Which data cleanup should we triage right now to get the best from AI implementations?

Not all data cleanup is created equal. If we try to “clean everything” before doing anything, we’ll burn time and budget without clear ROI.

Instead, we should triage ruthlessly and focus on the few areas where better data quality will unlock the most value from AI.

Here’s where I’d recommend we focus first:

1. Customer and Account Master Data
Why it matters:
Any AI initiative—whether it’s predictive lead scoring, churn prediction, or personalized marketing—depends on having a single, accurate view of each customer or account.
What to triage:

• Duplicates: Clean up duplicate records across CRM, ERP, and support systems.

• Basic Firmographics and Demographics: Ensure fields like industry, company size, location, and key contacts are populated and standardized.

• ID Linkage: Create or strengthen consistent IDs across systems so we can stitch together interactions.
  Impact:
  This cleanup is foundational. If we skip it, AI outputs will be noisy and unreliable.

2. Historical Transaction and Engagement Data
Why it matters:
Training predictive models—like purchase propensity or churn risk—requires complete, time-sequenced records of what customers did and when.
What to triage:

• Missing Transactions: Identify and fill gaps in historical order or usage records.

• Timestamp Consistency: Standardize date formats and time zones.

• Outliers: Flag and correct obviously invalid transactions (e.g., negative quantities, orders with no customer).
  Impact:
  This is the fuel for AI. If historical data is patchy or inconsistent, model accuracy will suffer.

3. Product and Pricing Master Data
Why it matters:
If we’re considering AI-powered pricing, cross-sell recommendations, or margin optimization, we need clean product hierarchies.
What to triage:

• Product Hierarchies: Make sure categories and SKUs are current, consistent, and linked to transactions.

• Pricing Records: Validate that historical price and discount data is accurate.
  Impact:
  This ensures AI models can correctly segment products and recommend pricing actions.

4. Key Behavioral and Marketing Data
Why it matters:
For personalization and campaign optimization, the most useful signals often come from digital interactions.
What to triage:

• Email Engagement: Validate that open/click data is tied to the right customer IDs.

• Website Activity: Ensure tracking pixels and logs are capturing consistent behavior.

• Campaign Attribution: Clarify how leads and opportunities are attributed to campaigns.
  Impact:
  This cleanup helps AI deliver relevant recommendations instead of generic outputs.

5. Metadata and Data Dictionaries
Why it matters:
Even if the raw data is clean, if no one agrees on what a field means, we’ll waste cycles debating definitions instead of building models.
What to triage:

• Critical Fields: For the datasets above, confirm there are clear definitions and owners.

• Data Dictionary: Start lightweight documentation—field names, formats, business definitions.
  Impact:
  This reduces confusion and speeds up both model development and adoption.

How to Approach This Triage Practically:
✅ Start Small and Prioritized:
Pick 1–2 critical domains (e.g., Customer Master and Transactions) to focus your first cleanup sprint.

✅ Align Ownership Early:
Assign data stewards or business owners for each domain to validate and approve changes.

✅ Set a Timebox:
Commit to 4–6 weeks of focused cleanup, then revisit progress.

✅ Tie Cleanup to Specific AI Use Cases:
For each cleanup task, link it to the AI initiative it enables—this builds clarity and momentum.

Bottom Line:
If we triage cleanup to these five areas, we’ll get 80% of the benefit needed to make AI work. Everything else can follow in parallel or after we’ve proven early wins.

I can put together a short project plan with owners, timelines, and deliverables so we can get this rolling immediately.