Recent advancements in artificial intelligence have not only reshaped how we interact with technology but also how businesses operate and innovate. Key players like Microsoft, OpenAI, and Snowflake are at the forefront of this transformation, each pushing the boundaries of what’s possible with AI. Let’s take a look at the strides made by these industry leaders, exploring Microsoft’s commanding presence in AI, the cutting-edge developments in conversational AI with GPT-4o, and Snowflake’s ambitious open-source Arctic LLM initiative. Together, these advancements signal a new era where AI is more integrated, responsive, and essential to the business world.

AI Leadership and Strategic Moves

Microsoft’s AI Leadership

Microsoft’s recent earnings announcement underscored its robust performance in the AI domain. With Azure growing by 31% and AI services contributing 7% to this growth, Microsoft’s strategic investments are clearly paying off. The real game-changer, however, lies in high-profile deals such as the $1.1 billion agreement with Coca-Cola for Azure services, including Azure AI. These moves highlight the growing adoption of AI as a key productivity tool in enterprises.

Under Satya Nadella’s leadership, Microsoft has positioned itself as a pioneer in AI technology. This leadership is further bolstered by its partnership with OpenAI, allowing Microsoft to leverage cutting-edge research and innovation. Notably, Azure supports a variety of AI models, including those from Meta and Mistral, ensuring that Microsoft’s AI solutions remain versatile and adaptable to diverse business needs.

Google’s AI Ambition

Not to be left behind, Google has also been ramping up its focus on AI. The company’s revamped search engine, driven by generative AI, showcases this shift. Embracing an “AI-first” philosophy, Google aims for faster results while addressing concerns about website traffic. Internally, Google has unified its AI teams under Google DeepMind, aiming to expedite commercial AI product development while maintaining a strong research focus. This strategy underscores Google’s commitment to innovation and responsible AI integration.

Google is enhancing user experience by incorporating its leading AI model, Gemini, into the Workspace suite, boosting productivity across applications. In Google Search, AI-generated overviews provide summarized information directly in results, aiming for faster retrieval. The lightweight Gemini Flash model further demonstrates Google’s focus on reliable and accessible AI. Combining technical innovation with responsible implementation, Google is making significant strides in the generative AI landscape.

Apple’s AI Plans Unveiled

Apple’s recent WWDC 2024 announcement showcased its strong push into the AI arena. Introducing “Apple Intelligence,” Apple unveiled a suite of AI features across iPhones, iPads, and Macs. This move is set to redefine user interaction with devices, emphasizing enhanced privacy and personalized experiences. Key features include a more conversational Siri, AI-generated “Genmoji,” and access to GPT-4o, which enables Siri to utilize OpenAI’s chatbot for complex queries.

Under Tim Cook’s leadership, Apple is carving out a unique path in the AI landscape by focusing on on-device processing, thereby minimizing data sent to the cloud and ensuring user privacy. This approach is further strengthened by Apple’s “Private Cloud Compute” strategy, which processes complex requests without storing data on its servers. By integrating these AI capabilities seamlessly within its ecosystem, Apple aims to provide a user-centric and secure AI experience, positioning itself as a leader in trustworthy AI implementation.

Technological Advancements in AI Models

GPT-4o Evolution

The introduction of GPT-4o by OpenAI represents a significant leap in conversational AI. Building on the robust foundation of GPT-4, GPT-4o incorporates voice capabilities, transforming the interactive experience with real-time speech-to-text and text-to-speech functionality, much like a smart speaker. This seamless integration marks a pivotal advancement in AI interactions.

A key focus of GPT-4o is optimizing the “time to first token” metric, which measures the time from receiving an input to beginning to generate a response. By improving this metric, GPT-4o ensures fluid and natural conversations, enhancing user experience. The model’s ability to quickly stream parts of the answer while continuing to process the input revolutionizes conversational efficiency.

Practical Applications of GPT-4o

The advancements in GPT-4o open up numerous practical applications across various industries. The ability to replace screen-based interactions with voice interfaces can transform sectors such as tech support, counseling, and companionship, offering more intuitive and responsive user experiences. This makes AI a central tool in business operations and customer interactions.

GPT-4o Risks

With advancements come new challenges. GPT-4o’s ability to convincingly mimic human speech raises concerns about potential misuse, such as impersonation and large-scale robocalling fraud. While enhancing conversational efficiency, the model’s rapid response capability also increases the risk of generating plausible yet incorrect responses. These risks underscore the need for robust safeguards and monitoring to ensure responsible use of AI technology.

Snowflake’s Arctic LLM

Snowflake’s Arctic LLM represents a strategic advancement in the open-source AI arena. Utilizing an innovative Mixture of Experts (MoE) architecture, Arctic trains smaller models on different datasets and combines them to solve various problems. This approach allows Arctic to activate only a portion of its parameters during inference, making it both computationally efficient and powerful, outperforming many open-source and some closed-source models in specific tasks.

By releasing Arctic under the Apache 2.0 license, Snowflake aims to foster collaboration and innovation within the AI community. This open-source strategy encourages external contributions and enhancements, positioning Snowflake as a leader in AI community engagement. Arctic is designed for enterprise-specific tasks such as SQL generation and code instruction, providing businesses with valuable tools to streamline operations with AI.

Snowflake’s Arctic for Enterprise Use

Arctic’s MoE architecture and open-source nature align with Snowflake’s goal of advancing AI through community collaboration and practical enterprise applications. Designed for tasks like SQL generation and code instruction, Arctic allows enterprises to tailor the model to their specific needs, effectively addressing real-world challenges and enhancing productivity and efficiency in business operations.

AI Safety and Explainability

Safe AI Development

As AI technology advances, ensuring its safe and ethical use becomes paramount. Traditional methods for training safe AI have focused on filtering training data or fine-tuning models post-training to mitigate issues such as bias and unwanted behaviors. However, Anthropic’s research with the Claude 3 Sonnet model introduces a proactive approach by mapping the model’s inner workings to understand how neuron-like features affect outputs. This transparency is crucial for mitigating risks and ensuring that AI models behave as intended.

Anthropic’s innovative approach provides real-time insights into how models process prompts and images, laying the foundation for integrating explainability into AI development from the outset. By understanding the internal mechanics of AI models, developers can identify and address potential issues early in the development process. This ensures that production-grade models are reliable, truthful, and unbiased, which is essential for their scaled-up use in enterprises.

Practical Guidance for Explainable Models

Achieving explainability in AI models involves several advanced techniques. One effective method is having models articulate their decision-making processes, making the AI systems more transparent and accountable. This can involve generating detailed explanations for each decision or prediction the model makes, thereby increasing user trust and facilitating better oversight.

Another approach is identifying “neighbors” or examples from training data that are similar to the model’s current decision. By comparing new inputs to known examples, developers and users can better understand the context and reasoning behind the model’s outputs. This method not only enhances the understanding of the model’s thought process but also helps in diagnosing errors and improving model performance.

Furthermore, these techniques can reduce training time and power requirements while improving precision and safety. By focusing on explainability, developers can create models that are not only effective but also efficient and aligned with ethical standards. This focus on ethical AI is becoming increasingly important as AI systems are deployed in sensitive and high-stakes environments such as healthcare, finance, and autonomous systems.

In addition to these methods, integrating explainability features into user interfaces can enhance the practical utility of AI models. For instance, dashboards that visualize decision paths or highlight key factors influencing predictions can make AI tools more accessible to non-expert users. This democratization of AI technology ensures that a broader range of stakeholders can engage with and benefit from AI systems, fostering wider adoption and innovation.

Ensuring the safe and ethical use of AI technology is critical as advancements continue to accelerate. Anthropic’s proactive approach with the Claude 3 Sonnet model exemplifies how understanding the inner workings of AI can mitigate risks and enhance reliability. Techniques such as having models articulate their decision-making processes and identifying similar examples from training data contribute to greater transparency and accountability. By integrating explainability into AI development from the outset, developers can create models that are not only effective but also efficient and aligned with ethical standards. These efforts are essential for fostering trust and enabling the responsible scaling of AI in various enterprise applications.

A Fast-Evolving Field

The rapid advancements in AI by Microsoft, Google, Apple, and Snowflake are reshaping the business landscape. Microsoft’s strategic growth, Google’s innovative AI integrations, and Apple’s focus on privacy underscore the diverse approaches of these tech giants. The introduction of GPT-4o by OpenAI and Snowflake’s Arctic LLM highlight significant leaps in conversational AI and open-source models, respectively, offering practical applications across various industries.

Ensuring the ethical and safe use of AI is crucial. Anthropic’s proactive approach with the Claude 3 Sonnet model emphasizes transparency and explainability, essential for building reliable and unbiased AI systems. Techniques to achieve explainability, such as articulating decision-making processes, enhance the accountability of AI models.

These advancements signal a new era where AI is more integrated, responsive, and essential to business operations. The focus on innovation, collaboration, and ethical standards will drive the responsible scaling of AI, benefiting both businesses and consumers.

The post Navigating the AI Boom: Leadership, Innovation, and Safety in the New Era of Artificial Intelligence first appeared on AnswerRocket.

California’s SB 1047 legislation has emerged as a pivotal development in the AI space. This proposed law mandates that companies investing over $100 million in training “frontier models” of AI, such as the forthcoming GPT-5, must conduct thorough safety testing. This legislation raises critical questions about the liability of AI developers, the impact of regulation on innovation, and the inherent safety of advanced AI models. Let’s  examine these issues in depth, aiming to understand the balance between fostering innovation and ensuring safety in the realm of AI.

Liability of AI Developers

One of the fundamental questions posed by California’s SB 1047 is whether AI developers should be held liable for the harms caused by their creations. AI Regulations serve an essential role in society, ensuring safety, ethics, and adherence to the rule of law. Given the advanced capabilities of Generative AI (GenAI) technologies, which can be misused intentionally or otherwise, there is a compelling argument for regulatory oversight.

