The data investment life cycle

And the importance of J-curve for investors. Again.

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  • How quickly can data scientists develop a solution leading to desired business outcomes?
  • What is the quality, reliability, and availability of the data?
  • What investments into data infrastructure will be needed?
  • How well will the organisation execute on the project?
  • What is the adoption of the data throughout the business; how data fluent are the teams?
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“Only with a complete picture of a company’s data and data-related capabilities can a PE firm expect to make fully informed decisions about whether or not to execute on deals and how to price them accordingly.” — Douglas Laney.

A deep understanding of data assets provides a meaningful information advantage, identifying hidden value that traditional commercial due diligence isn’t looking for. It spots data risks and opportunities, maturity, and true data monetisation — or better still, ‘insights monetisation’ (the real output of data science initiatives) — all of which ultimately leads to better investment decision.

Data scientist with corporate, consulting and start-up experience | avid cyclist | amateur pianist | Interim CDO at DataDiligence.com