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Author's profile photo Thierry AUDAS

The Answer to Scaling Analytics Adoption

The global big data analytics market is forecasted to exceed $105 billion by 2027, according to a January 2020 study by Acute Market Reports. With organizations making significant investments in analytics, they are also looking for ways to overcome the common problem of user adoption. A study conducted by Gartner in 2021 showed that business intelligence and analytics adoption among all employees was only 30%. That is only a small improvement from similar findings from the 1990’s when adoption was about 20%, according to Eckerson Group. What is driving the reluctance to use analytics and business intelligence? Finding and addressing the challenges could be the key to analytics delivering on its promise of faster decisions and confident action.

The Ongoing Analytics Gap

Connecting disparate forms of data from sources across the organization used to be the major problem affecting adoption. However, most analytics platforms have solved this and can aggregate data from virtually any source and offer functionality to analyze it. But still, adoption has lagged. To compensate, analytics leaders provide richer opportunities for training and access to analytics experts for support. While these efforts had some positive effects, they have not been perfect.

On average, 90% of an organization’s employees can be classified as casual users. Casual users who either interact with existing reports and dashboards and may occasionally want to modify or change what is available to them. Analytics vendors have also done their best to spark the curiosity of casual users by continually adding more and better tools for visualization, dashboards, standardized reports, and simplified customization. Even the increase in user-friendly functionality has moved the needle only slightly on casual user adoption.

Augmented Analytics Offers Hope

Enter the age of augmented analytics which is an approach to analytics incorporating artificial intelligence (AI) and machine learning (ML). Augmented analytics may finally be the answer to better adoption because it bridges the gap between analytics capabilities and how casual users think about data. Augmented analytics is comprised of a set of tools that help make analytics more intuitive and less daunting for casual users.

Natural Language Query (NLQ)

NLQ allows analytics users to type a question, statement, or keywords into the analytics interface to initiate an analysis of the data. For example, in SAP Analytics Cloud, you can analyze a data set consisting of your company’s sales from the last 5 years. Type “What are the sales results from 2020?” into SAP Analytics Cloud, and a few clicks later, you’ll have a report and visualization of the data.

NLQ has come a long way in its development, even being so forgiving to allow spelling and grammatical errors. NLQ can also interpret generic terms. For example, you could ask the same question, “What are the sales results from last year?” to get the same insights.

Assisted Analytics

Assisted analytics offers the analytics user the opportunity to click on a chart or metric within a visualization and get more detailed information. For example, you may see correlating data or a drilled-down view of a data point. Augmented analytics anticipates what users want to see and can learn from their actions to provide better insights over time.

Business Monitoring

By running assisted analytics functionality continuously, augmented analytics can enable intelligent alerts about changes in metrics that impact business operations. Users can spend their time on high-value work and address alerts when necessary.

AI Modeling Wizard

One of the more advanced functions of augmented analytics is the ability to use wizards to guide analytics users through the process of building their own modeling and predictive algorithms. They can do so without the need for a data science background or the support of data experts.

How Does Augmented Analytics Help?

Augmented analytics is helping to make analytics more accessible to both casual and advanced analytics users across organizations. Advanced users benefit from the efficiency and better insights generated as the tools learn. They may also benefit from spending less time supporting casual users. Casual users benefit from intuitive features that stimulate their confidence in the tools and in the data. Casual users may still experience challenges such as “I don’t know what to type” or “I can’t explain or defend the results.” However, augmented analytics is actively addressing these and, so far, offers the best chance at solving the problem of casual user adoption.

Scaling Analytics Adoption Organization-Wide

Get the Eckerson Group whitepaper to discover the “Analytics Adoption Framework,” which outlines the major factors contributing to the widespread adoption of business intelligence and analytics tools. The Analytics Adoption Framework provides a helpful guide for ensuring business users will adopt new technology.

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