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Business Intelligence (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 business intelligence partner only implements the solution as a technology project; they should also take full responsibility for your user adoption.

Organizations can take full advantage of business intelligence investments only when their users leverage insights and reports from the solution to make decisions.

Who is responsible for user adoption?

User adoption ensures your investment in business intelligence supports data-driven decisions in your business. Before choosing to work with a business intelligence partner, you should determine 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 business intelligence partner should support and strategize the user adoption initiatives for a smooth transition.

What Columbus brings to the table

At Columbus, we have implemented business intelligence solutions to clients globally for more than 14 years now. Some of these engagements involved converting data to intelligent reports, creating interactive dashboards, providing training on solutions such as Power BI, including solutions leveraging the data generated by applied artificial intelligence and machine learning. We have gathered a wide range of experiences and best practices through these engagements and structured them into a ‘user adoption catalog’ which simplifies user adoption.

The catalog contains many elements with details on 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 on with your partner towards implementing a business intelligence solution in your organization. Let’s understand the tasks associated with each level.

Basis:

  • Assess and improve data and artificial intelligence literacy in your organization as per need
  • Provide education and training (where to find, how to do) on how to use and visualize dashboards that are easy to use and explain the insights, say using Power BI
  • Define self-service governance and security models to restrict access to data and insights to the right profiles/ roles before their release
  • Ask yourself - What competencies does it take to execute a specific task? What training should be given to individuals? And what should you focus on in the future to build new competencies?

Interactive:

  • Preparation and implementation of the communication plan - newsletters and survey forms to understand and resolve user concerns
  • Collation and sharing of beneficial data and artificial intelligence products and discount opportunities
  • Workshops and events to enable users to share their success stories and achievements

Support:

  • 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 and artificial intelligence users to build reports, use generated insights, predictions, forecasting etc in core business processes
  • Management of services, tools and their licenses

Finally, I must point out one crucial thing: User adoption is not rocket science, but it is essential 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 business intelligence 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, preparing your organization to transition to a data-driven culture is of utmost importance. Access to data itself cannot ensure cultural change.

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 could 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 and artificial intelligence investment.

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