Applying Data Analytic Management Framework
Jim Sprague is the Vice President of Analytics and Data Science here at Cadmus. Jake Erikson is a Senior Analyst in the Distributed Energy Resources practice specializing in beneficial electrification. We spoke with Jim and Jake about how they use Cadmus’ data analytics management framework to ask better questions and consider the implications of current and future decarbonization initiatives.
How is your approach to data management helping transform how our subject matter experts and clients think about decarbonization?
Jim: There are so many sources of data that contribute to the efforts to decarbonize. Open-source public information and propriety data sources need to be validated and blended in a way that supports the kinds of analysis that we perform. Our approach to this data management challenge is to use a framework that puts into place a way to move all that data through an organized pipeline. For example, our Cadmus eSIM platform is an integrated data warehouse, development environment and workflow. As data moves through the eSIM workflow its value is enhanced by additional insights and modeling efforts until it ultimately makes its way to the end-product for our client, such as an interactive dashboard or a report.
Jake: It’s only been in the last 5, maybe 10 years that the emphasis on more flexible and innovative data capture and collection and analysis has really grown in the traditional energy space. As these opportunities expand all over the market, the question becomes what to do with that data, those insights, and what kind of questions can we ask of them. As we start considering the possibilities, the data management becomes not just a way to channel data from Point A to Point B and answer pre-determined business questions, but there’s a bigger opportunity to explore the data and consider new insights we may have missed otherwise. We can expand the scope of some of our analyses with really interesting real-world data.
How do we use the data we’re collecting from evaluations to inform next generation planning?
Jake: One of the things our frameworks like eSIM do really well is give us the ability to ask, “What’s the best next step?” Not only can we look at the current condition—who’s using what energy and how—we can also use that data for projections to begin thinking through what the future may hold based on where we are today.
Jim: We first review the data using classic business intelligence software. Then to take it to the next level, we start getting into the predictive world—the “what ifs.” What if certain conditions change, then how does that impact other energy, market, or socio-economic factors? What happens if you offer more rebates, incentives, government subsidies? This predictive, forecasting-type model is a powerful tool for our clients that can help them plan for new programs and manage existing ones.
Jake: I want to come back to that connection between disparate data sets and how we can use that. Increasingly our clients want to model the impacts of transformation scenarios such as reducing carbon emissions to net zero. The connections between systems—demographic data and energy use, building types and carbon reductions, physical infrastructure and regional economic systems—begin to give us the bigger picture views of what is truly an interconnected energy infrastructure. How are we thinking about the equity implications of electric vehicle subsidies? How does applying an incentive for a heat pump affect the building landscape over the next 20 years? We can ask these questions because data is being brought into a management framework that allows us to analyze the impacts within different systems as well as the interdependencies between systems.
What are some of the challenges in using data analytic management to create actionable insights?
Jake: Getting high quality data is certainly a challenge, as well as getting the right kind of data. It can also be challenging to reflect the ever-changing context in which we’re conducting the analysis. The lens through which we approach the analysis—market segmentations, perspectives, political context—do change over time so we need to think about how the data fits in that context. Something like the eSIM model enables us to apply that going forward and maintain more consistency.
Jim: A big challenge is considering how the data will be consumed. Picking the right way to interpret the data is important, is it an interactive dashboard? Or a traditional row and column report? We want to make sure we are not just pushing data back at people. We want to personalize the presentation of data so we can answer our clients’ questions clearly, as well as provide a means for finding and considering other potential questions and insights that can be drawn from the data. We work hard to deliver information back in a way that is relevant and impactful.
Jake: We’ve seen that who the audience is for some of these analyses can actually change as a result of the data that’s available. Rather than just being consumed by direct utility clients, these analyses are fueling insights and information for the public. The result of our data framework and some of the data visualization and communication tools that we’ve been able to integrate using the breadth of our Cadmus capabilities is sort of a democratization of the data and analysis. Together with our clients, we can communicate insights in ways that are more easily consumed by broader audiences.
Can you share some examples of how our data analytic management work is helping clients expand the way they think about decarbonization policies and programming?
Jim: A large city in California wanted to understand where to focus its electrification efforts. Cadmus blended together a rich set of public and private data to do a building stock assessment to determine where to start the move from fossil fuels to electrification. We presented the work in an interactive dashboard which quickly led to more questions. Because we used a well thought out data framework, we were able to add additional data sources quickly. Income levels, environmental factors and geographic information were added to the initial project work so they could see how to proceed in an equitable manner for their entire community while they evaluated each scenario. This is an example of where a good data framework allows you to not only forecast and predict, but then to prescribe various actions based on that information. This brings together technology and subject matter expertise to identify opportunities and recommend solid policies and actions based on what we’re seeing in the data.
Jake: We were recently involved in a project evaluating a pilot for an all-electric homes program. One of the interesting things the data revealed that would not have been visible otherwise was the concept of “what’s the best framework against which to evaluate these programs.”
In the pilot evaluation, we found that all-electric homes—if they’re roughly the same cost as dual-fuel homes to purchase—will be a huge benefit to the homeowner, especially low-income homeowners because utility costs are going to be cheaper for them. But initial evaluation of the program didn’t meet the criteria of something the utility would adopt more broadly. Even though we saw a tangible customer benefit that could be helpful for low-income customers, historical energy efficiency cost effectiveness analysis didn’t incorporate dimensions such as understanding societal impacts and equity.
So, when we approach problems like this through our data analysis framework, we can consider how to adjust the metrics and analysis we use to evaluate forward-looking programs and opportunities, especially as our clients look at broader decarbonization efforts. For example, we have questions about pure carbon reduction in some of the analyses, but we need to think about where is the carbon reduction happening and at what cost? How is that reduction distributed across different populations? What about other co-pollutants that come along with that reduction?
These types of questions can be readily incorporated into our data management/framework process. We have the expertise and what we can do with eSIM and other platforms is build those insights and bring in other subject matter experts across Cadmus to inform effective, impactful policy and program recommendations.
Jim and Jake, tell us a little about yourself and what brought you to Cadmus.
Jim: I have been the information technology field for more than 15 years. During this time, I focused on telecommunications design and data science. I always had an interest in how technology can benefit an organization’s core mission. I pursued an MBA and after graduating moved into various leadership roles with commercial companies that did public and private sector work. The work we continue to do at Cadmus keeps me excited and could not happen at a better time in our history to help make a positive impact on the environment.
Jake: I have worked in the energy space for the better part of ten years now. Much of that time was spent working on energy access and development in sub-Saharan Africa, where I really developed an interest in data analysis to tease out the important trends and effects of policy changes. I also have a pretty extended history in environmental work: mostly focusing on climate change policy in the U.S. and Marshall Islands. Coming to Cadmus was really my opportunity to bring these two parts of my life together: energy analysis and climate action. Our work on energy systems and data analysis is really exciting for me, and clearly makes a difference for our communities and our planet.
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