify what factors are impacting your KPIs, either positively or negatively. In this blog, we’ll cover what driver analysis is, the benefits and challenges of it, and how it can be automated with AnswerRocket.

What is Driver Analysis?

Driver analysis is a statistical technique used to identify the key factors that influence a particular outcome. In the context of business, it is often used to identify the factors that drive KPIs such as customer satisfaction, revenue, or profitability.

Driver analysis typically involves a three-step process:

1. Data collection: The first step in driver analysis is to collect data on a range of variables that could potentially impact the outcome of interest. This might involve surveying customers, analyzing sales data, or conducting market research.
2. Statistical analysis: Once the data is collected, statistical analysis is used to determine which variables have the greatest impact on the outcome of interest. This might involve running a regression analysis to identify the strength of the relationship between each variable and the outcome.
3. Actionable insights: The final step in driver analysis is to use the insights gained from the statistical analysis to inform business decisions. This might involve optimizing marketing campaigns, improving product features, or making changes to the customer experience.

Benefits of Driver Analysis

Driver analysis can help businesses optimize their performance in a number of ways:

• Targeted decision-making: By identifying the key drivers of success, businesses can make more informed decisions about where to focus their efforts. For example, if customer satisfaction is identified as a key driver, businesses can invest in initiatives that improve the customer experience.
• Optimization of marketing efforts: Driver analysis can help businesses identify the marketing campaigns and strategies that are most effective at driving customer engagement and revenue. This allows businesses to allocate resources more efficiently and optimize their marketing spend.
• Product optimization: By identifying the product features that have the greatest impact on customer satisfaction or revenue, businesses can optimize their products to better meet the needs of their customers. This can help drive growth and profitability over the long-term.
• Improved customer experience: Driver analysis can help businesses understand the factors that have the greatest impact on customer satisfaction, allowing them to optimize the customer experience and improve retention rates.

Challenges of Driver Analysis:

• Access to the right data: Within an organization, teams are often working on a multitude of platforms, all with their own reporting tools and interfaces. The onus is on individuals to share data and make the connection of how it affects KPIs. 
• Avoiding your own biases: It’s natural to lean on personal experiences and organizational habits when looking to data to glean insights. This is why an objective third party or tool can be useful in gaining a true understanding of what’s going on.
• Finding time to analyze data: Many business teams are seeing decreased headcounts and increased expectations for delivering results. The need to “do more with less” lends itself to busier employees and longer days, leaving little time to dig into data. 
• Identifying actionable insights: While it may be easy to see what’s changed, the challenge comes in identifying why something is up or down. Once you can identify the “why,” it’s much easier to take action to change or capitalize on a situation. 

Automate Driver Analysis with AnswerRocket

With AnswerRocket’s Driver Analysis, teams can easily track and understand why KPIs are changing—in seconds. The solution is designed for the end user who wants to monitor and understand business performance changes quickly. It’s perfect for answering the “what, where and why” questions that you may come across when analyzing your data. 

The KPI Dashboard

The Driver Analysis KPI Dashboard gives you a view of your key metrics at a glance, enabling you to quickly see what’s up, what’s down, and the WHY behind any changes.

Some of the types of metrics you can pull into a driver analysis dashboard:

• eCommerce 
• Google Analytics
• Email
• Social Media
• Sales Data

Criteria that you can refine your data by includes:

• Timeframe
• Comparison period
• Geography
• Brand

Users are able to click on a KPI in the dashboard and quickly understand why something is happening.

AnswerRocket does the heavy lifting with standard business intelligence capabilities like:

• What’s happening? 
• What was it year over year?

It will also be running a trend analysis in the background, to see if changes are above or below where we expected. 

Users can also easily see pacing as it relates your targets and growth rate over time. 

If the selected KPI is dipping or spiking, then we get into the next piece of “why did that happen?”. Scroll down further and you’ll see what the Top Drivers of that change are.

AnswerRocket Highlights Top Drivers

The Top Drivers section of the KPI analysis shows what’s having the greatest impact at a glance. 

In the example below, we can quickly see, spelled out in simple terms, that mobile was the main driver of the increase in Gross Sales.

AnswerRocket also features:

• Issues to Investigate
  
  Highlights things like device categories, source channels or campaigns that may not be performing up to par. Instead of guessing or searching around for a possible issue, users can focus their time and get to the root of a problem quickly.
  
• On the Bright Side
  
  Highlights things like device categories, products or product categories that are performing better than expected. This information can help users understand if a recent investment like a mobile update, or marketing campaign focused on a certain item is paying off.

Drill down even further by switching from “Summary” to “Top Drivers” view and seeing additional details within each segment and subsegment. Details such as:

• News 
• Current Period
• Comparison Period
• Amount Change
• Percent Change
• Driver Impact 
• Driver Rank

Normally, this analysis could take days or even WEEKS to complete.

In conclusion, driver analysis is an important aspect of data analysis that helps determine what segments are impacting your KPIs. While it can be time consuming, driver analysis offers benefits such as uncovering hidden information and providing value-focused insights. With the right tools and technology, it can be automated to minimize manual effort and generate useful results quickly. Whether you are trying to increase sales, optimize performance, or just make informed decisions about budgets, understanding the drivers behind your organization’s success is essential for any organization looking to evolve and iterate in the digital world.

With Driver Analysis from AnswerRocket, users get answers in seconds so they can take action quickly. These details help users respond swiftly to performance changes with insights that fuel growth.

The post Understand KPI Changes with Driver Analysis first appeared on AnswerRocket.

Augmented analytics is the combination of natural language generation and machine learning to automate insights. In practical terms, this means businesses can leverage the power of machines to analyze their data in seconds, so employees can spend more time setting business strategy instead of playing catch-up.

Speakers:

• Mike Finley, Chief Scientist at AnswerRocket
• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post Using Augmented Analytics to Gain a Competitive Business Advantage first appeared on AnswerRocket.

On May 5th, AnswerRocket took to the virtual stage for the 2021 Gartner Bake-Off: Modern Analytics and BI Platforms.

The Bake-Off is a mainstay of the annual Data & Analytics Summit, and we were honored to be selected as a featured vendor, along with Tableau, Power BI, and Qlik.

You can watch AnswerRocket’s full demo at the Bake-Off below, or scroll down to read our synopsis.

The Bake-Off tasked industry-leading vendors with analyzing COVID-19 data to demonstrate their product capabilities and differentiators. Gartner provided vendors with data and the directive to review the state and efficacy of vaccination efforts, as well as other containment measures. Rita Sallalm, a VP Analyst at Gartner, hosted the event and gave expert commentary on key differentiators.

We approached the task with a number of questions:

• How are vaccines affecting mortality rates?
• Which countries and regions are performing well in their vaccination efforts?
• How will vaccines impact unemployment, and when will we start to see the effect?

Then, we leveraged AnswerRocket’s augmented analytics to analyze data, diagnose drivers, and predict future outcomes. Our Chief Data Scientist, Mike Finley, presented our findings to a live audience, including pandemic expert Donna Medeiros.

Here’s what we learned.

Insights Highlight Reel

To create a comprehensive picture of vaccinations, the AnswerRocket team combined various data sources, including data from:

• Our World in Data
• Humanitarian Data Exchange
• The CDC
• The Department of Labor

AnswerRocket analyzed all of this data to generate insights like this:

The Insight: New deaths are down 4.8% worldwide

How We Got Here: AnswerRocket trended vaccinations and deaths from COVID using a RocketBot, a specialized analytical app that automates analysis and surfaces insights in the form of data stories. The most compelling insights were bubbled up to a browsable NewsFeed based on the end user’s interests. Whenever new data was added or existing data refreshed, the NewsFeed automatically generated insights, ensuring the most up-to-date vaccine information without prompting. The stories and insights you see in the NewsFeed below were all composed by AnswerRocket, using natural language generation.

The Insight: The majority of new vaccination increases month-over-month were concentrated in 3 countries: India, China, and the United States. Further, nearly 90% of the gains came from just 15 countries.

