In the game of scrabble, the word ‘data’ has a score of just 5.
Not too impressive to be honest.
In the world of business - however - data’s value is so much more. Yet few have the ability to value the intangible, which is madness when:
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 people, and money, hoping for much more than 5!
This is exactly why we created DataDiligence - to offer investors a structured, comprehensive approach to validating, valuing, and assessing the value-creation opportunities of data assets, both on the buy and sell side of M&A.
Further, our diligence methodologies also enable management teams to assess and identify potential use cases, latent within their customer, product and operational data for better decision making, automation/optimisation, and potentially new revenue streams.
When we first started DataDiligence, we had to squeeze ourselves into the due diligence process, often piggybacking on digital diligence. However, 24 months later, we have worked with almost 30 different private equity houses or their portfolio companies. In many cases, we have provided insights on multiple diligences and/or projects. Like us, our investment partners are learning, asking deeper questions, and shifting their investment thesis accordingly.
Sure, we are still asked the ‘reality check’ questions, like, how real and proprietary are the AI and machine learning capabilities of the target? Or, who owns the data? But, increasingly, these ‘defensive’, compliance-led questions are table stakes.
Rather we are now being asked to interrogate value-creation opportunities. ‘Offensive’, what-is-the-economic-value-of-data, and how-does-data- underpin-our-investment-thesis questions, such as:
❓How effectively are data and analytics optimising operations?
❓What further optimisation can be achieved?
❓Is there hidden potential in the data?
❓What are the internal or external data monetisation opportunities?
Why settle for 5?