Case study · HubSpot
I set a 3–5 year vision that moved HubSpot’s Data Quality tools from cleaning up bad CRM data to catching it early, then shipped the first proactive features.
Customers found CRM data problems, such as duplicates and missing values, only after those problems had broken a report or a workflow. Fixing them was manual, and the tools only helped once the damage was done.
I set a 3–5 year vision built on one principle: reactive, then proactive, then preventative. Then I shipped the first proactive features: alerts, a Missing Data tool, better duplicate management and in-line fixes.
Business context: Clean CRM data is the foundation for reporting, automation and, increasingly, AI across HubSpot. Data Quality was a growing part of Operations Hub, and data management was becoming a priority across the whole platform.
Customer problem: Admins and ops teams found data problems only after the damage was done: a broken report, a duplicate contact emailed twice, a workflow that misfired. Fixing them was manual and repetitive. [Add: specific pain points from customer feedback, e.g. the most-requested fixes.]
Evidence: I tracked customer feedback themes and internal questions in the #ops-hub-sales-help and #data-quality Slack channels, and started a monthly readout of CSAT and customer feedback. [Add: the top themes and CSAT figures from those readouts.]
“[Customer quote about finding data problems too late]”
Problem statement: How might we help customers catch and prevent data problems before they cause damage, rather than cleaning up afterward?
I owned: The UX vision and strategy for Data Quality, research and discovery (including the alerts research and the anomaly detection exploration), and end-to-end design of the features we shipped. I also owned a running list of small UX fixes and found ways to fit them into each quarter.
I partnered on: Setting up a regular process for capturing and sharing customer feedback, with the PM. Coaching a new APM. Cross-team efforts: Data Staging with Data Sync and Import; surfacing Data Quality insights in the CRM; and a Data Quality activation project with CRM and Workflows.
I joined from Data Sync and immersed myself in customer feedback and internal questions. I started a monthly readout of CSAT and feedback, and streamlined one-pagers so the team shared the same picture of customer problems.
Key insight: [Add: the pattern you saw, e.g. most issues were discovered downstream, long after the bad data came in.]
I ran research on alerts and an anomaly detection exploration, and led discovery and documentation for new duplicate alerts, a Data Quality email digest and automatic de-duplication.
I wrote “Data Quality in 2026,” a 3–5 year vision and strategy built on three principles: reactive, then proactive, then preventative.
I shipped the first proactive features, starting with alerts, followed by fixes customers had asked for. I also changed the team’s process so frontend engineers had more ownership during the high-fidelity mockup phase.
How I got buy-in: I used the vision deck and the principles to show the team how each quarter’s work led toward prevention, and the monthly feedback readouts kept customer evidence in front of everyone. [Add: a moment where the principle changed a roadmap decision.]
Alerts tell customers about data problems, such as new duplicates, as they happen, instead of leaving them to be found in a broken report. The research ran in 2023 and the design shipped in 2024.
I shipped improvements customers asked for: in-line editing, exporting issues, better duplicate management (with a custom duplicate merge in progress) and new out-of-the-box reports. A new Missing Data tool helps users find and fill empty fields, and it tested well in usability sessions.
I built a Data Quality component library so new features stayed consistent, and rewrote copy that was confusing customers.
The reactive, proactive, preventative principle still shapes the Data Quality roadmap after I moved to other work, and the team runs on processes I helped set up: monthly feedback readouts, a running list of small fixes, and engineers who co-own high-fidelity mockups. [Add: the strongest number you can get, e.g. alert usage or CSAT trend.]
Qualitative impact: My manager wrote that I moved the team “from a reactive to a proactive stance,” and that my “systems thinking and platform approach have elevated the work and the team.” A frontend engineer described an earlier project of mine (Sync Health) as “the dream” because of how openly I shared the process.
“[Add: a Data Quality-specific quote from a customer, PM or engineer]”