How We Got Here: AnswerRocket was able to answer a natural language query to produce these insights: “What were the new vaccinations given month over month by country in Mar 2021?” AnswerRocket understood we were looking to compare vaccinations across countries between two time periods. It produced a bridge chart showing month-over-month variance. It also generated natural language insights to explain the result and uncover additional interesting facts about the data.

The Insight: By June 5th, weekly vaccines will drive the unemployment rate below 4.5%

How We Got Here: With a natural language question (“When will weekly vaccines drive the unemployment rate below 4.5%?”), AnswerRocket generated an answer in seconds. AnswerRocket understood the intent of the question, selected the right machine learning Skill to answer it, and provided visualizations and insights that gave context to the answer. We used our Impact Skill, which leverages machine learning to predict an outcome based on modeling a scenario.

The Insight: When it came to containing the spread of COVID cases, countries with a higher prevalence of domestic travel restrictions and mass population testing measures faired better than those that relied predominantly on awareness campaigns.

How We Got Here: We used a custom Cluster Comparison Skill to automatically cluster different countries together based on their COVID testing rate and case rate, allowing the end user to easily compare containment measures between the leaders and laggards. While there is a Python code base powering this Skill, end users do not need to know how to work with code, they can simply ask a question or leverage a shortcut to invoke this interactive application.

This is how AnswerRocket enables operationalization of advanced analysis to business users.

Shifting the Dashboard Paradigm

With COVID data constantly evolving, meaningful insights must be 1) timely and 2) accessible to decision-makers.

Automation of analysis and insights generation achieves this. In our Bake-Off prep, it became increasingly clear that augmented analytics provides essential capabilities that traditional dashboards simply don’t possess.

While dashboards are important visualization tools that won’t be replaced anytime soon, they must be paired with accessible AI and machine learning techniques, as well as natural language technology, to enable end users to take action.

Our augmented analytics capabilities accelerate decision-making in the following ways:

• Conversational search with natural language processing enables frontline vaccination experts to get fast, up-to-date information on their own.
• Curated news and daily digests highlight insights based on interests, meaning end users get vaccination information without having to ask questions or trigger analysis in the first place. This helps to fill the gaps between what end users know to ask versus what they need to know.
• Skills leverage AI and machine learning to understand user intent, select the best possible model to answer questions, and automatically generate the appropriate insights and visualizations. End users have access to the best analysis techniques without having to learn SQL. Likewise, data scientists can fine tune models based on their in-depth knowledge with openly extensible AI.

AnswerRocket doesn’t approach data analysis from the same angle as dashboards. It’s not simply sitting on top of and visualizing data. It’s automating analysis, responding dynamically to the data, and enhancing discoverability.

We were thrilled to showcase AnswerRocket at the Bake-Off and demonstrate our unique perspective.

Do you have questions about AnswerRocket? Talk to our team!

Answer Your Questions and Solve Business Problems. Try AnswerRocket With Your Data!

The post AnswerRocket Joins Gartner Bake-Off: Analyzing The Impact of COVID Vaccines first appeared on AnswerRocket.

After all, companies must come away from these costly investments with actionable insights. Where’s the value if you can’t generate insights to help drive growth, cut costs, and ultimately move the needle on performance?

Many companies are facing the fact that their traditional business intelligence (BI) investments in reporting and dashboards don’t necessarily get them closer to actionable insights, at least not at the scale needed to go on investing in those solutions.

Instead, the hope is that a new generation of big data and machine learning will mean that employees can ask a myriad of complex questions specific to their department, function, or role and receive answers in seconds or minutes, without intervention or support from a data analyst.

Insights should make teams smarter, more agile, and more efficient. They must be embedded in the decision-making process, accessible to business people without technical expertise.

Insights produced by traditional BI platforms tend to lack the depth, speed, and ease of understanding to be truly actionable at scale. Employees are accustomed to reporting that is either irrelevant or fails to point out the key problems.

In this post, I discuss why traditional business intelligence is insufficient to scale insights generation.

Traditional business intelligence is insufficient

Traditional analytics platforms rely on dashboards to illustrate stats and trends in the data. Dashboards are typically composed of distinctive visualizations that answer simple questions, such as “what were sales in Q3?” or “Report on Sales In Q3.”

While dashboards can show data, they can’t explain the results with drivers and analysis. An analyst must still interpret dashboards to find insights and piece the story together.

In reality, analysts download data from dashboards into Excel to determine root causes and uncover meaningful, actionable insights. While dashboards can provide helpful visualizations, analysts are still performing the bulk of the work, often outside the BI tools themselves.

That means analysis is not uniform across departments because best practices are difficult to convey and enforce. The occasional big success punctuates an expensive and mundane flow of largely irrelevant charts and grids.

These limitations can lead to low adoption, since business people need support from analysts to get actionable insights. In turn, data and analytics leaders who’ve carried out traditional BI are left with lackluster ROI.

As such, there’s an impetus for companies to upgrade traditional BI and analytics tools to drive the results they want to achieve.

These considerations illustrate the depth of the need for better insights at scale:

• Surface-level insights aren’t sufficient. A BI tool may show a trend or perform statistical analysis, but this information doesn’t help a business person get closer to understanding why and what steps they can take to impact a business outcome.
• The cost of analytics is increasing, along with demand for insights. Analysts are challenged to better serve the business, while at the same time, they’re asked to reduce costs. The insights generation process is too manual and time-consuming— the same way it’s been for 10–20 years.
• Existing BI and analytics tools are not broadly accessible. While dashboards can provide helpful visualizations, they often require technical expertise to interpret or build.
• The turnaround time for quality insights is too long. Insights can quickly become outdated when it takes days or weeks to produce reports or build dashboards. Even fast analytics tools don’t solve this problem, as quick insights can be disparate and lacking context. A competitive environment requires speed.

To solve for these challenges, companies need fast insights that replicate the knowledge of a data scientist— insights that can be infused with the business expertise of a department or category manager, a supply chain guru, or a strategic executive. In other words, companies can’t sacrifice deep analysis to get quick insights, and vice versa.

The solution cannot be traditional BI and dashboards. These tools have been around for 10–15 years with user adoption rates stuck around 30%. To help companies achieve the outcomes that measurably improve performance, they must look to future-facing technology: augmented analytics and GPUs.

Operationalize data science with augmented analytics and GPUs

Augmented analytics leverages the power of AI to put data science capabilities into the hands of business people.

This data democratization — essentially, allowing business people to access and understand data without intervention from a data scientist or analyst — is enabled by two key components of augmented analytics:

1. Natural language generation (NLG) — NLG produces insights in plain language. Advanced NLG can produce entire data narratives that tell the story of business performance, trends, and opportunities.
2. Machine learning (ML) — ML algorithms perform exhaustive data analysis, surfacing hidden insights by testing every data combination. To accelerate ML, companies can pair augmented analytics with GPUs (more on GPUs in a moment).

With NLG and ML, augmented analytics automates the analytics workflow. Augmented analytics not only performs the analysis far more quickly and exhaustively than a person could, but it also mitigates the need for interpretation.

As discussed, a report generated by traditional BI would likely include visualizations with simple answers that state surface-level trends. These answers might state a percentage increase or decrease for important metrics like sales or market share, but would require technical expertise to understand the “why” behind the numbers and generate actual insights.

Augmented analytics, in contrast, can answer “why” questions directly; ML algorithms determine core contributors and detractors, developing a full data narrative that helps business people understand where to focus their attention.

There’s no need to bring an analyst into the conversation. Augmented analytics enables business people to drill-down, to ask follow-up questions, and to quickly gain an unbiased, 360° view of performance.

Augmented analytics is a powerful tool. To generate insights above the gold standard, augmented analytics requires significant processing power, especially for the large data sets common in enterprise organizations.

