Leading indicators and the advantages of data-driven insights  

Where a lot of private equity firms struggle is the sheer amount of data they’re presented during diligence, often dealing with fragmented insights into a company’s operations.

By Ajai Kumar, Persistent Systems

You’re planning a special dinner for your anniversary, and everything has to go right. You visit the restaurant website, check out their menu, read some reviews, visit their social media page for pictures, consult some friends, check the parking scenario, etc. These are data inputs for one night out – now, imagine the number of inputs that went into the recent $16.5 billion acquisition of Citrix. Probably a bit more involved.

Win before you begin

Using data to analyze an investment is nothing new. But where a lot of private equity firms struggle is the sheer amount of data they’re presented during diligence, often dealing with fragmented insights into a company’s operations and technology.

Ajai Kumar, Persistent Systems

PE firms traditionally used lagging indicators to forecast future success – poring over old quarterly reports to build a feasible performance model. Today, solely depending on this method is risky, especially with rising asset prices and the emergence of SPACs upping the competition.

Top PE firms are prioritizing leading indicators, using advanced analytics and artificial intelligence (AI) to crunch massive amounts of data to predict what might make an investment profitable.

The quality of quantity

PE firms must analyze the strength of two key areas of a target acquisition – especially for software companies: (1) processes, and (2) technology. This includes how those factors operate independently and work together based on external influences. Advanced analytics are opening the aperture wider in the PE firm decision-making process, and those who employ these tools earliest have a competitive head start.


For a software company, process can reference how the company manages and mitigates technical debt. It can be about how departments interact and perform with each other and how development and delivery teams are distributed nearshore or offshore. These are standard process questions that PE firms should assess and score using data analytics.

Technical debt is the result of trading speed to market over writing perfect code. When development teams expedite a project, delivery can happen quicker, but increases likelihood that the software will need code refactoring down the line. Being able to analyze and understand the development process (as well as the code itself) is important to managing future costs and resource requirements.

PE firms also need to be able to use data to conduct a product portfolio analysis of target software companies. In the same way that AWS and advertising are the main profit centers for Amazon and prop up the retail business, software companies may have similar P&Ls. PE firms need to rationalize unprofitable/low value areas so they can consolidate the tools uses for software product development and maintenance.

Another question PE firms need to understand is if a target acquisition company is undergoing a larger fundamental change to its business model based on macro trends. For example, as Apollo nears a $2.3B deal for Worldline’s Terminal business, it was suggested that new funding and management may be needed as Worldline moves to a SaaS business model, according to the Wall Street Journal.


If process is the “how”, then technology is the “what” when it comes to a PE firm understanding the ins and outs of the assets it’s acquiring. The better a PE firm understands the quality and stability of the tech and infrastructure that underpins any software-driven company, the better an asset will perform. Some of this insight can be derived from understanding the development process, but at a certain point, the software itself must be analyzed and valuated.

This technology due diligence may require a code quality audit as well as an intellectual property audit. There are many examples of software or platform valuations getting way ahead of the technology capabilities – especially with software or marketplaces that deal with new and emerging tech. While the case of OpenSea and its $300M Series C and $13.3B valuation is Venture Capital vs. PE, a Fortune article lays out the perils of a platform not being able to keep up with rapid growth.

When a PE firm acquires a company, it acquires its newest developed assets but also its legacy systems. This can drain resources and become an unnecessary distraction for engineering teams, often leading to low profit margins and a drag on valuation. Cloud computing software can create hybrid options, where companies can keep the legacy tech and evolve the rest.

Implementing this technology early on and coupling it with a user-friendly dashboard helps measure behaviors, identify underlying problems, and course correct to desired outcomes.

The bottom line

Want to know a few of the most dangerous words in private equity? “I hadn’t thought of that.”

When PE firms take a data-driven approach to technology transition and transformation, they can get the actionable insights they require to extend the life of their legacy products and create sustainable operations.

Ajai Kumar is global head of growth at Persistent Systems, a global services and solutions company delivering digital engineering and enterprise modernization.