Bad data quality is a persistent challenge in companies. In a fast-paced sales environment, every minute matters –  chasing prospects, speaking with customers, building new strategies. With so many priorities, logging activities and keeping CRM records up to date often feel like an afterthought. Let’s face it: every sales rep has, at some point, questioned the value of keeping their CRM records tidy. On the other hand, CRM administrators often do not prioritize this as their first task either. It is at the back of our minds that we have opportunities that haven’t been updated in years and are simply sitting in the CRM without any use. This includes outdated customer contacts, duplicated accounts, and many more issues that have yet to be discovered. But who cares until it becomes urgent?

In this article, I’ll share practical steps to clean up your CRM and improve data reliability.

Understanding the current state

It’s hard to fix a problem without fully understanding it. We often recognize that data quality is poor, but do we truly understand how many records are impacted? Are there recurring patterns behind the issues?

Because of this, the first and most obvious step is to understand the AS-IS state (the current reality of your data). If the CRM system had been in use for years without proper guidelines or validation rules to prevent bad data entry, it may feel almost impossible to identify every issue at once. The good approach is to start by addressing the most critical issues first, and then gradually move on to the less obvious and lower-priority ones.

To make the problems visible, you can build dashboards that answer questions like:

AreaQuestionExample
CompletenessAre the required fields filled in?– Opportunities not connected to the account/company
– Opportunities missing deal amount
– Leads missing lead source
AccuracyIs the data correct and valid?– Records assigned to inactive users
– Opportunities with the close date in the past
– Deals with inflated deal amount
TimelinessIs the data up-to-date?– Open deals not updated in the last 3 months
– Open deals with no recent activities
ConsistencyAre sales processes followed?– Deals skipping required sales stages
ActivityAre interactions logged?– Opportunities with no calls or meetings logged.
– Active accounts with no next steps recorded

The process of creating dashboards helps clarify what data truly matters, which records are unnecessary, and whether the sales process is being followed. This will become the first step in setting data quality standards.

These insights are also crucial in defining the magnitude of the issue and whether any quick wins allow for making the CRM cleaner and faster. For example, with the Accuracy dashboards, you may discover that some opportunities are assigned to people who left the company a while ago.

Now we have visibility into records that clearly have issues. What’s next?

Well, there is no simple answer – each company’s data and people can differ drastically. For foundational issues like missing fields, outdated ownership, or stale opportunities, it’s best to start with simple actions.

Defining data quality standards

This step is often overlooked in CRM systems. In many teams, the priority is speed: if validation rules or required fields make entering a new lead take a few extra minutes, people may skip them, leaving the accuracy and completeness of important business data up to salespeople’s minds. However, once the data quality dashboards are created and gaps are revealed, it’s impossible to ignore apparent issues any longer.

The question needs to be answered: “What business-critical data must we capture to make reliable forecasts, understand the sales pipeline, track performance, and make informed decisions?” Here, you want to involve sales leadership – they are people who are using data from CRM to make business-related decisions. Some examples of non-negotiables are: no records assigned to inactive users (people who left the company), no opportunities with missing deal amounts, and no opportunities with close dates in the past. It is always a good practice to document standards that were defined for visibility and clarity.

Implementing rules and processes in CRM

Once the non-negotiable standards are defined, it should be easy for the CRM admin to build validation rules and processes that will help to improve data quality. The goal here is to reduce guesswork for salespeople who input data – instead of assuming what fields they need to populate in the opportunity page, the system should prompt them to enter the sales stage, deal amount, close date, and all other information that is relevant for business. They do not need to remember to update an opportunity after the meeting with the customer; CRM should proactively remind them to do so through tasks or alerts. The ideal situation is when the system guides salespeople on what and when they need to enter. In many cases, it will not be achieved in just one iteration, but it might take several loops of implementation and feedback.

Clean up historical data

The work does not end with setting up data quality standards and preventing future issues – there are still historical records that require attention. With the dashboards, it should be clear which areas are most problematic. Depending on the nature of the issue and the team’s experience with CRM, you may take different approaches to the cleanup. Some sales representatives can go to CRM and fix the issues directly there. In some cases, you may need to extract the data into a spreadsheet and ask salespeople to make updates there, and upload changes to the system later. Sometimes, it is not necessary to involve a team in the cleanup exercise at all – for example, if there is a bunch of old closed lost deals still in open status, this can be mass updated by a CRM admin.

Set up communication cadence​

As they say, communication is key – and this is especially true when it comes to sales teams. It’s not enough to explain what needs to be done; it’s equally important to explain why it matters and how keeping the CRM clean benefits both the business and the salespeople themselves. It may take more than one meeting or email. Regular check-ins (weekly, biweekly, or monthly) help reinforce expectations, answer questions, and keep data quality top of mind. Following up by email with progress updates or simple metrics can make improvements visible. The key is consistency: repeated communication helps data quality become part of the team’s routine rather than a one-time initiative.

Cleaning your messy CRM is not a one-time project – it’s an ongoing effort that requires regular check-ins and monitoring. It may seem like an overwhelming task at first, but doing it step by step and being consistent will get things moving.

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