If you work in go-to-market (GTM) operations, you already know that bad data can quietly wreck everything—sales targeting, marketing campaigns, customer analytics, you name it. If you're using Tamr to wrangle messy data from all over your company, this guide is for you. We'll dig into practical ways to spot and fix data quality issues in Tamr, so you can trust your data and stop fighting fires.
No fluff, just what you need: how to set up monitoring, what to actually look for, what tools help, and how to keep things running smoothly. I'll also call out what most people waste time on (and what to skip).
Step 1: Get Clear on What “Data Quality” Means for GTM
Before you start poking around Tamr, get specific about what matters. Data quality is a fuzzy buzzword, but you can't monitor what you haven't defined.
Key data quality dimensions for GTM:
- Completeness: Are key fields (like account owner, industry, or email) missing?
- Uniqueness: Are you dealing with duplicates? (Two Salesforce records, same company.)
- Accuracy: Is the data actually correct, or just plausible-looking?
- Timeliness: Are you operating on stale info (like last year’s lead scores)?
- Consistency: Are fields formatted the same way everywhere?
Pro tip: Don’t try to solve everything at once. Start with the quality issues that actually mess up your GTM processes—usually missing or duplicate data.
Step 2: Set Up Monitoring in Tamr
Tamr has a lot of features, but not all are worth your time. Focus on what helps you catch problems before they hit downstream systems.
What to monitor
- Project health metrics: Get familiar with Tamr’s built-in dashboards for completeness, duplicates, and recent changes.
- Golden records: Pay attention to the quality of “golden records” Tamr generates—these are what get sent to your GTM tools.
- Source system drift: Watch for changes in your connected data sources. Did someone add a new field in Salesforce?
- Error logs and exceptions: Tamr logs all sorts of things. Don't ignore error spikes, especially after schema changes or large data loads.
- Data pipeline runs: Set up alerts for failed or delayed pipeline jobs.
How to set up monitoring
- Dashboards: Use Tamr’s dashboard to keep an eye on metrics you care about. Don’t let it become wallpaper—schedule a quick review every week.
- Automated alerts: Set up email or Slack alerts for pipeline failures or when metrics cross a threshold (like duplicate rates spiking).
- Export reports: For GTM ops, it’s often easier to export Tamr’s quality reports to a spreadsheet and annotate issues for your team.
What to ignore: Vanity metrics. If a dashboard shows you a “data quality score” but you don’t know what it actually means, skip it.
Step 3: Find and Diagnose the Root Cause
You spotted a data quality issue. Now what? The real work is figuring out why it happened.
Typical GTM data quality problems
- Duplicates: Same customer, different spellings, multiple systems. Tamr is supposed to merge these, but rules sometimes miss edge cases.
- Mismatched fields: One system calls it “Industry,” another calls it “Vertical.” Tamr’s schema mapping can get confused.
- Unmapped values: New drop-down options added in CRM, but Tamr doesn’t know about them yet.
- Bad merges: Sometimes Tamr makes a “golden record” that mashes two unrelated accounts together.
How to diagnose
- Trace the lineage: Tamr lets you see which source records were combined into a golden record. Start there.
- Sample problematic records: Pull a handful of records with issues and dig in. Are they from the same source? Did a recent import break something?
- Check recent changes: Did someone change a matching rule or add a new data source? These often trigger new problems.
Pro tip: Always look for patterns. If one field is consistently wrong, it’s usually a mapping or ingestion issue.
Step 4: Fix Issues and Prevent Them from Coming Back
Don’t just patch the symptom—fix the root cause. Here’s how to actually resolve data quality issues in Tamr.
Practical fixes
- Update mapping rules: If Tamr is merging the wrong records, tweak the matching rules. Don’t be afraid to get specific—sometimes you need to add an exception.
- Re-train models: Tamr uses machine learning for some matching. If it’s gotten things wrong, re-label a set of records and re-train.
- Adjust schemas: If new fields show up, map them appropriately. Keep naming consistent across systems if you can.
- Data source hygiene: Sometimes, the fix is upstream. Get your CRM or ERP owners to enforce required fields or run regular dedupe jobs.
- Manual overrides: For critical errors, Tamr lets you manually fix or “survive” certain values in golden records.
Preventative steps
- Document fixes: Keep a log of what you changed and why. This helps when the same issue pops up next quarter.
- Automate data checks: Build scripts or use Tamr’s APIs to run automated quality checks after every data load.
- Set up feedback loops: Let downstream users flag bad records and route them back to your data team.
What not to do: Don’t rely on one-off cleanups. If you’re regularly fixing the same problem, automate or fix the upstream process.
Step 5: Communicate Clearly with Stakeholders
GTM operations is a team sport. If you fix data issues in a vacuum, they’ll just come back.
Tips for effective communication
- Flag business impact: Don’t just say, “20% of records have missing industry.” Say, “Missing industry fields mean sales can’t segment by vertical.”
- Share before/after snapshots: Show a sample of data before and after your fix—people remember examples, not percentages.
- Set realistic expectations: Tamr is powerful, but it won’t solve every problem automatically. Make it clear what is and isn’t being fixed.
Pro tip: If you want people to care about data quality, tie it back to things they actually notice—like wasted ad spend or angry sales reps.
Step 6: Keep It Simple and Iterate
You don’t need an enterprise-wide “Data Quality Initiative” to get value from Tamr. Focus on the basics: monitor the critical stuff, fix what hurts, and keep improving.
What works
- Start small—pick one GTM process and get the quality right.
- Automate the boring checks. Let Tamr do the heavy lifting.
- Make data quality fixes part of your regular process, not a big annual project.
What to ignore
- Don’t chase “perfect” data. You’ll never get there, and it doesn’t matter for most GTM work.
- Don’t rely on dashboards you never look at.
- Don’t expect Tamr (or any tool) to read your mind. Human judgment is still required.
Wrapping Up
Data quality isn’t glamorous, but it’s the difference between a GTM engine that hums and one that sputters. Tamr can help, but only if you set up the right monitoring, fix real-world issues, and get everyone on board. Keep it simple, pay attention to what actually matters, and don’t be afraid to iterate as your GTM needs change.
If you’re stuck, start with one process, fix the biggest pain point, and build from there. You’ll get further—and stay saner—than trying to boil the ocean.