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Understanding the value of data has never been more important

Assessing the quality, reliability, risks, and potential of data assets across the investment cycle

Our services across the investment cycle

Identifying value

Deal
orgination

Optimise origination decision making, particularly relating to large, complex data sets

Need:

From time to time, investors ask us to support their origination process with additional advanced analytics to provided tighter insights into prospective acquisitions.

Validating value

Due
diligence

Discover hidden value and risks from data & AI across the full data ecosystem

Data & AI
due diligence

​Typical investor questions:

  • How efficient is the target in using data and analytics to power its business and operational models?

  • How ready is the target to deliver on the investment thesis? 

  • How real is the algorithm / data the company claims to have?

Accelerating value

Performance
improvement

Design & deliver data initiatives that solve business problems, improve decision making, optimise, automate and generate new revenue

Data & AI
strategy & delivery

​Typical investor questions:

  • How to efficiently unlock business value via data and analytics?

  • Are we shifting outputs to outcomes?

  • What capability & capacity do we require to go-to-market?

  • What are the complexities and potential synergies of blending data infrastructure, data sources, use cases, processes, and people in the event of a bolt-on?

Institutionalising value

Performance
management

Identify potential data linkages and economies of scale & learning between portfolio companies, and apply best practises with a portfolio-wide data & AI maturity diagnostic

Data & AI
portfolio diagnostic

​Typical investor questions:

  • Where should we focus and whom should we support?

  • Which data/AI activities should be drive at a Fund level, and what is more for individual portfolio companies?

  • What are the benchmarks, complexities and potential synergies across a portfolio of investee companies?

  • If an data & AI due diligence wasn't conducted pre-investment, what is the current state, risks and opportunities this company/portfolio post acquisition?

Realising value

Exit
preparation

Prepare & optimally position data & AI assets for vendor due diligence 

Data & AI
exit readiness & VDD

​Typical investor questions:

  • How AI-ready is the company?

  • What investment have we made that the next buyer will benefit from?

  • What investment will be needed to unlock future value?Can we increase confidence in financial forecasts?

  • How proprietary & scalable is the data ecosystem?

Download sample Data & AI Due Diligence Report

Sample report download

What our clients say?

"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

"While developing a data strategy and roadmap for one of our portfolio companies, DataDiligence addressed the technical requirements to ensure the right data was current, accessible, and usable across the organisation.

But more importantly, they provided guidance on the interplays between the company’s business and data strategies and highlighted the business opportunities related to the data itself.  They clearly articulated the resulting company strategy and used simple yet comprehensive frameworks to identify and communicate capability strengths and gaps. Then they even supported implementation of the associated interventions, including recruitment of a new exec to drive development and implementation of the digital strategy. 

 

DataDiligence’s recommendations are based on strong analytics, backed by deep cross-cutting experience, and delivered with hands-on and enthusiastic professionalism."

Operating partner

Sanari Capital

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Five due-diligence questions every investors should ask 

01.

How much does the target’s underlying data ecosystem play into value creation, today and in the future?

02.

Are we investing in data & AI due diligence proportionately to the perceived value?

03.

Do we have the experience — or know where to get it — to truly understand the AI & data nuances in this particular space and to develop unique insights?

04.

Is our data & AI diligence integrated with the broader commercial and financial due diligence, so the insights and recommended actions are consistent with where the value lies?

05.

Are these insights flowing directly into the value-creation plan to jump-start delivery on the investment thesis post-acquisition?

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