Why Change Management Increases Data Analytics Success

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Change management has only one purpose: to improve the success of your project.

People often talk about change management (CM) in language that is jargon-y, vague, and disconnected from business goals. It can sound like a luxury or a “nice to have”. CM is none of those things.

We view CM as a structured approach that improves the fundamental metrics of success in our data analytics projects: delivery against objectives, schedule, and budget as well as contribution to business objectives and outcomes. Those are what our clients care about, and that’s what we use CM practices to deliver.

Data analytics projects bring new tools and new ways of working. That’s exciting. The promise of better analytics is better insight, better decisions, and better business outcomes in the form of increased revenue or decreased costs. No matter what tools you use or what part of the business you have in focus, those are the ultimate aims of any dashboard, report, or data warehouse.

The problem is that your current ways of working are familiar—they are well-worn paths. Having a different option isn’t enough to cause people to change, and edicts from the C suite seldom get the intended results by themselves. In order for people to choose to embrace these new ways of thinking, they must have awareness of the new way, a desire to move toward something better, knowledge about what is required of them, the ability to enact the new behaviors, and reinforcement to make the new way their preferred way of working—a new set of habits. Absent those fundamentals, people tend to revert to old ways—even when the old ways are clearly inferior. 

The way we approach CM builds in the path to better performance from the start. When we design a dashboard, we not only ask what it will do—we also ask how it will be used and where in your workflow it fits. We want to know very specifically what the path to tangible business results will be, and we want to design both the tools and the processes surrounding those tools to follow that path.

Prosci (our primary CM approach) has amassed years of data on the contribution of CM to project success. They calculate return on investment (ROI) in part on “adoption contribution”—the percentage of a project benefits that depend on people changing how they do their jobs. For important projects, Prosci has shown that number to be 80% or higher. For new tools and approaches to succeed, people need to know why and how to change. That’s why CM is central to the way we think about data analytics.

Research consistently shows that only around 15% of data analytics projects succeed completely. Successful adoption and integration are key to getting into that 15%, and that’s how we use CM: to make your project a complete success.

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