Unleashing the Power of PE Data

• Investment due diligence: firms rely on data analytics to conduct thorough due diligence on potential investments, using financial statements, market trends and reports, customer data, operational metrics as well as a host of other ad hoc metrics to gain insights into the target company’s performance, growth prospects, and potential risks.
• Value creation: investment teams use advanced analytics and predictive modelling to glean insights, identify growth opportunities, optimise pricing, predict future trends and conduct scenario planning, both at an individual company and portfolio level.
• Operational efficiency: data can play a key role in identifying inefficiencies, overseeing and managing supply chains and improving and streamlining resources, both at portfolio and GP level.
• LP/GP relations: data is essential for providing more timely, visual, flexible reporting capabilities to LPs. While quarterly PDF reports aren’t quite a thing of the past, there’s also an increasing demand from LPs to have access to real-time data and intuitive dashboards.
• Performance measurement and benchmarking: establish robust data-driven performance measurement systems to track the financial and operational performance of their portfolio companies as well as benchmark at a fund level against their peers.
• Risk management: firms can improve their risk management practices by analysing historical data and identifying key risk indicators. Data also allows them to assess potential risks associated with investment decisions, monitor portfolio company performance, and take proactive steps to mitigate risks.
• Exit strategy: analysing market trends, competitor data, and customer data can help determine the optimal timing and method when exiting an investment.

An abundance of data is both a blessing and a curse. Having a greater number of data points can help you make more informed investment, strategic or operational decisions. But piecing that data together to really make sense of it all – filtering out the noise and deriving actionable insights – can be a challenge.

At first glance you might be forgiven for thinking that private equity doesn’t have a data abundance problem – particularly in comparison to the public markets. That’s certainly the case when it comes to structured, accurate and timely market data but there is a huge amount of unstructured (i.e., not organised) data out there: data that’s difficult to access, normalise and make useful.

For example, alongside financial databases and market research there are company presentations, alternative data providers and scraping of web, news and social media platforms. General partners also have access to unique data sets that are not available elsewhere – portfolio company data as an example – which, if properly utilised, may provide an edge.

But this all comes at a cost and 83% of managers cite the need for continuous investment in next generation technology as a major challenge to becoming data-driven*. In the absence of a sufficient budget for acquiring these datasets from a prestigious and established data vendor such as a Refinitiv, FactSet, Bloomberg or Morningstar, a private equity data team will need to acquire these data from a number of boutique data vendors or public sources, which could lead to high engineering costs and lead times. Maintaining the data pipeline and monitoring data quality from these disparate sources can often be onerous tasks as well.

Making unstructured and disparate data useful is one area where AI – particularly large language models like ChatGPT – can be transformative. These tools can take disparate sources and data models, translate them, and produce more useful data that drives better decision-making.

Just having access to and being able to assimilate a large and disparate web of data is only one piece of the puzzle. Simply having more data at your disposal does not make for good decision making; that data needs to be accurate, reliable, and relevant.

For private equity data, this is the crux of the problem – and has been identified as the single biggest challenge managers face when sourcing data. Because the data is inherently ‘private’, accessing reliable data is onerous. Take benchmarking data as an example – much of what is available is based on estimates built on general, outdated datasets. When you extrapolate this data, it can become wildly inaccurate and can’t really be trusted for decision-making.

As for AI, the technology is only as good as the data it has consumed. No matter how sophisticated the analyst or database is, the underlying data sources need to be inherently reliable and trustworthy. Firms that can figure out how to source quality data – coupled with the models and techniques to aggregate and analyse that data – will quickly realise the value and can will have a point of competitive differentiation.

Top ranked challenges faced when sourcing data

(Source: Mergermarket survey for Aztec Group, Q3 2022)

Even across lean firms, you’ll find that data siloes exist between functions. You might have the deal teams monitoring their investments, sitting on the boards of their investees gathering data to improve operational efficiencies or grow sales. Yet at the same time, the finance teams at the same portfolio company will feed monthly or quarterly accounting data to the GP to measure performance.

Not only is this inefficient, but it can lead to discrepancies in data, even if there are only slight differences in the approach or method to collect or calculate figures. For example, sales data pulled from the CRM system may differ from sales data taken from the accounting system. Both may be reported back to the GP differently, but which one should they trust?

