• Chelsea Wilkinson

Evolving data due diligence

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?

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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