I recently had a conversation with a CIO who, as his title would imply, was experienced in data management and cleanup. But he was not experienced in HubSpot. He asked how he should approach solving his company’s CRM data problems. The answer to that question is a little more complex than I could manage on the 15-minute call we had scheduled, but I kept thinking about it over the past few weeks and decided it would be the perfect topic for the first Hubsessed newsletter!
My intention is for these newsletters to be informative, actionable, and broadly applicable. If you have feedback about the content, format, or anything else about them, don’t hesitate to send me an email or hit me up on LinkedIn. And with that, here we go!
A 3-Step Guide to Clean Data in HubSpot
I’ve literally never seen a HubSpot portal that didn’t have data problems. Often, companies build new processes on top of legacy ones without doing any cleanup. That means workflows are still firing, duplicate properties are created, and generally, everyone is confused.
But, clean HubSpot data is the foundation for effective marketing, sales, and customer service. Poor data costs efficiency, revenue, and trust. This playbook guides you through diagnosing, cleaning, and addressing the root causes for preventing bad data in the future. With reliable, trustworthy CRM data your teams will be able to unlock HubSpot's full potential.
Step 1: The Data Audit – Diagnosing Your HubSpot Data Health
Start with a health assessment to identify systemic data problems.
Common Culprits: The Usual Suspects in Bad Data
HubSpot's Diagnostic Toolkit: Your CRM's Health Report
HubSpot offers powerful tools to assist in your audit:
Data Quality Command Center (Ops Hub Pro & Enterprise): Central dashboard, flags issues.
Custom Reports & Dashboards: Create tailored reports to visualize data gaps for essential properties, especially those that require manual filling
Deep Dive into Records: Review contacts, companies, properties, associations.
Active Lists: Identify "Junk" or "Imported" contacts for review. Always back up data with a full export.
Workflows & Integrations Audit: Review active workflows for goals, naming, re-enrollment. Check integrations for active use, necessity, and correct configuration to prevent errors.
Step 2: The Data Cleanup – Addressing the Symptoms Systematically
Once you’ve identified where the existing bad data exists, address it systematically.
Stop the Cycle: Cleaning Up Workflows & Integrations
This is always the first thing you should do. If you don’t, these automations may reintroduce bad data as you clean it up, erasing your hard work.
Workflows: Archive/delete unused, consolidate duplicate workflows, ensure enrollment criteria and re-enrollment settings are correct. Test to ensure correct output.
Integrations: Audit sync settings, address conflicts (duplicate rules, field mapping, validation). Ensure cross-platform consistency; set error notifications.
Conquer Duplicates: Taking off the Beer Goggles
HubSpot tools for duplicates:
Duplicate Identification: HubSpot’s Data Quality tool identifies duplicates and makes it easy to merge or reject duplicates.
Third-party tools: Integrations, like Incycle or Dedupely can automate the cleanup of duplicates.
Standardize and Enrich: Get Your Data In Line
Ensure consistency, fill gaps:
Bulk Editing (Spreadsheet): Export, clean, re-import with Record IDs.
Format Data Workflow Actions: Automate standardization (e.g., capitalization, phone formats).
Archive/Update: Audit stale records and make necessary changes.
Rebuild Connections: Fixing Missing Associations
Disconnected records lose context:
Manual Association: Link records directly.
Workflow Automation (Ops Hub Pro & Enterprise): Automate complex associations.
Step 3: Building a Sustainable Data Management Process – Solving the Underlying Problem
Your job isn’t done when your data is clean. If you stop here, you will have a CRM full of bad data again in 6 months or less. You need to redesign your data management processes for proactive prevention.
Prevention is Key: Stopping Bad Data at the Source
The most effective cleanup is the one you never need to do:
Clear Data Entry Guidelines: Document consistent formats/naming.
Property Validation Rules: Enforce accuracy at data entry.
Optimizing Custom Properties: Use dropdowns over free-text when possible.
Strategic Workflow Design: New workflows need clear goals, consistent naming, testing. Avoid inconsistent data automation.
Robust Integration Setup: Define objectives, map data, set ownership/sync rules. Pilot test thoroughly.
Permission Setting: Not every user needs to be a Super-Admin. Manage permissions so users can only view/edit the data that they need to in order to do their jobs.
Preventing bad data at the source is the most impactful, cost-effective long-term strategy, freeing resources for strategic initiatives and enhancing AI reliability.
Automating Data Hygiene with Workflows
Automates data hygiene using workflow actions:
Format Data: Automate standardization (e.g., capitalization, phone formats).
Breeze Workflow Actions: Use AI actions to correct formatting or categorize open text properties in order to set standardized dropdowns.
Ongoing Data Governance & Best Practices: Quarterly Reviews
Data quality is an ongoing commitment. Schedule quarterly data quality reviews to ensure your new processes are working as intended:
Regular Audits: Look at the Data Quality Command Center and custom reports to identify issues.
Continuous Workflow & Integration Monitoring: Ensure proper function, prevent automation issues. Monitor integration performance, address sync errors.
Strategic User Permissions & Training: Control access to data, workflows, integration settings. Train users on guidelines.
Leveraging AI: Clean data is crucial for HubSpot's AI (e.g., predictive scoring). MCP amplifies AI's use of CRM data, making data quality a strategic imperative.
HubSpot Features for Sustainable Data Governance & Automation
Your Clean Data Advantage
Clean, reliable data not only empowers marketing, sales, and service, but it truly transforms HubSpot into a strategic asset, allowing you to gain reliable insights and innovate with AI. You've probably heard me say before that AI is only as good as the data it's trained on. Well, with Breeze, it's trained on your CRM data. If your CRM data is garbage, Breeze won't be much use to you. But when it's clean and trustworthy, Breeze becomes a superpower that puts the smart in Smart CRM.