Data Maturity Assessment Checklist for Commerce

Summary

Data readiness determines whether a replatform takes three months or eighteen. This checklist helps agencies and consultants assess the real state of a client's data before migration scope gets locked in.

Data Problems Kill Replatform Timelines

Most replatform timelines blow up because of data, not platform configuration or frontend builds. The integration estimate was based on clean API endpoints that don't exist. The product catalog has 47 custom attributes no one documented. Customer records have three different address formats across two decades of orders.

You won't surface these problems in a kickoff meeting. They show up in week six when your engineers start pulling actual records.

Use this checklist during discovery to identify migration complexity, integration risk, and the gaps between what stakeholders believe about their data and what's actually true.

Treat This as an Evidence Gathering Exercise

Don't fill this out like a form. "Yes, our product data is clean" isn't an answer. "Here's a sample export and the last time someone audited it" is.

Assign an owner for each section. Flag anything that requires a technical resource to validate. Document assumptions explicitly, they become scope risks later.

Product Data Quality

Catalog Structure

How many SKUs exist in the current system versus how many are actively sold?
Are products organized by a single taxonomy or multiple overlapping hierarchies?
How are variants handled, as child products, attribute combinations, or separate SKUs?
What's the source of truth for product data: the commerce platform, a PIM, an ERP, or spreadsheets?
When was the last time someone audited the catalog for duplicates, orphans, or deprecated items?

Attribute Completeness

What percentage of products have complete descriptions, images, and required attributes?
Are custom attributes documented, or do they exist only as tribal knowledge?
How are product relationships (cross-sells, bundles, kits) modeled and maintained?
Do attribute values follow consistent formats, or are there free-text fields with inconsistent entries?

Content Readiness

Are product descriptions platform-specific, or can they migrate cleanly?
Where do product images live, and are they referenced by URL, embedded, or stored externally?
How are digital assets versioned and linked to products?

Customer Data Integrity

Record Quality

How many total customer records exist versus active customers in the past 24 months?
What's the estimated duplicate rate, and has anyone attempted deduplication?
How are guest checkout records stored compared to registered accounts?
Are B2B and B2C customers stored in the same structure or separate systems?

Address and Contact Data

How many address formats exist across the customer base?
What validation was applied at the time of entry, and has that changed over time?
Are phone numbers and emails verified, or just collected?
How are international addresses handled?

Account Relationships

How are company accounts linked to individual users?
Are buyer roles, approval workflows, or spending limits stored, and how?
What customer data lives outside the commerce platform (CRM, support tools, loyalty systems)?

Order and Transaction History

Historical Depth

How far back does order history go, and how much of it needs to migrate?
Are historical orders needed for operational reasons (returns, warranties) or just reporting?
What's the format and accessibility of archived orders?

Data Completeness

Do historical orders contain full product snapshots, or just SKU references?
How are discounts, promotions, and pricing overrides recorded?
Are tax calculations stored, or recalculated dynamically?
What payment and fulfillment data is retained per order?

Edge Cases

How are split shipments, partial fulfillments, and backorders represented?
What happens to order records when products are discontinued?
Are subscription orders or recurring transactions stored differently?

Integration Dependencies

Current Integrations

What systems currently send data to or receive data from the commerce platform?
Are integrations real-time, batch, or manual?
What's the failure mode when an integration breaks, and when did that last happen?

Data Contracts

Are API schemas or file formats documented for each integration?
Who owns each integration, internal team, vendor, or agency?
What authentication and security requirements exist?

Migration Impact

Which integrations must be rebuilt versus reconfigured for the new platform?
Are there integrations that bypass the commerce platform and hit the database directly?
What third-party systems assume specific data structures that will change?

Data Governance and Ownership

Accountability

Who is responsible for data quality in each domain (product, customer, orders)?
Is there a defined process for handling data issues, or are they addressed ad hoc?
What tooling exists for data validation, monitoring, or cleanup?

Documentation

Is there a data dictionary or schema documentation for the current system?
Are field-level definitions documented, or inferred from usage?
When was documentation last updated?

Compliance

What data retention policies apply to customer and transaction data?
Are there PII handling requirements that affect migration or storage?
How is consent tracked for marketing and communication preferences?

Migration Complexity Indicators

Volume and Scale

What's the total data volume across products, customers, and orders?
Are there performance constraints on export or extraction from the current system?
How long do full data exports typically take?

Transformation Requirements

What data requires transformation versus direct mapping to the new platform?
Are there known incompatibilities between current data structures and target schemas?
How much manual cleanup is assumed before migration can begin?

Testing and Validation

What's the plan for validating migrated data before go-live?
Are there known records or scenarios that should be used as migration test cases?
Who signs off on data accuracy post-migration?

How DigitalStack Handles Data Readiness

DigitalStack treats data readiness as a structured discovery domain, not a side conversation. Data sources, integration dependencies, and migration risks live in connected modules, so findings link directly to architecture decisions and implementation scope.

Survey responses from technical stakeholders feed into a unified view. You can trace a scoping assumption back to the person who validated it. When data complexity changes, and it will, the impact is visible across the engagement, not buried in a thread.

Next Step

Run your next discovery with structure. See how DigitalStack connects data readiness findings to architecture and scoping decisions, [request a walkthrough].