Data Analytics and Utilities

At the recent DistribuTECH 2014 conference and trade show, almost every session touched on big data and analytics in some way. Utility professionals are gradually taking a leap from implementing smart meters to reaping benefits from broader smart grid technologies. The question is: Will big data and analytics be the key that allows utilities to bridge the chasm between technology and people/process improvements?

Here are my DistribuTECH observations related to smart grid data analytics:

1. There's immense interest in a common data model. Many utilities mentioned the need for a common information model to converge data from multiple data stores. Forward-looking utility companies now understand the need to create insights from historical and real-time data to improve outage management, asset monitoring, and customer engagement. These utilities are finding that current technologies face new challenges and limitations regarding data management.

2. Data veracity is important. Many utilities are interested in big data -- but it was news to me to hear data veracity being mentioned in addition to the common "Three V's" of big data (volume, velocity, and variety). Data veracity refers to the biases, noise and abnormality in data. It indicates how much you can trust data as a basis for decisions and actions. I was surprised to learn that some utilities are concerned about data integrity across enterprise systems. These utilities hope that analytics will reveal gaps in information stored in their existing systems. And they hope to fill these information gaps with more accurate data from GIS and other sensors in the distribution grid.

3. Improved outage management is the prime scenario for grid analytics. Preventing and predicting outages is high on the wish list for many utilities, due to reliability metrics and requirements. Analytics can play a key role in achieving this goal -- by analyzing data from a variety of assets such as transformers, smart meters, synchrophasors, distribution management systems and outage management systems.

4. Engaging customers also requires analytics. Understanding customer energy consumption patterns, providing new services, implementing demand response, and revenue protection all require analyzing data -- as well as sharing it with marketing and customer operations departments. Furthermore, social media and mobile applications are important tools for delivering insights to customers. Analytics tools will play an important role in providing data to social media and mobile platforms.

5. Experimentation with analytics is growing. Many utilities are still in the earliest stages of using big data and analytics. However, some are experimenting with various analytics capabilities for data across systems. Some focus on selected use cases, while others are taking a sandboxed approach.

6. Analytics is a key skill for utility professionals. Judging by DistribuTECH vendor announcements, it's obvious that analytics is a sought-after path for many solution providers. Utilities will be eyeing analytics to solve both short- and long-term problems. Therefore, utility professionals should evolve their skill set to include analytics. At the same time, utility professionals should develop a framework approach to solving emerging issues via analytics, so that they can more easily track results over time.

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