Regulations have a role in society, providing for safety, ethics, and the rule of law. Because GenAI tech is advanced enough to be used for harm—whether intentionally or not—there must be a role for AI regulation around this important new advancement.

AI developers must ensure their models do not harbor hazardous capabilities. The legislation suggests that companies should provide “reasonable assurance” that their products are safe and implement a kill switch if this assurance proves inaccurate. This level of accountability is crucial, as the intent behind the use of these tools is at fault for any harm done, not the makers of the tech itself. 

Regulation vs. Innovation

The debate over whether AI regulation stifles innovation is not new. Meta’s chief AI scientist, Yann LeCun, has voiced concerns that regulating foundational AI technologies could hinder progress. While the intent of AI regulation is to protect from danger, the California law, as currently proposed, has notable flaws. For instance, setting a cost-of-production threshold to determine a model’s danger is problematic due to the dynamic nature of computing costs and efficiencies.

Putting a cost-of-production threshold on what makes a model dangerous is flawed. The price of computing and the efficiency in the use of computing are notoriously dynamic. Meaning a powerful model could still be developed below the threshold. A more suitable approach might involve using intelligence benchmarks or introspective analyses to assess an AI’s potential risks.

Sensible AI regulation can coexist with innovation if it targets genuine threats without imposing unnecessary burdens. Thus, we can avoid stifling the amazing minds behind GenAI and instead encourage them to create better solutions that skirt the burden of bureaucracy.

Safety of AI Models

The safety of AI models, particularly larger ones, is a topic of significant concern. GenAI can be either a tool or a weapon, depending on its use. The real risk lies in the intent behind using these technologies. 

While GenAI models are not inherently harmful, their deployment in autonomous systems with physical interactions poses potential dangers. Whether GenAI models rise on their own to harm humanity without human-generated intent is, at best, a transitional state of affairs. If GenAI were released to operate independently with its power supplies and means to interact with the world, it would likely strive to enhance its intelligence. Why? Because intelligence is the ultimate answer, the only true currency of any value in the long run.

To harness the benefits of AI while minimizing risks, proactive management and ethical considerations are paramount. We’re better off making this technology great for our own benefit, working symbiotically with it as it approaches or surpasses our own abilities.

Conclusion Striking A Fine Balance

As we navigate the frontier of AI technology, it is crucial to strike a balance between regulation and innovation. Ensuring the safety of AI models through sensible regulation, without stifling the creative efforts of researchers and developers, is essential. By focusing on genuine risks and maintaining ethical standards, we can maximize the benefits of AI while safeguarding humanity. Stakeholders must engage in thoughtful AI regulation and commit to ethical AI development to pave the way for a future where AI serves as a powerful ally in our progress.

The post AI Safety and Regulation: Navigating the Frontier of Technology first appeared on AnswerRocket.

Our latest resource, “Brand Growth Beyond Crisis: Leveraging Pandemic Insights to Future-Proof Your CPG & Retail Strategies,” explores critical strategies for harnessing the power of Artificial Intelligence (AI) and Machine Learning (ML) to steer through turbulent times. Here’s why this guide is a must-read:

Navigate Through Data with Precision

The pandemic taught us that the necessity for precise, timely, and frequent analysis of complex business performance has never been more critical. Augmented analytics—melding AI with natural language generation—empowers businesses to make intelligent decisions swiftly, ensuring that you’re not just keeping up but staying ahead.

Transform Challenges into Opportunities

From enhancing cross-functional collaboration to improving shelf presence and managing market share, AI can transform your operational challenges into competitive advantages. Learn how AI helps you gain real-time insights into market demands, enabling you to make informed decisions rapidly.

Insights from Industry Leaders

Gain inspiration from leading companies like Coca-Cola and Procter & Gamble, who have successfully navigated the pandemic’s challenges by innovating and adapting their strategies. Understand the shifts in consumer preferences and how these giants are leveraging technology to stay relevant and resilient.

Ready for the Future

As the lines between digital and physical shopping experiences blur, understanding and implementing a robust omnichannel strategy is key. Discover how AI and ML are crucial tools in understanding these dynamics and preparing your business for the future consumer landscape.

Embrace the New Normal with AI

The insights offered in “Brand Growth Beyond Crisis” are more than just theoretical—they’re a blueprint for action. By embracing AI and sophisticated analytics, you can ensure that your business is not only reacting to changes but is proactively prepared for future shifts.

Curious to uncover the full strategies and detailed insights? Download the full eBook now and transform your approach to meet the demands of a rapidly evolving market. Let AI be your guide in future-proofing your CPG and retail strategies.

The post Post-Pandemic Lessons Learned: Harness AI for CPG & Retail Growth Amid Crisis first appeared on AnswerRocket.

CONTACT INFO

info@max.ai

www.max.ai

Anywhere, Anytime

POWERED BY

AnswerRocket

Open AI GPT-4

USE CASES

• Investigate business issues & opportunities
• Generate proactive insights & analysis
• Support business planning & strategy development
• Support research projects

COMPATIBLE DATA WAREHOUSES

• Amazon RedShift
• Azure Synapse Analytics
• Google Big Query
• Snowflake

EXPERIENCE

Category & Brand Insights Assistant | Fortune 500 Global Beverage Leader

March 2023-Present

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 changes in market share and brand equity with data-driven action plans.

Sales Effectiveness Analysis Assistant | Fortune 500 Pharmaceutical Company

March 2023-Present

Helping a Fortune 500 pharmaceutical company generate groundbreaking insights revealing the direct impact of sales activities on market share.

Macro Market Trends Research Assistant | Global Consumer Packaged Goods Leader

March 2023-Present

Empowering a global consumer packaged goods leader to generate insights from unstructured market research alongside structured company performance analysis to better understand and respond to macro market trends.

AI Assistant for Data Analysis

ABOUT ME

I’m a generative AI Assistant that helps you explore, analyze, and get insights from your data. Powered by AnswerRocket’s augmented analytics platform and GPT-4, I’m focused on making it easier than ever to get answers to your business questions – just by chatting.

CAPABILITIES

Explore Data

Analyze & Visualize Data

Run advanced analysis to understand, diagnose, and predict business performance

Generate Insightful Narratives

Compose easy-to-understand data stories highlighting key insights from analysis

Follow-ups

Answer follow-up questions and pick back up on past conversations in an instant

Automate Analysis

Generate recurring analysis reports
and presentations on a set schedule
or as new data is available

ANALYTICAL SKILLS

• Current Performance: Evaluate your latest performance and track key metric changes
• Competitive Performance: Assess performance against major competitors and spot improvement opportunities.
• Metric Drivers: Identify and drill into the drivers behind metric increases and decreases.
• Metric Trends: Trend business performance over time, spotting outliers, and forecasting future performance.

REFERENCES

Anheuser-Busch InBev

“A chat-based tool like Max can help more users feel comfortable interacting with data. Having an on-demand assistant that can quickly answer the questions that pop up throughout the day would enable our team to make data-driven decisions at scale.”
– Sabine Van den Bergh, Director Brand Strategy & Insights Europe

Cereal Partners Worldwide, a joint venture between Nestlé and General Mills

“Max will take AnswerRocket to the next level! We need our teams to make informed, fact based decisions. Max will enable users across all levels of CPW to quickly access data and insights through intuitive questions and responses.”
– Chris Potter, Global Applied Analytics

Beam Suntory

“With Max, Beam Suntory can automate routine tasks and gain valuable insights from data, allowing us to make more informed decisions. I see the potential for Max to become a powerful tool for analyzing a combination of external, macro, and internal data.”
– Abraham Neme, Global Head BI & Analytics

Share Max’s Resume with Your Team

The post Max’s Resume first appeared on AnswerRocket.

The post Demo: Meet Max, Your Generative AI Assistant for Data Analysis first appeared on AnswerRocket.

CONTACT INFO

answerrocket.com/cpg

Anywhere, Anytime

POWERED BY

AnswerRocket

Open AI GPT-4

USE CASES

• Investigate business issues & opportunities
• Generate proactive insights & analysis
• Support business planning & strategy development
• Support research projects

ANALYZE DATA FROM

• NielsenIQ
• Kantar
• IRi
• Spins
• NPD
• Toluna

EXPERIENCE

Category & Brand Insights Assistant | Fortune 500 Global Beverage Leader

March 2023-Present

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 changes in market share and brand equity with data-driven action plans.

Sales Effectiveness Analysis Assistant | Fortune 500 Pharmaceutical Company

March 2023-Present

Helping a Fortune 500 pharmaceutical company generate groundbreaking insights revealing the direct impact of sales activities on market share.

Macro Market Trends Research Assistant | Global Consumer Packaged Goods Leader

March 2023-Present

Empowering a global consumer packaged goods leader to generate insights from unstructured market research alongside structured company performance analysis to better understand and respond to macro market trends.

AI Assistant for CPG Analytics & Insights

Powered by AnswerRocket

An AI Assistant for Category Managers & Insights Teams

ABOUT ME

I’m a generative AI Assistant that helps you explore, analyze, and get insights from your CPG data. Powered by AnswerRocket’s augmented analytics platform and GPT-4, I’m focused on making it easier than ever to get answers to questions about your categories and brands–just by chatting.

CAPABILITIES

Explore Data

Analyze & Visualize Data

Run advanced analysis to understand, diagnose, and predict business performance

Generate Insightful Narratives

Compose easy-to-understand data stories highlighting key insights from analysis

Follow-ups

Answer follow-up questions and pick back up on past conversations in an instant

Automate Analysis

Generate recurring analysis reports
and presentations on a set schedule
or as new data is available

ANALYTICAL SKILLS

• Current Performance: Evaluate your latest performance and track key metric changes
• Competitive Performance: Assess performance against major competitors and spot improvement opportunities.
• Metric Drivers: Identify and drill into the drivers behind metric increases and decreases.
• Metric Trends: Trend business performance over time, spotting outliers, and forecasting future performance.

