What is advanced data analytics?
Advanced analytics is a term coined by Gartner that refers to the “autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations.”
Let’s break down the components of Gartner’s core definition to more clearly define advanced analytics in simple terms (click to expand each definition).
Advanced Analytics Definitions
“Autonomous or semi-autonomous examination of data”
This means the advanced analytics tool, which we’ll refer to as the machine, doesn’t need help from a human to analyze data. This trait has generally been associated with AI-based technology, which can perform a process that typically requires human intelligence without being explicitly told to do so.
Sophisticated techniques and tools typically beyond those of traditional BI”
Traditional BI is capable of facilitating analysis, though it usually requires significant work on part of the user. For example, BI tools generally allow technical users like data analysts to create dashboards to illustrate data. Advanced analytics goes beyond this basic functionality and applies machine learning algorithms and models to automate data analysis.
“Discover deeper insights”
“Deep” insights require more than a regurgitation of trends. To draw a comparison, “sales increased by 5%” is a standard statistical insight that one could expect from a number of analytics tools. Advanced analytics differentiates itself by figuring out why sales increased and drawing the user’s attention to a more complete story that places the 5% increase in context with other metrics, like market share.
“Make predictions”
Advanced analytics predictions are rooted in a solid understanding of current data to forecast future events. Questions like “how can I outperform my competitors?” can be answered with this technology.
“Generate recommendations”
Insights and predictions refer to an output from the machine, but true recommendations require the machine to prioritize this output so that a human can act on the information they’re provided. This means that the machine breaks down the information into a sort of hierarchy, demonstrating which metrics most impact the numbers on the surface. In line with this concept, the machine should be intelligent enough to be discerning about the information it displays to the user.
What’s the difference between analytics and advanced analytics?
In short, advanced analytics leverages techniques like machine learning and statistics to analyze data, whereas traditional analytics stick to simpler computations that show what’s happening in the data, but not why.
Advanced analytics selects the appropriate algorithm or model to apply to the data based on the intent of the user. For example, a traditional analytics platform might show simple trends based on historical data, while an advanced analytics platform could apply forecasting techniques to make predictions for the future, all while explaining the root causes of the initial trend line.
As a result, advanced analytics helps businesses make better decisions by automating the generation of nuanced and in-depth insights and recommendations.
Now, let’s talk with more specificity about how advanced analytics accomplishes this goal.
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Advanced Analytics Techniques
Plenty of techniques and tools fall under the umbrella of advanced analytics. To ground the conversation, let’s take a look at three examples of advanced analytics techniques that demonstrate how these technologies represent the forefront of the BI market.
Machine Learning
In advanced analytics, machine learning is an AI technique that leverages algorithms to analyze vast amounts of data. Machine learning can determine commonalities between different data points to reveal relationships that aren’t readily apparent to an observer.
To do so, the machine exhaustively and deliberately combs through the data, essentially performing the work of a data analyst at a much faster pace than a human. As such, the machine is generally more thorough than a person because it can test infinite data combinations.
In testing these data combinations, the machine can determine what information is most valuable for the user. Specifically, machine learning demonstrates the quality of attention; that is, the machine knows what is important in the data and what the user should look at.
Advanced analytics puts the power of machine learning into the hands of the business user, enabling them to easily trigger algorithms to answer complex business questions. One of the most intuitive ways to trigger this process is through a natural language query, which we’ll discuss in the next section.
Natural Language Technology
Natural language technology refers to a machine’s ability to understand and reproduce human language.
This ability is becoming more commonplace in business intelligence, but advanced analytics demonstrates end-to-end semantic capabilities that speak to business people.
Natural language processing (NLP) is one example of semantic analysis that many users are already familiar with, as they likely leverage it in everyday life with robotic assistants like Siri.
NLP in advanced analytics involves a user saying or typing a business query or objective into the platform; the machine then translates the speech into a programming language like R or Python to understand and answer the question.
Natural language generation (NLG), on the other hand, has yet to take the same hold in the BI market. NLG refers to technology that produces an output in language that humans can understand. NLG in advanced analytics takes the form of insights written or spoken in plain language.
Together, NLP and NLG allow business people to have a conversation with their data, wherein they ask a question and receive an answer that they can understand.
Data Visualization
One of the most intuitive ways for users to understand their data is through visualization. Visualizations in advanced analytics generally refer to graphs and charts that tell the story in the data.
Paired with natural language insights, visualizations point the user toward the information that’s most relevant.
Intelligent visualizations analyze the answer to a query and determine how best to display the information. For example, the machine would logically determine that metrics related to specific states or regions should be displayed on a map.
Further, advanced analytics visualizations should be customizable for the user, who should be able to choose the graphical presentation that matches their unique needs. Intelligent visualizations will remember these personal preferences when asked similar questions, alleviating the burden of repeated customization from the users.
Now that we’ve discussed some of the more vivid techniques used in advanced analytics, let’s break down the advanced analytics tools and platforms that actually that businesses would actually use.
