Business intelligence (BI) platform licenses during the past twenty years have been held tight or were limited to a specific pool of users in IT. These licenses were often not shared with business users. In fact, there was no place in the BI deployment project plan to measure actual end-user usage and adoption of the BI applications built upon these platforms. And the results were painful: sunk costs with little to show for it and business decisions being made more by "gut" than supported by data, despite its availability.
That was then, this is now. Companies have spent millions of dollars to implement BI solutions to get their teams to make better data-based decisions, and they want to see returns in the form of better business outcomes and faster time to value. Nowadays, organizations require clear plans to drive BI and analytics adoption and to measure BI application usage.
BI platforms are being implemented as the "corporate standard" with the intent of eliminating silos and making more data and analytics accessible to business users on-demand. Achieving that standard, however, can be challenging because teams are comprised of different personas with different technology and data analysis skill levels: some advanced and some beginners. To achieve broader adoption, BI platform deployment teams must evaluate the different ways people adopt and use software and then tailor their BI applications to support each of those user types or personas. Ultimately, understanding and handling those different user types is the key to success.
In all areas of BI and analytics adoption, one approach to help refine and improve adoption is to create a simple hypothesis that you can then test for and analyze. For example, you can hypothesize that better data visualizations will improve adoption. Measure your current state and adoption levels and then implement visual dashboards and data stories; finally, re-measure the engagement and adoption to see the impact of the change(s) you made. For a complex scenario, you might hypothesize that a new, more "user-friendly" BI platform would improve adoption through self-service capabilities. To analyze such a complex case might require pilot studies or small roll-outs of the new platform. In any case, direct observation in addition to automated measurements of how people interact with the solution as they work is a supercritical component of the analyses.
It is also necessary to establish adoption benchmarks and set target goals. Each discrete BI application needs to be studied to ensure appropriate levels of adoption. To explore this a little further, take the example of integrating BI in a manufacturing firm’s supply chain department. Looking at supply chain BI adoption, you observe an excessive number of data exports from a specific set of users. In most instances, this might indicate a poor application design. However, in investigating the app's adoption, you find that users are exporting the data to deliver information to suppliers, proving instead that the application design was fine and fit for purpose. Without spending time with users, you might never discover the truth. Similarly, different usage patterns may be tied to business cycles (such as budget planning and board reporting) or specific times of the year (for example, year-end reporting or quarterly business reviews); understanding that the associated apps may only be used seasonally or in alignment with those cycles helps to accurately collect usage and adoption details.
To drive adoption you also need both the capability to measure adoption and engagement PLUS a codified plan to do so. Without this combination of capability and plan, it is impossible to know if companies are getting value out of their BI investment. Generally, the goal is to utilize all assets to drive growth, innovation, and profitability, and to reduce risk. Leaders need to understand if their BI platform investment is being put to good use to drive profits, avoid risks, and optimize business decisions.
Axis Group Enables Teams and Improves Adoption Rates
Axis Group recently worked with a large innovative manufacturing firm. The company was struggling to obtain data in real-time, as the tools were not in place to allow for rapid analysis and the creation of new metrics on the fly. Axis Group experts reviewed the BI implementation and determined that the issues of low adoption and poor analytics usage were responsible for all manner of challenges among the team. They had data and BI platforms, but they did not have a clear perspective of how systems could be accessed and utilized on a day-to-day basis by employees across functions. Axis Group focused attention on improving adoption. By driving awareness through marketing and targeted training, in a matter of months, the organization's culture was transformed. This led to a dramatic increase in the use of data to drive better decisions, and it improved how the business functioned overall. In partnership with data champions at this company, Axis Group fostered healthy competition and collaboration between business units leveraging analytics, resulting in improvements in decision making and data literacy.
A BI adoption tracker, such as the Axis Group solution illustrated above, is an essential tool to measure progress. A BI adoption tracker also measures the success of an organization's data literacy program. As the company gets feedback through the tracker, they can take proactive steps to improve and implement predictive measures to ensure effective use of analytics within their business.
Measuring adoption is a continuous journey. The first-time use of analytics tools is an important opportunity to inspire individuals' use of data and to set them up for long-term success. When measuring adoption, consider these four aspects of engagement:
- User Analysis
- Roll-Out and Promotion
User Analysis is the ability to monitor BI or analytics application usage. Usage entails tracking the target behaviors of the audience for a specific application. Out of that target population, seek to discover: “Who is actively using the BI application?” Drilling down a bit further, you might then ask, “If they are using the application, how long are they accessing the application, and for how long are they engaged?” A successive question you might then ask is “While engaged, how active are they within the application (number of clicks)?” Each of these questions helps provide more insight.
Education can be either formal or informal. Most BI software is packaged with formal education from the vendor in either a classroom or by way of computer-based instruction. The engagement with educational offerings is another detail that needs to be tracked. Tracking education details shows the progress the organization is making. Studying not only who has taken the courses and how they performed along with if and why people hesitate to move to the next level of education can provide powerful insights into your company's analytics adoption journey.
Community is a common area where users can share ideas, post questions, and receive mentorship. Measuring activity within the community is another component of adoption. High community activity is a proven indicator of higher adoption. Axis Group recently worked with a customer who was struggling with low adoption rates. While they could see their application usage metrics, they could not understand how employees were using the applications. Axis Group implemented a Community for analytics collaboration and knowledge sharing, which led directly to improvements in individual education as well as four times more engagement.
Roll-out and Promotion, finally, is incredibly important to measure as well, when aspiring to drive BI adoption. Certainly, it's necessary to have an implementation plan that includes the user community but that plan cannot be purely technical. It has to include targeted messaging and promotion that will drive both awareness and demonstrate the value of the analytics applications and systems in place. Promotional activities might include email newsletters, virtual or live events, and must also be measured to help you understand what is effective and what is not working. If they are not effective, you must shift gears and find alternative activities to drive BI adoption. Finding the optimal activities to motivate and inspire everyone to become more data-centric is the goal.
At Axis Group, we understand all aspects of analytics adoption and enablement. With more than two decades of experience in data and analytics, we have successfully helped analytics teams across a variety of industries get more value out of their BI and analytics platforms and applications. Schedule a FREE discovery session to learn more about how we can help you improve data literacy or complete your digital transformation here.
Matt Herdlein is a Digital Transformation Strategist at Axis Group. He leads employee customer engagement initiatives and oversees their Analytics Adoption Index — an emergent tool for global analytics adoption benchmarking.