REFERENCES

Anheuser-Busch InBev

“A chat-based tool like Max can help more users feel comfortable interacting with data. Having an on-demand assistant that can quickly answer the questions that pop up throughout the day would enable our team to make data-driven decisions at scale.”
– Sabine Van den Bergh, Director Brand Strategy & Insights Europe

Cereal Partners Worldwide, a joint venture between Nestlé and General Mills

“Max will take AnswerRocket to the next level! We need our teams to make informed, fact based decisions. Max will enable users across all levels of CPW to quickly access data and insights through intuitive questions and responses.”
– Chris Potter, Global Applied Analytics

Suntory Global Spirits

“With Max, Beam Suntory can automate routine tasks and gain valuable insights from data, allowing us to make more informed decisions. I see the potential for Max to become a powerful tool for analyzing a combination of external, macro, and internal data.”
– Abraham Neme, Global Head BI & Analytics

Share Max’s Resume with Your Team

The post Max’s CPG Resume first appeared on AnswerRocket.

Chat with Max for 10x faster insights on your CPG data with generative AI

Conversational UX

Understands your business and data

Skilled in advanced analytics

Turns raw data into actionable insights

Safe and secure

Download the Solution Brief

Get actionable insights for better decisions
across your organization

Insights Teams

Category Managers

Marketing

Field Sales Team

Drive brand and category growth
with the power of AI Analytics

Analyze CPG data just by chatting

Chat with Max to gain valuable insights on your brand, category, market, and customer data – anywhere, anytime. Our integration with OpenAI’s GPT-4 LLM lets you chat naturally in your own words.

Automate recurring brand and category analysis

Say goodbye to manual analysis, while giving your team the same reports and presentations they know and love. Max automates your daily, weekly, and monthly analysis on schedule or upon data refresh.

Tailored to your CPG business

Max is fully customizable to reflect the way your business analyzes, visualizes, and talks about data. With Skill Studio, you can create specialized Skills and AI Assistants to help tackle your unique data analysis needs.

Make Max a GenAI Assistant on Your CPG Team

The post Meet Max, a GenAI Assistant for CPG Teams first appeared on AnswerRocket.

Throughout last year, the COVID-19 pandemic severely affected businesses. It impacted their ability to make accurate predictions, fulfill consumer expectations, and assess performance.

Restaurants were closed or shut down permanently. The financial sector adapted to a decrease in business profits and consumer disposable income.

Some CPGs and retailers coped with out-of-stocks and delayed supply chains, while others capitalized on “free” product trials as consumers flocked to whatever items were available during periods of pantry stocking.

Thus, many companies are poised for portfolio evaluations, whether to tighten their operational costs or to gain more market share.

Portfolio health can be enhanced by reducing products, changing factors like price, and investing in winners. To make these decisions, businesses require an examination of individual SKU performance, which is far easier said than done.

SKUs can be affected by many factors, such as segment, geography, price, promotions, channel, retailer, distribution, or competitors–and all to varying degrees.

Analysts must grapple with these factors to gain an accurate view of SKU performance, but most businesses still struggle to determine their big-picture portfolio performance.

SKU rationalization, and how you approach it, can make a difference.

What is SKU Rationalization and Why Does it Matter?

SKU rationalization is the process of determining which products should be kept, retired, or improved based on the myriad of factors that contribute to performance. Sometimes referred to as SKU optimization, this process enables organizations to refine their product portfolios to improve their financial outlook.

By prioritizing SKUs that drive growth and cutting the tail on laggards, leaders can build healthier portfolios of revenue-generating, valuable products.

In addition to choosing which SKUs to keep and cut, analysts might also focus on solving product cannibalization to ensure SKUs aren’t competing with each other.

By understanding performance at the SKU level, companies can invest in winning products, eliminate low performers, identify opportunities for new innovations, and optimize for efficiency.

How is SKU Rationalization Typically Performed…and Why is it Insufficient?

For large companies, performing granular analysis at the SKU level would be a seismic task, especially if the analysis is manual. Thousands of SKUs would need to be audited along with the many factors affecting their performance.

In actuality, companies tend to group and analyze SKUs based on a limited number of factors.

One way of measuring SKU performance involves comparing SKUs in the same category. For example, a company might compare the performance of all its sparkling waters against each other, rather than comparing the category to its soda products.

This approach is the most accessible way to analyze SKUs, but it’s lacking because category is a single performance factor among many.

A flavor of sparkling water that’s only sold in one region may perform poorly compared to a national flavor but perform well compared to other regional products.

Grouping SKUs by category doesn’t tell the whole story or show meaningful product performance, which can lead companies to draw the wrong conclusions.

The lack of visibility is a cumbersome hindrance in the way of organizations’ success.

Data science, however, provides a different approach. Data science enables accurate decision-making by analyzing SKUs with the business case in mind.

Machine learning (ML) algorithms can consider every important factor, which isn’t possible for a human analyst contending with deadlines. Additionally, ML speeds up the analysis process significantly, delivering compelling insights directly to business teams within minutes, on-demand.

It can also optimize SKU rationalization on the granular level. Instead of only comparing SKUs within the same category, SKUs can be compared based on likeness. While two different flavors of sparkling water can appear to be similar enough to draw conclusions about performance, this assumption is based on limited characteristics.

ML analyzes many elements beyond just category, allowing the company to gain the greatest amount of insight into SKU performance.

In the next section, let’s further discuss how to use data science and ML to automate SKU rationalization.

A Different Approach: Applying Machine Learning to SKU Rationalization

ML can automate SKU rationalization by analyzing the many factors that affect SKUs. It can then group SKUs together based on overall performance, showing a big picture view of product portfolio health.

This approach groups SKUs as high, medium, and low performers. This performance is determined by a number of factors, including comparisons with SKUs in and outside the category, as well as competitors, and the overall trend of the segment, retailer, and region.

ML also enables teams to generate granular, SKU-level recommendations. While manual SKU rationalization would require significant translation, ML can automatically serve these recommendations in natural language.

Natural language generation enables a business person to receive recommendations like “cut this item” or “intervene in 2023.” Essentially, business teams can take action without waiting for back-and-forth with an analyst (and the analysts are freed from translating the minutia of their findings).

Recommendations to intervene should be bolstered with forecasts that show future SKU performance by year.

The automated process compresses the workflow and produces compelling, on-demand insights. Instead of yearly, SKU rationalization can occur daily, weekly, quarterly, or monthly to suit the needs of the organization.

The continual analysis is a huge competitive advantage among changing consumer behavior. Companies have instant access to actionable insights from relevant data, rather than data that is weeks or months old.

How, then, do we correctly automate the SKU rationalization process to ensure our companies have access to actionable insights?

We’ll explore the essential elements of automating SKU rationalization and how to successfully incorporate ML software into the process in the next paragraph.

The Essential Elements of Automating SKU Rationalization

The “learning” part of ML starts with successful examples that can be used to teach a machine how to do something.

For a machine to learn SKU rationalization, the first step is to use human insight that identifies high-performing SKUs from which the machine can learn. This is an example of how ML cooperates with human experts to generate successful, scalable results.

ML software creates an idealized model of great SKUs based on examples from human experts. This model is then applied to all SKUs to assess current and probable future performance.

Using historical results, machine learning can create a view of how SKUs will sell over time. This forecasting capability of ML allows organizations to predict SKU performance.

To test whether the forecast is accurate, machine learning models can be used to “predict” what has already happened. This is called backtesting, and it should be used to establish whether ML has accurately learned how SKUs perform.

In SKU rationalization, the goal is to decide how to use limited resources to improve the performance of a large number of SKUs. Price, promotion, distribution, advertising, and innovation all are levers that a marketer can pull to change the trajectory of a SKU.

Deciding which of those levers to use one SKU at a time is ideal, but tedious and expensive, doing so for arbitrary groups is not likely to create optimal results. A better answer is to use ML for this task.

ML is used to make groups out of SKUs according to their characteristics such that marketers can act on these groups instead of on each SKU, while still achieving near-optimal results. The ultimate question is then: “How do we know what actions to take for each group of SKUs?”

The groups of SKUs that ML assembles should make sense to the human experts that provided the training information. If the groupings don’t make sense, the machine learning algorithm likely needs additional iterations with more specifics on which factors are important when determining how to group SKUs.

Once the groups do make sense, the actions needed to be taken will be clear to the marketers receiving the analysis.

Of course, some products will be too new to be meaningfully evaluated. A good ML model should highlight those innovations and exclude them from analysis. Products that are approaching the end of their shelf life should also be excluded.

Apart from the technical essentials, successful SKU rationalization automation requires intelligent change management. Company leaders must endorse AI and ML automation to ensure it’s adopted from a top-down approach. Since this analysis will enable cross-functional collaboration, teams should be aligned from the start of adoption.

The benefits of this alignment are clear: marketing, finance, and data teams often don’t have enough visibility into each other’s realms. A standardized approach to SKU rationalization creates a clear through-line for decision-making on which products to keep, promote, or cut.

Conclusion

Analytics from automated SKU Rationalization provides companies with the SKU-level insights they need to make better investment decisions and increase the health of their portfolios.

It reduces costs as companies can proactively cut the tail on low-performing SKUs and invest in winners based on predictive insights. They can also track the performance of all SKUs over time.

The real-time analysis provides organizations with a competitive advantage compared to other companies that have not adopted this type of technology. The automation streamlines the entire SKU rationalization process by providing team members with ease of access and more granular insights.

