If you're wrangling sales data from a million sources—CRM, spreadsheets, marketing tools—you know the pain. Duplicates everywhere, missing fields, nothing lines up. If your enterprise sales team is tired of garbage-in, garbage-out analytics, this guide is for you. We're going to walk through setting up a data mastering workflow in Tamr that actually helps you get reliable, unified sales data—without drowning in buzzwords or “digital transformation” promises.
Let’s get into it.
Why Data Mastering Matters for Enterprise Sales
Before we start clicking around, here’s why this isn’t just a “nice to have”:
- Duplicate records: Multiple entries for the same customer means wasted outreach and reporting headaches.
- Messy source systems: Sales and marketing rarely use the same definitions or tools.
- Missed opportunities: Incomplete data means reps miss cross-sell or upsell chances.
You don’t need “AI-powered, next-gen data synergy.” You need a clean, unified view of your customers—period.
Step 1: Gather Your Data Sources
Don’t skip this. Garbage in, garbage out.
What to collect: - CRM exports (Salesforce, Dynamics, HubSpot, etc.) - Marketing lists (Marketo, Eloqua, Mailchimp) - Spreadsheets (yes, the ones tucked in random SharePoint folders) - Support tickets or customer success platforms - Any other system where customer/company info lives
Pro tip: Don’t try to boil the ocean. Start with the three most important sources—typically CRM, marketing automation, and maybe billing or support.
What works: Keeping scope focused. Start small, show value, then expand.
What to ignore: Fancy connectors or “data lake” projects if your main pain is just cleaning up basic customer records.
Step 2: Prep and Profile Your Data
Before you load anything into Tamr, get a sense of what you’re dealing with.
- Check file formats (CSV, Excel, database dumps). Tamr wants structured data.
- Look at sample data. Open files, scan for weird encoding, missing headers, obvious junk.
- Profile the data. Tamr can do this, but you’ll save time by fixing glaring issues first (like columns labeled “Company_Name_v2_FINAL” or fields with 90% blanks).
Pro tip: Make a quick spreadsheet mapping columns to what they should be called (e.g., “Acct Name” = “Company Name,” “Cust_Email” = “Email”).
What works: Cleaning up the worst inconsistencies ahead of time. It speeds up matching later.
What doesn’t: Trusting that “someone else already cleaned this.” They didn’t.
Step 3: Set Up Your Project in Tamr
Now, log into Tamr and kick off a new mastering project.
Steps: 1. Create a new mastering project. Name it something obvious, like “Sales Customer Master.” 2. Connect your data sources. Use Tamr’s connectors or import your cleaned files. If you hit a weird system, worst case, export to CSV. 3. Define your entities. For sales, this is usually “Account,” “Company,” or “Contact.” Get specific about what you’re mastering.
Pro tip: Don’t get lost in the weeds. If you’re not sure what an “entity” is, think: the real-world thing you want a single, trusted record for.
What works: One project per entity type (one for accounts, another for contacts).
What to ignore: Overcomplicating entities with dozens of attributes right away. You can add more fields later.
Step 4: Map and Standardize Data Fields
Tamr needs to know how your columns line up across sources.
How to do it: - Use Tamr’s UI to map source columns to your “master” schema (e.g., map “Acct Name” and “Account_Name” to “Company Name”). - Standardize values where possible (country codes, phone numbers, etc.). - Set data types (string, number, date).
Pro tip: If you have columns that do the same thing but are named wildly differently, document this now. It’ll save headaches.
What works: Being ruthless about dropping unnecessary fields, especially if nobody on sales/ops can explain them.
What doesn’t: Trying to “future proof” by adding every possible field. Keep it lean.
Step 5: Configure Matching Rules
Here’s the real value: Tamr’s matching engine uses machine learning to spot duplicates and match similar records across sources.
What to do: - Start with out-of-the-box rules (Tamr gives you templates for common scenarios). - Identify key match fields—usually company name, domain, phone, maybe address. - Run a sample match and review the results.
What works: Using company website domain as a strong match key. It’s more unique than company name.
What doesn’t: Relying only on company name—lots of duplicates or variants (“Acme Corp,” “Acme Corporation,” etc.).
Pro tip: Don’t be afraid to tweak thresholds. Tamr lets you adjust how strict the matching is. Loosen it up for messy data; tighten if you’re getting false positives.
Step 6: Review, Train, and Improve Matches
Tamr’s matching isn’t magic—you need to check its work.
- Review clusters of records Tamr thinks are the same entity.
- Accept, reject, or split matches in the tool. This “trains” Tamr to do better next run.
- Bring in sales or ops power users for feedback. They know the real-world quirks (“Acme East” is actually a separate division).
What works: Frequent, short review sessions with people who know the data.
What doesn’t: Letting IT run this in a vacuum. Business context matters.
Step 7: Consolidate and Author “Golden Records”
After matching, Tamr will suggest a “golden record” for each entity—a single, trusted version.
- Decide field survivorship rules (e.g., for phone number, always take CRM value unless blank).
- Configure rules in Tamr. You can set priorities by source, most recent update, or custom logic.
- Preview golden records before pushing them live.
What works: Keeping survivorship rules simple. When in doubt, make CRM the tiebreaker—salespeople usually have the latest info (or at least think they do).
What to ignore: Overengineering survivorship with complex hierarchies or 5-level fallback logic.
Step 8: Publish and Sync Back to Downstream Systems
Now, make the data usable.
- Export golden records to CSV, database, or via APIs.
- Push cleaned data back into CRM, analytics tools, or wherever sales needs it.
- Set up regular syncs if you want this to be ongoing, not just a one-off cleanup.
Pro tip: Start with a manual export/import. Automation is great, but only after you trust the results.
What works: Communicating to sales and ops: “Hey, your customer data just got way better. Here’s what changed.”
What doesn’t: Quietly swapping out data without warning—people hate surprises, especially when a familiar record looks different.
Step 9: Monitor, Iterate, and Improve
Data mastering isn’t “set and forget.” Stuff changes.
- Set up alerts for new data sources, big changes, or matching errors.
- Schedule regular reviews—monthly or quarterly—to tweak rules as new edge cases pop up.
- Get feedback from end users. Are they seeing fewer duplicates? Is reporting cleaner?
What works: Treating this like an ongoing process, not a one-time project.
What to ignore: Vendor promises that “the AI will learn everything automatically.” Tamr’s good, but your business is unique.
Real-World Tips and Common Pitfalls
- Don’t try to master everything at once. Get one entity (e.g., Accounts) right, then expand.
- Document as you go. Future-you (and your teammates) will thank you.
- If Tamr’s UI feels overwhelming, focus on one step at a time. Its documentation is decent, but don’t be afraid to ask for help.
- Keep stakeholders in the loop. Especially sales—they’ll notice changes, so make them part of the process.
Wrap-Up: Keep It Simple, Keep It Useful
You don’t win by having the fanciest data mastering setup. You win by making your sales data trustworthy and easy to use, so your team can close more deals and waste less time. Start small, fix what hurts, and don’t get sucked into endless configuration rabbit holes. Iterate as you go.
And if something isn’t working? Change it. The perfect is the enemy of the good—especially in enterprise sales ops.