Consider this:
🤔 Dry powder (unspent commitments to private equity funds) reached US$3.4 trillion globally in 2021, according to Bain & Company.
🤔 ~90% of the S&P 500 market value being composed of intangible assets (IP rights, reputation, and data), according to Ocean Tomo.
That’s a lot of money chasing ‘unseen’ assets!
This is exactly why we created data due diligence - a structured, comprehensive approach to validating, valuing, and assessing the value-creation opportunities of data assets for investors, both on the buy and sell side of M&A.
When we first started DataDiligence 18 months ago, we had to squeeze ourselves into the due diligence process, often piggybacking on digital diligence. However, we are now seeing some ‘early adopters’ deepening their data due diligence scopes, and requesting special areas for deep dives.
So, WHAT QUESTIONS ARE INVESTORS ASKING ABOUT DATA IN THE DUE DILIGENCE PROCESS?
In our experience, their questions can typically be grouped into three themes:
❓ A REALITY CHECK - How real and proprietary are the AI & machine learning capabilities?
❓ WHAT INSIGHTS IS DATA OFFERING? - Are the data and analytics effectively optimising operations and/or decision making? How can they support further optimisation?
❓ WHAT MORE CAN BE DONE? - Is there hidden potential in the data? What are the internal or external data monetisation opportunities?
Of course, they also want to test for ‘red flags’.
But the material shift we’ve seen is a move from ‘defensive’, compliance-led data diligence, to ‘offensive’, what-is-the-economic-value-of-data and how-does-data-underpin-our-investment-thesis mandates.
Which is our raison d'être!