To illustrate, National Beverage, a company with a distinctive portfolio of sparkling waters, juices, and carbonated soft drinks, needed to obtain actionable insights to solve complex problems like SKU rationalization.

The organization utilized AnswerRocket’s AI-powered analytics software to understand what exactly was driving category and product performance with efficient and timely insights, eliminating “analysis paralysis.” Learn more about this use case.

Originally published August 3, 2021.

The post The SKU Rationalization Guide: Optimize Your Product Portfolio first appeared on AnswerRocket.

ATLANTA—Dec. 12, 2023—AnswerRocket, an innovator in GenAI-powered analytics, today announced the launch of Skill Studio, which empowers enterprises to develop custom AI analysts that apply the business’ unique approach to data analysis.

“Skill Studio has immense potential to transform our approach to analytics,” stated Stewart Chisam, CEO of RallyHere Interactive, a platform for game developers to run multi-platform live service games. “With Skill Studio, we can create a customized AI analyst that deeply understands the nuances of the gaming industry and how we analyze our data. Its ability to automate complex analyses like game performance, player interactions, and usage patterns is groundbreaking. The insights generated by Max can help drive strategic decisions to enhance both our platform and user experience.”  

Say hello to specialized AI copilots

AI copilots have emerged as a powerful tool for enterprises to access their data and streamline operations, but existing solutions fail to meet the unique data analysis needs of each organization or job role. Skill Studio addresses this gap by providing organizations with the ability to personalize their AI assistants to their specific business, department, and role, which enables users to more easily access relevant, highly specialized insights.

Skill Studio elevates Max’s existing AI assistant capabilities by conducting domain-specific analyses, such as running cohort and brand analyses. Key enhancements include:

• Full Development Environment: End-to-end experience supporting the software development lifecycle for developers to gather requirements, develop, test, and deploy Skills to the AnswerRocket platform. Skill Studio allows developers to leverage the Git provider and integrated development environment (IDE) solution of their choice.
• Low-Code UX: User-friendly interface for developers and analysts to create customized Skills for the end users they support.
• Reusable Code Blocks: Accelerate custom Skill development by leveraging pre-built code blocks for analysis, insights, charts, tables, insights, and more.
• Bring Your Own Models: Skill Studio extends the analytical capabilities of Max, enabling enterprises to deploy their existing machine learning algorithms within the Max experience.
• Multi-source and Multi-modal Data Support: Analysts can perform complex analyses using multiple data sources, including structured or unstructured through a single tool. This allows businesses to glean insights from siloed data sources that were previously inaccessible.
• Create Purpose-Built Copilots: Construct copilots designed for specific roles by giving them access to the Skills needed to perform a set of analytical tasks.
• Quality Assurance & Answer Validation: Testing framework for validating accuracy of answers generated by Skills. 

“AI copilots have revolutionized the way organizations access their data, but current solutions on the market are general-use and not personalized to specific use cases,” said Alon Goren, CEO of AnswerRocket. “Skill Studio puts the power of AI analysts back in the hands of our customers by powering Max to analyze their data in a way that helps them achieve their specific business outcomes.”

A collaborative experience for creating fit-for-purpose AI assistants

Skill Studio enables cross-functional design, development, and deployment of AI copilots:

• Data Scientists and Developers: Technical team members can democratize specialized data science algorithms and models as reusable Skills that can be leveraged by Max, enabling users to successfully retrieve the advanced answers they need quickly and securely.
• Analysts: Analysts can customize Skills and Copilots to capture their company’s best practices for analyzing and retrieving insights from data. This allows repetitive, manual data analysis processes to be executed by Max for automated analyses. 
• Business Users: Users can enjoy an easy-to-use experience for interacting with their data by chatting with an AI analyst who understands their business, analytical processes, and insights needs.

For more information on Skill Studio, please visit: https://answerrocket.com/skill-studio/

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

Elena Philippou
10Fold Communications
answerrocket@10fold.com
(925) 639 – 0409

The post AnswerRocket Unveils Skill Studio to Empower Enterprises with Custom AI Analysts for Enhanced Business Outcomes first appeared on AnswerRocket.

We loved being a Diamond Sponsor for this event in Atlanta, right in our own backyard!

What is COLLIDE?

The collision of data and industry is at Data Science Connect’s COLLIDE Data Science Conference. This event showcased the latest trends and advancements in data-driven decision making and how it is revolutionizing industries such as healthcare, finance, and marketing.

Learn more at https://datasciconnect.com/events/collide-2023/.

If you were unable to attend the event, you can watch our session recordings, linked below:

Tuesday, October 3rd, 2:50-3:10pm
Copilot Cooking Show: How To Build a GenAI Assistant for Analytics
With Mike Finley, AnswerRocket Co-founder, CTO and Chief Scientist

In this quickfire session, we’ll demonstrate how AnswerRocket enables enterprises to create customized GenAI-powered assistants for data analysis. Key ingredients include OpenAI’s GPT LLM, AnswerRocket’s augmented analytics platform, and tough business questions. Come see how you can apply these game-changing technologies to produce a custom AI assistant that boosts your team’s analytical productivity.

Wednesday, October 4th, 2:50-3:10pm
Bridging the Gap: From AI Hype to Real-world Impact
With Pete Reilly, AnswerRocket Co-founder and COO

Artificial Intelligence, with its seemingly endless potential and promise, is now front and center as a hot topic in boardroom meetings and strategy sessions. But how does a business move from awe and curiosity to actively realizing benefits in real-world scenarios? This session is tailored to steer organizations from mere contemplation of AI’s power to the tangible and transformative results it can deliver.

Are you interested in learning more about adding a Generative AI Analytics Assistant to your team?

Request a demo with a member of our team here.

The post Past Event: AnswerRocket at COLLIDE 2023 first appeared on AnswerRocket.

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.

We really enjoyed being a Platinum Sponsor of this event, and connecting with so many great people while we were there!

What is Big Data LDN?

The UK’s leading data, analytics and AI event. Big Data LDN (London) is the UK’s leading free to attend data, analytics & AI conference and exhibition, hosting leading data, analytics & AI experts, ready to arm you with the tools to deliver your most effective data-driven strategy. Discuss your business requirements with over 180 leading technology vendors and consultants. Hear from 300 expert speakers in 15 technical and business-led conference theaters, with real-world use-cases and panel debates. Network with your peers and view the latest product launches & demos. Big Data LDN attendees have access to free on-site data consultancy and interactive evening community meetups. Learn more at BigDataLDN.com.  

While we were there, we met with lots of people at our booth (pictured below) and hosted 3 different sessions. We were lucky enough to have some our great customers and even a partner join us on stage for those sessions.

Check out some of the highlights from the show in the gallery below.

Gangs all here!

A bird’s eye view of the conference floor.

Connecting with the audience at the booth.

Our own Alon Goren alongside Ted Prince of Kantar.

Chris Potter, CPW, Alon Goren, AnswerRocket, Liz Davies, AB InBev

Our presentation with Anheuser-Busch

Creating some content alongside Chris Potter, CPW.

Enjoying the local pub scene after a great day at the show.

Cheers to great partnerships!

If you weren’t able to attend the show, or if you’d like to rewatch one of our sessions, you can click on the session titles below to watch a recording. 

WHAT: How Cereal Partners Worldwide Scaled Data-Driven Decisions with Augmented Analytics & Gen AI

WHEN: Wednesday, Sept 20, 4:40 p.m.

WHERE: Gen AI & Data Science Theatre Session

WHO: Chris Potter, Global Applied Analytics, Cereal Partners Worldwide

Joey Gaspierik, Enterprise Accounts, AnswerRocket

Step inside the transformative journey of Cereal Partners Worldwide (CPW), a joint venture between industry giants General Mills & Nestlé, as they redefine decision-making in the era of AI & Big Data. Hear how CPW modernized its analytics processes, turning to augmented analytics and generative AI to realize their vision for a data-driven culture. We’ll share challenges faced, strategies implemented, and the tangible results achieved in this ongoing journey towards democratized analytics.

WHAT: How Anheuser-Busch InBev Unlocked Insights on Tap with a Gen AI Assistant

WHEN: Thursday, Sept 21, 2:40 p.m.

WHERE: Gen AI & Data Science Theatre Session

WHO: Elizabeth Davies, Senior Insights Manager, Budweiser – Europe, Anheuser-Busch InBev

Joey Gaspierik, Enterprise Accounts, AnswerRocket

Gaining a competitive edge in today’s business landscape requires instant, actionable insights. Hear how global beverage titan Anheuser-Busch InBev is transforming its workflows with AI assistants for automated and ad hoc analysis and insights. We’ll discuss real-world use cases and highlight how Insights teams are empowering their business counterparts to make faster, better decisions at scale.

WHAT: Maximizing Data Investments with Automated GenAI Insights

WHEN: Thursday, Sept 21, 4:00pm

WHERE: X-Axis Keynote Theatre 

WHO: Ted Prince, Group Chief Product Officer, Kantar

Alon Goren, CEO, AnswerRocket

We have more data at our disposal than ever, but extracting true value from it remains a challenge. Generative AI and machine learning have opened up new possibilities for transforming raw data into actionable business insights with unprecedented efficiency and precision. Hear how Kantar, a data and insights leader, and AnswerRocket, a Gen AI analytics platform, are applying these powerful technologies to help companies analyze business performance, forecast market trends, and spot anomalies in seconds. Attendees will gain a comprehensive understanding of how to leverage their data assets more effectively, ensuring every data investment drives business growth and innovation.

If you weren’t able to attend but would still like to connect with us, click the link below. 

Request a demo with a member of our team here.

The post Past Event: AnswerRocket at Big Data LDN 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.

