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Tackling the analytics adoption roadblock

Written by Koen Vanbrabant

As organizations continue to navigate a rapidly changing business landscape, the importance of data-driven decision making has never been more apparent. The adoption of analytics solutions can provide a competitive edge, but it requires a new approach to data and technology.

Companies are exploring cutting-edge technologies such as machine learning, artificial intelligence, and advanced analytics to gain new insights and make better decisions. These technologies require skilled data professionals who can design, implement, and maintain complex systems. But even the best technology is useless without user adoption. That's why companies are focusing on creating a culture of data-driven decision making, where employees understand the value of data and feel empowered to use it to drive business outcomes.

We put the business in the spotlights

One key strategy for ensuring successful adoption is to involve business users from the very beginning of the analytics journey. By collaborating with domain experts, data professionals can better understand the specific challenges and opportunities within the organization, and design solutions that are tailored to meet those needs.

As an example from a client “we only properly started to understand how an advanced analytics model could help contact center employees after spending time shadowing our new colleagues and listening to a series of customer calls. We started to understand the context of the client conversations, but we also learned that our solution needed to be simple and fast. The contact center employees had a maximum of 60 seconds to lead to a call to resolution. A model that is too complex, or a dashboard that is too slow to navigate would not have been possible to use.”

Making life of the business easier

Another important aspect is creating intuitive and user-friendly interfaces that mirror existing workflows and tools. This can ensures that the new analytics solutions feel like a natural extension of existing processes, rather than an abrupt change.

As an example from a client “…when building a predictive model to advise on the set-point selection of a chemical production process, we kept the excel interface of the tool that the production engineers build themselves. We changed the underlying business rules with the output of a predictive model. This allowed us to test the new model, without training the user in a new interface or tool.”

A use-case driven approach

Finally, companies are taking a use-case driven approach to analytics, focusing on small, manageable projects that can demonstrate clear business value before scaling up. This helps build trust and momentum for analytics initiatives and can help ensure that the organization is able to fully realize the benefits of its investments in data and technology.

As an example from a client “when testing and implementing a customer loyalty program, we first focused on one customer segment to define and roll out data-driven actions and show impact before going to other segments. The risk of starting too big a project makes it uncontrollable, resources are spread too thin by which they lack focus, and it takes too long to show the impact that can be made.”

Overall, the focus of analytics adoption is essential, as organizations continue to explore new technologies and strategies for gaining insights and driving business outcomes. By focusing on user adoption and creating a data-driven culture, companies can ensure that they are able to capitalize on these opportunities and remain competitive in the years to come.

If you are interested in hearing more about overcoming the adoption roadblock or if you have any questions on how to get started, reach out to Koen Vanbrabant.


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