Most analytics platforms currently run on central processing units, or CPUs. That’s the kind of tech that powers the computer you’re using to read this. While CPUs work well for lots of different types of analyses, such as trending and contribution, their use is limited for AI and ML. CPUs simply don’t have the speed that can provide a competitive advantage. As companies move to reap the benefits of AI and ML, they’ve also seen the need to move from CPU-based systems to GPU-based systems.

GPUs, or graphical processing units, can cost-effectively accelerate augmented analytics, scaling insights generation through massive processing power. Originally developed to create the kind of realistic 3D environments now seen in video games, GPUs can now be applied to advanced AI-driven technologies well beyond that humble start.

Where a typical CPU server may have 10, 20, or 25 cores, a GPU server can have over 5,000.

As such, GPUs enable complex analyses and insights generation that simply can’t be accomplished with CPUs. This GPU revolution runs ML algorithms faster, meaning insights can be delivered in seconds, even for complicated questions.

GPUs scale insights generation in four primary ways:

1. GPUs help augmented analytics scale up from a handful of experts to enterprise rollout. For companies to achieve true intelligence with insights, they’ll need enough processing power to support every department and role that needs fast, on-demand answers.
2. GPUs enable proactive analysis, finding insights before business users even ask the question. AI can leverage processing power to understand a user’s interests and behaviors, generating insights that are relevant to them in a newsfeed. As data changes, the platform can highlight notable changes or anomalies without being prompted.
3. GPUs can help to understand the intent behind a user’s question. One question can contain a multitude of questions within it. Nested in a question like “why are sales increasing this year” is the question “explain the drivers of sales success this year, and identify any areas where we were not successful.” GPUs can answer the entire question, quickly, combining automated research with the work of many collaborators to build a coherent, comprehensive and actionable response.
4. GPUs help advance the sophistication of questions that AI can answer. GPUs can support more complex ML algorithms without sacrificing speed or depth, unleashing enormous potential for the future of automated analytics workflows.

Together, augmented analytics and GPUs allow users to get answers to complex questions quickly. Pragmatically, this unique combination is currently implemented with two leading technologies: AnswerRocket’s RocketBots and RAPIDS— an open source initiative started by NVIDIA.

RocketBots

RocketBots are powerful ML algorithms for proactive insight generation.

Here’s how they work:

1. RocketBots are invoked with a natural language question or through scheduled or event-triggered analysis.
2. The RocketBot gathers the data and uses ML to analyze all possibilities, producing meaningful analysis and insights.
3. The RocketBot composes a clear, concise story showcasing the most relevant visualizations and a high-quality insights narrative.

RocketBots allow companies to operationalize data science on demand. After a user asks a question, RocketBots get to work, automating the analytics process so that business people can focus on taking action (instead of waiting for answers or trying to divulge meaning from static dashboards).

Data scientists can also take advantage of this technology by launching their own ML algorithms within AnswerRocket’s platform and adjusting RocketBots based on their deep understanding of the business and its data.

RAPIDS

RAPIDS is a software suite for GPU-accelerated data analytics and machine learning. Pioneered by NVIDIA, RAPIDS enables faster, deeper data insights powered by ML and AI. Because it’s open source, software developers can leverage this technology for their custom needs.

The NVIDIA accelerated computing technology has enabled breakthroughs in AI across industries— from driving intelligent retail to optimizing content creation workflows. It’s no surprise that NVIDIA would power the next wave of disruptive AI-driven analytics.

RAPIDS allows RocketBots to analyze an entire data warehouse and return the best answer at lightning speed. For companies with enormous amounts of data and complex analysis needs, RAPIDS provides unprecedented insights that would take a team of analysts days or weeks to uncover.

Together, RocketBots and RAPIDS can automate game-changing analysis.

For example, the Category Overview RocketBot performs a deep-dive analysis into CPG categories, providing insights tailored to category managers. With this one RocketBot, category managers can:

• Uncover the key contributors and detractors impacting their category.
• Compare category performance to competitors with comprehensive market share analysis.
• Gain insight into product attribute performance.
• Forecast future performance.

Essentially, RAPIDS makes sure that RocketBots leave no stone unturned because they have the processing power to evaluate every aspect of a question, every assumption in analysis, and every intention of the user.

What RocketBots and RAPIDS mean for the future

RocketBots and RAPIDS ultimately enable teams to achieve better business outcomes.

For companies that want to identify and drive growth, leaders need to know more than what they’ve gained or lost. They need to know which gains and losses most affected the end results and where to focus their attention to net the most growth.

When these insights are fast, exhaustive, and intelligent, business leaders can achieve an enormous competitive advantage. Understanding “why” in seconds means companies can move the needle in several respects.

First, companies can reduce analytics costs and better distribute resources to high-priority projects, instead of allocating data analysts to churn through routine reports (reports that often contain outdated insights by the time that they’re produced).

Second, companies can act proactively instead of reactively. While business leaders are waiting days to simply understand what happened last quarter, the market is moving. The ability to quickly gain a 360° picture of performance enables companies to move faster than their competition.

GPUs and augmented analytics can scale insights generation for companies, where traditional BI and analytics tools lack the power to do so.

By operationalizing data science, companies can gain the deep and fast insights that they need to achieve critical business outcomes.

The post Scaling Insight Generation with Augmented Analytics and GPUs first appeared on AnswerRocket.

In the past, data storytelling was a data analysis method restricted to data scientists or analysts. This has now changed with the introduction of self-service business intelligence (BI) tools such as AnswerRocket.

Data Visualization as a Tool for Storytelling

Most effective data stories begin with a relevant visualization. Relevance refers to both the visualization’s depiction of the data as well as its usefulness to the audience in question.

For example, a relevant visualization of regional sales figures could employ a map that illustrates these numbers intuitively. For a salesperson who wants to use the visualization in a presentation, the map may be perfect.

For the team member who wants to dive deep into the nitty-gritty numbers, a pivot table may be more appropriate. A visualization tool that’s relevant, and therefore effective, should provide customization options so that said team member can quickly pick the chart type that makes the most sense.

Finding a Data Narrative Using BI Software

Your data story is not complete without a proper narrative.

Historically, data narratives were culled together and put into a report by data scientists and other analytics experts. Then, the business user would be left to draw conclusions and build out data stories from that predefined narrative.

Now, with the help of AI and machine learning, a non-technical user can go straight to their business intelligence tool and ask their first question. For example, a sales leader might be interested in, “What were sales by territory for Q4?” The ability to ask an every day business question in conversational, organic speech is the beauty of natural language processing, one of many technical advancements brought by AI.

Once that answer is generated in a matter of seconds, the user can leverage results to go further down the process of building out a data story. AI speeds up this storytelling process immensely.

Context for your Data Story

When storytelling with data, the audience should be considered when choosing the way data is framed or positioned.

“It’s the context around the data that provides value and that’s what will make people listen and engage” – Gartner.

The context of the data story will help guide the audience to an elevated understanding of the data being presented.

For example, an increase of 10% of sales in Q4 will look promising until provided the context that the goal of Q4 was a growth of 20%. Further, positive trends can overshadow stagnating metrics and opportunities for immense growth if taken at face value.

It is also important to consider who you are building the story for, as a salesperson is likely to have more interest in data that’s actionable for their role than in data that’s targeted to the finance department.

As we discussed before, the ability to ask questions in natural language dramatically increases access for non-technical people, allowing them to tailor data storytelling to their own needs and interests.

Data without context is just that, just data. It can contain certain insights, but these insights can only be unlocked through the use of context. Random data points will mean nothing to the audience or user until you show them what to look at and what to compare them to.

The Importance of Storytelling with Data

Data storytelling can assist non-technical members of an organization by simplifying complex sets of data into digestible, relevant content.

It can empower entire organizations down to the employee level to make informed business decisions to optimize business operations.

AnswerRocket and Data Storytelling

Through the use of an AI analytics tool such as AnswerRocket, data storytelling can be largely automated. AnswerRocket is a query-based data analytics software that can automatically detect trends and other insights based on the questions you ask.

