Augmented analytics use cases and examples

By Brynne Henn - July 26, 2021

The buzz around augmented analytics continues to get louder.

In fact, Gartner in 2021 highlighted augmented analytics as the biggest battleground in business intelligence, pointing to recent use cases at large companies like Microsoft. Augmented analytics is the use of machine learning and AI within business intelligence—a market that’s expected to reach $30 billion globally by 2027 (annual compound annual growth rate of nearly 30 percent), according to industry analysts.

If you’re still relying on traditional BI tools and working without an augmented analytics platform, you may feel a little behind the times because of their popularity right now. But even though it sounds complicated, augmented analytics has never been more accessible.

Augmented analytics can be incorporated seamlessly into your existing data analytics stack, helping to reduce technological barriers, rather than contributing to them. That’s because augmented analytics eliminates much of the rote work of data preparation and processing, as business users worldwide have demonstrated. Augmented analytics frees up human capital to work on the deeper insights of datasets.

When equipped with machine learning capability—the self-learning component of AI systems that allows them to improve over time without human intervention—companies that invest in augmented analytics can become more competitive and valuable.

What augmented analytics won’t do is automate the complex business decisions that are better suited for human judgment and deliberation. Adopting these systems will not mean turning over control of your company to AI. Instead, what it will do is unlock insights from your data in more efficient and meaningful ways.

Here’s how unlocking these insights impacts industries of all types.

What are the benefits of augmented analytics?

Over the past decade, the promise of Big Data gave way to the realization that data is more like oil than gold. Data requires refinement to be a real resource, and companies are struggling to do that. A recent Gartner survey reported that 73 percent of business leaders say that their data “has not had the expected influence.”

Augmented analytics expedites the analysis step of data management and your analytics tools, generating faster data analysis that is also cleaner and capable of higher-level insights.

Let’s start with data preparation. In the world of data science, the acronym ETL stands for extract, transform, and load, which is the process of copying datasets from multiple sources, unifying them under one system, and cleaning up their discrepancies.

Depending on your current data analytics tools, the ETL process can be a major drain on resources and siphon time away from analysis to surface actionable insights, which is the ultimate goal. Some companies have data scientists spending upwards of 75 percent of their time on ETL. Augmented analytics reverses that ratio by automating many steps of that process.

Augmented analytics platforms also make it easier to ask questions of your data, going beyond the point-in-time visualizations of BI dashboards. Thanks to natural language processing (NLP), you can ask the computer a question in your own written or spoken word, one of the emerging benefits of AI in terms of data management. Natural language processing is the ability of computers to interpret human language—and not just numeric data or code—and research into this field is expanding at a rapid clip.

What this means for augmented analytics is that it’s getting easier to translate human questions into computer queries. And with that comes the streamlined potential for drawing actionable insights from your datasets.

Although traditional BI tools like dashboards and KPI metrics are a simple way to reorient business decision-making around data, they only provide insights on past and current performance. Advanced analytics (which includes augmented analytics, along with other systems that don’t rely on machine learning) are forward-looking, drawing deeper insights from data sets and making data-informed decisions about the future.

They can do the work of a data science team in a fraction of the time. This allows stakeholders to jump more quickly into the difficult business decisions about what to do with the data analyses, rather than spending time creating data reports.

Augmented analytics: Use cases and examples

Industries far and wide are incorporating machine learning and AI into their essential systems and decision-making using augmented analytics tools (as Kitu Super Coffee did in this Sisu case study).

Here are just a few real-world examples of how industries use augmented analytics:

Financial services

Almost every bank is using AI in some form, from chatbots to growth models. For instance, JPMorgan Chase & Co. has emerged as a leader in the adoption of augmented analytics. In 2016, the bank launched its Emerging Opportunities Engine, which uses predictive algorithms to identify which clients are primed for issuing or selling equity, based on an AI analysis of granular datasets.

Emerging Opportunities has been highly successful at reducing costs and enhancing revenue, while paving the way for more targeted promotional campaigns and improved customer satisfaction. Other banks, like Goldman Sachs, have since followed suit in creating like-minded engines, while JPMorgan Chase has accelerated its adoption of augmented analytics in other areas.

Manufacturing and retail

Coca-Cola tapped into AI and Big Data to drive success in making decisions around products, branding, and its supply chain. Using AI image recognition, the company has kept track of who uploads photos of its products (and those of competitors) on social media. Then, Coca-Cola creates specialized advertising to reach those people.

Other augmented analytics are plugged into datasets like weather patterns, satellite images, and crop yields to maintain consistency of its products and the ingredients required to make them. The company is even using augmented analytics to discover marketable new flavors.


Nearly one in four people who are discharged from a hospital end up getting readmitted within 30 days. It’s why more medical networks are leveraging AI-fueled analytics to address avoidable inpatient readmissions. In fact, a recent study looked at how these systems were used at a Wisconsin hospital that’s part of the Mayo Clinic and found that augmented analytics does help reduce readmissions.

After implementing predictive models that determined which patients were at the highest risk of coming back, and deploying more resources accordingly to those patients, “the relative reduction in readmission rate was 25 percent,” according to the study.

Sisu makes it easy to use augmented analytics

Sisu’s decision intelligence engine leverages augmented analytics to give your company a holistic, real-time view of your data. By leveraging AI and machine learning techniques, the Sisu engine considers the metrics that matter to you the most and crunches all your data in seconds to discover what changed and why.

Sisu allows analysts to forgo the time-intensive process of querying and manipulating data to run one analysis at a time like in a BI tool or dashboard.

Request a demo to see how you can get the facts with Sisu or review our webinars to see how customers like Samsung, Mastercard, Wayfair, and Gusto are using Sisu to realize more returns on their data investment.

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