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5 Ways to Evaluate Business Intelligence Adoption

There’s been no shortage of advancements in business intelligence (BI) and data analytics technology over the past few years. And yet so many organizations still find themselves struggling to maximize the value they’re getting from all that data they’ve stored.

What gives?

One area proving to be an ongoing challenge for organizations as they try to move toward data analytics maturity is user adoption. The tools a company deploys does matter, but only if enough people are utilizing them for the right purposes — and often enough.

Lackluster employee BI adoption rates hamper an organization’s ability to turn data into actionable insights capable of driving favorable business outcomes. The determining factor is how effectively employees use BI tools and factor information into informed business decisions. It’s important to use analytics tools and apps like Einstein Analytics, which enable business owners to view data. In 2017, the world’s #1 CRM platform, Salesforce, released their newly rebranded analytics tools for viewing data – Einstein Analytics (formerly known as “Wave”). Einstein Analytics is an app used to visualize the activity occurring in your Salesforce environment. Whether you use Salesforce for sales, marketing, or service, this visibility tool offers insights into the data (like contacts, campaigns, or accounts) your users add to the CRM every day.

While there are a number of ways to evaluate business intelligence adoption, here are five of the primary ones to consider.

Number of Unique Users

Perhaps the most straightforward way to take the temperature on BI adoption is to look at how many people are using these tools. More specifically, you’ll want to find out how many unique users are pulling data insights within a given time period like a day, a week or a month. This approach will help you differentiate unique users from power users, which is the only way to determine whether more people are embracing BI or whether it’s the same group of core power users doing so.

Once you’ve determined the number of unique users, you can figure out the percentage of people with data access who are actually using self-service analytics tools. If this percentage seems low, don’t get discouraged — Gartner reported only about 32 percent of employees within an organization had adopted analytics in 2017. 

Average Session Duration

Just like ecommerce companies want to know how long people are spending on each landing page of their website, organizations can evaluate analytics adoption through the lens of how long users are spending actively logged into their BI platform — viewing dashboards, entering queries, clicking on interactive charts, etc.

If the average session duration is very low, this may be a sign that users are encountering roadblocks with usability. Perhaps users would spend more time each week analyzing data if they received additional training on all the features available to them, or if the expectations for usage were laid out more clearly for them.

Use this metric as a gauge to begin determining how much value users seem to be getting from your BI setup. Think about ways you can encourage users to keep drilling down into data and experimenting with the features at their disposal.

Report Execution Time

Something that undoubtedly impacts BI adoption is how long it’s taking for users to pull data insights. The lower this number, the more likely users are to stick around long enough to get the full report and use it.

Your BI platform will determine how long it takes users to create reports and extract insights. Case in point: One Fortune 200 Pharmaceutical firm was able to reduce the time it took researchers to get results of drug trial results from three months to three minutes on ThoughtSpot’s platform. This, in turn, enabled them to reduce their time-to-market, a task that has been an ongoing challenge for pharmaceutical companies. As Scientific American writes, “Consumable and consistent data could also help bring drugs to market faster.”

Whatever industry you’re in, optimizing your data strategy to reduce report execution time can boost adoption rates and help drive better business outcomes.

Reports/Queries Executed

Simply put, taking a more granular look at the reports created and queries executed will provide a deeper understanding of who is seeking what information. Then you can begin to think about why certain information is useful to certain teams. For instance, the marketing manager may realize they can help make their team more data-driven by adding a few more relevant metrics to the team dashboard.

Number of Decisions Made with Data vs. Intuition

Last but not least, track the number of decisions driven by data vs. those driven by old-school intuition — and the outcomes for each.

There are a few key ways to evaluate BI adoption: how many people are using it and for how long, plus what they’re querying and how long it’s taking them to get results.