The software is also simple enough for anyone in an organization to use by implementing natural language processing (NLP) and natural language generation (NLG) in the platform itself. This means that a user can ask a question of their data in natural human language, and the system will output an answer in the same easy-to-understand language. U

pon asking questions of your data, AnswerRocket will use machine learning algorithms to analyze a large scope of data to uncover many different insights that may not be visible on the surface level.

These insights can provide a narrative to your data story and answer the “why” questions the user may pose. The platform will also perform analyses on your data to discover the perceived best visualization based on the question asked.

AnswerRocket also provides the ability to continue to ask questions that stem off the first initial query in order for the user to come to a productive conclusion, which is an essential component of data storytelling. In short, using AnswerRocket can provide full data stories and data storytelling tools when asked just one question.

The post Data Storytelling – Explained first appeared on AnswerRocket.

First, Google announced that it would be acquiring Looker on June 5, reportedly to leverage the platform’s data visualization capabilities for Google Cloud services.

Days later, Salesforce announced its upcoming acquisition of Tableau, a deal scheduled to close on October 1st of this year.

It’s not yet clear how these analytics platforms will be made accessible to business users or how they’ll be entrenched in Google and Salesforce’s mainstay software.

Yet, in light of this news, the analytics space is buzzing with questions about the state of the market and the role of analytics in the larger scheme of business intelligence.

What the Looker and Tableau Acquisitions Signal About the Future of Analytics

These back-to-back purchases demonstrate that tech companies are investing in analytics as a critical aspect of their enterprise suites.

Users, perhaps now more than ever, need tools that can interpret the astronomical amounts of data that most companies wield— a demand that Salesforce and Google, in their data-centricity, would be well aware of.

For the average business user, analytics are a means of transforming abstract data into something more actionable. The data visualization capabilities of platforms like Looker and Tableau are one method of providing more insight into metric relationships by indicating general trends, outliers, and so on with customizable graphs and charts.

But, the hunger for analytics solutions seems to point to a larger need for the kind of tangible insights that lead to data-driven decisions.

Beyond visualizations, advanced technology like natural language insights can weave complete data narratives that address the causes beneath the numbers on the surface. Data exists as a complex web of relationships, and trends that can be captured in visualizations are part of a larger story.

The more companies invest in tech that drives insights, the more innovative, intuitive, and routine insights will become in the decision-making process.

As the need for user-driven, insight-fueled analytics in business intelligence software grows, so too has interest from outside software companies, like Google and Salesforce. Likewise, this interest will help propel the analytics market forward, as independent platforms continue to refine their offerings to differentiate themselves with better, faster, and more advanced insights capabilities.

The market is ripe for innovation— which brings us to the next frontier in the space.

AI: The Next Frontier of the Analytics Market

Analytics solutions help users make smarter decisions based on data. In a sense, they enhance our insight and intelligence.

It’s a logical, natural step that the future of analytics will be led by AI. 

AI continues to bridge the gap between a user and the data they seek to interpret. AI is adept at the tasks that are time-consuming, laborious, and often frustrating for employees.

For example, AnswerRocket’s AI-driven analytics software leverages machine learning and natural language technology to generate the data narratives discussed prior. In practice, this means AnswerRocket parses through an entire data warehouse to identify the most important and relevant data relationships, triggered by user queries like “how did Brand A perform last quarter?” or “why are sales down?”

Machine learning algorithms perform this analysis in minutes, a feat that would take a person hours or days, depending on the complexity of the query and the amount of wrangling they’d have to do to gather, prep, and analyze all of their relevant data sources.

AnswerRocket makes quick work of evaluating all potential combinations of data, testing every possibility without bias.

Once the analysis is complete, AnswerRocket generates data visualizations and natural language narratives that explain and reveal hidden insights from the data, such as key drivers, trends, correlations, and anomalies. Where possible, the opportunities with the most ROI are also presented, guiding the decision-making process for business users.

This level of AI is innovative and unmatched in the analytics market.

Learn more about AI analytics with this ultimate guide.

How the Google and Salesforce Acquisitions Can Expand the Breadth of Analytics Use Cases

These acquisitions provide an interesting opportunity to see how analytics can be adapted and refined for the wide variety of use cases that customers of Google Cloud and Salesforce no doubt have.

At AnswerRocket, AI has been the means of tailoring analytics to different roles, departments, and industries.

Specifically, AnswerRocket employs specialized machine learning algorithms purposefully designed to address specific business use cases. CPGs can, for example, easily automate time-intensive market share and brand health analysis with algorithms that break down these metrics in-depth. Algorithms designed to run financial analysis quickly reveal core contributors to top-line growth and bottom-line profits.

We’re continually developing new algorithms for automated analysis to meet the growing and diversified needs of companies who’ve invested in our AI-driven analytics.

The analytics buy-in from Google and Salesforce can similarly lead to more nuanced applications of analytics as a whole. In a future state, companies could come to expect tailored analytics that speak to their unique needs.

Ready to get ahead of the analytics curve? Try AnswerRocket today!

The post Looker & Tableau Acquired: The State of the Analytics Market first appeared on AnswerRocket.

This was the central question for the Show Floor Showdown, an event hosted during the 2019 Gartner Data & Analytics Summit. In this event, six analytics software companies were invited to demo their products in a 10-minute presentation aimed at uncovering correlations between technology and loneliness.

AnswerRocket was honored to be one of the lucky six.

Prior to the Summit, Gartner presented each company with data from the Kaiser Family Foundation, the Economist’s survey on loneliness, and Gartner’s Consumer Values Survey. We were tasked with preparing and structuring the data for analysis within a timeframe of two weeks— a challenge we were eager to take on.

From there, we used AnswerRocket’s advanced AI and machine learning features to dive deep into the data. AnswerRocket SVP Pete Reilly presented our findings during the Show Floor Showdown to a crowded room of AI enthusiasts. We were thrilled with the engagement we received around our software, and more so, we were excited to contribute to an important conversation around such a pressing and timely issue as loneliness.

So, what did we find out?

Who is most affected by loneliness?

Curiosity is at the root of discovery, so we began our data dive by asking questions.

We started with the basics to determine which demographics were most affected by loneliness (and with AnswerRocket’s natural language processing, we were able to ask these queries as they came to mind):

We found it interesting that millenials ranked highest for loneliness, considering the attention paid to loneliness in elders.

Additionally, the disparity between loneliness in each country caught our eye.

We decided to iterate on our original “frequency of loneliness by country” question to learn more:

• Even though Japan ranks lowest in loneliness, almost 50% of respondents in Japan considered loneliness to be a major problem, whereas only ~20% of UK and US respondents thought the same.
• Only ~20% of Japanese respondents reported being “Very Happy” compared to ~50% of US respondents.

This information added more context to the original demographic data and reminded us not to jump to any conclusions. Data, after all, is complex, and software like AnswerRocket is well-poised to direct us away from our biases, assumptions, and tendencies to read correlations as causations.

Plus, this was only part of the story — we hadn’t yet discussed the role of technology’s impact.

How does technology impact loneliness…in our perceptions vs. reality?

According to the data, approximately 45% of respondents thought technology was a major reason for loneliness.

Though this sentiment was captured in the survey, a closer look revealed a much more complex relationship between technology and loneliness.

We decided to analyze how people use technology to connect with others in tangible ways by zeroing in on a specific scenario: how often respondents used technology to communicate with their families.

We discovered an interesting effect, where 23% of respondents who were always lonely used technology to communicate with their families every day, yet 25% of respondents who were never lonely also used technology to talk to their families every day. Further the greatest percentage of respondents who were never lonely (29%) used technology to communicate with their families a few times a week.

Based on this information, the question of technology’s impact on loneliness was more complicated than it first appeared.

Though people perceived technology as a negative influence on loneliness, people who were never lonely also tended to use technology to connect with their family members at least a few times a week.

So what was going on with this divide?

