Big Data

Making Big Data Smaller

Making Big Data Smaller

Posted by CIO Talk Radio onin Big Data

Did you know that as the Smart Grid is implemented to update utilities’ aging infrastructure, if you were to accumulate data at 15 minute intervals for an average population of about 100,000 meters across the period of a single year, you would manage to collect as much data as you’ve collected on nearly four million meters historically?

That’s a lot of data, and with Smart Grid, utilities can now bring in data points from countless substations and customer meters at any frequency they wish, so the infinite possibilities and sum of information is no doubt overwhelming.
But Mark Wyatt, the VP of Grid Modernization with Duke Energy and the person who shared that above statistic on our Viewpoint

The Smart Grid Big Data Challenge”, made a very simple point: If there’s a cost associated with bringing back all that data, only bring back data when you need it and can leverage it, nothing more.

Wait, what? You can do that?

“Big Data is not as big as most people think,” Wyatt said. “You can bring back hundreds or thousands of points, but you need to limit the points that you bring back when there is a business value to use it. And if you do it well, it does not overwhelm your system, it does not overwhelm your employee, and you have a much higher probability of getting value on that data.”

That seems to be the key to lessening the whole Big Data Challenge: look past the technical hang-ups and focus on the more important business need of finding real value. If the raw processing power is there, how do I make use of all that real time information to give customers the billing information they need, how do I quickly address outages, and how do I keep five-nines reliability?

“While the meter is capable of being read on a very timely frequency, you need to understand the business process that you need to put around it to leverage that data,” Wyatt said, implying that Big Data can’t do much for utilities unless the right information can be understood in real time. What algorithms need to be in place to filter out the important elements only? What training do my employees need to put analytics around that real time information?

Right now, Wyatt explained that most processes are not set up for real time interactions. Calls from customers come in with a request for service or a complaint about a bill, and the time it takes to deliver digitally enabled data to people in the field is often days, weeks or months old. So on a fundamental level, not a technical standpoint, what are utilities doing to solve this?

“I really hope that we’re starting to balance the conversation around Big Data,” Wyatt said. “It’s not as scary as most people think.”

Hear more from Mark Wyatt on our Viewpoint, “The Smart Grid Big Data Challenge.”

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