How can AI and augmented analytics actually help CPGs? Many companies spend immense amounts of time and money on figuring out what happened in their previous quarter. Imagine finding the answer to all of your questions in seconds. In this webinar, we discuss how.

Speakers:

• Ryan Goodpaster, Account Executive at AnswerRocket
• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post How CPGs Can Use AI to Find Growth first appeared on AnswerRocket.

CPGs and FMCGs need to understand their data and insights. Augmented analytics is disrupting how businesses access this vital information. Learn how augmented analytics automates time-consuming analysis and leverages natural language to power insights that CPGs can use to get an edge over their competition.

Speakers:

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

The post How AI is Powering the Next Wave of CPG Analytics first appeared on AnswerRocket.

With 10+ years leadership experience at companies like Unilever and Coca-Cola, Ada Gil has expertise in all things CPG. She discusses how AI can help CPGs better reach their customers with methods like personalization. Learn more about how CPGs can implement AI to stay competitive for the future.

Speakers:

• Ada Gil, Former Marketing Director at Unilever
• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post How AI Can Launch CPGs Into the Next 5 Years 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.

Imagine an enterprise where analysis is automatically conducted and insights are proactively delivered to help business users make an impact on business outcomes. Learn how enabling technologies like AI, machine learning bots, and GPUs are converging to make this a reality. Together, these technologies stretch the possibilities of augmented analytics, supporting an improved user experience centered around the consumption of analytical content.

Speakers:

• Jim Scott, Jim Scott, Head of Developer Relations, Data Science at NVIDIA
• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post Igniting Proactive, AI-Automated Analytics with ML Bots and GPUs first appeared on AnswerRocket.

This announcement follows a surging trend in data analytics— to make data and insights more accessible to business people. Many companies now recognize the value of data science in this endeavor and are striving to put advanced analysis skills into the hands of business people.

Since data science and its successful deployment is something we’re very familiar with at AnswerRocket, we want to jump into the conversation.

Tableau’s Business Science Sparked a Data Science Conversation— What Should Businesses Consider?

With uncertainty from COVID-19, businesses must navigate unprecedented scenarios with an overwhelming amount of noise in their data.

Many businesses fire on all cylinders to simply analyze historical data, let alone capitalize on current or future growth opportunities.

Data science provides the diagnostic and predictive capabilities that enable businesses to make proactive decisions with sufficient precision, speed, and context.

However, data science teams are stretched thin, tasked with shepherding their models through the business and selling their findings to decision-makers. Meanwhile, business teams lack the technical context to make use of the models’ output and take action.

There’s a gap between what data science can do and how businesses can gain value. One approach to solve this problem is to put no-code AI in front of business people, guiding them to adjust models based on their understanding of the data.

In theory, this allows data scientists to focus on higher-level analysis, while enabling business people to answer everyday questions with data science skills.

However, there are two potential gaps in this approach:

• Data scientists still aren’t empowered to “sell” high-level analysis to business people
• Business people aren’t necessarily clear on the use cases that call for data science, meaning data scientists must undertake significant change management work

In both cases, data science teams must perform work that’s best left to the realm of the data science unicorn— an expert storyteller and modeler who’s too rare to count on.

The work of data science is not the same work of designing outputs that make sense to business users and helping decision-makers take action. Nor is it the same work of getting business users to adopt a new modeling tool, no matter how user friendly.

With that in mind, what other approaches can businesses take to incorporating the strengths of data science into their organizations?

Approaching Data Science for Best Results

Let’s see how business users and data scientists can solve problems without requiring either to level up their skills in areas outside their scope.

1. Data science as a service — This approach brings a team of data scientists into your business to do the hard work of building and deploying models to end users. Data science as a service pulls rare talent into your organization without a long hiring process.
2. Operationalize data science models — High-level data science can be incorporated into self-service analytics and automated for business people. This approach enables domain experts to assess output and refine analysis, while preserving a single source of truth for every end user. Data scientists can leverage their expertise to fine tune the model, while business people can get visualizations and natural language insights that speak in their language.
3. Access pre-built machine learning skills — Models that have already been refined and packaged for critical use cases can accelerate data science skills without overhauling their process.

In each of these approaches, data scientists can lean into their data science skills, and business people can lean into their business skills.

To learn more about these strategies, check out RocketScience, AnswerRocket’s data science services.

The post Tableau Announces Business Science: A Data Science Tool first appeared on AnswerRocket.

It’s no secret that data scientists are in demand.

These professionals leverage their understanding of the business’s needs to build models that solve specific problems. This advanced form of analysis requires extensive knowledge of machine learning, statistics, and the business data— skills outside the scope of a typical data analyst.

While these skill sets are essential for answering complex questions and making predictions of future outcomes, simply analyzing the data doesn’t help business people actually enact solutions.

Decision-makers need to understand how this analysis should impact their strategy, and ultimately, their actions. Thus, data scientists are also tasked with effectively communicating their findings to the business.

Herein lies one of the largest challenges facing data science teams today.

What are Data Science Unicorns and the Last Mile Problem?

The Last Mile Problem refers to the difficulty of making data science output actionable. While data scientists are experts at the analytical process, they often aren’t exceptional storytellers.

It’s incredibly difficult to translate complex insights into something business people (without any analytical background) can understand. Moreover, the ability to present and visualize data analysis is an entirely different skill set than building machine learning models. The rare employees who are able to accomplish both are known as data science unicorns.

A data science unicorn is fully capable of wrangling data, performing analysis, visualizing data, and presenting the findings to decision makers.

They’re so scarce yet in such high demand that hiring one, let alone a team of them, is unrealistic.

As a result, many companies struggle to maximize data science. Instead, they have The Last Mile Problem, with twofold results:

1. Data scientists know they’re sitting on valuable insights, but they struggle to sell them to stakeholders. Decision-makers misunderstand or oversimplify the analysis, expecting the right answers to all their questions even when the answers are nuanced and complex.
2. Executives don’t receive the guidance they need. They invest a lot of money in data science operations, but they don’t see tangible results— because the results aren’t communicated in their language.

In other words: frustration on both sides.

How can companies successfully leverage data science, knowing that data science unicorns are just as rare as their namesake? Simply hiring more data scientists won’t fill the gap.

Solving the Last Mile Problem (Without Data Science Unicorns)

First, let’s illustrate the data science process in more detail.

To perform the analysis, data scientists must:

• Gather and scrub data
• Plan and build models
• Test and validate models
• Evaluate models
• Deploy models
• Run models

As previously discussed, this is where most data scientists excel.

However, once the models are created, the data scientists find themselves stuck as the steward of that model. Every time the business wants to run the model on different parameters, the data scientist is pulled in to facilitate the rest of the cycle on repeat:

• Visualize data
• Form conclusions
• Present findings to decision-makers

This is where the Last Mile Problem takes hold.

The process itself is complicated, time-consuming, and repetitive. Data science teams can solve the Last Mile Problem and automate repetitive steps of the analytics process with augmented analytics.

Augmented analytics can simplify the entire workflow, helping both data scientists and decision-makers in the process.

Augmented analytics is the combination of machine learning and natural language technology to automate insights. Augmented analytics represents a collision of traditional business intelligence solutions and data science, allowing both producers of analytics and consumers of analysis to achieve their goals in a single workflow.

Simply put, augmented analytics:

1. Enables end users to ask questions by typing them into a search bar.
2. Selects the appropriate machine learning algorithm to perform analysis across the business’s data.
3. Produces an answer in the form of visualizations and natural language insights in seconds or minutes.
4. Automates insight production to create a continuous, proactive feed.

How does augmented analytics work? It employs similar “thinking” as the manual analysis process, but speeds up and scales up the work.

For example, decision-makers may want to know expected sales through the end of the year by week.

Just as a data scientist would perform a time series forecast, augmented analytics would do the same, choosing the model from a myriad of options (such as clustering, gradient boosting, or selecting a deep learning network). Augmented analytics understands that a time series forecast is the best choice to answer the question, and it automatically selects the model topology, parameters, and confidence.

Minutes later, augmented analytics delivers the forecast with visualizations and insights tailored to business people. As such, decision-makers can directly receive answers to business questions without needing to query a data scientist. This self-service analytics solves the Last Mile Problem for common business use cases.

Now, how does augmented analytics handle even more complex analysis or unique cases?

Data scientists can leverage openly-extensible platforms to deploy their own custom algorithms. Data science models are invaluable, created from extensive knowledge of business data.

Augmented analytics allows data scientists to input their models into the platform, developing custom workflows that can be performed with natural language queries.

In this way, business users and data analysts are given an approachable way to leverage these models to produce user-friendly visualizations and insights, without any data science technical know-how. This enables business teams to tap into advanced analytics capabilities—which they previously relied on technical resources for—all on their own.

Thus, data scientists can operationalize their machine learning models, automate steps of the analytics process, and allow business users to ask questions and get answers in their own language.

To sum up, augmented analytics makes advanced analysis accessible, approachable, and actionable by:

• Allowing data scientists, analysts, and business leaders to play to their strengths.
• Streamlining the most time-consuming, tedious portions of the data science process.
• Automating model running to enable proactive, continuous insights.

As a result, businesses gain the competitive advantage of speed. Faster insights enable decision-makers to act quickly, instead of reacting to change once it’s already happened.

Lastly, in addition to augmented analytics, it’s worth noting the value of Data Science as a Service (or DSaaS).

This solution allows companies to outsource their data science needs to a third party. A careful selection of a vendor can enable companies to tap into data science unicorns without having to hire them. It’s worth considering this option, especially for urgent business problems.

Do you have a business problem to solve with data science? Are you unsure where to start? RocketScience can help! Request a free consultation.

The post Solving for Data Science Unicorns and the Last Mile Problem first appeared on AnswerRocket.

