Maintain Data Quality with Processes and Audits

Data is only as useful as it is clean. This section gives you the tools and processes to maintain data hygiene—removing duplicates, fixing errors, and ensuring accuracy—so your metrics, dashboards, and decisions stay trustworthy.

Why it's Important

Prevents incorrect insights from skewed or missing data.
Builds stakeholder confidence in data-driven decisions.
Saves time by reducing cleanup and rework later.
Supports automation and integration by standardizing formats.
Avoids embarrassing mistakes in reporting or communication.

How to Implement

Create a basic data hygiene policy: required fields, formatting, validation rules.
Use automated tools for deduplication and formatting (e.g., Insycle, OpenRefine).
Implement naming conventions for campaigns, tags, and properties.
Run monthly audits across CRM, forms, and spreadsheets.
Assign clear ownership for data management responsibilities.
Create a “dirty data” log to track recurring issues.
Educate your team on data quality basics and input best practices.

Available Workshops

Data Quality Deep Dive: Review a week of data to find issues.
Dirty Data Hunt: Spot duplicates, errors, and missing fields.
Standardization Sprint: Align 3 commonly used fields (e.g., job title, source).
Input Practice Drill: Simulate real-time data entry with feedback.
Monthly Audit Game: Create a leaderboard for data fix wins.
“Would You Trust This?” Review: Ask, “Would you make a decision based on this?”

Deliverables

Data hygiene checklist and policy.
Role-based data ownership document.
Monthly audit report with findings and fixes.
List of recurring errors or inconsistencies.
SOPs for form setup, import processes, and manual edits.

How to Measure

% of fields completed correctly across lead records.
Time spent fixing bad data (aim to reduce).
Audit scorecard or cleanliness score over time.
CRM or dashboard error reports (goal: zero).
User trust/satisfaction with data (via internal survey).

Real-World Examples

Segment

Used internal “data SLA” processes to maintain event accuracy across pipelines.

Mailchimp

Built rules to auto-correct dirty input data (e.g., email formatting, names).

Zapier

Documented strict field usage rules and automated validation across teams.

Get It Right

Make data hygiene a shared team responsibility.
Automate common cleanup tasks to reduce friction.
Set and reinforce clear formatting and field rules.
Tie data quality to performance metrics (e.g., lead conversion).
Celebrate clean data milestones (gamify it).

Don't Make These Mistakes

Assuming someone else is “taking care of the data.”
Letting tools import bad data without checks.
Creating custom fields without documentation.
Forgetting to clean inactive or unused records.
Ignoring errors until they show up in an investor deck.