Aligning the stars to unlock the power of data for value creation
Data is finding its way into private equity. Investors — just like their business executive counterparts — are viewing data as a strategic asset that can drive value creation. In fact, some private equity fund managers have gone even further, establishing their own data science functions.
One such example is HgCapital. Their ~30-strong data analytics team is supporting the particularly ‘data-rich’ software and service businesses within their portfolio by making data science and technical skills and tools widely available.
And, as many people in the private equity space have noticed, it pays off. Hg’s case study of using data science to drive cross-sell is an illustrious example of the economic value of data. Who wouldn’t want to see 60% uplift in converted sales orders, right? But don’t be mistaken that it’s easy to achieve such results! First, there is a survival bias that might cause us to overlook the shockingly high share of unsuccessful data projects. And second, quite a few stars need to align to see the value of data translated into the P&L.
Stars have aligned
After the initial ‘Wow’ as you read the case study, it’s sobering to read it again and contemplate how realistic is doing a similar project in your portfolio? Here you need to start jotting down a few key points from the case study:
Rich data — relating to a large, diverse customer base is mentioned. Multiple products are needed (which is obvious if the opportunity you go after is cross-sell).
Then, there are machine learning models, built by skilled and talented data scientists. Their data expertise was paired with product and customer knowledge of the sales reps — the domain experts.
The models also needed to be transparent and understandable and their outputs integrated into day-to-day operations. That way, the sales reps were equipped with information they understood and considered beneficial to their work.
But there is more!
It is obvious that the company treats data as a strategic asset and their leadership is using the data to solve a very particular business problem — cross-sell in their case.
And arguably the most important success factor is an aligned shareholder, who is driving momentum, providing capital, talent and expertise. Plus, the shareholder deeply understands the difficult, R&D-like, nature of data projects.
Aligning the stars
When asked about when does a company need a data strategy, I respond: When there is a business problem. For a given problem, an experienced data leader can often figure out a way data can help (or at least contribute towards a solution) and encapsulate it within their data strategy.
Then, the leadership team needs to be aligned behind the data strategy, and a data culture nurtured. Data-driven decision-making processes need adopting. And data fluency increased. Data must be collected and machine learning models trained. Data talent needs to be hired and infrastructure (which is now available as a product) built. Then, nothing is in the way.
However, this all takes time and is quite resource heavy. Plus, don’t forget about the failure rate of data initiatives — execution of data strategies is notoriously difficult.
So, why then go after data? And how big is the prize?
Well, we would argue that without a proper data due diligence, it is nearly impossible to assess how large the opportunity is and what it would take to grasp. Or — crucially — what could be the ROI.
Data due diligence
This is why a thorough data due diligence is beneficial. One that treats data holistically — not just as tables with information — and considers the strategic, as well as technical and people elements of data. And remember, ‘data’ for most businesses is vastly wider than financial information. It could be customer lists, vehicle tracking, supply chain measurement, production line indicators, point of sales, labour, inventory, … The list really is almost endless.
A data due diligence process, built on a robust framework, will help you validate and evaluate data for hidden value. Ultimately leading to:
better investment decision-making
true insight and trend analysis
identification of break-out opportunities
improved returns
To the benefit of investors and their portfolio companies alike.
***
This article was first published on Medium: https://medium.datadriveninvestor.com/why-investors-need-data-due-diligence-801ee96d6437
Adam Votava, the author, is a co-founder of DataDiligence. A specialist consultancy providing data due diligence & data delivery services into private equity and M&A.
As ever, I’m indefinitely grateful to Chelsea Wilkinson for patiently shaping my thoughts into a publishable format.
Comentários