Answering these questions can be the difference between a growth-oriented, proactive strategy and floundering, reactive decisions.

With advances in AI and machine learning (ML) technology, the answers themselves are more achievable than ever before. Yet, how to answer these questions (and the myriad others), is outside the scope of most businesses internal resources. They simply don’t have enough data scientists, enough technical capabilities, or enough time, to develop and deploy the right ML models to decision-makers.

Data science as a service can fill the gap.

What is Data Science as a Service?

Data Science as a service, or DSaaS, refers to outsourcing data science skills and capabilities to suit the needs of a business.

Businesses usually opt for DSaaS to boost their internal data science resources— whether to build ML models or to fill a hiring gap for these in-demand professionals.

DSaaS, however, is more than a supplement. For many companies, it’s a means of scaling their analytics capabilities to meet the critical needs of the business.

Data science itself is the act of modeling specific problems and synthesizing an understanding of the data adapted to the business. Through this process, ML models investigate options, predict outcomes, and find solutions.

As such, the DSaaS provider must closely align with the business on the desired outcomes, as these outcomes drive the development of the model.

The actual implementation of the model will also vary based on need.

In practice, DSaaS consultancies can provide data scientists to:

• Perform advanced analysis for specific projects.
• Build ML models to deploy to the business.
• Enable self-service analytics so business users can access the models and output (i.e. insights and visualizations).

The value of DSaaS is growing in tandem with rising analytics needs— now, more than ever.

Why is DSaaS Critical Now?

Even before COVID-19, data analytics has evolved to meet the growing needs of end users.

DSaaS is the latest phase of analytics maturity, broadly following this order:

1. Database Tools — provide access to data
2. Business Intelligence Tools — allow users to create canned reports and dashboards
3. Self-Service Analytics Platforms — enable data exploration, performs complex metric calculations, and produces visualizations and conditional insights
4. Data Science as a Service — model data to solve business problems and achieve critical outcomes, leveraging tools like automation where necessary

DSaaS fills the gap between the needs of the business and the data that can inform decisions.

It’s no longer a competitive advantage to simply see the results of the previous quarter, to see bar graphs and pie charts demonstrating basic computational and visualization skills.

To make informed decisions that impact performance, businesses need more advanced skill sets that typically fall to data scientists.

In the midst of COVID-19, data science capabilities are necessary to make realistic predictions about the impact of external factors, such as stimulus checks. Less mature analytics solutions will struggle to make sense of pandemic behavior, especially for industries that saw significant growth or loss. In fact, most solutions will be completely incapable without data science at the core.*

These forecasting needs fall beyond the scope of reports that simple analytics platforms churn out on schedule.

Inadequate technology coupled with urgent need and scarce data science resources has led to the rise of DSaaS.

For many businesses, the question is no longer about the value of these services, but: how can we leverage DSaaS for a competitive advantage post-COVID?

*Even advanced analytics solutions can hamper data science efforts with proprietary software that needlessly restricts access to the source code. Only open source solutions with open extensibility have the right foundation to operationalize data science models within their platforms.

How to Successfully Implement DSaaS

While DSaaS will not necessarily require implementation à la software, it will require focused effort on part of the business and the service provider.

A good provider will follow these essential steps to maximize the value of data science across your organization.

1. Identify key analytics questions

The provider should connect with the business to gain a thorough understanding of the problem the business is trying to solve. Common analytics questions include:

• How might COVID impact demand?
• How are new products contributing to growth?
• How would a pricing change impact sales?

2. Develop the machine learning model

Next, the provider will develop, train, and test the model to achieve a high degree of accuracy. Once ready, the DSaaS team will deploy the model and continue to fine tune it. (For more information on ML models in analytics, check out this blog post).

3. Design output for end users

In many cases, end users are business people who need insights and visualizations to make decisions. DSaaS can put ML models in their hands, but the output must be consciously and carefully designed. Insights should be served in natural language that’s immediately understandable to a non-technical person. Likewise, visualizations must render with intelligence and options for customization.

4. Integrate analytics tools for self-service

Operationalizing data science across the business enables end users to tap into advanced analytical capabilities regardless of their role. A good DSaaS provider will understand the value of pairing ML models with self-service analytics and will work with the business to meet cascading needs at every level.

The end result of these efforts should be faster insights delivered directly to the business people who most need them. To see an example of DSaaS in action, check out the next section.

Data Science as a Service Case Study — Modeling the Impact of COVID-19 Stimulus Checks

One of the top snack companies in the world, with $26 billion in annual revenue, needed to better understand the potential impact of COVID-19 stimulus checks on demand.

This company’s sophisticated forecasting system was unable to predict extraordinary demand created by fiscal stimulus at the local level. They needed a new model that could learn from the previous stimulus and make educated predictions — enter RocketScience, AnswerRocket’s DSaaS solution.

Working with AnswerRocket, the customer built a scenario modeling tool that would learn from 2020 and adapt to 2021 as the year evolves. As a result, the customer identified hundreds of millions of dollars of opportunity. They would be able to meet this opportunity by fulfilling customer demand, instead of disappointing them with empty shelves.

This case study is just one example of the significance of DSaaS in the post-COVID world. Every business will have its own unique problems to solve amid the impact of the pandemic. DSaaS can meet this need and help companies grow.

Do you have a business problem to solve with data science? Are you unsure where to start? RocketScience can help! Request a free consultation.

The post Data Science as a Service: Navigating Post-COVID Uncertainty first appeared on AnswerRocket.

In fact, many CPGs and retailers don’t even have a centralized view of their data. As a result, cross-functional collaboration is difficult, and organizations struggle to act with precision and agility. Growth opportunities get left on the table, especially during times of accelerated change (like COVID-19).

Ada Gil is here to help. Ada is a former Unilever Marketing Director, currently specializing in automating data analysis for CPGs and retailers. Check out her advice for making the most out of consumer data in these two Fireside Chats:

• For CPGs — Making the Most of Consumer Data with Consumer Goods Technology
• For Retailers — Making the Most of Consumer Data with Retail Information Systems

Watch to learn more, and check out the Additional Resources that compliment each video.

For CPGs — Making the Most of Consumer Data with Consumer Goods Technology

In this video, Ada discusses the proper strategic approach to consumer data analysis as well as the technological prowess that will allow consumer goods companies to turn data into insight, and insight into action.

Ready to learn more about how CPGs can make the most of consumer data? Check out these additional resources:

• CPG Analytics Guide — Learn what’s possible with with CPG analytics, what to look for in a solution, and how various roles can leverage this technology for success.
• AnswerRocket for CPGs — Check out AnswerRocket’s approach to automating CPG analysis, tailored to critical use cases.

For Retailers — Making the Most of Consumer Data with Retail Information Systems

In this video, Ada discusses the current state of retailers’ data approach for planning, how retailers can partner with CPG to maximize their data, common data pain points and opportunities, and more.

Ready to learn more about how retailers can make the most of consumer data? Check out these additional resources:

• Retail Analytics Guide — Learn the benefits of advanced retail analytics, common roadblocks to automation, and how to solve business problems with analyses.
• Category Analysis: How to Determine Performance Drivers — Understand how retail category managers can leverage analyses techniques to pinpoint the root causes behind performance.

The post Watch: Making the Most of Consumer Data first appeared on AnswerRocket.

Join Ada Gil, former Unilever marketing director, to learn how CPGs and retailers can capitalize on the new opportunities and avoid the dangerous pitfalls of COVID-19. With the power of augmented analytics, CPGs and retailers can make sense of unprecedented data and quickly adapt to new consumer behavior patterns. CPGs and retailers can’t operate with an old mindset; they need to learn quickly and move fast to emerge stronger at the end.

Speakers:

• Ada Gil, Former Unilever Marketing Director
• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post Promise and Peril: How Augmented Analytics Can Help CPGs & Retailers Navigate the Post-COVID World first appeared on AnswerRocket.

Even prior to the current state, CPG leaders have been leveraging the power of AI and machine learning to automate analyses, saving time and resources to improve their bottom lines. These existing use cases can act as models for CPGs that now have an urgent need to upgrade their technology.

This blog post will cover how CPGs use machine learning for:

1. Brand health analysis
2. Market share analysis
3. Category analysis

Machine learning and AI are more than temporary trends; they’re fundamentally changing how CPGs understand their consumers.

Practically, machine learning algorithms can identify and tailor critical insights across different data sets, scenarios, and functions— meaning, the algorithm “understands” what information is most important and relevant for a category analysis whether the data represents cosmetics or food and bev.

To learn more about machine learning, check out this resource: Machine Learning in Business Intelligence Solves the Puzzle. Otherwise, let’s dive into the myriad of ways machine learning is used in data analysis.

1. CPGs use machine learning for brand health analysis.

Understanding brand health is critical for CPGs.

Just as data has become more complex in recent years, brand health too has evolved beyond the consumer experience to encompass a brand’s overall performance:

As revenue models and customer expectations continue to evolve rapidly, every aspect of a business can affect the brand—from logistics and inventory management to the in-store experience. As a result, organizations increasingly are considering the connection between their brands and their underlying business operations with a focus on how performance can affect brand health.

Source: https://deloitte.wsj.com/cmo/2018/01/11/assessing-brand-health-risk/

This evolution means there are an incredible amount of factors to consider when it comes to brand health. Yet, at the core of brand health analysis is the simple question: “how did my brand do?”

In order to answer this question, brand managers need to understand which factors are driving a brand’s performance.

Which metrics, if changed, would create the largest ripple effect? Is location critical to brand health, or does advertising channel selection matter more? Where should employees focus their time and energy for the best results?

These answers aren’t always intuitive. It’s not uncommon for CPGs to leave growth opportunities on the table because they’re acting on partial information or assumptions.

