9 steps to improving CRM data quality

In private capital, having access to accurate and high-quality CRM data is critical to staying competitive and improving data-driven dealmaking. Yet, a Harvard Business Review report found that only 3% of enterprise data meets basic quality standards.

You’ve likely invested countless resources into implementing the right CRM system for your firm. But your CRM is only as useful as the data that’s housed inside it. 

Keep reading as we explore the ins and outs of CRM data quality and the steps you can take to improve the quality of your organization’s data. 

What is CRM data?

Any data that can be stored within your customer relationship management (CRM) platform falls into the category of CRM data.

Historically, we’ve often considered CRM data to include static information, such as contact details and company demographics. However, as CRMs have become more dynamic, we’ve seen the rise of real-time and fluid CRM data including relationship and dealmaking insights. 

Read more: How to use your CRM data to close more deals

What is CRM data quality? 

CRM data quality is the measurement of how useful your data is. In the context of private capital dealmaking, it’s the degree to which your data can be used to drive deals forward.
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There are many ways to determine the quality of a dataset, but it generally comes down to a few core considerations:

• Is the data complete? Does it include all the data points you need to make deal decisions?
• Is the data accurate? Is the information provided up-to-date and error-free?
• Is the data consistent? Is the information available the same across all records?
• Is it relevant? Is it the data that you actually need? 
• Is it available? Can you access the data when you need it?

Why CRM data quality is important for deal teams

Whether you’re screening a company or negotiating the final details of a deal, data can inform every decision that’s made within the deal funnel. It enables investors to identify the most valuable deals and approach every interaction with the context needed to build stronger relationships and drive deals forward.

However, when dealmakers are left with fragmented, inaccurate, or absent data, it impacts their ability to make the best decisions. It can distort their perception of a deal, or worse—put a deal in jeopardy if they come across as misinformed or disorganized. 

Quality data also empowers deal teams to unlock the full value of their deal pipeline. Accurate and complete datasets can easily be turned into richer deal reports and dashboards to analyze relationships and engagement activities—so dealmakers know that they’re focusing on the most lucrative opportunities.

Poor data goes beyond simple inconvenience. It can have a serious financial impact on your firm. A Gartner study found that bad data costs the average company as much as $15 million a year. And as the use of data in firms continues to rise, we can almost guarantee the losses attributed to dirty data are even higher today. 

The value of quality CRM data in private capital deals

CRM data management is a big problem for many private capital firms. While relationships are a key part of effective deal sourcing, clean and accurate data is critical to turning those relationships into closed deals.

With data becoming a growing component in the investment process, an extended enriched dataset is needed to drive consistent deal flow and optimize the deal sourcing process. With this, teams can spend more time nurturing relationships and reduce the risk of failed investments.

The most common CRM data quality issues

Let’s look at some of the most common data quality issues faced by private capital firms.

Duplicate data

Duplicate records often occur when there are multiple sources of data or a CRM isn’t synced in real time.

For example, one analyst or associate might create a new company or founder profile, not realizing another has already created one, resulting in duplicate records. This can cause data to become split between the two records, which impacts productivity as it leaves teams working off of different data, creating confusion regarding which record is most up-to-date.

Incomplete data or missing data

Deal teams should have a list of data fields that make up a complete profile. When fields are missing, it renders that profile less valuable.

Data can often be incomplete due to: 

• Data loss during data transfer or migration.
• Human error during manual input.
• Sellers failing to collect the necessary information.

Inconsistent data or inaccurate data

Even when data is present, it’s not always usable. 

When data is difficult to decipher or incorrect, it gives deal teams an inaccurate picture of the deal at play. This can lead to inefficient processes and missed opportunities. 

Even something as simple as inconsistent date formats can have a significant impact on the ability of a team to run accurate reports or extrapolate actionable insights.

Irrelevant data

While incomplete data is often more common, there are times when dealmakers will over-input data, including information that isn’t relevant for analysis or decision-making.

Having more information is usually better than not having enough, but irrelevant data points can make it difficult for dealmakers to parse out the information they actually need to drive a deal forward. 

Siloed data

This is an issue when data points are available but stuck within different platforms or tools.
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Siloed data limits visibility and leaves sellers stuck threading together individual pieces of information. Not only is this valuable time that could be better spent on driving deals forward but it can delay deals or lead to missed opportunities altogether.

Outdated data

Your deal pipeline is constantly changing which means your data records should too. New deal activities should be added, points of contact should be updated, and irrelevant information should be deleted or archived. 

When information isn’t regularly updated or validated, it can lead to teams pursuing deals with outdated and stale information. For example, trying to reach a contact who no longer works at a target company. 

How to improve CRM data quality

Clean CRM data is critical for managing and closing deals as quickly as possible. Fortunately, regardless of the current state of your CRM data, there are proactive steps you can take to ensure you have the highest-quality data possible. 

1. Ensure your CRM system houses all of your data

Private capital firms often have tech stacks that are complete with several different data sources. While these tools are valuable for collecting data, it’s important to aggregate all your data points in one place: your CRM.

Having one place that centralizes your data makes it easy for dealmakers to access that data and make educated deal decisions. If you have to spend hours searching for information you need, it often goes one of two ways: wasting valuable time sifting through data silos or making less-than-ideal decisions based on fragmented data. 

That doesn’t mean you should burden your team with manual data transfer either. When you manually manage data entry, the risk of human error and bad CRM data goes up substantially, leading to further data integrity issues. CRM automation tools and integrations can help your data flow seamlessly between your other business solutions and your CRM.

