Data is crucial now that showing off supposedly impressive top-line growth figures no longer impresses private equity investors. Exits of businesses engaging with PE funds are slowing down due to uncertainties. In this context, reliable information, and extracting sharp insights, really matters.

In this article, Anush Newman, CEO at JMAN Group discusses the tension between commoditised and proprietary data in private equity. It is an important point illustrating how private equity firms can turn information into the insights that earn returns. The editors of this publication are pleased to share these insights; the usual editorial disclaimers apply to guest contributors' views. Email tom.burroughes@wealthbriefing.com and amanda.cheesley@clearviewpublishing.com if you wish to comment.

In H1 2025, private equity (PE) exits reached their highest levels in three years, driven by increased corporate acquisition activity and a higher number of continuation funds. But that is not to say that this rebound has eased the pressure. 

Exit activity is now broadly stalling due to increased geopolitical uncertainty, higher interest rates and growing economic caution which has made buyers more reluctant and lengthened holding periods. At the same time, the rapid pace of change driven by advances in AI means that even seemingly strong investments can deteriorate quickly during the hold period.

For businesses eyeing a successful exit, particularly when engaging with sophisticated private equity firms, this increasingly requires a robust approach to data. Put bluntly, the era of simply showcasing impressive top-line growth is over. Today, data reigns supreme. It's the bedrock upon which compelling value stories are built, the lens through which operational efficiency, defensibility of revenue and scalability are scrutinised.

A solid data strategy, coupled with the ability to extract meaningful insights, is no longer a ‘nice-to-have’ but a fundamental requirement for de-risking transaction processes and securing a lucrative exit in today’s competitive landscape.

But even though a robust dataset is a crucial factor for PE exit success, it is also one of the biggest challenges. According to EY’s latest Private Equity Exit Readiness Study, 72 per cent of PE respondents cited access to a robust set of data and key performance indicators – both to validate historical performance and forecast future trends – as their biggest challenge from a financial perspective. Notably, this ranks well ahead of the next two biggest concerns: inexperienced CFOs (63 per cent) and underdeveloped financial systems and controls (48 per cent).

In our experience, one of the biggest reasons for this major data-related hurdle lies in the complex nature of PE-driven growth. Many PE-backed businesses –especially in the mid-market – have grown through intricate, complicated buy-and-build strategies. That growth often comes with fragmented systems, inconsistent reporting frameworks, and a lack of centralised data governance. Even for businesses that have scaled organically, the data used to run day-to-day operations often falls short when it comes to supporting the level of visibility required by an exit process.

The focus on growing revenue and EBITDA often outstrips operational and data maturity in the organisation.

At the same time, data ownership has been historically confined to the IT or finance department via monthly reporting handled by accountants and viewed as a lesser priority.

But the landscape has changed. As PE firms become much more data-led, they are looking at a host of metrics to build a much more holistic picture of how a company is performing now and, with predictive analytics, will potentially fare in the future. Everything from customer service data, product revenue, transaction levels, retention rates, organic versus inorganic growth, ARR, to customer profiles and team performance metrics are all of serious interest to investors. Apart from a greater breadth of metrics, the depth of granularity required is now much greater as well.

Unlocking this level of insight requires a cultural shift where data is seen as a strategic management priority with its own dedicated infrastructure, staffed by specialists who can turn raw information into actionable insights.

Looking ahead, the role of data in shaping exit valuations will only intensify. We anticipate that the level of scrutiny and the expectation for data maturity and insightful analysis will continue to rise. When it comes to performance and trends; just saying profitability has grown by X per cent year-on-year is now not enough – it needs to be evidenced by granular data and solid analytics. 

The historical “data cube,” which shows a snapshot of the business at the point of transaction has now evolved; it is now expected that this will be a scalable data “asset” which will work long after acquisition, giving firms the confidence that they will have the tools and infrastructure to manage the next stage of growth.

Investors want to know what’s working now and how your company can scale post-acquisition. By providing the context behind the metrics, it makes it easier to showcase opportunities for further growth, with potential investors being able to leverage these data assets to underpin their investment cases. With higher investor expectations, those who fail to do so risk undermining their valuation potential or, worse still, failing to secure the deal. Furthermore, companies will need to start demonstrating how they are leveraging data to capitalise on the value that AI can bring. This could range from using AI-powered analytics to identify at-risk customers to employing machine learning to optimise pricing strategies or internal operations. However, this advanced application of AI is only possible when the fundamental data infrastructure, governance, metrics and insights are already firmly in place.

So, how can firms proactively use data to build a compelling value story that resonates with potential acquirers? It boils down to demonstrating operational efficiency, scalability, and long-term viability. Data can paint a vivid picture of operational efficiency. Metrics such as customer acquisition cost (CAC), lifetime value (LTV), and the ratio between them are not new; but tracking them at individual customer level over extended time periods can demonstrate a sustainable and profitable customer acquisition engine. Analysing support ticket volumes, resolution times and customer satisfaction scores can highlight the efficiency of customer support operations.

Even granular data on engineering productivity and release cycles can showcase the efficiency of product development.

Demonstrating scalability requires showcasing the ability to handle significant growth without a disproportionate increase in costs. Data on infrastructure use, the cost of serving an additional customer and the automation of key processes can provide compelling evidence of scalability. What’s more, tracking cohort performance over time can illustrate the long-term value and potential of the customer base.

Finally, long-term viability is underpinned by data that demonstrates customer loyalty, product stickiness and the ability to adapt to market changes. High customer retention rates, low churn, and positive net promoter scores (NPS) are crucial indicators of a healthy and sustainable business. Data on cross-sell and upsell success demonstrates the potential for future revenue growth within the existing customer base; this is particularly relevant for M&A-driven inorganic growth strategies in a challenging PE market, differentiation is paramount. 

For the forward-thinking firm, a well-executed data strategy and the ability to present that data in a compelling equity narrative can set a company apart from its competitors. Supporting that with a roadmap that shows how you are and plan to utilise AI as a key lever will only enhance your position.

Crucially, management teams need to demonstrate that they really own their data and use it in day-to-day decision-making – this gives Investors huge confidence that teams are capable of executing their post-acquisition growth plans The good news is that the earlier in the investment cycle a management team can start this journey, the more value they will extract from their data. The majority of “exit-ready” metrics and insights can also drive huge value creation through the hold period; it will also help shape equity stories well ahead of time, before external advisors are appointed for a deal. As the PE landscape continues to evolve, businesses that leverage data effectively will command stronger valuations and unlock greater exit opportunities.

About the author
Anush Newman has a diverse work experience in various industries. Starting in 2010, they worked at JMAN Group as the managing director, where they focused on providing simple and actionable solutions for different companies across sectors. Prior to that, from 2006 to 2010, Newman worked as a consultant in the Energy Practice at Arthur D Little, gaining experience in strategy and operational projects in the oil and gas, nuclear energy, R&D, and telecoms sectors. In 2005, they were an Analyst at BT One IT, specialising in operations improvement and process design. Before that, Newman had research roles at GlaxoSmithKline, where they developed anti-cancer and anti-asthma drugs and received recognition for their achievements.

Anush Newman attended John Henry Newman School from 1994 to 2001. Later on, from 2001 to 2005, they pursued a degree in chemistry at the University of Cambridge, earning an MSci.

JMAN Group is a data consultancy that helps private equity firms and their portfolio companies turn operational data into insight that supports value creation and deal readiness.