The data investment life cycle
And the importance of J-curve for investors. Again.
Most companies understand that data represents a significant (and growing) business opportunity. In of itself, data can be of significant value. Then there is the economic value of data — where data is used to make better decisions, improve efficiency of operations or create new revenue streams. And ultimately, generating business outcomes in the form of increased profit or company valuation.
However, converting the potential value of data to the bottom line starts with an investment. And often not an insignificant one. Data science projects can cost up to £300,000 as a ballpark. So, imagine you are a private equity investor, with multiple companies in a portfolio! The data investment could appear prohibitive if you don’t carefully break down the potential return on investment.
Firstly, your investment will likely include hiring or subcontracting data scientists, data engineers and other professionals as few firms (particularly within the size range of typical private equity) have such capability in house. Then, there are infrastructure costs (both setup and ongoing), licences and so forth. Plus, beyond these financial investments, strategic and adoption investments are significant — both in time and education — as without them, research indicates that there is a huge failure rate of the data initiatives, killing any potential ROI.
The life cycle of investment into data (or a data project) often takes the shape of a J-curve — a concept highly familiar to all private equity professionals.
Source: Image by author
The actual shape of the data investment curve is influenced by many factors (let’s name a few):
How well linked is a data science project to solving a relevant business issue?
How quickly can data scientists develop a solution leading to desired business outcomes?
What is the quality, reliability, and availability of the data?
What investments into data infrastructure will be needed?
How well will the organisation execute on the project?
What is the adoption of the data throughout the business; how data fluent are the teams?
The ‘dipping’ phase (where you are investing hard, without returns: as the line drops below the horizontal) often lasts few of months, if not quarters. [Noting that there are typically multiple data projects going on in parallel, with different timelines.] Meaning it is difficult to judge the future impact of data on the P&L and company’s value without taking a deeper look into the data assets and their utilisation.
Take a look at the image below. At what point in time is a company most valuable? A, B, or C? Hard to tell even based on a rigid financial and commercial due diligence.
Source: Image by author
In the first scenario, company A on the image hasn’t embarked on the data journey yet. It would likely take some time to compel the leadership to make a strategic decision about data, design a data strategy, work out the plan of action and get the investment signed off.
All of that would take us to the situation company B is in. They are about to invest in the data — hire or contract data talent, setup data infrastructure, integrate data sources, build initial models, run first pilots. Just like space shuttle launch, everyone is nervous if all the assumptions and estimates are correct and praying that the expected J-curve won’t turn into a disastrous L-curve.
Company C, however, has already made it back to ‘black’ numbers and the data initiative is starting to generate profit. The main unknown is what will be the ultimate ROI? Either way, they might consider the data investment a success already and start new data initiatives benefiting from the data assets in place and hoping for even better ROI.
All things being equal, I’d argue that company C is — by far — the most valuable. Even if it’s not obvious from scrutinising the balance sheet and P&L statement.
Which is actually great news for private equity investors! Now, imagine that your firm has a pre-investment insight into whether the acquisition target is more like company A or company C.
“Only with a complete picture of a company’s data and data-related capabilities can a PE firm expect to make fully informed decisions about whether or not to execute on deals and how to price them accordingly.” — Douglas Laney.
A deep understanding of data assets provides a meaningful information advantage, identifying hidden value that traditional commercial due diligence isn’t looking for. It spots data risks and opportunities, maturity, and true data monetisation — or better still, ‘insights monetisation’ (the real output of data science initiatives) — all of which ultimately leads to better investment decision.
Data due diligence provides investors with exactly that.