Although business intelligence (BI) and data analytics become more of a competitive differentiator with every passing day, some companies remain hesitant to invest in this technology. Why? They want to know they’ll be getting their dollars back — and then some — if they do.

It makes sense organizations are always looking for ways to derive more value from BI; here are three.

Create Data-Driven KPIs for Business Goals

Yes, business intelligence (BI) can help teams across an organization track whatever aspects of performance fall within their scope — from product sales by SKU to new subscribers by campaign.

But what about establishing and measuring key performance indicators (KPIs) for BI itself? Companies aiming to get the most value possible from BI and data analytics set goals for these tools and carefully gauge the results to determine their return on investment (ROI). The exact KPI metrics a business measures for its BI and data analytics strategy will depend on a few things. Among them is what the business objectives were for investing in this tech in the first place.

Say a company wants to see more of its employees outside the data team incorporating BI into their workflows, something that has been challenging for many organizations trying to become more data driven. It rolls out an advanced self-service BI system that will give users across the company access to insights whenever they want or need them. Making user adoption rate a KPI is a good way for the organization to track the percentage of its employees using the tech and how often they do so over time. Only by setting this KPI could the company determine whether they’re meeting that goal of increased adoption — an important facet of getting value from BI.

Setting KPIs to measure data goals, like adoption rate, gives organizations visibility into the ROI of their data initiatives. It also helps maximize value because it holds companies accountable for investing in data analytics tools up front, keeping close tabs on the success of the data strategy — and making improvements as needed if the KPIs are conveying underwhelming results.

Use AI Analytics to Uncover Insights

What often comes to mind when we think about BI is a user entering a search query and receiving an answer in return. Search-driven value can provide a lot of value to users, to be sure. But what about all those questions nobody has yet thought — or had time — to ask?

An artificial intelligence (AI) analytics engine can help identify insights, like an overlooked trend or a surprising anomaly, hidden within millions of rows of data. This task can easily take human analysts days or weeks to accomplish manually due to the sheer amount of data most companies store today.

Without AI analytics, companies are leaving these hidden insights on the table. What’s even more promising, teams can provide feedback to machine learning algorithms to “teach” these them which types of insights are most relevant.

Infuse Shared Portals with Analytics

Giving employees direct access to speedy self-service analytics is a great first step toward maximizing the value of BI. But there’s a way to take this idea farther, expanding the reach of data analytics to partners and customers within shared applications.

British Telecom (BT) gave its customers direct access to embedded analytics through the company’s website. Instead of having to call customer service to ask questions about billing, over one million customers now use a simple search tool to analyze their billing information and figure out which service plan would best provide the features they need for the price they can afford.

The potential benefit here is multi-fold: Customers and partners who gain instant access to data insights are empowered to make more informed decisions. Businesses that connect users directly with data eliminate the inconvenience associated with gatekeeping information — like how BT customers can now their own questions rather than having to always call customer service about billing.

Deriving more value from your BI strategy is a matter of setting and measuring goals, harnessing the power of AI analytics and giving more users access to quality information they can incorporate into decision-making.