The Importance of Data Quality for Private Equity Deal Teams
Three steps to an effective private equity data management strategy.
This article is part of ACG’s series for executives at private equity-backed companies, sponsored by RSM US LLP, a leading accounting, tax and advisory firm dedicated to the middle market. The article originally appeared on RSM’s website.
Middle-market private equity investment activity continues to heat up, and there is a heightened sense of urgency to complete deals ahead of anticipated tax policy changes. Whether you are a PE buyer or seller, time delays can make or break any M&A transaction and success often depends on speed and data quality.
Decision-making is only as sound as the data that supports it, yet organizations often don’t fully appreciate how proper data management can drive business outcomes and smooth due diligence efforts. Poor data quality is one of the common reasons why business initiatives fail, which explains why PE buyers are more likely to invest in data-driven businesses that demonstrate they care about quality.
“If your goal is to increase investor interest and drive higher valuation multiples, you want to focus on high-quality data that eliminates uncertainty and directs the focus on the strength of the business and value creation opportunities.”
Director, Technology Consulting, RSM US LLP
Don’t Let Poor Data Quality Delay Your Exit Strategy
In private equity, the due diligence phase of the transaction timeline is critical and poor-quality data can hinder the closing process. Yet, more often than not, PE firms make the mistake of ignoring data management until they plan the exit strategy, at which point it is too late. The result? PE firms and portfolio company management teams have to spend countless hours manually addressing data quality issues.
“When conducting IT due diligence, private equity deal teams tend to ignore the ‘I’ and focus on the ‘T,’ which sets them up for acquiring portfolio companies with poor data quality,” says Faisal Muhammed, technology consultant for RSM US LLP, who provides data and analytics services for clients.
During his experience working with PE firms, Muhammed has observed that most deal teams will postpone fixing data quality issues at the portfolio level until they are ready with the portfolio company’s exit plan; this can lead to costly delays. He recalls compiling sell-side analytics for a PE-backed client with poor-quality customer data. When it came down to crunch time, the buyer asked to see sales and profitability by various customer attributes (e.g., industry; original equipment manufacturer vs. maintenance, repair and overhaul), but the seller was unable to deliver. This delayed closing by more than a week, during which time the client spent money and internal resources to update missing customer attributes. Fortunately, the lack of quality data did not tank the deal, but it did cause considerable discomfort during the delay.
“If your goal is to increase investor interest and drive higher valuation multiples, you want to focus on high-quality data that eliminates uncertainty and directs the focus on the strength of the business and value creation opportunities,” says Muhammed.
3 Steps to an Effective Private Equity Data Strategy
Implementing a data management process takes a recurring commitment but not extensive resources. Once the process is established, PE companies should aim to implement the same methodology to improve data quality for any add-on acquisitions as a knowledge-sharing best practice to help save time and ensure success. Here are steps for PE firms to get started:
Work with portfolio companies to help them understand the impact of data quality on the objectives of the business.
The key is to get business stakeholders to treat data as a strategic asset, like any of their traditional assets. As an example of how poor data quality can lead to sales ineffectiveness, Muhammed recalls a client that struggled with duplicate prospects in its customer relationship management system, which caused two different sales representatives to work on the same prospect, leading to inefficiencies, lower conversion rates and other problems.
Establish data quality metrics that are relevant and important to the business.
Accuracy: Take the time to collect and validate customer data, such as phone numbers and email addresses. Not only does this build consumer trust, it is also critical to the success of marketing and outreach campaigns.
Completeness: Ensure that all the important and relevant customer fields are populated with data. This is key when a PE firm is ready for an exit strategy to sell the portfolio company, as buyers often want to analyze business revenue/profitability by various customer attributes.
Ensure continuous monitoring and improvement by establishing an ongoing process to systematically detect, correct and prevent data quality issues.
Detective controls: Finding errors or irregularities in the data based on the metrics that are relevant and important to the business.
Corrective controls: Taking measures to address any data quality issues, which can be as simple as creating a service ticket and assigning the responsible data owner to the ticket, or programmatically correcting the issue based on established rules.
Preventative controls: Identifying the source of issues and establishing controls to prevent entry of incorrect or incomplete data; for example, using drop-down menus in applications to limit the choice of entries.
Who Is Responsible for Maintaining Data Quality?
It is a common misconception that data quality is an IT responsibility; however, IT doesn’t inherently generate data, nor does that department necessarily understand the nuances of the data. Therefore, it is more accurate to view data quality as a business initiative supported by IT. It is most effective if every functional area implements a data quality monitoring and improvement process for each of its datasets. To do so efficiently and effectively, it helps to assign a data owner and data custodian.
- A data owner is a business stakeholder who is accountable for the quality of their datasets. Typically, this is someone who is very knowledgeable about the dataset and would be most affected by data quality issues.
- A data custodian is responsible for storing, maintaining and securing data in accordance with business requirements.
After implementing controls, it is important to measure improvements to show the positive impact on data quality. PE operating partners should list these improvements in the report for buyers as a strategic enhancement when it comes time to sell.
Prioritizing Data to Create Value and Grow the Enterprise
Poor data quality is often underestimated by private investors focused on closing deals. PE firms need to take the target company’s data management processes and data quality into consideration when assessing the true market value, as well as their level of confidence in the numbers on which their calculations are based. Apart from due diligence during the purchase phase, PE owners should advise portfolio companies on how to establish an ongoing data quality monitoring and improvement process. This benefits both the fund and portfolio company, as good data quality is essential to the value and growth of the enterprise.