By identifying the potentially disparate and duplicated data sources, identifying the golden source of truth and creating a centralised data platform with it – where all information is generated from – firms will be able to bolster the reliability of the data and the story that it tells.

Private equity GPs will typically have a rear-view mirror approach to operational data. By the time a portfolio company collates their data, it gets audited and is fed to the CFO they could be looking at data that is weeks or perhaps even months old. In a fast-paced world where valuation-affecting sentiment towards a company or industry can shift in an instant, thanks to the power of social media, that isn’t good enough.

Accessing current, reliable data enables decision-makers to assess situations more accurately, anticipate trends and understand the impact of market developments that may lead to an exploration – or reappraisal of – investment opportunities, or help portfolio companies take immediate corrective actions. Timely data aids investment decision-making, risk mitigation and ultimately leads to more positive outcomes.

Having addressed data accuracy, timeliness and availability, making data easy to use and understand is the next important piece. Those who access data may reside anywhere within a firm or even extend to the LP. Thus, making data self-service by using configurable, intuitive dashboards is critical if the value of data is to be fully realised.

Just because the underlying data might be complex does not mean the story you’re trying to tell using that data needs to be. Turning complex data into simple storytelling, aided by visual cues, enhances clarity and decision-making. Especially when not all decision-makers are data gurus.

And it won’t always be you telling the story; increasingly users want to tell their own stories. For example, each LP will have their own specific reporting requirements – preferred metrics, formats, timescales and reporting layouts. Standardised reporting doesn’t work, and tailored reporting can be laborious, so increasingly firms are moving towards self-serve reporting and analytics with nearly half (45%) of managers implementing self-service analytics in the last year*.

Giving LPs direct access to data and analytics enables them to conduct their own analysis and build the reports they need, it also helps improve efficiency, deepens the GP-LP relationship, and can relieve a potential source of tension. Around one in four (79%) managers believe that advances in data analytics will positively impact LP-GP relations, with one in three (35%) describing it as transformative*

One area where private equity really lags behind public markets is benchmarking. Is there a fund out there that hasn’t claimed to be “top quartile”? The available benchmarks can be as meaningless as the claim. Notwithstanding the incompleteness and inaccuracy of available benchmarking data, there’s the inability to find benchmarks that really represent a fund. For example, if you’re a European tech investor you should be comparing against other European tech investors – as opposed to European generalists, or global tech investors.

It’s long been assumed that in private equity, data is private and GPs are reluctant to share data. However, as the industry has matured and the market has evolved, the ability to assess performance alongside a ‘true’ group of peers has become increasingly important. GPs are now prepared to provide anonymised, audited fund and portfolio-level data, if they are able to accurately assess their own performance in return.

And it goes beyond performance benchmarking, to operational benchmarking at the GP level. GPs often find themselves at a data disadvantage to LPs, when it comes to operational insights. They only have access to their datasets, while LPs can perform their own benchmarking across their GP relationships when it comes to accounting or management metrics – for example – loan facility costs, audit fees or deal costs. By having access to that detail across their peer group – on an anonymised basis – a GP is better placed to understand and demonstrate their true performance, fees and all.

Many private equity firms have data roles, but nurturing a data-driven culture across the organisation is crucial to really drive operational excellence and performance. Most, if not all, employees are often encouraged to use data to make decisions. But the question is whether they are empowered to do so. From the HR team recruiting and retaining talent, to the finance team realising operational efficiencies, or the investment teams supporting deal sourcing and driving investee profitability – the key to nurturing a data-driven culture is democratising data across the firm.

Empowering teams with access to data, analytics and dashboards will encourage employees to make better data-driven decisions. But that’s easier said than done, and becoming data-driven starts at the top. Senior management must fully support and lead efforts through example and embedding the right processes and tech within a firm.

Across all managers, more than one in three (38%) have established a data strategy that drives deal-making, and this increases to 89% of firms with funds of €1 billion.

One firm pioneering the use of data in private equity is EQT which uses its Motherbrain AI investment platform to source deals and help its investment teams make more informed decisions. As they say on their website: “thousands of new start-ups are founded every day, no human can rate them all”. To date Motherbrain has assisted EQT Ventures source 15 investments, representing €200 million of invested capital.