Centralization vs Virtualization: The Best Way to Unlock Your Energy Data

Energy data is everywhere.

Literally. It is all over a typical utility organization, across OT and IT, throughout transmission and distribution, and all the way to the edge. And some energy data even lies beyond the scope of utilities, such as in the cases of weather forecasting, wildfire trajectories, and flooding patterns.

In fact, one of the most common challenges I hear from utilities trying to leverage their data is that they often simply can’t locate what they need. And that’s because energy data is in too many places for them to easily find and access it.

But as we all know, harnessing energy data is no longer optional for modern utilities. You need an easy way to identify and utilize exactly what data you need. But what does that look like?

That’s why I wanted to write this blog. There are two main methodologies for bringing together scattered energy data: data centralization and data virtualization.

And today we are going to talk about both.

First off, centralization. Data centralization is a very simple process: it involves gathering up all of your data and storing it in a single, central repository like a data lake or warehouse. From this central storage point, you can more easily find and access the data you need for a given purpose – simply because it’s all in one place!

Now, does that sound like an intuitive solution? Yes, it probably does.

But it really isn’t. Putting all your hard-to-find data in one place may seem logical, but there’s a catch. When you relegate all energy data to the central repository, you are removing it from the care and control of the entities across your organization who own it – not to mention those who consume it too. As soon as the data enters the repository, it becomes much more difficult to access and maintain.

As I’ve seen many times in my career helping utilities with data integration, this creates significant issues and disputes about who is now responsible for updating, maintaining, and ensuring the quality of the data. Now that the data owners (the ones who understand it best) no longer control it, there is little they can do to ensure it remains accurate and high-quality. Inevitably, the data slowly loses its quality and accuracy over time – thus skewing the outcomes of the use cases it is needed for.

So centralization may seem easy enough, but the negative effects on data it causes are not worth it.

Then there’s virtualization, which is the methodology enabled by our GridOS® Data Fabric. With virtualization, there is no relocating of data at all, nor change of ownership. Instead, virtualization involves using a grid data fabric to visualize all your disparate data sets. Thanks to data federation, within a single pane of glass you can view and peruse all the energy data available to you, and from there feed it into the applications that need it. Our data fabric also uses a metadata catalog to categorize data sets based on specific attributes, making it much easier to find what you need.

Overall, virtualization helps utilities combine and contextualize internal and external data, creating a unified view that spans transmission, distribution, and the edge. With GridOS® Data Fabric, utilities can access and leverage energy data more efficiently, enabling intelligent use cases, AI- and ML-powered applications, and data-driven decision-making.

For more information about GridOS® Data Fabric, check out our Data Fabric webpage and whitepaper.

To learn about GridOS Data Fabric’s data integration layer, GridOS Connect, watch our recent webinar on the subject.