2. Make it easy to update your CRM

An Affinity survey showed that 35% of respondents say teams don’t use their CRM because they don’t have enough time to add new records or activities.* 

With users already spending over 4 hours a week on CRM updates, it’s understandable why updates often fall through the cracks. 

However, when your CRM isn’t kept up to date, the quality of the data declines. This makes it less useful to your team, meaning they’re even less likely to use or update it in the future. It’s a vicious cycle that ultimately impacts your team’s ability to close deals efficiently. 

Automated activity capture tools, like Affinity and Affinity for Salesforce, eliminate the need for manual data entry by creating and updating contact records through email and meeting analysis. 

3. Make the system as user-friendly as possible

Similarly, it’s also important to make information as easy to access as possible. Regardless of how valuable your data is, if your team feel like it’s slowing them down, they’re not going to use it. 

Take the time to identify friction points in the process and remove them so you’re delivering your sellers a seamless CRM experience without impacting deal velocity.
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For example, Affinity CRM and Affinity for Salesforce bring your CRM data directly into the tools you already use every day, such as LinkedIn or your email inbox. Investing in these tools and the user experience can be the difference between a CRM full of valuable information and a potentially failed investment.

4. Define a standard CRM data management process

When it comes to improving CRM data quality and data hygiene, process is key.

Dealmakers should know when they’re expected to collect data, when they should use it, and where that information should be stored. They should also know what to do when they come across data that is missing or not updated.

When data capture and usage are standard parts of your workflows, teams no longer view them as optional. While onboarding that process will come with a learning curve, data management will eventually become second nature for deal teams and keep data cleaner in the long run.

5. Use data validation fields to ensure you input clean, reliable data from the start

Data validation helps standardize your data from the outset. When you set up validation parameters in your CRM, it alerts dealmakers when data isn’t inputted correctly or if information is missing. 

For example, you can make certain fields mandatory, such as contact information and names. Or you might set a requirement that phone numbers must have nine digits and follow a specific format.

Effective data validation rules will reject data that doesn’t meet the criteria, reducing the risk of incomplete or inaccurate data finding its way into your CRM.

6. Audit your CRM data regularly

Putting a strategy in place to protect your data quality can help reduce your risk of dirty data. However, the data in your CRM is—and should be—constantly changing as dealmakers go about their daily activities. Regular data audits make sure that nothing falls between the cracks. 

When conducting a CRM data audit, focus on validating records and identifying areas where data quality is coming up short. It’s also an opportunity to reevaluate your CRM data management strategy to make sure your process is still minimizing the risk of errors. 

The first few audits will likely feel tedious. But over time, as your data quality improves, these audits and the resulting clean-up will quickly become less time-consuming.

7. Train your team and provide support 

Maintaining CRM data quality is almost impossible without the support of your team. It’s important to deliver training that improves CRM use and adoption.

This can include skills-based training, such as CRM tutorials and practice sessions, that help deal teams use and update CRM data efficiently throughout the sales process. However, support should go beyond execution. 

It’s difficult to secure buy-in if dealmakers don’t see the value of clean data on their own performance and earnings. Training should also highlight the value of data in closing deals and the consequences when it gets neglected, so dealmakers understand the importance of data management.

8. Implement a data governance strategy

The more data you need to manage, the more structure that’s required to keep that data organized and protected.

Studies have shown that firms with poor-quality data are 450% more likely to say that there’s no one responsible for managing CRM data than companies with higher-quality data.

A data governance strategy defines responsibilities and accountability for the data within the firm. It also takes into consideration data quality, security, and compliance to help your firm make the most of that data and ensure that it’s driving business outcomes. 

9. Find the right data cleansing and enrichment tool

The easier it is to maintain CRM data quality, the easier it is to make it a priority. 

The right data hygiene tools can help you: 

• Improve access to data through browser extensions.
• Expand your existing dataset with data enrichment tools.
• Reduce manual record creation with automated activity capture.
• Prioritize the right deals with comprehensive reports and dashboards.

Automate high-quality CRM data entry with Affinity

Data management is key to maintaining high-quality data. But it shouldn’t consume your team’s entire week. The right tools can free up valuable hours to focus on the high-value, relationship-building activities that help source and close more deals. 

Top private capital firms use Affinity to elevate and enrich their CRM data—and save up to 200 hours of data entry per person every year. Affinity helps by:

• Automatically managing and updating contact profiles from your inbox and calendar information.
• Rolling relationship and deal insights into the tools that dealmakers use every day, from emails to their browsers. 
• Enriching reports and data with contact engagement and history, while assigning relationship scores so dealmakers can stay on top of every opportunity.
• Unlocking new deal opportunities by uncovering paths of warm introduction that close deals up to 25% faster.
  

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CRM data quality FAQs 

How can I improve the quality of my CRM data?

There are many ways to proactively and reactively improve the quality of your CRM data. Some of the most effective ways to elevate CRM data quality include conducting regular data audits, using data field validation tools, reducing human error with automation tools, and defining a data management process. 

What are the metrics for CRM data quality?

Common metrics used to measure CRM data quality include:

• Data accuracy
• Data completeness
• Data recency
• Data relevancy

How do I clean up data in my CRM?

The best way to keep your data clean is to proactively maintain data quality. However, if you find yourself saddled with dirty data, there are a few steps you can take to clean it up. Including:

1. Validate data for accuracy and relevancy.
2. Remove inaccurate and irrelevant data points.
3. Set standards for data entry.
4. Invest in tools, such as Affinity, with functionality to automate and enrich data within your CRM.

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* Data from Affinity’s 2024 survey of 250+ business leaders across investment banking, media and communications, real estate, professional services, healthcare, financial services, manufacturing, and enterprise technology.