Poor data quality costs organizations millions annually in wasted resources, missed opportunities, and operational drag. According to Gartner's estimates, dirty data costs companies an average of $15 million annually. Another study suggests that bad data may cost companies up to 25% of their potential revenue.
When we talk about data in this context, we mean B2B contact data and firmographics: contact information, job titles, company size, tech stack details, and more. Poor-quality data shows up as missing fields, outdated contacts, duplicate entries, inaccurate firmographics, and non-normalized data formats. These are examples of bad data that plague CRM systems across every industry.
Here's the root problem: Data decays the moment it's collected. B2B businesses move fast. Personnel changes happen. Companies get acquired. Product offerings shift. Without continuous maintenance, your data becomes stagnant and inaccurate within weeks.
Here's how bad data wrecks GTM efforts, with real examples of how data problems cost big time and how diligent pros can solve for it.
What Poor Data Quality Costs Your Business
Poor data quality costs B2B organizations between $15 million annually in operational waste (per Gartner) and up to 25% of potential revenue in missed opportunities. These costs compound across three areas: revenue leakage from missed deals, productivity drain from wasted selling time, and decision risk from inaccurate strategic intelligence. Most companies (60% according to Gartner) don't measure these costs, meaning they operate blind to how much waste bad data generates.
The costs break down into three core categories:
Revenue leakage: Sales teams chase wrong contacts or miss buying signals, causing deals to slip through. Territory planning built on bad data misallocates resources, and forecasts based on incomplete pipeline data miss the mark.
Productivity drain: SDRs waste 27% of potential selling time following bad data. That's more than a full day per week on dead ends and outdated accounts.
Decision risk: Strategic decisions made on inaccurate data misguide GTM directions before anyone catches them. Annual planning, product launches, and team deployments depend on reliable market intelligence.
Data decay accelerates these costs. Without continuous enrichment and verification, the problem gets worse each quarter.
How Bad Data Hurts Sales and Marketing Teams
Dirty data disrupts every GTM motion: lead generation, prospecting, nurture, account prioritization, and customer growth. Clean data enables focused action that pinpoints opportunities and aligns outreach with customer needs.
Here's where bad data hits hardest:
Wasted selling time: Wrong numbers and outdated accounts waste hours that could've been spent selling. Incomplete buying committees, low-level contacts, and outdated account records give SDRs and AEs an inaccurate picture of prospect challenges. In a market where efficiency drives results, this time loss directly impacts quota attainment.
Missed buying signals: Bad data causes teams to miss critical buying signals like champion moves, funding rounds, buying group changes, and hiring plans. Without comprehensive contact data, these signals become background noise. When you can't react to executive appointments or funding news, competitors seize the opportunity first.
Email deliverability damage: Bad data in email lists drives bounce rates, spam reports, and spam traps that harm sender reputation. Excessive bounces can get you blacklisted, suspending your email account and blocking critical business communications.
Flawed forecasts and territory plans: Unreliable data leads to inaccurate forecasts and flawed territory planning, especially costly during annual planning, product launches, and team deployments. Misguided strategic directions frustrate reps who can't hit targets, driving employee churn and eroding market reputation.
Overpaying for bloated databases: Many GTM tools price by data volume stored in their systems, including marketing automation platforms that charge per email address. Old, inaccurate data inflates your tool costs unnecessarily.
Bad data wrecks team morale and brand credibility. When sales teams know bad data is preventing them from hitting quota, they leave. Word spreads among sales professionals about where selling is fruitful, tarnishing your employer brand.
Bad data creates misspelled names, undelivered messages, account mix-ups, and duplicate communications. These errors seem minor individually but at scale severely damage market reputation. Multiple issues compound into negative brand impressions that cause churn and lost deals.
AI amplifies bad data problems. AI learns and acts based on your data, so inaccurate inputs produce scaled damage. Poor decisions and misdirected actions happen too fast for human quality control to catch.
Case Example: Tradeshift, a leader in supply chain payments, faced escalating costs from poor data quality:
Compliance risk: Database duplicates made managing opt-outs nearly impossible
Incomplete records: Event and purchased lead lists lacked data for segmenting, scoring, and prioritizing
Sales inefficiency: Teams bought leads they couldn't use, wasting budget and time
Tradeshift deployed ZoomInfo Operations to cleanse, deduplicate, and enrich its database. Result: solved compliance concerns and used resources 10x more cost-effectively.
How to Measure the Cost of Bad Data
RevOps and GTM leaders need to quantify data quality costs to build the business case for fixing them. The metrics below help you measure impact and track improvement:
Duplication rate: Calculate the percentage of duplicate records in your CRM. High duplication inflates database costs, creates compliance risks, and confuses teams. Track by contact, by account, and across both.
Field completeness: Measure what percentage of critical fields are populated: email, phone, job title, company revenue, industry, tech stack. Incomplete records can't be segmented, scored, or prioritized.
Bounce and connect rates: Track email bounce rates and phone connect rates as data validity proxies. Rising bounces signal decay; low connect rates indicate outdated information.
Rework hours: Estimate time spent manually researching contacts, cleaning data, or correcting errors. Multiply by loaded labor costs to quantify productivity drain.
These metrics give you a baseline. Track them quarterly to measure improvement and justify continued investment in data quality programs.
How to Prevent Data Quality Issues Before They Hurt Revenue
Even with a trusted vendor like ZoomInfo, data flows in from untrusted sources: first-party form fills, purchased lead lists, and third-party integrations.
Here are critical steps your team can take to improve and maintain B2B data quality:
Continuous Enrichment and Verification
Data decay is constant, so your enrichment and verification processes must be too. Here's how to stay ahead:
Enrich at point of entry: Use database maintenance tools to clean existing data and fill gaps. Partner with a reputable data provider like ZoomInfo to fix inconsistencies. Vet every provider on accuracy, coverage, and consistency using our evaluation guide.
Automate refresh cadences: Map every data entry point and commit to continuous improvement. Consistent cleaning, appending, and updating builds trust and reliability.
Verify before outreach: Build verification into workflows: check email deliverability before campaigns, validate phone numbers before dialing, confirm job titles before personalizing.
Data Governance for GTM Teams
Data governance isn't an IT function. It needs to be owned by the business and driven cross-functionally so the right rules and processes are in place to serve your overall needs:
Own it cross-functionally: IT doesn't understand territory rules, sales stages, or marketing segmentation requirements. Your data squad needs representation from every function affected by data.
Prioritize critical fields first: Identify the most critical data elements for leads, contacts, accounts, opportunities, and billing. Focus initial efforts there to generate wins that build buy-in.
Balance automation with flexibility: Use automated enrichment for most fields, but give sales teams permissions to update key fields when needed. Appoint data stewards to oversee and correct inconsistencies.
Clean Data Is a Revenue Strategy
High-quality data drives sales and marketing that connects with your audience. Bad data alienates potential customers.
The cost of poor data quality far exceeds the investment required to fix it. Consider the ROI in data quality and the revenue you lose by making the wrong choice.
Ready to see how clean, verified B2B data can transform your GTM performance? Talk to our team to get started.