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5 Attributes of Data Quality for Private Equity Firms to Measure

Dive into what's missing from dealmakers' data toolkits, including the five key attributes of data quality they should measure and tips for realizing the benefits of good data.

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October 24, 2023

As the business world gets increasingly cluttered and complex from the daily influx of new companies — and as private equity firms become more targeted and specialized in their focus — dealmakers will need every advantage they can get. Data is that edge.

However, we recently found that while 84% of dealmakers claim to be “strategic” about the way their firms manage and use data, 40% also say that their data is often outdated, inaccurate, and incomplete. Our survey also uncovered that nearly three-quarters of firms aren't confident that they've overlooked or lost a deal to a competitor due to a lack of actionable, relevant, high-quality data.

Let's dive into what's missing from dealmakers' data toolkits, including the five key attributes of data quality they should measure and tips for realizing the benefits of good data.

What Is Data Quality?

Unfortunately, labeling data as “good” or “bad” isn’t as simple or straightforward as it sounds. Instead, there are five key attributes that firms can use to measure and judge the quality of their data. These are: accessibility, accuracy, actionability, completeness, and freshness. While each of these attributes is valuable on its own, only when data scores high across all five dimensions can it be effectively categorized as high quality.

We recently asked dealmakers to rate their data using these five attributes. Three in five respondents generally felt good about the accessibility of their data, with accuracy ranking second at just over half of dealmakers (52%). But for actionability and freshness, the ratings quickly dropped, with more respondents specifying their lack of trust than asserting confidence. And just one in three respondents felt the completeness of their data was in a good spot.

The 5 Key Attributes of Data Quality

Because information about private companies is so often scattered, messy, and difficult to find, private equity firms must be exceptionally diligent about ensuring the information they use to find and evaluate investment opportunities is high quality. Let’s dive deeper into our attributes of data quality and examine the tools and tactics firms can use to improve their rankings across all five.


Data accessibility involves ensuring those who need access to data have the right information at their fingertips at the right moments. This means keeping data in an accessible location — but properly secured and provisioned, of course — and formatting it in an approachable manner.

Achieving data accessibility requires establishing a "single source of truth" for all data — often a CRM. With a CRM, you can create a centralized hub to store all relevant deal data for contacts, target companies, and more. Most will even allow your firm to set user- and group-based permissions that allow the right people to get only the data they need to do their jobs.

To maintain this source of truth, it’s important to integrate your CRM with the other tools in your technology stack — especially your private equity data providers. This way data is automatically passed back and forth between the tools and made immediately accessible to all. Some service providers also offer tools like browser add-ons that make it easy for teams to access and add CRM data about top targets without ever leaving the companies’ websites.


For data to be usable, it must be factual and correct, no matter where or when it is used. Ensuring data accuracy involves both gathering accurate data initially as well as maintaining data accuracy as it moves throughout and is transformed in your tech stack. This is known as data integrity. Integrating your technology stack is one way to help boost data integrity.

However, more private equity data providers are turning to artificial intelligence (AI) to improve data accuracy. Unfortunately, AI alone isn't enough, as it cannot understand important concepts such as intent or context. As Sourcescrub CTO Jon Dodson cautions, “Data is error-prone, and eventually everything has to be supervised to ensure data quality and accuracy.”

While AI can process massive amounts of data far faster than a human ever could, it lacks human reasoning. Because of that, it’s critical to pair humans and AI together to ensure that the models AI relies on are consistently tuned and updated, as well as that the data AI collects is quality checked and validated before use. This process is referred to as “expert” or “human” in the loop machine learning.


As we touched on earlier, data that you cannot act on is of no help to your firm. Actionable data gives enough insight so your next steps are clear. Often, your chosen CRM will have custom reporting capabilities to help you better understand your data and make decisions with it. But actionable data doesn't just mean having a colorful report or dashboard.

Actionable data needs the proper context. For instance, if you know that three of your target opportunities have an estimated revenue of $10 million a year, that data isn't helpful on its own. But if you compare those in the list against each other and contextualize the revenue information with growth signals, that data becomes much more actionable.

Deal sourcing platforms can take this further and help your firm create the private equity data analytics and proprietary insights necessary to get ahead of your competition. Custom scoring and analytical models keep you one step ahead and make it far easier for your team to determine their next steps.


Getting a holistic picture of private companies requires dealmakers to do extensive manual research and connect the dots across multiple fragmented sources — an extremely time-consuming and error-prone process. It's no surprise that data completeness was the lowest confidence attribute in our survey results, with fewer than 6% of respondents rating their firms’ data completeness as "excellent."

The answer to this problem lies in sources-first data, which is generated by weaving information together from across hundreds or even thousands of individual sources to create deep and detailed company profiles. Connecting all the available data about a company and properly tying it back to its origins results in a web of insight that enables firms to quickly zero in, get up to speed, and keep tabs on target companies, their competitors, and their categories.

It also offers the ability to see beyond the obvious and derive additional private equity data analytics and insights from key data signals. For instance, if a company profile includes signals such as open job roles, increased conference attendance, and rapid growth in headcount, dealmakers could intuit that the company may be a more investment-ready opportunity than others in their target list.


The last data quality attribute is freshness, or the recency of information. From executive and board member replacements to employee turnover and general market shifts, company information is never stagnant, and keeping up with those changes is a constant struggle for dealmakers.

On top of avoiding embarrassment from reaching out to former execs and board members, data freshness can ensure your firm finds opportunities and makes contact before your competition. This way, your firm can more easily and quickly build the relationships that help you win more.

To get fresh data, your data sources must regularly and frequently update, and then push that information to the rest of your tech stack — yet another reason why integrations are key to experiencing the benefits of good data quality. The best private equity data providers will also notify dealmakers when information about a target opportunity is updated or new data becomes available.

Make Data Your Advantage

In today's market, private equity firms that want to succeed must up their data game. You not only need to collect all the information you can to identify potential opportunities, but you also need to ensure the information you're getting has all the attributes of quality data. The first step? Your deal sourcing platform.

However, not all deal sourcing platforms are equal. With hundreds of thousands of sources with millions of data points sourced by AI and vetted by humans, Sourcescrub can help your firm create a proprietary data advantage. Request a demo today to see for yourself.