Data and analytics is a major driver and source of great value for private equity firms. The best private equity firms know the full power of data and analytics. They realize that portfolio company enterprise data is typically the crown jewel of an acquisition or deal target.

Data and analytics are also the foundation of financial and operational transformation. Quickly pulling data from their portfolio companies, and consolidating it into actionable information, will enable and accelerate financial and operational value opportunities, driving up EBITDA. Even better, the creation of data monetization revenue opportunities unlocks hidden sources of value creation. And down the road, a data-driven organization will always yield much higher financial valuations and returns to their investors.

Due Diligence to Accelerate Data Value Creation

Most firms doing due diligence on potential targets will only do basic due diligence. They will focus on assuring financial valuation and risk assessment. Therefore, most PE firms will conduct standard IT due diligence, analyzing expense budgets, hardware and software capital assets, license and service contracts, and headcount/staffing. They will seek to understand IT architecture, as well as assess the network in terms of capability. Because it is top of mind, the due diligence effort will also heavily focus on cyber and network security, and the architecture built to protect the portfolio company and its data. Typically, they will declare the due diligence effort complete.

Beyond classical IT due diligence, most dealmakers try to understand their data assets once the deal has closed and they begin operating the acquired company. However, best practice says otherwise. To accelerate the data and analytics value creation curve, it really starts at data due diligence. Precise data due diligence serves as the foundation for portfolio data strategy, as well as uncovers hidden sources of potential and opportunistic strategic value. Doing data due diligence will give the PE firm and portfolio company a running start on data value creation once the deal has closed.

What should deal firms look for when doing data and analytics due diligence? Here are key areas and questions for investigation and analysis when investigating a target portfolio company.


Step 1: Determine the target company’s current overall approach to managing and analyzing its data.

Develop an understanding of the target company’s current approach to accessing and analyzing their data. Understanding their current approach will let you know the effort needed to accelerate potential data value creation.

  1. Does the target company have a comprehensive data strategy to transform the company into a data-driven enterprise?
  2. Does the company have a single source of truth for data, analytics, and reporting?
  3. What is the target company’s usage of data-driven business decisions in operations, marketing, sales, and finance?
  4. What cloud services, architectures, and tools does the company use to manage its data?
  5. What is the on-prem data environment and architecture?
  6. What kind of cloud data and analytics proofs-of-concept does the company have in place to build out its capabilities?
  7. Has the company identified and implemented value prop use cases for data and analytics, realizing tangible ROI?
  8. Where is the target company on the data and analytics curve?

Step 2: Identify the data sources, what data they contain, and how clean the data is.

Data value depends on breadth and quality of the target company’s data and data sources. Document what the data sources are, what purpose they serve, how the target company currently integrates data sources for analytics, the existing security and data governance measures, and the overall quality of the data.

  1. Inventory all of the company’s data sources, including a data dictionary, size, physical and logical location, data architecture, data model, etc.
  2. How many of the data sources have an API for ETL (extract, transform, load) to pull data into the data warehouse?
  3. Does the target company have a data warehouse, and are all of its data sources feeding the data warehouse?
  4. How much history does each data source have? Obviously, the longer the history, the greater the value of the data source.
  5. What kind of data security is in place to protect all data sources?
  6. What kind of data quality assessment for each source has been conducted?

Step 3: Assess the quality of the target company’s analytics and reporting.

Review how the target company approaches reporting and analytics. This step should include a review of their tools and technologies, KPIs and metrics, and reporting (i.e., self- service, interactive, dashboards, Excel reports, reports delivered by IT, etc.).

  1. What kind of reporting does the company use?
  2. Does the portfolio company have a heavy dependence on Excel for producing reports?
  3. Describe the KPIs that are in place for each functional area. How has the company been tracking against these KPIs?
  4. Does the company enable self-service analytics across the enterprise?
  5. What is the inventory of all reports generated by the company?
  6. What percentage of the reports are delivered by way of dashboarding?

Step 4: Review the people and processes involved in data management and analytics.

Determine the extent of the target company as a data-driven organization by examining the people and processes behind the data strategy. Document which FTEs are involved with data and analytics, how much time is dedicated to reporting and report development, as well as the current processes for analytics.

  1. How many FTEs are engaged in financial and operational report development?
  2. What does the data and analytics team consist of, in terms of data engineers, data scientists, data administrators, and others with data titles?
  3. What kind of data governance is in place for the target company to regulate the structure of data, as well as where and how data can flow through the organization?

Step 5: Find opportunities for target company data value creation.

Assess, understand, and determine the opportunities for marketing and operational improvements, cost reduction, untapped areas of growth, data monetization, cash flow improvement, and more.

  1. Which of the following advanced data and analytics use cases does the portfolio company have in place?
    • Customer acquisition
    • Marketing channel excellence
    • Working capital rationalization
    • Fixed asset deployment and maintenance
    • Operational labor transformation
    • Forecasting predictive analytics
    • Automated customer reporting
    • Supply chain optimization
  2. What use cases does the company conduct for data science predictive and prescriptive analytics?
  3. What is the target company’s data monetization strategy, and where are they with implementation?
  4. What is the company’s usage of big data to enhance marketing, sales, and customer service understanding and strategies?
  5. What third-party data does the company use to supplement internal data to drive enhanced insights into marketing and operating?


To accelerate data and analytics value creation for a portfolio company target, start the process during due diligence. Gaining tremendous insight into the potential for data will accelerate the plan once the deal is closed and allow for a running start on data analytics value creation. With these insights, the PE firm, in partnership with their portfolio company, will generate fast data ROI and enable financial and operational transformation, EBITDA growth, and enhanced cash flow.

At 2nd Watch, we help private equity firms implement comprehensive data analytics solutions from start to finish. Our data experts guide, oversee, and implement focused analytics projects to help clients attain more value from modern analytics. Contact us for a complimentary 90-minute whiteboard session to get started.


Jim Anfield, Principle and Health Care Practice Leader