Understanding the value of data & AI has never been more important
Assessing the quality, reliability, risks, and potential of data assets across the investment cycle
Supporting private equity and their portfolio companies across the invesetment cycle
Pre-investment >
Due Diligence
Discover hidden value and risks from data & AI across a target company's full data ecosystem
-
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?
Value creation >
Diagnostic
Post-investment assessment to identify AI-readiness, plus data and AI opportunities in individual companies or across a portfolio
-
If an data & AI due diligence wasn't conducted, what is the current state, risks and opportunities this company/portfolio post acquisition?
-
Which data/AI activities should be drive at a Fund level, and what is more for individual portfolio companies?
-
Where should we focus and whom should we support?
-
What are the benchmarks, complexities and potential synergies across a portfolio of investee companies?
Value creation >
Strategy
Discover and design data and AI initiatives that solve business problems, and enhance visibility, optionality, and predictability
-
Discovery phase to assess how to efficiently unlock business value with data and analytics?
-
What capability & capacity do we require to deploy data products (internal & external)?
-
Design phase to craft the infrastructure, processes, data inputs and output, teams and best practices to build, deploy, maintain and innovate data & AI initiative/products that solve business problems & reinforce the business strategy
-
Strategy principles to guide delivery decision-making and emphasise flexibility, replaceability, and no technical debt.
-
Roadmap, milestones and investment case realise the value of data & AI
Value creation >
Delivery
Efficiently deliver data and AI projects that improve decision making, optimise and automate operations, and generate new revenue
-
Tiered-delivery approach, tailored to client budget and needs
-
Senior data lead project management to drive the shift from data outputs to business outcomes.
-
Management of all delivery partners
-
Regular, detailed shareholder and stakeholder engagement to elevate alignment and transfer skills.
Value realisation >
Exit readiness
Prepare and position data & AI assets for vendor due diligence
-
How AI-ready is the company?
-
What are the threats and opportunities from emerging data & AI tools and technologies?
-
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?
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
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?