Importance of Data Hygiene for CRM

If you treat data hygiene like spring cleaning and only tackle it once a year when things get unbearably messy, you're killing your opportunity for new growth channels.   Instead, clean your data on a schedule—not when it becomes a crisis.   Think about it like cleaning your apartment. You don't wait until you can't find your laptop under piles of laundry. You have a routine:    • Dishes every day  • Floors every couple days • Dusting every week   Your CRM data needs the same approach.   The goal here isn’t to keep your data clean and organized for the sake of it. It’s to keep it clean enough so that you can pull a segment and be able to trust it.   Imagine you want to test a new strategy targeting all VPs of Marketing in New York who downloaded your whitepaper and took a meeting with your team. If you have messy data, you'll spend weeks cleaning and deduping before you can even start the to run your play.   And by then, the opportunity is gone.   Because here’s the thing about data: it goes stale. Fast. People leave companies, get promoted, or move laterally. Companies grow, shrink, and open new offices. Parent-child account relationships shift.   If you're not updating this regularly, you might target a completely wrong person or branch. Or even waste time and resources on a dead-end lead.    The advice I give to every GTM team we work with: Set a refresh schedule for your firmographic data. Update headcount every six months. Keep your segments current. Make it routine, not reactive.   Because when you exhaust your current growth channel, be it LinkedIn, Reddit, Google Ads, you need to be able to pivot fast.   What's your data maintenance rhythm? Or are you still treating it like spring cleaning?

Last week, I shared a framework for structuring #datagovernance within #CRM platforms. This week, double-clicking on the #impact: why it matters and how to think about the outcomes it unlocks. One lens I’ve found helpful, previously used at the enterprise level, but also powerful at the data asset level, is the offensive vs. defensive framework. We can use it to make the case for #datamanagement not as overhead, but as a foundation for both protecting the business and enabling growth. 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐑𝐌 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 start with a clear data model, including consistent field definitions and metadata to ensure clarity in what’s captured. Strong reference data and hierarchy management brings structure to key entities like customers and products. A connected Customer 360 view ties everything together, while data quality rules and monitoring enforce standards from the start. Together, these are the scaffolding for both regulatory compliance and scalable value creation. On the 𝐝𝐞𝐟𝐞𝐧𝐬𝐢𝐯𝐞 side, governance ensures regulatory alignment, audit readiness, and risk reduction. This is especially important now. For one major client we worked with, the no. 1 data privacy concern was unstructured text in CRM notes, where reps were entering sensitive personal information, unknowingly triggering global privacy risks. Governance helps classify, restrict, and manage access to that kind of data before it becomes a liability. But 𝐨𝐟𝐟𝐞𝐧𝐬𝐞 is where things get exciting. Clean, reliable CRM data directly powers better segmentation, smarter recommendations, more accurate forecasts, and faster service response. Governance doesn’t slow these things down—it enables them. Attached, you’ll see seven CRM use cases where governance acts as a multiplier. Together, they can generate 𝟓%+ commercial impact. But 𝐧𝐨𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞𝐦 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐭𝐫𝐮𝐬𝐭𝐞𝐝 𝐝𝐚𝐭𝐚.

One of the most common questions I receive from fellow executives: "How do we maintain high-quality CRM hygiene that enables better GTM decisions?" I've found two approaches to consistently work: 1) Attach a portion of sales incentives directly to CRM quality. Your CRM data is your navigation system. Without clean data, you're essentially swimming in muddy water with sunglasses on. Here's a simple framework: Tie 10% of sales incentives to CRM data quality. Define clear parameters for what constitutes "quality" data. Then, at the end of the quarter, randomly select deals for evaluation against these standards. This small financial nudge creates meaningful behavioral change without feeling punitive. 2) Enforce a single source of truth in leadership reviews. When conducting executive reviews (especially CEO/CFO-level discussions), commit to using only CRM data. If someone claims "the data is wrong" and presents alternative numbers, pause the review and request they fix the CRM data first. While this may initially feel like a bottleneck, it quickly establishes the CRM as the definitive source of truth. Teams learn to proactively maintain clean data rather than creating shadow systems. The orgs that practice this discipline gain a tremendous advantage: The ability to make strategic decisions with confidence rather than constantly debating whose numbers are correct. Facing such a challenge in your org? Would love to exchange notes. #crmhygiene #fpna #revops

Sales leaders say “trust the data.” But what if the data is lying to you? You’re building your Q2 forecast. You’ve got pipeline stages mapped. Revenue projections locked. Then you look closer… 42% of your opps are missing next steps. 30% of required fields are blank. 67% of close dates have changed multiple times. And somehow, the dashboard still says you’re “on track.” 📉 Gartner: Poor data quality costs companies $15M per year. 📉 Forrester: 85% of B2B forecasts miss by more than 5%. 📉 Salesforce: Only 35% of reps even trust their CRM data. That’s a leadership problem. Your dashboards are only as good as the data feeding them. And right now? That data is starving. Manual cleanup. Field gaps. Misaligned CRM hygiene. Garbage in → garbage forecast → garbage GTM motion. Great strategy can’t save broken data. Want to fix it? Start here👇 Use Momentum.io which can update 98% of all possible unique field types that can be updated by a human. (ps, most other companies that you could rattle off to me can only do 30-40%) Identify top Salesforce fields impacting forecast accuracy Track how often reps update them (and when) Automate enforcement with real-time workflows Create visibility for missing or outdated info Fix it before it hits the boardroom This is how modern GTM teams win. Not just by selling harder. But by cleaning the pipes. What’s your biggest CRM data pain right now?

Companies that haven't implemented data governance in their CRM will face serious challenges when they start using AI workflows and automation. Poor data quality compounds over time, and continuous monitoring with proper guardrails must be in place for reps to trust their CRM system. The foundation matters more than you think New reps shouldn't have the ability to create contacts and accounts until they understand what "good data" looks like. Without this experience, they'll pollute your database from day one. Here's what happens when your CRM contains bad data If your contacts don't match your ICP or aren't true target contacts, you'll waste money monitoring signals for accounts/contacts who will never generate pipeline. When those signals trigger outbound campaigns, you're burning through rep time and budget on contact research, and phone/email enrichment for bad prospects. The ripple effects get expensive fast Wrong LinkedIn profiles lead to improper data matching, giving you incorrect phone numbers for contacts. That's money spent on bad data plus time wasted on failed calls. Even worse - if you're tracking a past champion but have the wrong LinkedIn profile, you're monitoring job changes for the wrong person entirely. The bottom line Everyone wants to implement AI and automation in sales, but if you haven't defined your target accounts and ICP's with basic data hygiene (correct website URLs, LinkedIn profiles), your system will collapse at scale. Get your data foundation right first. Then scale with confidence.