A few practical questions

We primarily work with private equity investors and their portfolio companies, typically mid-market businesses where data and AI can support value creation, operational efficiency, and growth. We also support management teams preparing for investment, transformation, or exit.

Our work typically aligns with the investment lifecycle: Pre-investment diligence – testing the data and AI assumptions in the investment case During ownership – acting as Data & AI Operating Partners to define strategy and deliver value Pre-exit – preparing the data and analytics story for buyer diligence

We act as an extension of the leadership team, providing strategic direction, delivery governance and accountability for outcomes. Rather than delivering isolated projects, we help define priorities, assemble the right expertise, oversee execution and ensure value is realised.

Yes. We are not a reseller, implementation partner or licensed representative of any technology platform. We recommend technologies based on the needs of the business, not vendor relationships or sales targets.

Yes. We regularly support portfolio companies assessing and integrating acquisitions. This includes data due diligence, integration planning, KPI harmonisation, reporting alignment and creation of a scalable data architecture capable of absorbing future acquisitions.

All due diligence, strategy, roadmap development, architecture, solution design and programme leadership is delivered directly by the DataDiligence team. We do not outsource advisory work. Our clients work with experienced senior practitioners who have led hundreds of data, analytics and AI programmes across multiple sectors. For implementation and delivery engagements, we often supplement our core team with carefully selected specialists from our extensive delivery network. This gives clients access to deep technical expertise across data engineering, AI, machine learning, cloud platforms and analytics without the cost and overhead of maintaining a large permanent bench. Importantly, DataDiligence remains accountable throughout. We define the direction, design the solution, assemble the right team, govern delivery and ensure knowledge is transferred back into the business. This model combines the strategic rigour of a specialist consultancy with the flexibility and scale of a broader delivery capability.

We operate as the delivery lead and remain accountable for outcomes. Depending on the engagement, we may deliver directly, work alongside internal teams, or assemble specialist expertise from our trusted delivery network. DataDiligence provides the architecture, governance, programme leadership and quality assurance throughout.

Yes. We support the design and deployment of AI applications, copilots and agents. However, we focus first on ensuring the underlying data, governance and business processes are suitable for AI adoption.

We provide both strategy and delivery. Our work ranges from diligence and strategy through to implementation of data platforms, analytics, machine learning, and automation initiatives. We often work alongside existing technology teams or implementation partners.

A Business Data Twin is a business-friendly representation of how an organisation operates - in data. It creates a common language across systems, teams and reporting, providing a stable foundation for analytics, automation and AI. As systems change or acquisitions are integrated, the Business Data Twin ensures reporting and insight remain consistent.

Yes. We help management teams prepare for buyer diligence by assessing data maturity, documenting capabilities, validating AI initiatives and articulating the data and analytics story that underpins future value creation.

We focus specifically on the investor context. Our work combines technical data expertise with commercial understanding of value creation, ensuring that analytics, automation, and AI initiatives translate into measurable operational and financial impact.

No. Many of our clients do not have an in-house data leader. We frequently operate as an interim Chief Data & AI Officer, providing leadership and governance until internal capability is established.

For diligence engagements we typically request access to: Data room materials System architecture overview Sample datasets or database access Key operational systems (ERP, CRM, platforms) For strategy or delivery engagements we begin with management interviews and system discovery.

Engagement duration depends on the scope. Typical timeframes include: Data & AI diligence: 1–3 weeks Data strategy: 4–8 weeks Delivery and operating partner support: multi-month programmes aligned to value creation plans Exit readiness and VDD: typically 3–6 weeks depending on scope

Most of our work can be delivered remotely, particularly diligence and analytical assessments. For strategy and delivery engagements we often combine remote collaboration with targeted on-site workshops, particularly when working closely with management teams.

Most engagements are delivered on a fixed-fee basis aligned to a clearly defined scope. For longer delivery programmes or operating partner roles we may work on retained or phased engagements aligned to the portfolio company’s value creation plan.