To gain some clarity, we leveraged AnswerRocket’s machine learning capabilities to identify drivers behind the data. That is, AnswerRocket analyzed data relationships and patterns to identify the causes behind the numbers on the surface.

We found that location played a much larger role in loneliness than we would’ve guessed. The largest role in fact, with Ohio leading the charge for loneliest respondents. Other factors, like the amount of personal confidants and attendance of religious services, also impacted loneliness.

These drivers, working in the background and influencing the participant’s responses, built a more complete picture of loneliness and how technology isn’t the sole, or even most important, factor. The relationship between technology and loneliness doesn’t exist in a vacuum, and AnswerRocket was able to quickly pinpoint the other factors that exhibit stronger correlations.

Now, that’s a lot of info to pack into a 10-minute presentation. Imagine what we could illuminate about your company.

To get a sample of AnswerRocket’s industry-leading AI-powered analytics, sign up for a custom demo. We’ll work with your data!

The post Technology and Loneliness: Insights from the Gartner Show Floor Showdown first appeared on AnswerRocket.

Tableau’s new tool, Ask Data, is a natural language processor that allows users to ask questions in plain language and get answers about their data in the form of a visualization.

At its core, natural language processing (NLP) refers to a machine’s ability to understand words and phrases in normal human speech. Think of the conversational questions we ask virtual assistants like Siri or the way we type a Google search query.

In the realm of business analytics, NLP has become an increasingly common feature and something of a standard.

For users, the ability to ask questions about their data in natural language is becoming an expectation— and those who do not expect it soon will.

More broadly, natural language features in analytics platforms have the potential to influence enterprise software companies on a large scale (take a look at these recent acquisitions by Salesforce and Google, for example). The point being, the natural language is a hot topic that’s gaining momentum.

Since this natural language technology is something we’re very familiar with at AnswerRocket, we’re excited to jump into the conversation that’s been generated in the aftermath of Ask Data’s arrival.

So what exactly is the natural language processing that Tableau’s Ask Data has us talking about?

As mentioned, natural language processing helps people ask questions of their data.

NLP deviates from the historical use of keywords in analytics platforms. The difference is between typing “sales, q1, category” and asking “what were sales in q1 by category?”

The second example reads into a person’s intentions. Good NLP solutions will generate the same answer even when the question is worded differently (ex: “what were sales by category last quarter?”).

For businesses who want analytics platforms with accessible AI, this understanding of intention is key.

That said, natural language processing is only one component of natural language technology.

Natural language generation (NLG) is another similar technology that’s used in business analytics, though not as commonly.

Natural language generation in business intelligence refers to the production of insights in plain language. In other words, once users ask a question, they receive an answer that makes sense to the average person. NLG fills in some of the gaps of visualizations, highlighting key takeaways like outliers, pareto, and trends.

In advanced implementations, NLG can relay an entire data narrative, explaining complex concepts like why sales are increasing, how your company’s market share compares to competitors, or an end-to-end summary of the performance of key KPIs.

To learn more about advanced NLG in the context of business intelligence, check out “The State of AI in Business Intelligence.”

When it comes to selecting a solution or engaging with the conversation around these tools, it’s important to understand what natural language processing means in the context of practical, every day business operations.

So let’s talk about why natural language is important and what questions to ask when you’re considering a platform.

People don’t think in keywords.

Stringing together a list of disconnected keywords disrupts workflow. When you have a question at front-of-mind, it’s distracting to translate what you know you want to ask into parsed language.

Plus, keywords rely on specific data structures and dimensions. Look for a natural language search that allows business users to build a glossary of terms and synonyms so that employees can ask questions like humans.

When we ask questions, we rely on business vernacular and use phrases like “compare” to signal the need for ratios and percentages. A good natural language processor should fill in the gaps and build calculations around our intended meanings.

As such, natural language tools allow you to focus all of your brainpower on the answers you receive so that you can more quickly set business strategy.

Natural language is familiar.

The search engines we access in our daily lives depend on natural language. Why should our business tools be any different?

To be clear, it’s not just about convenience; it’s about increasing access to analytics for the non-technical user.

This accessibility of natural language makes it easier for business people to use these tools and feel confident in the answers they’re getting — because they’re asking questions that make sense.

We ask questions to understand “why” not “what.”

Ask Data is an interesting tool because it gets people talking about natural language. But we want to emphasize that natural language can go beyond questions like “what are sales by brand” or “penetration by state.”

The extension of asking “what” questions is getting to the “why.” As we said before, natural language is about understanding a person’s intention.

A great natural language tool should enable customers to ask “why are sales down” and “how can we grow our brand” — questions that business people ask all the time. AI empowers people to ask these day-to-day questions and get answers in seconds.

Natural language paves the way for better AI.

Perhaps most importantly, natural language connects people to the most advanced versions of AI-powered analytics; in fact, the more these algorithms develop, the more vital natural language becomes.

First, let’s establish some context.

The full implications of AI in business are beginning to emerge as more companies look to implement machine learning into their BI tools. Ask Data has arrived on the cusp of AI-driven tech trends in industries such as the consumer goods and retail space (just look at resources like Consumer Goods Technology, which details how CPGs implement AI to respond to radical market changes).

As such, technology that prioritizes organic communication is well-poised to snowball in industries that are hungry for more automation, and as more businesses invest in these technologies, the more these technologies can develop and grow.

Plus, the better we can communicate with these future-facing tools, the better we can leverage their knowledge and implement them into business strategy.

It’s easy to think of AI as a separate robot entity in your computer, but AI that works from natural language is more akin to a fast, efficient data scientist that you can access at any time. So sure, a robot, but one that you can talk to like a person.

We think that more people valuing natural language in their AI tools is a great thing because it opens the door to even more advancements that help people get what they actually need.

In our case, it’s answers to tough questions. And we’re happy to provide.

For more advice on shopping for business intelligence software with natural language processing, check out our complete guide to BI in the age of AI. 

The post Tableau Announces Ask Data: A Natural Language Processing Tool first appeared on AnswerRocket.

But I was also intrigued why Gartner recognizes Cool Vendors. I love the association – but I wondered why the Gartner analysts landed on that particular adjective.

I gained some insights by reading a Gartner article about their selection guidelines for Cool Vendors. They defined Cool Vendors as emerging companies that shared three qualities:

1. Innovative — enables users to do things they couldn’t do before.
2. Impactful — has or will have a business impact, not just technology for its own sake.
3. Intriguing — has caught Gartner’s interest during the past six months.

With those criteria, I understood better what Gartner saw in AnswerRocket. Here’s how I think we check each of those boxes:

Innovative
Analytics software has been around for decades. The problem is that these software packages were designed for technical analysts who write SQL and understand database schemas. In today’s data-driven business environment, these specialists can no longer keep up with the data and analytics needs of business users. Many of these traditional BI solutions tried to solve this by extending their reach from technical specialists to business users – but these tools are just too complex.

AnswerRocket was built from the ground up as a self-service analytics tool for non-technical business users. This was the persona we focus on, versus more technical users who are comfortable handling raw data. We provide an interface they use every day: a search box that accepts natural language questions and returns answers in seconds.

That’s pretty innovative – but we aren’t stopping there. Our Smart Briefing gives an executive-level view of your business with automatically generated charts and natural-language-generated insights. This leaps past the KPI dashboards of today, by alerting business leaders to trends in their data, identifying outliers, and reporting progress against goals in easy-to-digest articles generated by the system.

Impactful
One of the best parts of my job is hearing about the positive difference we’re making for our customers. AnswerRocket delivers significant bottom-line impact – in fact, some of our customers experience an ROI within a few weeks of implementing AnswerRocket.

Check out SnapAV’s experience of finding a $1M opportunity within just a few minutes of reviewing their data. Or listen to how AnswerRocket delivered groundbreaking analytics to the game designers and marketers of Hi-Rez Studios.

