Generative AI is one of the most important developments to come out of data science and will play a key role in fuelling the AI revolution. Done well, it can produce faster, personalised customer communications based on historical data and customer interactions around the clock. It can help businesses qualify leads and answer common queries much more quickly, deliver internal transformation efforts more rapidly, and speed up operations. In the future, it could even help generate everything from new product names to strategic proposals.

Amongst all this though, one of the most exciting developments afforded by this unfolding tech category comes in the opportunity for startups – especially those working towards securing an investment or exit – to build a compelling data-driven business narrative.

Over the past decade, data has transformed the investment sector. Historically, investors wanted visibility into the key financial trends. Now they have much higher expectations for transactions on a data front row, and are much more interested in understanding the ‘why’ as well as the ‘what’.

This is redefining decision-making. Traditionally, private equity firms have successfully relied on their relationships and experience to assess potential targets, usually supplemented by a basic level of technical analysis. But rising asset prices and a competitive market can make it risky to rely solely on these methods. The result is a shift towards data-driven strategy where intuition makes way for a much greater emphasis on data and analytics.

Founder-led businesses often only begin to seriously consider their exit strategy after several years of operation. By that time they have most likely scaled to several hundred employees across several countries. Depending on the business model, they may have hundreds, thousands or even tens of thousands of customers. They will often work with dozens of partners and agencies, have already taken several rounds of investment and have multiple products with different revenue streams. In short, the business will be complex with a sea of information swirling through different platforms, teams and departments.

Traditionally, getting all this information sorted out to present to investors was a difficult but not impossible task. This is because most investors have previously prioritised a few important metrics including liquidity, cash flow and expense control. Most well-run businesses will have a good grasp of these fundamentals and will merely need to get their ducks in a row in order to prove it.

Now, the situation is changing. As PE firms become much more data-led, they are looking at a host of new 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. Investors have gone beyond looking at the ‘what’ of the performance, but now are expecting management teams to be able to have the data to answer the ‘why’ as well. Unfortunately, a surprisingly large number of businesses can not readily provide this information, nor do they have the data infrastructure or expertise to begin to gather it efficiently.

This can be a costly problem. It is much more difficult, expensive and inaccurate to try and collect and analyse data after the fact. Retrofitting data management infrastructure into already mature systems can be disruptive. Upskilling or hiring a team to correctly manage, interpret and visualise the data is also time consuming. This means that a business leader who is already embarking on their exit strategy has two choices – face the delays and expense of getting their data house in order – which could mean market conditions end up turning against them - or run the risk of receiving much less favourable terms from investors who consider the lack of insights available a risk that needs financial mitigation.

Both options are obviously far from ideal. So let’s consider a third path – building your company with data at its heart. I’m sure most of us already know just how much value data insights can provide to a company’s operations and how it serves its customers. However, there is still a reluctance from many business owners to start investing in data infrastructure and expertise because either it seems like an unnecessary early capital outlay or a low priority when they are trying to keep the fundamentals of their business operating. This is the wrong mentality. If the ultimate goal of your business is to exit or list – you are going to be better placed to achieve this target if you build the data policies, expertise and infrastructure you need into the fabric of your business. Everything does not have to be in place from day one, rather you need to create a strategy that will enable you to ramp up to gathering all the critical data points you will need to answer every question an investor will ultimately ask. Doing this also lays the foundations to take advantage of the latest generative AI advances. AI applied to a shaky data foundation is unlikely to get you results, but applied to the right data foundations can transform the value of your business.

Luckily, the data points that PE firms now really value are the same insights that will make a fundamental improvement to how effectively you make decisions as your business scales. The important thing to remember with any data project is to start with the questions you want to answer. This means understanding modern PE professionals. Ask yourself, what metrics, beyond simple revenue figures, will tell the story of your company’s success and potential? It could be the diversity of your customer base - both geographically and by sector. It could be how strong your recurring revenue figures are. Perhaps it’s the longevity of your products or the exponential growth of a new service you have launched. It may even be the approach you have to customer service and marketing and how that links to customer retention and growth. When you have a clear picture of where your real strength and USP exists, the next step is to develop the data collection, management and analysis systems and policies that will prove what you know to investors.

The benefit of this approach isn’t confined to proving your company’s worth in its initial pitch. As mentioned above, investors are increasingly using near real time data analysis to monitor the performance of their portfolios. If you have your data in order, it is much easier for investors to undertake this type of ongoing analysis. Consequently, it can be more attractive to them knowing that their potential acquisition or investment can easily plug into their existing systems. In addition, for buy and build firms that prioritise the compatibility and synergy of their acquisitions, deep data insights are very attractive. Most importantly, it will make it easier for you as leaders to run the business day-to-day, know where to spend your time and where to place your bigger bets.

Finally, this shift also necessitates significant cultural and organisational changes – such as upskilling and retraining staff so they have the skills and knowledge to use data effectively. This should include everyone, including all senior teams. Even today, it still surprises me how few business leaders are able to understand and interpret their core business data, instead relying on a handful of experts. After all, it’s impossible to know what you don’t know – and a second-hand account of somebody else’s understanding, no matter how advanced it may be, could never substitute for your own personal analysis. By building up your own expertise now, you and your senior team will be well-positioned to present your company’s data-led narrative with confidence when the crucial investment or exit pitch comes around.