GPs ride the data wave

Demanding LPs and AI advancements have moved the dial on data. But what does a data strategy during a holding period look like? Xhulio Ismalaj reports

With a challenging exit environment, GPs are having to hold onto portfolio companies for longer. As such, there is even greater tenacity needed to squeeze value out of investments.

The role of data is central to all of this, with many GPs deploying data workstreams as part of their value creation toolbox. Further, with advancements in LLMs and more broadly with AI, there is an increasing focus on data quality.

Accurate data at a granular level helps GPs report to their LPs, getting beyond the portco’s P&L statement and finance team. Vera Huang, UK sales director of data services at fund administrator IQ-EQ, says: “LPs are increasingly coming to GPs with more ad hoc requests, becoming more and more demanding, in a good way, for better transparency, to find out how GPs are creating value, how portfolio companies are doing and have been performing over time.”

While financial and commercial metrics are usually the place to begin for tracking value creation, many are now looking at non-financial metrics.

“A couple of such metrics that have gained popularity over the years would be net promoter score to track how likely your clients are going to recommend your product, or annual contract values, which is more prominent in the SaaS industry,” says Huang.

Of course, with a growing emphasis on ESG and the energy transition, GPs are increasingly asking for ESG metrics to be tracked too.

Business transformation

The first step in a portco’s data journey starts with a high-level value creation plan, says Marco Ferrara, operating partner at Limerston Capital, a UK investor targeting businesses valued between £20m and £200m.

Then it’s about identifying the KPIs throughout the different layers of the business needed to hit the high-level value creation objectives, and translating them into data points. Limerston’s Ferrara likes using the objectives and key results framework to cascade the business’s key priorities throughout the organisation.

“It aligns everyone in the company on what the key results that the business needs to deliver are,” he says. “If management makes those critical KPIs that you need to hit as a business clear to everyone, and there’s a clear understanding of how each layer in the organisation will contribute to delivering those KPIs, then every employee knows what they need to do to deliver success.”

To give the investor a sense of the quality and robustness of a company’s data pre-acquisition, Limerston identifies whether there is a single source of truth that exists for any given data point in the organisation.

Ferrara elaborates: “Some of the key things we’ll test in DD are whether responses to queries that we’d expect to come from a single system come back in a timely fashion and are consistently formatted across multiple data requests. We ask for the same datasets to be sliced in different ways, and we run checks for inconsistencies across datasets that should reconcile.”

Of course, there is potential for conflicting information in different systems. “We test this in DD by looking at customer-level pricing and cost data. Often, things like one-off pricing or discounts might not be held in your finance system; they might be added manually or held in a separate piece of code,” the Limerston operating partner notes.

“That then helps us understand that you've got issues with either the processes through which you're capturing your data, or in the integrations between the different systems as the information isn't being effectively sent from one to the other. Sometimes you might have both issues, so you might have people keying information in two different systems or capturing information in two different systems, and then there isn't an immediate reconciliation mechanism to make sure that you're cleaning that data at the outset.”

Automation means that the sponsors and the portfolio companies can focus their time on value-creating tasks rather than populating Excel templates

Vera Huang, IQ-EQ

Automation enabler

Many sound, profitable midmarket businesses do not have a data infrastructure, let alone dashboards. Some, even, still use paper, while many use spreadsheets, which can be labour-intensive and subject to mistakes.

Excel is still being used to collect data, despite being one of the least mature ways of doing so. For example, a GP would request its portfolio companies to submit data in a predefined Excel template to be sent over email or a portal, before manipulating it further or keying it into another system.

However, as GPs launch more funds and invest in more companies, Excel becomes an ineffective way of gathering information, as it is difficult for all that data to be taken from Excel and put into a more centralised place so that GPs can provide reporting, both for management and their LPs.

IQ-EQ’s Huang says: “Nowadays, for a PE sponsor to be able to look at how much value a company has created over time, they need to have some sort of technology that allows the portfolio companies to track those metrics. We do see that more and more sponsors are looking at using tools and portals to interface directly with a portfolio company, or working with service providers to help them with that.”

For instance, a GP will set up a data request in the portfolio, for which a portco’s CFO logs onto the portal every quarter to submit the data. The use of technologies to streamline the data collection process is, naturally, considered a more mature way to operate.

“The less manual intervention, the better. The more automated, the better,” says Huang. “Because it means that the sponsors and the portfolio companies can then focus their time on value-creating tasks rather than populating Excel templates. They don't want to be doing the very mundane and boring tasks of collecting data; they want to be doing data analysis, instead of collecting data, so the more we can automate that away, the better.”

On tools, Limerston chose the Microsoft path and standardised it across all its portfolio companies. “We use the same infrastructure approach and productivity tools as much as we can, and the same data capture and then data representation in terms of management information tools across all of our portfolio companies, which helps us get a consistent view of what's happening across companies in different sectors and of different sizes,” says Ferrara.

Altogether, when considering business value, it is important that PE players start with the ‘what’ first instead of running to the ‘how’. For example, what is the value of a GP giving a portco a dashboard, system or ML model?

Can it give more flexibility on pricing? Can it give opportunities to understand the best performance across sales teams in different jurisdictions to assess the organisational structure? If the answer is yes, how much money can they make from it, or lose from not having it?

Data platforms, it seems, should not be built for their own sake but rather with an understanding of the business context.

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