Intriguing
Gartner’s annual Data & Analytics Summit brings together all of the key players in this space, along with thousands of enterprise leaders looking for analytics solutions. We were honored that Gartner asked us to help kick off the Summit by giving a demonstration of AnswerRocket at their Innovative BI in Analytics session. Our smart data discovery solution clearly struck a nerve, as our booth was packed the rest of the Summit with people wanting to learn more.

I give demos of AnswerRocket every week, and I regularly get astonished reactions when people see our analytics tool for the first time. It seems too good to be true – until they see how quickly we can take their data and allow them to get answers and insights in seconds.

Being named a Cool Vendor is certainly cool. But we’re not going to rest on our laurels. We remain even more committed to deliver innovative solutions designed to positively impact our customers and intrigue those seeking analytics solutions.

The post It’s Cool That Gartner Thinks AnswerRocket is a Cool Vendor first appeared on AnswerRocket.

Alan Turing (you know, the guy who decided computers only needed 0s and 1s) thought that the ultimate test of a machine’s intelligence is whether it can hold a conversation with a person. If the machine has intelligence, the person will be fooled into believing that a human mind is on the other side of the dialogue.

According to Turing, mastery of language is intelligence. Since Turing’s time, the idea has been researched and extended by other scholars. The test has been attempted, and algorithms have passed limited forms of it (see Eugene Goostman). But every time a computer passes the test, the results are challenged and the test is made more difficult. Researchers are egging each other on to push the boundary further and further toward a machine that understands the thoughts of a mind. Conceptually, the bar could be pushed high enough that some humans would not pass! At least that’s the pattern we have seen with other AI developments like games and handwriting.

In contrast, consumers now have the commonplace experience of posing natural language questions to machines, including Amazon’s Alexa or Amy from x.ai. Far from the esoteric idea of machine minds that might one day “talk back,” businesses are focused on that evergreen consumer desire: convenience. Product announcements in the area of linguistic user experience – LUX, to coin a phrase – are now a weekly event. As much as we love our web browsers and smartphones, it turns out we’d rather use words than buttons. And while it’s fun to tease Siri into an amusing retort, Siri is really a productivity application that’s there to get something done for us.

The disparity between the Turing test and commercial practicality could be another example of the bumblebee buzzing around while scientists at the whiteboard prove it can’t fly. In Turing’s test, the machine proves that it is intelligent by responding to a human mind through language. That’s because when Turing formulated the test 12 U.S. presidents ago, the idea of ordering a pizza online had not occurred to anyone (well, maybe Nikola Tesla). Arguably, performing the right task as requested is a great way to know that the machine “understands.” We’ve all had conversations with people that definitely didn’t reveal any intelligence, and there have been plenty of times when software did something we found delightfully clever!

Like any new technology, LUX applications began by providing a better way to do something we could already do. Digital assistants that take dictation or set appointments are nice. Wolfram Alpha promised to “understand” our questions, and it is an amazing piece of work. But these early steps don’t mean that the tech will take off. Remember Graffiti on the Palm Pilot? Some great tools are nonetheless a dead end.

LUX needs to pass the next threshold to change the future: Language has to allow systems to do something new. The killer app remains on the loose. Convenience and speed are enough to win the consumer market, but business won’t tip to LUX until these new systems provide capabilities outside of merely activating on keywords.

What’s interesting about LUX is that there is already an existence proof for the killer app. We speak to each other every day using our evolutionary wetware and creating great business value. Language is effective. If systems could understand what we say, we could cut costs and improve efficiency by transferring intent directly into the systems that implement it. Sort of like, “Alexa, order me a pizza,” writ large. Imagine, “Alexa, increase profits in the paper division.” People can’t be cut out of the system because they originate the intent, but LUX implements it at the speed of thought. FastCompany recently profiled the business chatbot phenomenon. A number of companies, including my own AnswerRocket, are applying LUX to business intelligence. The list goes on.

Philosophers can argue about what makes intelligence and the nature of “mind.” In the meantime, LUX-enabled software will continue to proliferate, even as it sometimes makes comical mistakes like Alexa’s responding to its own TV commercials, or Siri’s injecting an answer at a White House press conference. Iterations and innovations will improve the technology until it takes its place in the arsenal of systems that improve our lives. Like the stray dogs that ride subways in Moscow, we don’t have to understand how it works to embrace and enjoy it. Our only mistake would be to underestimate what’s possible and not to prepare for what’s next.

Continued Reading

Natural Language & Analytics: A Cheat Sheet for Business People — Now that you have learned the larger context behind AI and the evolution of natural language computing, it’s time to dive into how natural language factors into the business world. More specifically, this comprehensive web page covers the scope of natural language possibilities in the analytics space.

Ian Lamont CC BY

The post AI and the Evolution of Natural Language Computing first appeared on AnswerRocket.

If your business intelligence software doesn’t have natural language query, every request you make could be just as frustrating as seeing Senor Cabrera deny nature’s call (and yours). Only, where your high school teacher wanted Spanish, a lot of BI software wants a programming language like SQL. Even if your BI solution doesn’t require coding knowledge, it may still be unwieldy enough that you’ll need someone from IT just to make a request.

Want to avoid this sort of roadblock? Consider a BI solution that uses natural language query.

Rather than using code, natural language query lets a computer “understand human speech as it is spoken.” If you’ve seen the ads for virtual personal assistants like Alexa or Google Home, you’ve seen natural language query in action. You’ve also seen how easy it is. To get that same kind of ease in your business, AnswerRocket is a great choice. Here are four ways natural language query meets your needs without you even asking.

1. Natural language query moves at the speed of curiosity

Good questions are like bad party guests: they arrive at their own pace. They don’t follow our schedules. Ever kept a pad and pen by your bed in case a good idea comes to you as you’re falling asleep? Then you know how assertively random curiosity can be.

Natural-language generation dynamically increases the volume and value of insights and context in data analytics.

How often have you forgotten a perfect question or idea in your head because you couldn’t get it onto the page fast enough? Interruptions and mental speed bumps—like trying to think how to phrase something—can stymie your creativity, or make you misinterpret what sounded good in your head.

And if you have to translate that query into a different format entirely? Say goodbye to that million-dollar question.

With natural language query, however, there’s no need to stop and phrase something in the computer’s language. Just type your question in plain English, and you’ve got an answer. Natural language query moves at the speed of inspiration and curiosity. That’s why Gartner analysis could say that “natural-language generation dynamically increases the volume and value of insights and context in data analytics.”

And, so long as we’re talking about pace, AnswerRocket’s natural language capabilities already puts them ahead of the curve. Natural language processing, which powers natural language query, is key to several of Gartner’s Top Ten Strategic Technology Trends for 2017.

2. Natural language query naturally incorporates data governance

Data governance isn’t just about security, it’s also about making sure data is consistent and useful for all your departments. If you don’t happen to speak the jargon of sales or marketing, you might be asking the right question in the wrong terms, and wrong terms don’t get answers.

Rather than specialized silos, your data lives in an environment where anyone can get to it, asking the same sort of questions they’d ask of a coworker.

Natural language query is above jargon: no matter what department’s asking, the same everyday speech is good enough when you ask your question. Rather than specialized silos, your data lives in an environment where anyone can get to it, asking the same sort of questions they’d ask of a coworker (without having to take that trip downstairs and lose your focus).

Furthermore, natural language query gives you the flexibility to use different kinds of terms. Rather than having to switch between everyday language and the specific terminology your business uses, you can use either vocabulary with NLQ.

3. Natural language query makes adoption easier

The average adoption rate of BI software is 22%. Even in baseball, that average is unimpressive.

When AnswerRocket worked with SnapAV, they found a company that wanted to get out of the other 78%. Employees were hesitant to use their BI solution because of its difficulty. Asking questions and getting answers from the program took the help of an IT analyst, which “might be a couple of hours” or “might take a couple of days,” according to SnapAV President Adam Levy.

On top of the delay for their business users, the analysts were also tied up answering simple data questions rather than delving deeper into more meaningful data analysis.

