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For many businesses - especially Private Equity owned companies which have been heavily optimised using traditional operating methods - leveraging data and AI might very well be their 'last mile' in efficiency, or, data insights might be the next product or indiscovered revenue stream.
Identifying these opportunities pre-deal, means that investors can rapidly target value-creation strategies. And a data strategy can be crafted or refined in the first 100 days.
What are the five questions investors should be asking themselves:
What are the five questions investors should be asking themselves:
How much does the target’s underlying data ecosystem contribute to value creation, today and in the future?
Are we investing in data and AI that is in proportion to the perceived transaction value?
Do we have the expertise or access to it, to truly understand the AI and data nuances in this particular space and to develop unique insights?
Is our data and AI due diligence integrated with the broader commercial and financial due diligence effort, so the insights and recommended actions are consistent with where the value lies?
Are these insights flowing directly into the value-creation plan to jump start delivery on the investment strategy in the post acquisition phase?
The Challenge
The Challenge
Our inboxes and news feeds are inundated with headlines such as: 'data is the new oil,' and businesses demonstrating data-driven behaviour reportedly enjoy more than 150% greater market-to-book value than the market average, according to the International Data Corporation. This is attracting high-levels of investment from large enterprises and private equity houses seeking to generate revenue from data - data-as-a-service, selling raw data, derived metrics, insights, and building AI systems.
Yet very few investors and companies have the capacity to deeply interrogate data ecosystems, datasets, and the AI algorithms to determine reality and inform their pricing and value creation strategies.
How can we help?
How can we help?
Advanced Analytica's Data Observatory takes a new approach to data due diligence by offering a secure environment and unique pre-diligence processing toolkit to prepare data and accelerate evaluation and privacy risk scoring of databases and datasets. Our approach utilises configurable AI processing pipelines to prepare, analyse, score and present highly structured and customised due diligence reports for inspection by regulators and potential investors, and validation by legal teams, M&A advisors and other domain experts.
Diagnostic programme
Diagnostic programme
Drawing on our data due diligence expertise, we follow an end-to-end process from problem definition to recommendations and the way forward by asking questions via our online due diligence questionnaire that poses questions across the data ecosystem - strategy, analytics and data, people, and infrastructure. This is coupled with targeted information requests, so we are able to build a strong understanding of the 'as is' data ecosystem.
Definition: What is your investment strategy or business problem we are aiming to solve? What is the scope and specific areas of investigation?
Assessment: What are the levels of data maturity, privacy risks and opportunities? How do these impact the consumer, business and investment strategy (RAG - red/amber/green)? What are the gaps between current to desired state?
Recommendations: What is required to fill the gap(s) between current state to desired state? What does the remediation programme roadmap look like?
Value potential: What are the data use cases and possible outcomes (ROI)? What are the potential economies of scale and learning?
Moving forward: Does the company need a full or light data or AI strategy and to validate findings? When do we kick off delivery, PoC, MVP, etc.?
The process
The process
From inputs to outputs, the diagnostic programme typically takes 3 weeks, with limited demands on executive time, and can be be delivered in-person, or remotely.
Step 1. Inputs (~1 week from start date)
Step 1. Inputs (~1 week from start date)
Pre-project:
Agree engagement and set up kick-off
Scope project and special items
Contract: fees, access, teams and timelines
Step 2. Assessment (~2 weeks from start date)
Step 2. Assessment (~2 weeks from start date)
Online kick-off call:
Define investment strategy and business objectives
Information request:
You complete questions and upload data to the observatory
Product and data demos
Deep-dive meetings:
Two to three 30 min meetings with leaders and domain experts
Step 3. Outputs (~3 weeks from start date)
Step 3. Outputs (~3 weeks from start date)
Overall report and risks:
Review and final amendments
Results:
Factual check with client or target
60 min presentations plus Q&A
Road map and next steps
Steps to realisation, for example:
Data strategy
Minimum value product
Proof of concept/value
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