The regulated energy industry is data-rich. Just think about the millions and millions of pages of filings available on public record! It’s exactly this breadth of data that makes it possible for industry pros to make smart, calculated, substantiated business decisions. It’s just that—working manually—it takes a whole lot of effort uncovering the data needed to get there. Our tools can help.
But before we get into all that, let’s take a look at the value of the data itself.
In this blog, we’ll examine just some of the data available (the tip of the iceberg, really) and walk you through how exactly these data points lead to better decisions.
Let’s start with a common example of an oft-referenced filing.
Data Points of Interest from FERC Form 1
DATA POINT: Total Electric Sales Revenue
WHY IT MATTERS: This figure provides a comprehensive view of the utility's market share and demand. Analysts can gauge the utility's overall business performance. This can help them better project their own level of opportunity. Or, if the number is particularly good or bad, it can inspire deeper research into unique elements of the company’s operating model which could provide ideas for things to try or avoid.
DATA POINT: Average Price to Retail Customers
WHY IT MATTERS: The average price to retail customers reflects the revenue generated for each customer in different customer classes. Analysts closely monitor this to understand pricing strategies. This is useful when reflecting on their own pricing strategies and spotting opportunities to raise rates.
DATA POINT: Total Net Income or Loss
WHY IT MATTERS: This figure serves as a comparative measure, providing insights into industry performance and financial health. By studying this metric, analysts can draw parallels between companies, identify industry best practices, and make informed recommendations to enhance their own company's financial strategies.
DATA POINT: Revenue per MWh sold as Resale/Wholesale Power
WHY IT MATTERS: This metric directly impacts revenue generated from wholesale transactions, influencing cash flow and profitability. Analysts use this data to understand market dynamics, assess the competitiveness of the company, and gain insights into optimizing their own wholesale pricing structures.
DATA POINT: Total Fuel Used
WHY IT MATTERS: Fuel consumption is a significant cost for utilities. What’s more, it’s highly variable and susceptible to world events. By analyzing total fuel used, analysts can identify trends, assess efficiency, and predict future operational costs.
DATA POINT: Operation and Maintenance Expenses
WHY IT MATTERS: This figure provides insights into the efficiency of a utility's operations. High operation and maintenance expenses may indicate inefficiencies or maintenance challenges. Looking at companies with low O&M costs can inspire new strategies to try whereas looking at companies with high O&M costs tells an analyst what their own company should avoid.
DATA POINT: Number of Customers
WHY IT MATTERS: The customer base is crucial for revenue generation. Analysts track the number of customers to assess market penetration, growth potential, and customer acquisition strategies. Looking at what peers are doing well here is another change to glean insights and ideas for the analysts’ own strategies.
Enter: Regulatory AI
You can see why data points like this play such a significant role in leadership decision making. Now the real conundrum is how to get them without breaking the bank in terms of time invested digging through data manually. What’s where Regulatory AI comes in.
HData's Regulatory AI is the first-ever artificial intelligence technology built to empower complex analysis of regulated energy data. Users can simply ask Regulatory AI a question, and the tool searches the hundreds of thousands of pages of FERC and state filings that have been curated in HData’s Library. You can even include your own documents in your queries: ask questions, find specific terms, drill down with follow up inquiries, and find citations buried deep in the data.
Bottom line: Data can be scoured in minutes. Year-over-year comparisons are a breeze. Useful information can be exported in tables, charts, or visualizations. And every bit of intel comes with a source link and verifiable attribution. Analysts can finally spend time contemplating data instead of collecting and arranging it.
Using the FERC Form 1 data examples above for inspiration, here are some real questions an analyst could ask Regulatory AI:
- What are the major changes between this year and last year for this filing?
- How many residential customers does [company name] serve?
- What is the average price for electricity for [company name]?
- Is there/what is the trend in operating revenues at [company name] over the last 3 years?
- Summarize peak demand for [plant name].
- Summarize [company name]’s capital structure, focusing on changes to their debt over time.
- Summarize the financial performance of [company name] in a table.
- Does this report reference stranded assets?
- Can you find the consulting fees?
- Help me understand the debt ratio of [company name] relative to their total assets.
- Can you list [company name]’s generation assets and their plant value?
- What are the key financial and operational data points collected in the forms that help regulators assess the financial health and performance of the company?
- How do the income statements in these documents compare quarter-over-quarter?
Regulatory AI was designed to make this exact process of information seeking more efficient and insightful. Analysts can have a casual conversation with Regulatory AI and the tool actually understands the sentiment of their inquiries. It’s not a simple search, and results aren’t based on keywords. The tool finds key information and contextualizes its replies to provide logical, fact-based responses directly relevant to the sentiment underlying your question. And users can ask follow-up questions based on a shared understanding of the conversation before.
It makes information retrieval feel easy.
Turning Intel into Decisions
Unearthing valuable insights is just the first step. The real magic happens only when that intelligence is translated into actionable strategies. Generally speaking, this can be done in three steps.
STEP 1: The data should be contextualized within the broader industry landscape, considering market trends, regulatory changes, and technological advancements. Analysts need to discern patterns and correlations within the data, identifying key drivers that influence the utility's performance. For instance, understanding the correlation between total net income and operational expenses might spotlight areas for cost optimization or investment.
STEP 2: The various departments within the utilities company must collaborate. Cross-functional teams can synergize diverse expertise, aligning financial insights with operational realities. This collaborative approach ensures that proposed strategies are not only financially sound but also operationally feasible. For instance, optimizing average retail rates might necessitate adjustments in the customer service or marketing departments to maintain customer satisfaction and loyalty.
STEP 3: In the final stage, data-driven insights need to be communicated effectively to decision makers. Visualizations, reports, and presentations should distill complex data into clear, comprehensible narratives. This facilitates decision-makers, from executives to operational managers, in understanding the implications of the data and empowers them to make strategic choices that align with the utility's overarching goals. The continuous feedback loop, incorporating lessons learned from the outcomes of implemented strategies, completes the cycle, ensuring that data-driven decisions evolve with the dynamic landscape of the energy sector.
Are you ready to supercharge strategic decision making for your organization? Learn more about using Regulatory AI. Request a copy of our ebook today: Unlocking the Value in Your Regulated Energy Data.