Augmented analytics explained: definition, use cases, benefits, features, and more

Modern data is complex and sometimes difficult to interpret and understand. That’s where a tool like augmented analytics comes in handy. Harnessing machine learning and artificial intelligence to make data easier to understand is a no-brainer. But what exactly is it, and how does it work?

In this article, we’ll cover augmented analytics topics such as:

• Definition
• Major benefits
• Features
• Use cases
• Challenges
• Best practices
• Role in BI 

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.

From global research and advisory firm Gartner : “Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.”

The role of machine learning in augmented analytics

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 to 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 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.

Augmented analytics vs. automation

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.

Who is augmented analytics for?

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 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.

Agility: Increasing speed to insight

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 by 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.

Accuracy: Providing a more complete picture

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.

Efficiency: Automating operational tasks

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.

Confidence: Powerful analysis in context

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 question without disrupting their analysis—and in some cases, no additional steps to prepare the data are necessary.

Automatic data identification

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.

Statistical techniques

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.

Smart data prep

During data preparation powerful algorithms work behind the scenes to help users prep data faster, minimizing manual cleanup. Augmented analytics systems can index and group related words by pronunciation or common characters to save people time from manually searching for and updating fields and values.

In some instances, the system may also recommend cleaning steps, like removing null values or splitting fields into separate columns.

Recommendations

A marquee feature of many augmented analytics systems is the ability to make AI-driven recommendations to users. Recommendations span data prep to discovery, analysis, and sharing. For example, a system may recommend data sources to join or cleaning steps during prep, or recommend effective chart types to use based on which rows and columns of data the user brings into view.

People also receive suggestions to explore analytical content based on their role, team, and analytical browsing behavior—just as many businesses offer “you may also like”—which can help new users onboard faster and find the data assets most relevant to them.

Natural language interactions

Natural language query is an augmented analytics capability that allows a user to type a question in plain language to query the data, rather than using a data query language or code. The system provides a guided experience by translating the text into a query and making suggestions to fill gaps in an attempt to understand the intent and context behind the user’s questions. This helps many people get insights from their data without having to understand the underlying data model.

Natural language generation creates textual descriptions of insights from the data, which can include explanations of data visualizations. Having these explanations in plain language helps people understand stories in their data without needing deep expertise of navigating and interpreting visualizations.

Misconceptions of AI and ML

Due to underlying complexities of AI and machine learning, there’s still a prominent focus on the technology itself, rather than how regular people will interact with it and benefit. Alongside misconceptions of machines taking people’s jobs, this can stall the adoption of solutions that offer practical benefits to people who work with data. People won’t use AI and augmented analytics if they don’t understand and trust in the value.

Augmented analytics limitations

On the other hand, some people may have inflated expectations of what these kinds of technologies can achieve and offer. This can result in sunk costs if big investments are made without understanding how the technology can actually help people, or without a clear strategy for implementing and supporting it.

This also requires understanding where humans vs. machines excel. It’s difficult for a machine to understand a person’s intent within a limited context. The machine has the data itself but doesn’t grasp the bigger picture in the same way a person with domain expertise can. Through monitored usage behavior and user feedback, machines will have to learn people’s preferences over time.

Data literacy and analytics proficiency

Tools and technology are certainly important parts of the greater movement, but employees must also learn to think critically about data. Acting on the wrong data—or the wrong recommendations from an AI system—will lead to bad decisions and wasted resources. This is where data literacy, critical thinking, and people development come in.

Augmented analytics is only successful when organizations have prioritized analytics proficiency across departments so people can confidently speak and understand the language of data. An explanation or recommendation is only useful if the consumer understands data concepts and how they relate to their own business data. For example, a human with business context is more likely to confirm or deny causation when a machine discovers a correlation.

Data governance, management, and curation

Data is the foundation of an AI system. Therefore, the quality and reliability of AI-enabled prescriptive recommendations or automated tasks are directly correlated to the quality and reliability of the data used to train the system. Organizations that have not invested in sound data governance or data management practices or have struggled to build traction and confidence in their BI deployment stand little chance of successfully embracing AI.

Ethical use of AI

As algorithms and models become more sophisticated, it’s critical they don’t become incomprehensible. In other words, organizations need to be weary of “black box” AI solutions. The concept of transparent and “explainable AI” is a powerful one—people should be able to understand the operations and logic that were applied to come up with an answer. This not only helps to ensure organizations aren’t using biased models, but builds people’s conviction that the answers are trustworthy for informing decisions.

Start with a solid foundation of modern analytics

Modern BI has opened the doors for users across all skill levels to answer their own questions, while balancing agility with the need for security and governance. For modern BI platforms, AI and ML features are an extension of this paradigm. They represent another step towards digital transformation, nudging organizations away from traditional BI and reporting, toward a modern, self-service environment where everyone can ask questions of their data.

Prove success before scaling AI analytics investments

With bold new ideas, the best way forward is to start with a scoped test and not seek to boil the ocean with the perfect system. Start with a very specific scope, like a certain department or use case. Then, once the value of your investment is proven, bring it to broader groups across the organization.

Demystify and educate to build trust and data literacy

An impactful data education requires both practical and creative skills. Introducing AI analytics into business processes will require trust in these technologies alongside good judgment from the workforce. Data scientists may hesitate to trust a machine when they themselves have tried and true experience; novice analytics users will need to learn how to interact with and validate augmented analytics recommendations, or to interject human knowledge to correct the course.

Nurture success with collaboration and community

Since these features will be embedded into existing workflows, strong communication between data champions and domain experts will help users find success and encourage adoption. Analysts that build dashboards for others should be aware of how people are using AI and machine learning features, and encourage open communication about the explanations and the data itself. Analysts can set up domain experts with the right data and the right context to drill down into the data points that matter to them; for example, a starter dashboard that allows for interactivity, exploration, and adaptation.

Data is a critical fuel in creating better customer experiences, more efficient operations, and opening new revenue streams. The organizations who are best at analyzing data will be the most competitive and impactful. As such, many are turning to AI analytics technologies and augmented analytics—including machine learning, natural language interactions, and complex algorithms—to find an edge and further enhance their people’s analytical abilities, drive digital transformation, and build business resilience in the face of change.

Augmented analytics promises to better translate human curiosity into pertinent answers. These capabilities will broaden the use of analytics and reach a lot of people who are less comfortable working with data. This helps to give everyone in an organization a way to confidently get business questions answered from their data.

At Tableau, we use AI to help people answer questions and drive meaningful decisions with data. From smart data prep and one-click statistical analysis to natural language queries, our augmented analytics capabilities allow more people to learn what they need to know from their data with increased confidence in their results. Our capabilities empower the wider business audience to answer questions with data, helping organizations leverage their growing amount of data.

Learn more about augmented analytics from Tableau.