BI implementations often do not yield anticipated results because of a lack of user adoption and cultural change. Therefore, it is not enough that your BI partner implements the BI solution as a technology project; they also take full responsibility for your user adoption.
Organizations can take full advantage of BI investments only when its users leverage insights and reports from the solution to make decisions.
Who is responsible for user adoption?
User adoption makes sure your BI investment supports data-driven decisions in your business. So, before you choose to work with a BI partner, find out how they enable user adoption and take co-responsibility to build a data-driven culture. Of course, your leadership must take responsibility for leading the user adoption based on your business needs. However, a BI partner should support and strategize the user adoption initiatives for a smooth transition.
What Columbus brings to the table
At Columbus, we have implemented BI solutions to clients globally for more than 14 years. Some of these engagements were conversions of data to intelligent reports, creations of interactive dashboards, training of solutions, such as those on Power BI as well as those using the intelligence generated by applied AI/ ML. Through these engagements, we have gathered a wide range of experiences and best practices and structured them into a ‘user adoption catalog,’ which simplifies user adoption.
The catalog contains many elements that all point towards how to become data-driven and how to reach anticipated goals. These elements are structured into three levels - Basis, Interactive, and Support, depending on the maturity level of your business.
This structure can be a useful, overall framework to work together with your partner - where you can, for example, ask for:
- Assess and Improve Data and AI literacy of the organization as suitable to your organization
- Education and training (where to find, how to do) - both how to use and visualize dashboards that are easy to use and explain the insights -- say, using Power BI
- Governance and security models to provide self-service and access to data and insights to the right profiles/ roles before its release
- Organization and roles - What competencies does it take to execute a specific task? What training should be given to the individuals? And, what should you focus on in the future to build new competencies?
- Preparation and implementation of the communication plan -- newsletters and survey forms to understand and resolve user concerns
- Collation and sharing of beneficial Data & AI products and discount opportunities
- Workshops and events to enable users to share their success stories and achievements
Don’t reinvent the wheel
- Easy access to support at all levels of the organization -- through training for super users, Q&A sessions, and a dedicated support function helpdesk
- Sessions for hardcore Data & AI users to build reports, use generated insights, predictions, forecasting, etc. in the core business processes
- Management of Services, Tools and their licenses
Finally, I have to point out one crucial thing: User adoption is essential – but it is not rocket science, in the sense that you necessarily have to invest heavily to achieve it.
On the contrary, make sure that the tools available for user adoption become an integral part of your BI implementation project. It is seamless and effective when you follow structured and simplified processes.
Remember that your management must take the lead
While the tools to build the right culture are already available, don’t overlook the risks associated with any Data & AI project -- preparing your organization to transition to a data-driven culture. Access to data itself cannot ensure cultural change.
So, let me conclude with a real-life example from one of our clients. The management, along with the CEO of a global company, took the responsibility of setting the right, positive note for user adoption after we implemented a solution. The CEO’s team reviewed the newly developed business dashboard every week and initiated a dialog on how well they were able to use the factual findings of progress, what they could do differently, and so on before they made a decision.
The client experienced increased growth and employee satisfaction because of a shared database and access to a single source of information. This transparency allowed each employee to contribute independently -- a testament to how businesses can get optimal value from their Data & AI investment, quickly.