Augmented Analytics: All You Need to Know About It in 2021 – Analytics Insight

Augmented analytics is a class of analytics powered by artificial intelligence (AI) and machine learning (ML) that expands a human’s ability to interact with data at a contextual level. Augmented analytics consists of tools and software that bring analytical capabilities—whether it be recommendations, insights, or guidance on a query—to more people. Machine learning, an area of computer science that uses data to extract algorithms and learning models, is a core technology in many augmented analytics features. Machine learning helps people in analysis, often by reducing or eliminating tedious work so that people get insights and make decisions with data faster. This spans cleaning, shaping, examining, and filtering data for more accurate and deeper examination. Machine learning capabilities within BI platforms often surface the results of advanced algorithms as recommendations. Additionally, some applications of augmented analytics leverage ML to learn the industry and organizational semantics, as well as user preferences over time, so that questions and results are more personalized and effective in the context of the business during analysis.

Automation is a common feature in augmented analytics solutions, but it’s important to understand the difference between automating tasks, as many technologies do, vs. automating the decision-making that analytics informs. Automating data-driven decision-making takes away the need for human capability, whereas augmentation provides a methodology for underlying technology to guide users to uncover insights they might not see or discover otherwise. Domain knowledge has always been important for analysis but augmented analytics, fueled by AI and machine learning, make this skill set even more critical. There are often gaps where humans need to fill in the necessary context and use the insight gained from analysis to help them make the best decision for the problem at hand. Business users and executives get incredible value from augmented analytics because these technologies help them get value from their data quickly without the need for deep, technical skills or expertise in working with data. Augmented analytics helps business users and executives more easily find relevant data, ask the best questions, and quickly uncover insights in the context of their business. While much of the benefit of augmented analytics focuses on enabling those without deep analytical expertise, it also helps analysts and advanced users to perform a more thorough analysis and data prep tasks faster.

Augmented analytics can make analysts’ work faster, more efficient, and more accurate. Machine learning and natural language technologies also help to bring domain experts—people embedded in the business—closer to their data by removing technical barriers to analysis, including making more advanced techniques available to people with less mature data skills and experience. AI-powered augmentation can accelerate the search for insights by trimming the search space, surfacing relevant data to the right person at the right time, and suggesting fruitful paths for analysis. By broadly tracking user behaviors, systems can provide smarter defaults and recommend actions, and tune and personalize them over time based on how people respond. When people answer their data questions faster, they can focus on more strategic tasks and spend less time combing through data for insights. Because machines don’t sleep, they perform repetitive tasks and calculations extremely well. AI and ML technologies behind augmented analytics can effectively look under every rock so the user can make the most informed decisions based on a thorough analysis. This type of complete view helps humans avoid confirmation bias in their conclusions.

Machine learning and artificial intelligence have made tremendous progress in applications where algorithms are fueled by highly specialized, repetitive tasks. (Think of websites serving up “you may also be interested in…” suggestions for related content or products, or even fraud detection programs.) Augmented analytics offers task automation that saves people time and energy when working with data—whether in data preparation, data discovery, running statistical analyses, and more. Augmented technologies are often easy to use, making working with data more approachable, and insights more easily attainable for broader groups of people. Augmented technologies can be tailored to model and surface data in context allowing you to confirm instincts and be confident in the quality of your conclusions. While business users may not deeply understand analytical techniques, they do know their field or industry and can apply this expertise when evaluating how to use the findings delivered by augmented analytics. Some augmented technologies are built into business workflows and integrated with other tools and software, which enables people to quickly explore their specific questions without disrupting their analysis—and in some cases, no additional steps to prepare the data are necessary.

Some modern BI platforms use AI to automatically detect certain attributes of data, like if a field contains geographic information (such as a postal code) or personal information (such as phone numbers or email addresses). Additionally, the system can read tables of data in formats like PDFs and text documents, automatically removing special formatting and converting them for effective analysis. Augmented analytics technologies can also automatically select from the best forecasting, clustering, and other statistical algorithms based on which offers the most certainty. In some systems, models run automatically to surface and offer insights within data that users may not have seen. These techniques can explain the “why” behind a data point, such as the drivers behind an outlier or an unexpected value in a data set. For an end-user, these capabilities are just a click away, rather than requiring the expertise of writing calculations or code.

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