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Welcome back to our series on private equity and data! If you’re just stumbling across this article, you can read our first article on modern approaches to onboarding portfolio companies significantly faster for analytics. Ultimately, individual portfolio company metrics aren’t helpful without context. PE firms need to look across companies in similar industries and stages of funding to get meaningful insights. Today, we’ll discuss how to effectively do that through maturity comparisons with reporting powered by a modern data stack.

PortCo maturity comparisons are the next step.

Being able to assess PortCo maturity across your portfolio is a basic function. Fundamentally, you need to know what investments to make when in what companies, understand when a company is ready for exit, and identify potential issues with investments to profit. This is an urgent need: Not being able to fully understand PortCo maturity can lead PE companies to sell the wrong companies, sell at the wrong time, make suboptimal investments in PortCo teams, and make other strategic decisions that don’t lead to profit. And considering the scale of the mistakes—with many PortCos owning dozens or even hundreds of companies—the pressure is on to make these decisions faster.

Unfortunately, too many PE companies lack standardized comparison metrics. Their PortCos are, after all, unique companies with their own idiosyncrasies and data management practices, creating a knowledge gap where key stakeholders don’t have an effective understanding of company maturity. So with various sources of data and KPIs to consider, how can a PE firm accurately measure and compare PortCo maturity?

Curate Data for Efficient and Effective KPIs

The answer is in smarter data management. Traditional data warehousing and analytics tools were built to run one company, not one hundred, and they can lead to siloed information at PE companies. Questions about what the data is saying need to flow through technical leaders, keeping your company from being agile. A modern data stack, on the other hand, is much more flexible. Tools that are built to respond to PE companies’ needs can put key KPIs and metrics in the hands of stakeholders who can use them for business decisions. Modern data ingestion and data engineering allows for data to flow quicky from the source to the through to the target analytics destinations enabling quick insights for decision makers.

The data warehouse plays a major role in maturity comparisons.

To get to better maturity data, companies should begin with modernizing their data warehouse capabilities. A modern data stack should follow ELT (extract, load, transform) best practices of extracting the data from the source, loading it into a data warehouse solution, and then transforming it for analytics use. Using data ingestion tools like Fivetran, data is ingested quickly using click-not-code capabilities. This raw data from various sources is connected to a downstream data warehouse like Snowflake to capture insights that might not be available in one source alone.

Your data warehouse should provide more than just storage of data. It provides a landing place for data to be aggregated, serving as the elastic storage and compute layer on top of which transformations are performed. Balancing storage and compute resources effectively can make vital data readily available in a cost-effective manner. Dashboards can query the data directly to create real-time KPIs and data insights. The transparency and efficiency gains from a modern data stack add day-one value for PE companies across the portfolio.

Transform raw data into useful information.

While the data in your warehouse will be cleaned and organized, for maturity data, you’ll need a specific data model—and data transformation capabilities to get it there. Transparency to timeliness and business logic is essential to build trust in the data. Make sure your transformation system gives you visibility into how data is changing and how it flows from the source to the final model—as well as the right formatting, descriptions, and KPI calculations.

Your data transformation tool should provide a low code environment where data engineering and transformations make the data work for upstream insights. Real-time source to end tool pipelines can be created, and data usability can be improved in multiple stages. Data transformations follow a precise layer approach so data engineers and analysts alike can understand the changes happening in the data. If you set this system up right, using a tool like dbt, it should be simple to use, allowing business analysts to have ownership over data without advanced coding skills.

Build a Modern Reporting Structure.

Your reporting system will be the most visible part of the system, and where the true business value comes from. Different personas in your company will need different dashboards: Analysts will need to see the details, your C Suite will need the main points, and departments will need department-specific data. If your company specializes in a specific industry, you’ll also have your own specific metrics to track. Most companies, however, will benefit from a few key data points:

  • Customer lifetime value
  • Customer acquisition cost
  • Scale to revenue per employee
  • Valuation metrics such as price-to-earnings ratio and price-to-book ratio

Your maturity comparisons dashboards should be interactive, allowing users to filter by categories like portfolio company, time period, and business segment. Of course, they should also allow for quick comparisons—letting you see how multiple companies measure up on an even playing field.

Data transformation can advance your profitability.

Modern data tools that let companies make these kinds of analyses and comparisons will provide a keen competitive advantage. Spaulding Ridge recently implemented a data transformation for Shore Capital, a private equity firm, delivering PE-specific data tools that delivered 3x ROI across the company. As the PE market heats up, companies with the tools to make smart decisions fast will be better positioned. In our next article, we’ll discuss how to take your portfolio management to the next-level through ML-driven predictions—until then, we’d love to hear from you.