Good afternoon!

Here's what UAI's utility members have been talking about over the past two weeks:

• IoT Center of Excellence: As utilities start to expand their use of IoT, how do they provide appropriate governance and oversight, training, consultation, and management? 
• Short-term load forecasting: What is a typical error rate for short-term loading forecasting?
• Self-service vs. governance and control: How can utilities properly maintain a level of governance and controls as business intelligence and analytics grow across the enterprise? 
• The data scientist experience: How does the data scientist balance learning the business and the data problems if they haven't been around for 20 years to know the business inside and out?
• EV load forecasting: How are utilities forecasting EV adoption numbers and the required capacity to support charging? (Note: UAI is releasing a podcast on this topic at UA Summit titled, "Paving the Way for EVs". Register to UA Summit to hear the episode of subscribe to Beyond the Data podcast!) 
• Data strategy: Data is the foundation of analytics, what resources are utilities referencing in the buildout of their data strategy?
• Single solution platform for analytics: Is there a single platform out there for all utility current and future analytics and business intelligence needs? 
• Data responsibility and data democratization: What is the most effective way to train staff on how to use and share data? 
• Executive operations dashboard:  How do utilities build an executive level operations dashboard and what should it include? 

If you're interested in how utilities are answering these questions, then join UAI today! There are different types of memberships--utility or solution provider--that get you access to UAI Connect, which is where these discussions take place. 

Have any questions for the UAI staff or interested in what we're thinking about? Shoot me an email at [email protected]. 

Happy reading!

Erin Hardick, Sr. Research Analyst

“We have a lot of data, but we still don’t have a lot of information.”

If this sounds familiar, you’re far from alone. Utilities today continue to struggle with the journey to efficiently operationalize data from around the organization into repeatable business value. It’s not for lack of trying; digital strategy is in place and teams are being structured to meet these challenges, yet the conversion point from data into valuable information still seems to be more manual than automatic.

Put simply, data really can’t turn into information without context. In operations, data must be anchored to its appropriate place in the process – usually to an asset hierarchy, process diagram or even a network. Skilled subject matter experts understand this context natively, having developed it over time. But for many employees, analysts and data scientists, this data exists in various formats and silos across the organization–most often lacking the quality, the context and the relationships that make these individual points valuable to operations. This translates into longer time for analytics and slower digital deployment cycles.

Overcoming the challenges that utilities face today seems overwhelming at times. Yet, utility issues and solutions all have one thing in common: utility concerns are spatial. It’s hard to think of a utility process that ignores location. Around the globe, utilities use maps. Why? Because utilities work better when using location intelligence.

Virtually every utility uses geographic information system (GIS) technology as a foundation for mapping. However, limiting GIS use to a digital replacement of paper maps is a profound underutilization—a lost advantage to address a challenging environment.

Changes in the utility environment and the explosion of data both demand better ways of managing, examining and communicating information. The energy industry asks for new digital tools that show an inclusive picture and provide powerful insights…insights that include exceptional visualization on any device, anywhere, at any time.

Utility companies frequently rely on digital tools to help make operational decisions, meet regulatory requirements, plan capital spending and respond to incidents. The key to success in all these endeavors is accurate, complete and timely data. Data governance is the process of effectively managing, utilizing and securing that data. It establishes internal policies and processes to protect the integrity, availability and usability of data within the organization. This type of system generally includes a governing body, a defined set of standards, and a plan to implement and maintain the new data procedures throughout the organization.

With well-executed data governance, organizations can avoid the headaches that go along with inconsistent data because it standardizes definitions and increases accuracy across diverse platforms and teams. More importantly, a well-organized data governance system will increase security and ensure compliance with internal and external data regulations while providing decision-makers with reliable information.