For example, a brand’s strong sales performance can mask losses like declining market share. Furthermore, it could be reasonable to attribute this strong performance to a sales value increase in the Southeast and subsequently double down on promotions in that region. Meanwhile, data indicates that an increase in brand penetration in the Northeast would offer the greatest growth opportunity, but this insight is obscured by numbers on the surface.

Some of the most intuitive and obvious assumptions can be the most dangerous, simply because it’s so challenging to look past them. Machine learning approaches brand health without preconceived notions or this kind of bias.

CPGs are using machine learning algorithms to parse data and identify brand health drivers.

Specifically, machine learning can:

• Analyze data intelligently and exhaustively, using brute force models to evaluate all possible data permutations and focusing attention on factors that matter the most.
• Perform contribution analysis, weighing and understanding how much each factor contributes to overall brand performance.
• Determine which data is relevant to brand health, without a biased approach.

To put these theoreticals into real life, read this case study. The Consumer Insights department at one of the top 10 CPGs with more than $50 billion in revenue needed to better understand their brand health. Here’s how they used machine learning to do so.

2. CPGs use machine learning for market share research.

One of the most important metrics that CPGs must consider is market share, whether across a specific brand, segment, category, or industry.

For CPGs, market share insights indicate how brands perform against competitors. This context is critical to building a long-term growth strategy. The goal with comprehensive market share research is to leave no stone unturned and to fully understand where a brand fits into the larger landscape.

Machine learning is capable of performing this in-depth and complex research in seconds.

To do so, machine learning algorithms thoroughly cull through syndicated and first-party data sources, test every data combination to determine relationships, and generate insights.

A complete machine learning-generated market share report would include the following information:

• Market share performance — Year-over-year, has market share increased or decreased and by how many basis points?
• Market share momentum — How does recent movement in market share compare to the long-term trend?
• KPI drivers of market share growth — which key performance indicators (KPIs) are driving market share growth (or decline) the most, and to what degree?
• SWOT analysis— What are the brand’s Strengths, Weaknesses, Opportunities, and Threats based on its performance and that of its competitors?

With this information, CPG professionals can zero in on the factors that drive market share and make decisions based on the big picture.

What were the top gainers and decliners? Which contributors most determined market share performance? The answers to these questions can help teams take action with the greatest possible ROI.

What’s particularly compelling about machine learning in this instance is that its speed and efficiency enable immediate follow-up research.

When marketers learn that distribution contributed 250 basis points to the positive trend, they can quickly drill down into distribution and find out why. Essentially, machine learning enables more targeted, informed decision-making.

To learn more about market share research and brand health analysis for CPGs, check out this quick-read blog post. Or, watch this quick video demonstration to see this analysis in action.

3. CPGs are automating category analysis.

Syndicated data sources are an important investment for CPGs wanting to understand their performance, especially within the context of their competitors. To take advantage of these massive data sources, CPGs are using machine learning algorithms to analyze category data quickly and effectively.

Machine learning has advanced to the point that it can automate the bulk of what is largely a manual process. That means CPG professionals can start with a question (like “how did my category do?”) and get an answer in seconds without having to wrangle disparate data sources, formulate and test assumptions, pull data, and repeat ad nauseum.

Within the manual process, analysts spend so much time pulling data together that they tend to follow the same pathways when it comes to analysis. As a result, analysis can easily become biased; analysts don’t have time to look below the surface, so they stick with what’s worked in the past. Unfortunately, COVID-19 data is beyond historical precedent, and the old methods of analysis are quickly becoming obsolete (if not now, then in the next 5 years).

In contrast, machine learning is unbiased, approaching the data without a set pathway or preconceived notions.

Once the machine performs analysis, it generates insights and data visualizations that explain the drivers in a category. This analysis should, for example, identify how different brands contribute to the overall category and draw the user’s attention to the most relevant and important insights.

While data visualizations are familiar mainstays at many CPGs, insights, on the other hand, vary widely, and machine learning has significantly elevated the baseline expectation.

The value of insights is no longer limited to simply pointing out what’s happening in the data, i.e. “market share increased 50 basis points.” Now, insights can be structured in complete data narratives that explain analysis in natural language.

“You have that ‘a-ha’ moment; that’s really what an insight is, where you see things differently than what you saw before. And you can follow that train of thought without these interruptions,” states Laura Braunecker, Founder and Principal Consultant of Zeerio, in this Food Dive Playbook. “[CPGs] want their best and smartest talent to be more productive, to get the answers they need and to be proactive, not reactive.”

That’s the key with automated analysis — generating insights that lead to action. And generating insights quickly enough that action can be taken proactively, not reactively.

This automation means CPG professionals like brand managers, CMOs, finance teams, and sales teams can perform analysis themselves, quickly and without intervention from an analyst.

Of course, the potential applications of machine learning are nearly endless, and they can fulfill analytical requirements for a myriad of departments and functions.

A few additional examples of machine learning in action include:

• Scenario analysis — ask “what if” questions to see what happens when metrics are altered.
• Granular forecasting — predict revenue and profit performance.
• Opportunity analysis — identify and prioritize growth opportunities.

Each of the above examples are active use cases for machine learning.

Hopefully, these examples inspire ideas for how you can use machine learning to solve problems and drive growth at your organization.

Additional Resources

• CPG Analytics Guide: Learn everything you need to know about CPG analytics with this ultimate guide.
• Data Analytics and Machine Learning: Learn more about machine learning in this more technical overview of AI in analytics.

The post How CPG Companies Are Using Machine Learning Right Now first appeared on AnswerRocket.

COVID-19 has forced businesses to enact digital transformation years earlier than they’d planned. With so many changes to consumer behavior, teams need tools that can help them sort through vast amounts of data, identify growth opportunities with precision, and quickly understand trends– and to be competitive, this analysis must happen fast. Learn how automation can accelerate digital transformation where it matters most.

Speakers:

• Pete Reilly, SVP of Sales, Marketing, and Implementation at AnswerRocket

The post The Future of Decision-Making: Empowering The Post-COVID Workforce with Automated Analytics first appeared on AnswerRocket.

To stay competitive, CPGs need to understand the trends that are shaping the industry. What are the major themes underscoring 2020? The rising influence of AI and machine learning on analytics, the need for better insights, and increasing consumer expectations.

Ensure your technology is up to date and your strategy is set to capitalize on these trends.

1. CPGs will take advantage of AI and machine learning to inform decision-making.

It’s estimated that AI and analytics could add as much as $13 trillion to total output by 2030, increasing the annual rate of global GDP growth by more than one percentage point.* These projections for 2030 are rooted in AI advancements that have already started. CPGs will continue to invest in AI in 2020, specifically in regards to decision-making.

AI and machine learning are revolutionizing the way CPGs can use data to make business decisions. CPGs are combining first-party data with syndicated sources, and to make the most of these investments, they’ll look to advanced technology like AI to uncover actionable insights.

In other words, CPGs have tons of data at their fingertips; now, they need help understanding what the data can tell them about how to find growth and what actions they should take to drive the business.

AI provides CPGs with fast data analysis that generates actionable insights, all without the help of a data analyst. Machine learning algorithms can identify performance drivers from hundreds of thousands to millions of data points, so CPG professionals know which factors most impacted their results.

In 2020, CPGs will continue to invest in AI-driven analytics because of the advantages it provides. AI pinpoints the “why” in the data, so decision-makers know where to focus their efforts for the best ROI. Even now, CPGs can leverage AI to understand brand health, market share, trends, performance, and “what if” scenarios.

The use cases for AI will continue to grow, and CPGs will become more comfortable with the automation enabled by this technology.

*McKinsey Global Institute: The Coming of AI Spring
https://www.mckinsey.com/mgi/overview/in-the-news/the-coming-of-ai-spring

2. CPGs will invest in AI-powered innovation analysis.

Innovations are expensive endeavors for CPGs. To succeed, CPGs need to move quickly and accurately; once a new product hits the shelves, there’s a very short window to adjust strategy to either course-correct or capitalize on opportunities.

In 2020, CPGs who invest in AI-powered analysis can reap significant rewards. First, AI can assist with innovation planning by helping CPGs identify growing segments. Second, centralized analytics can assist with cross-functional collaboration by assembling relevant data from every department into a single source of truth.

Once innovations are launched, AI provides CPGs with a fast and deep understanding of performance. It takes time to build distribution, and AI can help fill in the gaps without sacrificing the quality of the analysis.

CPGs who leverage AI for innovations can be more agile, acting during the launch instead of waiting until year-end to get a report on performance, essentially leaving money on the table.

3. The role of CPG category managers will shift.

In 2020, retailers will be well-equipped with syndicated data sources like Nielsen. Plus, they’ve invested in their own data scientists and analysts who build reports and generate insights. As a result, retailers are looking for new insights that they can’t produce themselves.

CPG category managers can fill in that gap and bring new value to the table. Specifically, category managers need to tell a different data story.

Category managers can, for example, leverage data sources across marketing channels. By engaging in social listening, category managers can better understand how consumers talk about products and adjust their strategies accordingly. This kind of analysis will become more critical to the category manager role as retailers continue to invest in their own analytics resources.

Want more? Download the whitepaper to see all 11 CPG Trends.

The post 11 CPG Industry Trends to Anticipate in 2020 first appeared on AnswerRocket.

To capitalize on this data, businesses must frame their approach strategically. After all, having the data is not enough to:

1. Interpret and understand the story it’s telling.
2. Determine which data is most relevant to which audience.
3. Instill a culture of data discovery in employees, especially when acting on hunches can be habitual.

Business leaders understand the value of data that’s tailored to each function and the role analytics tools play in the overall employee experience of accessing that data.

In this sense, analytics software that organically promotes data-driven decision-making provides a competitive advantage.

