Two years ago, DataDiligence created a new category of due diligence – the assessment of Data Assets to better inform investment decisions for buyers & sellers alike.
Data due diligence has evolved rapidly. The graphic below talks this evolution, & the typical situations & questions that investors asked us:
1 DATA-NATIVE TARGETS
Situation: (Seemingly) sophisticated, ‘data-native’ target making strong claims of being AI-driven and developing proprietary insights
Complication: Investor lacks in-house data science skills to validate advanced analytics, code, and process
Investor question: How real & proprietary are the AI & machine learning capabilities?
2 MOVING CLOSER TO THE CONSUMER
Situation: Digitalisation, plus the ‘pandemic effect’ mean that target is moving closer to customers & has likely started to curate significant volumes of customer data
Complication: Target’s decisions are not always data-driven, & much of the data is dormant; intuitively, management & the investor know that the data has value
Investor question: How is – and how can – data support the strategy & operations of the target?
3 PLATFORM PLAYS
Situation: Investor has ambitions to build a platform of symbiotic assets that harvest data for cross sell, upsell and growth
Complication: Investor requires deep, strategic analysis of full data ecosystem, testing for weaknesses, synergies, and breakthrough opportunities
Investor question: How compatible are the systems, data & analytical capabilities (‘acquiring for weaknesses’)? How is data disrupting?
4 VENDOR DD & PRE-VENDOR
Situation: Target is being prepared for exit; the data assets and data opportunity need to be positioned for maximum buyer confidence
Complication: Target may or may not have a mature data strategy and delivery story; information memorandum needs to be accurate and compelling in its positioning of the data opportunity & use cases
Investor question: How scalable is the data & infrastructure? Can the team deliver? How to package data assets for maximum valuation and future potential?
5 EVERY DEAL IS A DATA DEAL
Situation: Irrespective of sector or maturity, investors recognise that all companies generate data that can be leveraged beyond its immediate use; data is baked into the investment thesis
Complication/solution: Investor seeks independent data experts to assess and make recommendations across the entire data ecosystem to maximise data’s economic potential
Investor question: Is there hidden potential in the data? What are the internal or external data monetisation opportunities?
This year, we have led 20+ data diligence engagements, across sectors and geographies.
This is what one of our private equity clients said of their experience working with us:
“I see a growing need for data due diligence. DataDiligence's findings enabled us to make a faster, more informed investment decision.”
Managing Partner, US-based PE Fund