No better way to make something fail than turning a simple process into a multi-step equation. With AnswerRocket, though, SnapAV’s employees found adoption to be as easy as a Google search. By the time they’d finished asking questions, they realized they’d already adopted the program.

It’s also worth considering how much people are coming to expect question-and-answer searches. A lot of employees in any field are used to asking Siri things, and they’re used to Googling (the fact that “Googling” is a verb should tell you something). Natural language query will feel, forgive my word choice, natural to them. When business processes become as natural as asking where the nearest sushi place is, you’re one more victory toward winning the adoption battle.

Where this feature shines especially? With Millennials and other digital natives, who are used to intuitive, natural user experiences.

4. Natural language query helps give you fast returns

80% of BI users said the current pace of insights did not match the pace of their business requirements, and 52% said that that was due to how much time it takes IT to deliver those insights.

If you’re looking for fast returns from your business intelligence software, you may want to readjust your assumptions. A surprising number of BI users find their solution is slow and unsteady: 80% said the current pace of insights did not match the pace of their business requirements, and 52% said that that was due to how much time it takes IT to deliver those insights. Add to that the average of 6.1 days it takes to build a report, and business intelligence becomes a waiting game.

Natural language, however, helps get you the info you need at the pace of business. That’s what game design company Hi-Rez Studios found when they switched to AnswerRocket. Hi-Rez was frustrated by how long it took to get information with their previous BI program: “Queries had to be constructed exactly as intended, and it would usually take a lot of time for our more technical people.”

With AnswerRocket’s natural language query, however, Hi-Rez can adapt to their business’s needs more readily. For instance: Hi-Rez now has the agility to figure out the difference between things like “the behaviors and tastes of our players” in different countries, or the popularity of different characters. Rather than waiting for a report, employees can more quickly tell if gamers like a character or not (if Nintendo had had AnswerRocket, one of the ‘80s most tragic video game-related phenomena might never have happened).

With natural language query, you’re not “executing a business query.” You’re asking a question. If you’re looking for a business intelligence option that answers your questions before they’re asked, AnswerRocket is an option you’ll want to consider.

The post 4 Ways NLQ Meets Your Needs Without You Even Asking first appeared on AnswerRocket.

Big innovations come in unexpected bursts. We grow accustomed to life and work as we know it, until something apparently simple brings about bold change.

For example, we used phones for 100 years, but making them mobile transformed the world; we had the Internet for decades before the Web browser put digital education, entertainment and shopping in the hands of billions; and we documented our lives with physical pictures, paper records, CD-ROMs and thumb drives until Jeff Bezos brought us “the cloud.”

When individual creativity is enhanced by technical ingenuity, new behaviors and capabilities emerge.

Of course, every new idea has a band of detractors predicting the worst-case scenario. Like the notion that mobile phones will give us cancer. Or that Big Brother is tracking your every move online. We do need to be smart about innovation, but usually the detractors are the people who have the most to lose. Just look at how Big Oil and Big Auto struggled to make electric vehicles work before Tesla proved a technical path and viable market existed.

Artificial intelligence is the next obvious controversy. It’s around us every day, helping singles find a mate, or routing traffic or diagnosing disease. But will it one day take over like the Terminator? Make us obsolete and slothful like WALL-E? Enslave us like The Matrix?

Leave the blue pill for the Doomsday preppers and take the red pill of reality with me.

Intelligence has delivered all of the progress we enjoy. And, artificial or not, we must be careful with intelligence, or indeed any kind of innovation. Alfred Nobel understood this when he harnessed the destructive power of dynamite, yet later funded a legacy of progress for mankind through his namesake prizes.

In a world of truly challenging problems like famine, terror and disease, it’s hard to argue that more intelligence will leave us worse off.

People can’t understand new ideas if their livelihood depends on the old ones.
-Upton Sinclair

On a smaller scale, it’s also hard to argue away machines that help us drive cars when we know they see better, react faster and have more information than we do. Most of us have retirement accounts that are directed, at least in part, by non-human decisions. Machine-assisted surgery promotes faster recovery and better outcomes.

Man-machine symbiosis is already used in thousands of applications where a curious mind found an unsolved problem and thought of extra-human intelligence as the right tool. It’s just another extension of mankind in a long chain that started with club, fire and wheel.

What’s new in this chain is the precise way in which the new AI tools (and robots in general) extend us. Masahiro Mori realized nearly 50 years ago that tools appearing to impersonate people in any way could provoke hate. Just a few recent examples:

• After chatbot Tay was manipulated into posting offensive tweets, Microsoft’s corporate VP of research, Peter Lee, blamed those who acted with “malicious intent.”

• HitchBOT was conceived as a social experiment to see how humans would interact with a friendly, hitchhiking robot. Soon after starting its journey across the U.S. last summer, HitchBOT was decapitated.

• Six years after Apple introduced Siri, people are still annoyed by the intelligent personal assistant precisely because she sometimes is less than human. Who hasn’t raised their voice at Siri?

Humans aren’t exactly raging against the machine. But some are feeling frustrated, anxious or hostile about artificial intelligence.

When individual creativity is enhanced by technical ingenuity, new behaviors and capabilities emerge.

How we perceive AI at work may not be so blatantly destructive — but still illustrates our unease. In 1934, Upton Sinclair wrote that people can’t understand new ideas if their livelihood depends on the old ones.

The intelligence revolution may be just that kind of idea for today’s white-collar staffers. It was easy to embrace automation when it took away mundane tasks, but the new breed of big data applications looks a lot more like a smart co-worker than a fax machine. Once again, people face a choice between fearing the unknown and plunging forward with progress.

The choice for progress now is as clear as it has ever been. A better competitor, or a clever new employee, or an economic downturn could spell disaster for an unprepared knowledge worker, regardless of the role of extra human intelligence. Those who learn new ideas and become better at their jobs will stand out and succeed as they have always done. It’s about value creation, not about entitlement.

Machines won’t replace the need for business or profit or growth. That’s why entrepreneurs will always lead us toward the best use of new technology. And they will need curious employees who understand Sinclair’s paradigm to power their dreams, no matter what tool comes next.

Bryce Durbin

The post Be Kind to Artificial Intelligence first appeared on AnswerRocket.

Business intelligence is a broad term that encompasses the technology and techniques that companies use for data analysis.

Get a better grasp on business intelligence, BI tools, and where AnswerRocket fits in with this BI guide.

Table of Contents

1. What is Business Intelligence?
2. Business Intelligence and Data
3. Business Intelligence Software Basics
4. The Top BI Tools
5. Bottom-Line Benefits of BI 
6. AI, BI, and AnswerRocket

What is Business Intelligence?

The term business intelligence has been around since the 1800s. In the modern era, it’s a fairly simple umbrella term that encompasses many complex business processes and activities.

Business intelligence (BI) refers to technologies and services used to analyze business data and uncover actionable findings and insights for executives and other corporate users to leverage.

Essentially, business intelligence seeks to make sense of big data, not only managing the sheer volume but generating strategy-focused takeaways.

Forrester explains the business intelligence definition well, stating, “Business intelligence (BI) is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information. It allows business users to make informed business decisions with real-time data that can put a company ahead of its competitors.”

Every business is searching for the same thing — a competitive advantage and sustainable stability. With business intelligence, companies position themselves to strive for that goal.

Get the eBook: “The State of AI in Business Intelligence”

With this eBook, you’ll learn about the latest development in the business intelligence market: AI.

AI is revolutionizing BI by automating the production of meaningful insights with natural language technology and machine learning, augmenting analysis so that business people get their questions answered faster.

Download Now

Business Intelligence and Data

With the rise of omnichannel marketing, companies have access to more data than ever before. The customer journey is no longer a simple circular pathway, but a complex web of interactions. With each opportunity for engagement comes an outlet to receive more data. At the same time, the diverse ways in which businesses can interact with their customers can lead to incredibly complex data sets.

How do business intelligence solutions accommodate such data? Well, it depends on how the data is structured.

