Deploying analytics has become a top priority for utilities and is driving significant investments. By now, most utilities know that there’s a slew of well-documented analytics use cases with proven value. Analytics technology can provide answers to complex problems in seconds.
However, technology alone is not enough. Without strategy and intent at the root of analytics initiatives, the risk of jeopardizing analytics investments is high.
How should utilities go about de-risking analytics investments? Who better to ask than utility leaders that have learned the hard way. We recently interviewed utility veterans who shared some of the more nuanced, but critical, aspects of analytics deployments. The result is a handy guide – Ensuring Success in Analytics: A Playbook for Utility Executives – that you can use to de-risk analytics investments.
Whether you have started on analytics or not, the guide provides insights into things you may not know. Competition for analytics expertise is fierce. Where will talent come from? Analysis of data may lead to unexpected conclusions. How do organizations trust results?
Analytics require constantly changing inputs and outputs and rely on quality data. How does IT architecture accommodate flexibility? How does the organization insure accuracy, security and privacy of data? Utilities make a major commitment to analytics are looking for quick returns and reduced operational costs. Is Agile or waterfall project management best suited to meeting those objectives? How do organizations use analytic investments efficiently across the organization?
Just to give you a taste of what you can learn, here’s one thing you may not know about making the most of analytics technology investment.
Organizational learning creates efficiencies and advances analytics within the organization
Leading utilities have established communities of practice that meet on a regular basis. At the beginning, staff presence is often required. Usually, meetings are inclusive across business units with participation from finance, HR, operations, engineering and customer operations and seek to build networks that can be tapped later by project teams.
Communities of practice and analytics champions help to spread the word about the value of analytics, but analytics advance when teams can build on the progress of others. There are a handful of mathematical models that can be quickly re-purposed for use throughout the enterprise and are not necessarily restricted to specific problems. For example, a model used to predict transmission asset failure may use the same underlying model that is used to predict food expiration at a frozen food counter. Projects often reveal which models are suitable to the problems being solved.
How do you make the case for organizational learning? Organizational learning does take time and effort – for meetings, collaboration, applying models to use cases, along with budget for incentives to participate, attendance at conferences, and analytics model repositories. Appeal to your company’s interest in decreasing operational costs and enabling self-service of analytics across the enterprise – two of the most frequent objectives utilities have for investing in analytics.
So, in this case, as in others, the guide provides best practice alternatives and ways for you to make the case for investing time, budget and effort to getting things right from the beginning. According to one utility analytics veteran, “We always come back to that we can’t just talk about this. Analytics requires action and investment; it doesn’t have to be large. You must get started, go solve it and get confidence. Technology alone doesn’t get you there. Planning must go into this to make it happen. That is the realization that leadership has to have….”