Top 3 ways data analytics can improve your change management process

Data analytics and the possibilities opening to organizations (via IoT (Internet of Things), ML (Machine Learning), AI (Artificial Intelligence), robotics & automation) have grown rapidly in the last few years. Organizations stand to benefit enormously. In the change management and implementation space, the story is no different.

Digitization and digitalization of the change space is occurring rapidly, enabling live implementation dashboards, instantaneous feedback loops, real-time updates of digital dashboards, and virtual engagement that will approach and soon overtake face-to-face engagement in its effectiveness and ability to engage and reduce resistance to change.

Many organizations are at different stages in this journey, and accordingly will benefit by matching correctly their needs for real-time and other data with the type of analytics best suited to their needs.

Here are three critical dimensions for assessing where data analytics can help your change: descriptive analytics, predictive analytics and prescriptive analytics.

Descriptive analytics:

For the most part, the time-sensitivity and the challenge of gathering, collating and bringing together previously unrelated or unconnected data from different parts of the organization means that change implementation can be significantly enhanced through descriptive analytics. When you had no idea that implementation risk enabled you to compare and contrast risk appetites, and that that is a leading indicator of resistance, just knowing that can be revealing. It can also be threatening - and so such information needs to be carefully evaluated so that it can be used in the most helpful way.

Predictive analytics:

The second type of analytics, predictive, is the ability to make sense of and intelligent choices from chaotic situations, where ambiguity and/or lack of data is high. Or conversely, where choices and ambiguities stemming from an overload of data is high. In such situations, it’s not the answer per se that is important, but the “top 3” factors that are known to be present - and testing for them - that brings value. When situations are fluid and unknowns are high, predictive analytics in change implementation bring significant value by identifying previously unseen patterns, reducing ambiguity and unknowns, and enabling hypothesis testing to create success pathways faster and at significantly lower cost to the organization.

Prescriptive analytics:

The third dimension, prescriptive analytics, enables organizations to make statements about situations with extremely high degrees of confidence. It’s very likely that you will rise in the morning and stand up, given that a significant data set exists that demonstrates that that is exactly what happens when sleep ends and the day begins. Such high-confidence statements are examples of prescriptive analytics, and also increasingly they are finding their way into the change implementation space.

The application of data analytics in change implementation will continue to grow, but these categories will enable a good conversation over priorities and benefits of their application. Additional considerations such as where you are in your portfolio maturity, how large a proportion adoption benefits (vs installation benefits) are in your solution, and the level of sophistication of your data gathering infrastructure can help provide context to your decision criteria.

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