Over the past decade, one common factor that stood out across all the Digital Transformation Projects we have delivered is the consistent need for a flexible Data Testing Automation Platform that supports multiple scenarios depending on the project’s context. For example, a migration project from Oracle to Snowflake needs tooling that can be deployed immediately for a project; Content Migration to Veeva PromoMats needs a slightly different framework because metadata + content testing parameters differ; a production environment needs an Observability framework so that enterprises can reconcile data continuously. Unfortunately, I believe Data Testing does not get enough attention that it deserves. Folks talk about trusted data but there is often a significant gap between Intent and Execution; in my experience, this happens because of the following reasons
- No internally approved tooling that can be deployed instantly for projects requiring data testing.
- Limited Python expertise to address these challenges in a timely manner.
- Insufficient time allocated for SIT.
- Teams often do not recognize the importance of Data Testing until issues begin to surface.
I strongly believe that organizations should invest in platforms that can support multiple data testing scenarios as a strategic priority to avoid risks associated with compliance issues, like Fines from Regulatory Agencies, Bad Customer Experience, too much manual effort, etc.
This is critical for Life sciences commercial organizations because most of them are in the middle of a major transformation. CRM platforms are moving to Salesforce Life Sciences Cloud or Veeva Vault CRM. Data warehouses are being migrated to Databricks or Snowflake. Integration tooling is shifting from legacy platforms to MuleSoft or other integration platforms. Reporting is being consolidated onto Power BI (in many cases because of the pricing advantage). These are not small technical upgrades. They are enterprise-level programs that touch field sales, medical, marketing, compliance, analytics, and customer engagement, and at the center of every one of these transformations is DATA.
The Hidden Complexity
Migration as an Example
Commercial data in life sciences is deeply interconnected. A single HCP record may be linked to an HCO, Territory, Consent History, Call Activity, Samples, Approved Content, and many other entities. A single territory change can ripple into rep alignment, incentive calculations, and reporting. A single content asset carries claims, approval status, expiration dates, channel restrictions, and version history.
This interconnection creates serious migration risk. A migration may appear successful because record counts look close, but broken hierarchies, missing relationships, orphaned records, mismatched picklists, and incomplete audit trails may only surface after users go live. By then, the cost of remediation is far higher.
There is also a structural gap in how most organizations approach this work. QA teams write custom Python scripts, face long approval cycles, and test only sample records. But large-scale migrations cannot be treated as sample-based exercises. They must be treated as controlled, measurable, evidence-driven programs, validating 100% of the data in every cycle.
Rebates in Production as an Example
In Rebates related applications, there is sales data that comes periodically from distributors and it’s important that the quality of the data is factored in before rebates are calculated. Post calculations, it’s important that the numbers are validated based on rules and contracts.
Why Automated Testing and Reconciliation Must Work Together
Two capabilities are essential: migration testing automation and data reconciliation.
Migration testing automation validates that the migration process works correctly. It checks record counts, field-level values, required fields, transformation rules, parent-child relationships, picklist mappings, and integration readiness. In a CRM migration, for example, it can verify whether active HCPs remain active in the target, whether territory assignments have carried over, whether call activity is still connected to the right customer and rep, and whether tables restructured between platforms (a single Veeva CRM table might become three or four tables in Life Sciences Cloud) are complete and traceable.
Data reconciliation validates data trust across the broader ecosystem. CRM, MDM, data lakehouses, content platforms, consent systems, and analytics must all tell the same story. If CRM shows one HCP count, MDM shows another, and Power BI shows a third, trust breaks down. Reconciliation detects these exceptions and makes them actionable so teams can answer the practical questions that matter: Are golden records in MDM reflected correctly in CRM? Are approved content assets migrated with the right metadata? Do field activity reports match the underlying migrated records?
Together, these capabilities create a much stronger control layer. Neither alone is sufficient.
An Enterprise Capability, Not a Project Task
The organizations that handle the above scenarios well are not running manual checks. They are building a persistent enterprise data quality capability: an Enterprise Data Quality and Reconciliation Tower. They will be the ones that can prove the data is accurate, reconcile data across systems, monitor continuously, and use data with confidence.
This is not just a dashboard. It is a centralized function that combines automated testing, reconciliation rules, exception workflows, data ownership, compliance evidence, and continuous monitoring. It gives the organization a single place to assess the health of commercial data across MDM, CRM, content platforms, integrations, and analytics.
In a migration program, the tower supports every phase. Before migration, it profiles source data, surfaces duplicates, and defines reconciliation rules. During mock loads, it runs automated comparisons across every cycle, detecting mapping issues, tracking defects, and measuring whether quality is improving. At cutover, it provides objective evidence of readiness. After go-live, it continues monitoring integration alignment, downstream reporting accuracy, and data drift.
This approach shifts data quality from a project activity to an enterprise control function. Instead of asking “Did we test enough?”, teams can ask “What does the evidence show?” Instead of reacting to field complaints after launch, they can monitor exceptions proactively.
The Bottom Line
Large data sets require a different level of discipline. The volume is too high for manual review and too risky to be validated through limited sample-based testing. The relationships are too complex for record-count checks. The compliance stakes are too significant for informal evidence.
To execute this well, teams need an enterprise-grade platform purpose-built for migration testing and reconciliation, one that is ready when a project starts rather than assembled from scratch each time. Platforms such as DataGaps are purpose-built for exactly this need.
When the next transformation program begins, testing teams should not be scrambling for scripts. The core capability should already be in place before the project begins and there should be consistency in how Data and BI get tested within the enterprise. Once you have this in place, then you can talk about your AI Dream Projects.