Structured Data

Structured data is data with a definitive format and length, usually stored in a database. Examples of structured data include numbers, dates, or groups of words called strings. Structured data can be stored predictably in columns or rows, like that of a spreadsheet. As a result, it’s much easier to process than unstructured data.

Unstructured Data

Unstructured data is information that does not mold well to a database and is not formatted consistently. A common example of unstructured data is text, which is produced and collected in a large variety of formats (email, word documents, social media posts). The majority of most business data is unstructured.

What Data Structure Means for BI

Data structures operate on more of a sliding scale than binary categories. Semi-structured data, for example, refers to data that is largely unstructured but may be labeled with structured meta-data.

Business intelligence tools can automate the analysis of structured and semi-structured data to provide insights in seconds. Structured data also holds immense potential in the hands of BI with machine learning capabilities, as these tools can analyze thousands of data combinations to determine complex data relationships and pinpoint the source of trends.

When BI tools can do the heavy lifting of structured data, business people can spend more time focusing on setting strategy and analyzing unstructured data from a human perspective.

BI for unstructured data is far more complicated and requires reduction of unstructured data into a data model before it can be analyzed by a BI interface.

Business Intelligence Software Basics

Business intelligence software is designed to accomplish many necessary functions for its users. Here’s a breakdown of what the technology typically has the capacity to accomplish, broken out by role, as well as some of the BI software features you’ll encounter as you research the solution that is best for your company.

Data Scientists and Analysts

At its core, BI software should make the jobs of data scientists and analysts far easier. Reporting should be simpler. To start off with, a business intelligence platform needs to let you connect to your data, no matter where it is. Whether your data is on-premises, cloud-based, or a mix of both, you should be able to access it. From Salesforce to Excel to Microsoft SQL to Hadoop and beyond, pick a platform that connects with all of your critical data sources.

Once you’re confident that you’ve selected a platform that will work with your various data sources, look for an option that checks the following boxes:

• It is designed to make prepping and modeling data easier.
• The advanced analytics features are user-friendly and accessible for those who are capable of taking full advantage.
• The software enables you to create interactive, customized reports for your business’s unique needs.
• Reports and visual analytics are readily accessible and digestible for non-technical users.

However you look at it, it is clear that business intelligence is deeply embedded in the roles and responsibilities of analytics departments, so it is crucial that you put your best forward with an advanced and capable software solution.

Marketers

The right BI software can be a huge win for marketing teams. The better a marketer understands past and current performance, the better they can strategize for driving customer acquisition. Data is the key to that enhanced understanding.

Business intelligence technology should help pull together your data, run various marketing analytics, and report on the findings, all as quickly as possible — and preferably — in real time.

From social and web analytics to customer data, your BI solution should be ready and able to meet your marketing needs while answering your marketing questions.

Salespeople

Sales is metrics driven. Your sales team is going to be asking questions like “Have we reached our monthly sales quota?” and “How is our region performing compared to other regions?” You need a solid BI solution in order to answer those questions effectively and efficiently.

Look for software that can help you perform sales pipeline analytics, win-loss analysis, opportunity studies, and KPI tracking. Then, you can leverage your enhanced data capabilities to identify opportunities to shorten the sales cycle and grow revenue. 

Generally speaking, your entire sales team should be able to leverage dashboards and scorecards to track performance against targets and business requirements. Pick a platform that equips your team with the visibility it needs to close deals and bring in revenue.

Financial Analysts

Business intelligence provides a huge opportunity for financial analysts. Make a greater impact given your time and resources with an advanced BI solution.

Those in finance need to track professional success metrics like company growth compared to competitors, percentage growth over time, profitability, market share, and more.

While looking to convert raw data into those metrics, your average financial analysis team is also going to be simultaneously struggling with forecasting errors, uncertainty, disparate data, and lack of collaboration and communication.

Business intelligence technology can help your team solve for those challenges while tracking the right metrics.

Learn More About Business Intelligence By Role

Learn More About AI in Business Intelligence

The new eBook, “The State of AI in Business Intelligence: The Features You Should Look for Today,” explains how AI is augmenting the workforce and revolutionizing business analytics.

With AI-driven BI, business people can get faster, more comprehensive insights that drive action. This resource helps leaders understand how AI can be leveraged as a competitive advantage as more companies invest in this technology.

Learn more with the eBook.

The Top BI Tools

The market is full of business intelligence solutions, which is great for buyers. While the selection process can be intimidating and getting buy-in from leadership comes with its own set of challenges, developments in business intelligence software help make the case for the new investment.

If you’re looking to see what analytics platforms are out there, below you’ll find a BI tools list that highlights some of the best offerings in the industry.

AnswerRocket

Tableau

Microsoft Power BI

Domo

Qlik

Sisense

Learn More About Shopping For BI Solutions

The Bottom-Line Benefits of BI

Action

Ultimately, BI should help people act on the information they learn. BI tools provide insights into data that would take a person more time and effort to generate. As such, people can leverage BI tools to respond to opportunities and challenges alike. Moreso, advanced BI tools enable employees to be proactive, instead of reacting to static or outdated reports.

Accessibility

By displaying complex data as visualizations, for example, BI tools make data accessible to decision-makers. Often, the people who determine business strategy aren’t as technical as analysts or the IT department. As BI tools have become more user-friendly, more employees are empowered to make data-driven decisions.

Speed

The value of speed cannot be understated. When employees can use BI tools to assist them in understanding their data, they can work much more efficiently. Time that would be spent culling through reports and spreadsheets can be put toward setting business strategy instead. The cost of cumbersome data processing is then invested in growth efforts.

AI, BI, and AnswerRocket

Artificial Intelligence

Artificial intelligence (AI) is a huge topic in tech. In the data and analytics space, the combination of machine learning and natural language processing are changing the way companies approach their data. It’s making it possible for business users to forecast future trends and automate analysis in a way that was previously unimaginable.

Business Intelligence

Data analytics providers have been working for years to incorporate AI technology into business intelligence tools. Now, a few years into this trend, the most established version of this pairing is augmented analytics. Gartner coined the term, explaining that augmented analytics automates insights using machine learning and natural-language generation.

AnswerRocket

AnswerRocket is a leading augmented analytics platform, leveraging AI and BI to help its end users find growth opportunities faster than the competition. With business intelligence features such as dashboards and data visualizations and AI technology that automates processes and offers deeper insights, the AnswerRocket platform is a leading solution in this new era.

Learn More About AnswerRocket’s Business Intelligence Software

The post Top 9 Business Intelligence and Analytics Trends for 2016 first appeared on AnswerRocket.

Jet Ski on Your Data Lake – Natural Language Query on Hadoop from Pete Reilly on 

The growth of big data has created a whole new set of challenges. Namely, the ability for expanding groups of users to leverage this data to drive profitable action. Self-service analytics has been talked about for decades, but only a handful of technicians in each company can use the current generation of data exploration and visualization tools to create new visualizations and insights.

In this talk at Strata + Hadoop World in New York City, Mike Finley discusses the advent of big data and the changes needed to empower business users with self-service analytics using natural language search on big data. Mike shows a demo of AnswerRocket using retail data and natural language search to make big data discovery accessible to the everyday business user.

The post Natural Language Search on Retail Big Data first appeared on AnswerRocket.

AnswerRocket enables self service big data exploration and visualization.

Despite the proliferation of self service analytics tools, enterprises still struggle with democratizing their data. To answer unanticipated questions, users must enlist the help of technical resources. And wait. We think there is a better way.

AnswerRocket empowers people to access their enterprise data without technical help. AnswerRocket provides this Google™-like, search-driven analytics experience by converting natural language questions into queries against Apache Drill, Spark SQL, SAP HANA, Amazon RedShift and many other sources. AnswerRocket answers these questions with visualizations any business user can understand, explore, and share. Without waiting.

The post Natural Language Data Discovery and Visualization first appeared on AnswerRocket.