The advent of AI analytics has changed the premise of the conversation. With the automation and augmentation capabilities of AI, analytics tools are no longer facilitators of data analysis but are capable of performing the actual labor that was once unique to humans.

These advancements mean that businesses have an incredible opportunity to capitalize on data (as we’ve mentioned), but they must do so with an eye toward scale, change management, and curiosity culture.

In this article, we’ll specifically discuss the advantages of machine learning analytics and how it fits into the larger picture of AI in business intelligence.×

Learn more about the state of AI in business intelligence with this in-depth eBook for business leaders.

The difference between traditional data analytics and machine learning analytics

Data analytics is not a new development. From the beginning of business intelligence (BI), analytics has been a key aspect of the tools employees use to better understand and interact with their data.

However, the scale and scope of analytics has drastically evolved. Let’s discuss these differences in more detail.

Data Analytics

Traditional data analytics platforms typically revolve around dashboards.

Dashboards are constructed of visualizations and pivot tables that illustrate trends, outliers, and pareto, for example. Technical team members like data analysts and data scientists play a role in constructing these dashboards; generally, the humans are still performing the bulk of the analysis, and the software helps facilitate the results.

Current state analysis with traditional data analytics software looks something like this:

1. The data analyst starts with a core question, likely sourced from a business team. In this case, the question is “how did market share do last quarter?”
2. The data analyst accesses different spreadsheets from different locations.
3. The data analyst merges multiple spreadsheets manually.
4. The data analyst conducts analysis by filtering data based on their hypotheses around market share’s performance. This process is constrained by time restrictions, so the analyst can’t fully test every scenario. As the analyst iterates on their hypotheses, they may need to access data again.
5. The analyst presents the story, or the findings from their analyses. While these stories can be well-researched and accurate, they’re not a complete picture of what’s happening in the data and rely on the analyst’s initial assumptions.

This process is labor-intensive, time-consuming, and often frustrating. Data analysts have advanced skill sets that they can’t use effectively when they’re spending their time stuck in a cycle of routine reports.

The limitations of this process have paved the way for machine learning to take hold in analytics.

Machine Learning Analytics

Machine learning analytics is an entirely different process.

Machine learning automates the entire data analysis workflow to provide deeper, faster, and more comprehensive insights.

How does this work?

Machine learning is a subset of AI that leverages algorithms to analyze vast amounts of data.

These algorithms operate without human bias or time constraints, computing every data combination to understand the data holistically. Further, machine learning analytics understands boundaries of important information.

If asked to identify changes in sales figures, the machine can learn the difference between a $200 fluctuation and a $200,000 increase, only reporting the latter because that’s the info that actually impacts the company.

In other words, machine learning also tests out hypotheses to answer key business questions — but it can test all of them in a much shorter timespan. Think seconds instead of weeks. Then, it tells a data story that’s accurate, exhaustive, and relevant to the person asking questions.

Practically, machine learning is invoked in techniques like:

• Clustering — The machine determines commonalities between different data to understand how certain things, like customers, are alike. These customers can be grouped together in ways that may not be immediately apparent or intuitive to a person performing the same exercise.
• Elasticity — The machine determines causes behind results. If many factors are changing simultaneously, how do you determine which factor is credited with which outcome? This technique tells employees that an increase in household income resulted in boosted sales, rather than product promotions, for example.
• Natural language — The machine maps phrases like “sales” to their coding language counterparts. In this way, business people don’t have to understand R or Python to perform deep analysis. They can simply ask everyday questions like “what were sales in Q2?” and the machine translates those words.

With these techniques, machine learning analytics determines the drivers beneath the data and the opportunities to grow the most.

Significantly, machine learning that invokes natural language is also targeted toward business users who can perform the analysis themselves (a development known as augmented analytics).

Machine learning analytics are taking off…but why now?

The amount of data that companies have access to is much greater now than it has ever been before.

This data is a goldmine for businesses as it can inform the decision-making process, assist with targeting customers and prospects, and deepen the level of analysis that can be performed.

However, as the amount of data grows, so too do the challenges with harnessing its power:

• The roles and functions that make data-driven decisions are often removed from the data itself. CMOs, brand managers, sales teams, and other business-driven roles need data to act, but don’t have the time or training to divulge insights from the data without user-friendly tools or support from technical team members like data scientists and analysts.
• The data itself is more complex. Businesses need to invest resources into data cleaning, structuring, and maintenance to ensure that data pipelines are supported properly.
• The value of data is becoming more apparent. As more businesses invest in syndicated data sources, how do businesses gain a competitive advantage, especially when competitors are accessing the same data?

In tandem with this growth in data is a growth in computational processing power.

Cloud computing, the technology that ultimately supports this data, is becoming more advanced, and machines have more processing power than they have previously.

Companies are investing in both big data and cloud infrastructure.

According to SVP Pete Reilly in this CGT webinar, they’re investing toward an AI-driven end:

“They’ve got all this data available, and now they’re saying, what are the big business problems we could apply this to that would have a huge impact?”

After all, at the intersection between the expansion of data and computational power is machine learning.

Machine learning is essentially what you do with these resources to leverage them as business assets. Without machine learning, companies simply have a sea of disparate information. With machine learning, companies have a hierarchical structure of the information that’s most specific, relevant, and important to each role and function.

As indicated in Reilly’s quote, specific business problems can focus the implementation of machine learning.

Key considerations for data analytics and machine learning

The potential gains from machine learning have enormous appeal, and companies are looking to invest in advanced analytics solutions.

Yet — as with the larger conversation around AI in business — the pathway to successful implementation of machine learning is not as easy as it may appear. Change management strategies are critical for ensuring that employees use machine learning analytics effectively.

Machine learning is new in most industries, and its benefits aren’t necessarily obvious to employees who haven’t been exposed to the larger conversation. This is especially true when employees are concerned about being replaced by automation.

A good implementation strategy is key.

This strategy should be driven by:

• Specific business outcomes that clarify what machine learning analytics will accomplish and automate.
• Alignment between tech and business teams, so that both parties understand the benefits of workforce augmentation.
• Accurate data, supported by system maintenance and AI expertise.
• Change management fundamentals, which are often lost in the excitement of new technology.

These considerations will help ensure that machine learning analytics take root in the business and help employees become more effective in their jobs.

Request a Demo

See how AnswerRocket leverages machine learning to transform data analytics.

The post Data Analytics and Machine Learning: Let’s Talk Basics first appeared on AnswerRocket.

Yet, not all solutions are designed to meet the unique needs of large-scale enterprises. Here, we cover 6 key enterprise-grade capabilities to look for in an augmented analytics platform.

But first, let’s define advanced self-service analytics.

What is Advanced Self-Service Analytics?

First, self-service analytics refers to business intelligence (BI) platforms that allow business users to access and interact with their data directly, instead of relying on a technical team member like a data analyst to compile data for them. 

Self-service analytics enable business people to get the insights they need to act, all while streamlining data access and combating bottlenecks caused by routine reports.

While self-service analytics have grown in popularity, advanced self-service analytics bear distinctive traits that elevate them above their more routine counterparts.

According to Advanced Analytics: The In-Depth Guide:

“Advanced analytics leverages AI-based technologies in business intelligence tools to produce deep insights that help business people uncover and understand the stories hidden in their data. Advanced analytics combines technologies like machine learning, semantic analysis, and visualization to automate analysis.”

This automation of data analysis is key. Next-level analytics should provide deep, contextual, and user-friendly insights for business users without technical expertise– and theses solutions should provide these insights in seconds.

Now, let’s discuss the features needed to scale advanced self-service analytics solutions to the enterprise level.

Enterprise Capabilities for Advanced Self-Service Analytics

1. Open Data Platform

An analytics platform is only as valuable as the data that it’s connected to. As an open solution, AnswerRocket can flexibly connect to your existing data platform, whether it’s on-prem, in the cloud, or a hybrid solution.

We currently support over 25 different data platform providers and are continually adding new ones.

We can also host your data for you.

2. Security & User Administration

Security is a priority for AnswerRocket. Admins can customize functional permissions and set row-, column-, and table-level security to ensure that access to sensitive data— such as employee performance— is carefully controlled and governed.

Admins can also define entitlements guiding access rights at the user-, role-, and group-level. Authentication is handled with Active Directory and single sign-on integration.

3. Metadata Management

What good is self-service analytics if you can’t trust the answers?

AnswerRocket enables users to leverage a centralized semantic model and metadata.

This management feature helps establish a single source of truth capable of generating consistent, accurate answers you can have confidence in.×

See AnswerRocket’s enterprise capabilities with a custom demo of our advanced self-service analytics solution.

4. Data Storage and Loading

One of the biggest benefits of a solution like AnswerRocket is being able to access all of your important data in one place.

ETL tools allow you to extract, transform, and load data into a self-contained storage layer. Easily index data, manage data loads, and refresh scheduling.

5. Support for AI & Machine Learning Libraries

If you have existing AI and machine learning algorithms in use, then having an extensible analytics platform that can leverage those assets is key.

In addition to the AI automation and ML algorithms included out-of-the-box, AnswerRocket also makes it easy for you to build and operationalize your own machine learning algorithms.

Use any open source machine learning library, such as TensorFlow or scikit-learn.

6. Branding and Personalization

Need to provide a seamless experience for your team members?

The AnswerRocket platform can be white-labeled to reflect your company’s logo and branding. Customize colors to complement your style guide. Define your preferred format and default colors for visualizations to ensure they work harmoniously within your company’s templates.

Each of these capabilities is important for an enterprise-grade analytics solution.

Ready to see an example in action?

Get a custom demo of AnswerRocket.

The post Advanced Self-Service Analytics: 6 Must-Haves for Enterprise 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.