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Data analytics in today's context are largely centered on collecting, managing, and understanding
what data is there and what data we can use. We are seeing a lot of new data so it's about
getting a handle on what's in the data and understanding what the data is telling us.
As we progress forward, we are going to get more and more confident that we understand
what's in the data and we're going to start to look at ways to use more advanced math
techniques to actually understand how we can affect results. How we can predict what could
happen and how we can change the behavior to address that.
I think today one of the benefits of data analytics is being seen in the industry as
the focus on energy efficiency. It's a good example to really talk about what analytics
can provide in terms of a framework for the overall effectiveness of the grid. Without
meters or without other data sources it's not possible to understand what energy is
really flowing. If you don't understand how it's flowing, its really hard to understand
how to make it flow more efficiently.
I think the biggest problem we've seen in terms of a barrier to implementing analytics
is understanding what the ROI is or the payback. Many cases, the foundation of an analytic
is really going to solve or address many different business cases and business values will evolve
over time.
I think the key takeaway for analytics is we're really on the cusp of a new frontier
in being able to take advantage of these large new sources of data that provide a much richer
context of what's happening and what needs to happen in terms of grid resiliency and
operation. We can discover through those analytics ways to maturely improve the operation and
resiliency of the grid.