Utility analytics and I have had a very long, sometimes frustrating, sometimes rewarding relationship. Having started as a SCADA (Supervisory Control & Data Acquisition) market analyst nearly three decades ago, and SCADA being arguably an early version of something we call “edge analytics” today, I have had the good fortune to have a front row seat to many of the interesting market and technical developments in the utility industry that have brought us to today’s utility analytics market. It’s a great time to be in this industry!
As utilities embrace analytics and move towards data and analytics-driven decision-making, there is definitely a movement towards more of an enterprise (versus a localized or even “desktop”) approach to analytics in most utilities that are now investing in analytics and (hopefully) seeing a return on these people, processes, technology, and data investments. As the title above suggests, analytics strategy really is not an oxymoron anymore, although just a few short years ago it might have been. Remember when one-off analytics projects, often bordering on a “skunkworks” approach was the norm?
Thinking about some of the numerous discussions I have had with C- and VP-level utility executives as the utility analytics market has evolved, a few themes leap out as to both the “why” and “how” a cohesive analytics strategy is critical to successfully leveraging the power of data across a large, complex enterprise like a utility. The flavor of these discussions follows, along with some ideas on how to move an organization’s analytics strategy forward.
Focus on the business
The discussion usually started with something like this from a C-level executive: “We have tons of data. We have invested in smart grid and smart meters and have spent a king’s ransom on an IT infrastructure that requires constant upgrades. Where do start if we want to see value from all of this data? And don’t tell me about more dashboards or reports!” I have had some form this discussion many times over the last 6-7 years. While every organization has its differences, be they profound or nuanced, I am convinced that a few themes ring true. Boiled down and at the highest level of the organization, building alignment comes down to this:
- Do you have a mission statement? (“Yes.”)
- Do you have a short list of key strategic imperatives that support and drive the major initiatives in support of the mission statement? (“Yes.”)
- Focus on these key imperatives. The analytics will naturally fall out of these. Remember, the goal is typically some form of performance improvement and, hopefully, moving towards a more predictive world. Aligning very explicitly and tightly with these key strategic imperatives will drive the value into your IT and analytics investments.
One more note on this concept of alignment: There is often an organizational urge to adopt or deploy technology for technology’s sake which can quickly place an analytics initiative out of alignment with the business objectives. A little institutional discipline (i.e., reminding folks of a published objective) will go a long way in keeping analytics where it needs to be in the organization.
Who owns the initiative?
This goes beyond the assignment of an executive sponsor or a project lead, who might have been “voluntold” about his or her role leading the new analytics initiative. Not that these leadership roles are unimportant, but they are really just the beginning. A few thoughts around initiative ownership include:
- What it is the role of IT vs the role of the business? In “pure” IT projects the lines of responsibility are often more easily drawn, but in my experience this is not always the case with analytics initiatives. For instance, how do you build trust with IT when the business owner knows the actions or predictive outcomes they seek, yet IT is only focused on a seamless integration with low maintenance?
- Is there a clear path identified for IT’s role transitioning over the life of the project? At the beginning of a project I would argue that IT has a bigger role (developing the infrastructure, for example), but this role must transition as the project becomes more about the specific requirements and goals of the business unit. Would it make sense to have IT responsible for achieving call center metrics, or would it make sense for a distribution engineer to be responsible for the setting up a Hadoop cluster? Of course not. These are extreme examples, perhaps, but a clear path for responsibilities and how they transition will be critical.
- One other question that is more difficult to nail down: who has the passion to see that the project succeeds and is he/she given what is needed to be successful? It’s one thing to put someone’s name on an org chart as the project lead or executive sponsor, but we all know that some people take a “check the box” approach while others will eat, drink, and sleep that project because they have a passion for analytics and a passion to succeed. Yet the trick here is to get executives to ask for outcomes or meaningful KPIs. You’d be amazed at the passion around surfacing success stories when named accountability is established.
Treat data as an Asset. No, really.
We can probably all agree that “treating data as an asset” is a good thing, as we have been told at countless industry conferences. However, do we really know what this means? I was never sure until my colleague, Kimberly Nevala of the SAS Best Practices Group, posed the “Data as an Asset Quiz” to a group of us at a recent industry roundtable. Here are the five questions to this quiz:
- Are you giving it resources comparable to your other corporate assets?
- Are you dedicating technology comparable to your other corporate assets?
- Are you allocating funding relative to your other corporate assets?
- Do you measure the cost of poor, missing, or inaccurate data?
- Do you understand the “opportunity cost” of not delivering timely and relevant data to the business?
If you answered “yes” to all five, congratulations! For the rest of us, we have some work to do.
A final point on the data asset mindset: why do you think the EU was so motivated to enact GDPR (General Data Protection Regulation) policies? The governments fundamentally believe companies owe it to the citizens and customers to be protected and have privacy when desired. You don’t get traction on such global policies without the belief that data (in this case, personal data) is an asset to be respected at the highest level.
I love it when a plan comes together
While the band of renegades in the 1980’s American TV series “The A-Team” often lucked out and made their crazy missions “come together”, luck is not the name of the game in utilities. And at some point well-planned work needs to get done when it comes to strategically leveraging analytics. While the “tips” presented here are not the end-all, be-all for leading successful analytics initiatives, these ideas will hopefully help keep you and your organization focused on what matters: Stay focused on the business, provide clarity and passion for project leadership, be accountable for showing learnings or results and be honest with your assessment of how data is managed in your organization. As The A-Team’s “Mr. T” fondly barked, “I pity the fool who …” (fill-in-the-blank with adverse behavior). It’s my hope we work together to avoid being the fool with an analytics strategy.
I look forward to learning more about your analytics initiatives and progress!