How Data Enablement is Impacting Data Management

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Click to learn more about author Kevin W. McCarthy.

As we know, Data Management is nothing if not a barrage of the latest terminology designed to spur us into action. Big Data and analytics are terms that might be relatively new, but they are practices that have been around since the stone age (or at least back in my youth).

The latest Data Management term to pick up momentum is the concept of data enablement. Data enablement is the practice of empowering individuals in a business with the support and tools they need to responsibly leverage trusted data to achieve real business outcomes.


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As a self-proclaimed “data nerd,” I am all about finding ways to take the valuable data I love and put it into the hands of more people across the enterprise. We are still a long way from that happening, but I think more businesses are starting to make real strides.

In our new research study on how businesses are facilitating data enablement, we find data enablement can be boiled down to a couple of core concepts: data literacy, user-friendly technology, and an enterprise-wide data culture. Let’s do a deeper dive into these trends.

First, we see a lot of organizations are trying to bridge the gap between the business and IT, which is exactly what data enablement is all about. There are more Data Management initiatives going on with more mature organizations and they are more likely to have data specialist roles in place, like a chief data officer.

Second, we see several data technology initiatives going on, starting with Data Quality (62%), Data Analytics (56%), and Data Governance (48%). These initiatives make perfect sense, and in some ways, they build off one another. Personally, I’m happy to see Data Quality at the top of the list given I know that it is the foundational work needed to make all other data initiatives successful!

With Data Management, there is no one-size-fits-all fix. But, when we look at these data initiatives, we see two fairly polarizing approaches: the view of Data Quality or Data Governance as either a project or a discipline. A project is something you do as a one-off, like performing a data migration to a new system. A discipline is more of a cultural endeavor that is ongoing (“Everyone needs to understand the importance and business value of data.”). There is a night-and-day difference here: one-time projects have a start and end, whereas a data management discipline with an organization must have constant support and nourishment from the senior level down to individual contributors. Instituting Data Management as a discipline has greater long-term benefits but is also a much larger undertaking and journey.

Finally, we see companies creating a cultural shift. The biggest challenges to enabling the use of data are a lack of communication between departments, a lack of skilled human resources, insufficient budget, and a lack of data literacy. In addition, 69% say most Data Management initiatives occur in individual departments, only a few at the enterprise level. While I am all about companies experimenting with data, a lack of efficiency and scale can cause problems. We have to create a culture where we are curious about data and want to responsibly share information, not keep learnings and insight within individual silos.

These are three simple concepts to speak to but not so easy to implement in a business with existing staff, technology, and culture. You can lead a horse to water, but you can’t make that horse build a sophisticated infrastructure to and from the water, put filtering and purification mechanisms around the water, and teach all of the other horses how to drink from the water in an optimized fashion (at least not without senior equestrian buy-in!). That is why the journey to data enablement will take some time and effort. 

But, as companies travel their way down the data maturity curve, they are realizing the fruits of these efforts. These companies will gain an advantage and unlock the power of data, which can help take them to the next level.

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