Advanced Analytics Tools
The Rise of Self-Service Analytics
Advanced analytics tools encompass a wide range of platforms, from bare-bones environments that allow data scientists to build algorithms from the ground-up to complete end-to-end self-service analytics solutions designed for non-technical users.
Tools that facilitate programming, like Hadoop, enable users to build multivariate statistical models and machine learning algorithms that run deep analysis. However, these tools require a degree of technical knowledge, starting with literacy in a programming language (typically, R or Python). These tools do offer an environment to create sequences that run the analysis, but the bulk of the work is on the individual.
In contrast, self-service advanced analytics tools come with these algorithms pre-built into the interface. From a user’s perspective, they can easily and readily trigger advanced analysis from the framework of what they want to accomplish (without having to bother with the nitty-gritty details of the programming and figuring out which algorithm they should use).
For example, an average business user may not know whether they need to run cluster analysis or pattern matching algorithms, but they do know that they want to group consumers together based on the consumers’ potential to generate sales value. Advanced analytics platforms should enable users to approach the platform from this mindset.
Even further, people often think in terms of “what if?” — What if we started this campaign in the summer? What if we grouped these products together on the shelf? What if we dedicated more resources to this department?
Self-service advanced analytics enables people to ask these scenario analysis questions directly, without having to translate their initial thoughts into a specific command for the machine.
“Self-service advanced analytics enables people to ask these scenario analysis questions directly, without having to translate their initial thoughts into a specific command for the machine.”
Historically, analytics tools were relegated to data scientists and analysts, who held primary responsibility for building models and performing analysis. In contrast, business people interacted with analytics in a more passive sense, submitting research requests and getting back analysis in the form of dashboards or reports.
Now, the dynamic is shifting.
Advanced analytics self-service models remove the barriers between the machine and user. With techniques like semantic analysis, machine learning, and visualization, advanced analytics allows users to ask questions in natural language and receive in-depth answers that they can understand.
Self-service analytics invokes the power of advanced techniques and puts the output directly in the hands of the business user who can act on the information.
Open Source VS Proprietary
Open source advanced analytics tools are generally more well-regarded than their proprietary counterparts. Why?
The machine learning algorithms that drive advanced analytics aren’t valuable without data. Andrew Ng, Director of the Stanford Artificial Intelligence Lab and one of the foremost experts on AI, explains this well: Once machine learning algorithms are developed, they can easily be replicated.
That’s why open source platforms that offer open extensibility are more desirable.
For one, they enable data scientists and analysts to write their own extensions. Bigger picture, as advanced analytics empowers business people with self-service opportunities, it also empowers data scientists and analysts to put their skills to use.
Instead of spending their time servicing requests for routine reports, data scientists can build advanced statistical models, for example.
Open source advanced analytics facilitates the creativity of data scientists and analysts that ultimately leads to better, more sophisticated data analysis.
AnswerRocket – An AI Analytics Tool
AnswerRocket is an advanced analytics tool that is currently helping business people make data-driven decisions, fast. AnswerRocket’s advanced analytics offers features like:
How Does Advanced Analytics Help Business
Advanced analytics saves time with automation.
Ultimately, advanced analytics performs an intense amount of labor faster than a person could. The actual process of analysis is sped up to a remarkable rate.
Because of the intelligence of advanced analytics, the insights that are generated are also highly actionable to the business user, meaning they can put the information to use as soon as they receive it.
As such, business people can be much more proactive in their approach to data, instead of reacting to the results of Q1 in the first week of Q2. Immediate answers mean that action plans can be created more quickly, enacted more quickly, and evaluated more quickly, leading to more agile and responsive workflows.
Advanced analytics goes deeper.
Advanced analytics is smart. Because of its efficiency and power, it can undergo exhaustive analysis without a great deal of time or energy.
Rather than evaluating a small handful of hypotheses, advanced analytics is capable of analyzing every possibility. In doing so, it uncovers information that could otherwise be hidden or overlooked.
After all, surface-level metrics, especially when they seem positive, can be distracting and hide the true narrative that lies underneath. Advanced analytics is unbiased — it can’t be swayed by stumbling on a good story and instead will always hunt for a comprehensive, fact-based data narrative.
Advanced analytics seeks to paint a complete picture, in high definition. For instance, it may find that although sales are increasing in a category, market share has been lost in the southeast, identifying the drivers, opportunities, and outliers contributing to the observed performance. In this way, businesses are given better context on how they are actually doing.
Advanced analytics augments the human workforce.
One of the most noteworthy benefits of advanced analytics is its ability to turn business people into citizen data scientists. Advanced analytics performs the grunt work of data scientists (who are likely grateful to have their time freed up for more advanced tasks) so that business people can always get answers to their questions.
Plus, with automation, advanced analytics can continue this work non-stop. This means that advanced analytics can push information to the user even when the user isn’t asking for a specific piece of information.
The machine is smart enough to know that metrics like sales value are important, and it’s smart enough to discern which changes to the metric are noteworthy and which changes are routine or otherwise unimportant.
The user in turn has access to more information than ever before and can focus their attention on what is important, immediately, instead of having to decide where to focus in the first place.
