TSA Exit Acceleration
Land the warehouse cutover without losing the analytics. On the TSA timeline.
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Carve-out data warehouse separation is the work of rebuilding Newco's analytical data estate, the warehouse, the lake, the pipelines, and the consumption layer, so Newco has its own working analytics environment by TSA exit. The track is one of the more underestimated streams inside the broader carve-out advisory program. Operational systems get all the early attention. The analytical layer often gets a generic TSA service and a vague exit plan. By month six the gap is visible. By month twelve it is blocking exit. Treat the warehouse separation as a first class workstream and the analytics keep running.
A typical enterprise data warehouse is the most heavily shared technology asset in a carve-out. It ingests data from dozens of source systems. It feeds business intelligence dashboards used across the enterprise. It contains conformed dimensions like customer, product, and geography that bridge multiple business units. Carving out Newco from the warehouse means untangling the threads cleanly without breaking any of the analytical consumers on either side.
The legal and regulatory complexity adds another layer. Customer data, employee data, supplier data, and financial data carry data protection obligations. Newco can only inherit the data that pertains to the Newco perimeter. Seller data has to be excluded by design, not by best effort. The buyer-side advisor brings the legal and compliance leads into the discovery phase so the perimeter is defined before any pipeline gets built.
The right framing is that the data warehouse is the seam of the carve-out at the data layer. The seam needs deliberate engineering. The work pairs with carve-out data separation and GDPR.
The scoping question is which rows in each table belong to Newco. Customer records associated with Newco entities. Product records for Newco SKUs. Transactional records where Newco was the seller or the buyer. Employee records for transferring employees. Asset records for transferred assets. Each domain has its own perimeter rule and the rules have to be documented before any extraction happens.
Some domains are clean. A customer that bought only Newco products falls entirely on the Newco side. Others are messy. A customer that bought both Newco and seller products has records that span the seam. The buyer-side advisor works with finance, legal, and operational data owners to set the policy for these cases: do they go to Newco with relevant subset, stay with seller, or end up in both with appropriate masking.
The scoping work also feeds the deal level data room and the post close transition. Buyers benefit from a precise data perimeter because it shortens diligence on subsequent transactions. The work pairs with the TSA due diligence checklist.
The Newco target analytics architecture is almost always a cloud native platform. The dominant options are Snowflake, Databricks, BigQuery, and Redshift, with the choice often shaped by which cloud Newco's broader IT estate runs on. The buyer-side advisor pressure tests the architecture against the standalone Newco scale rather than copying the seller's design, which is usually sized for a much larger enterprise.
The architecture also includes the consumption layer. Power BI, Tableau, Looker, or a mix. Newco picks a primary tool and standardizes there, rather than carrying every dashboard tool the seller had. Tool rationalization runs alongside the architecture decision and feeds the application portfolio rationalization track.
A common decision point is whether Newco needs a full lakehouse model with raw data, curated layers, and dimensional models, or whether a simpler dimensional warehouse is sufficient. The answer depends on the data science and machine learning roadmap. Newco that plans on AI workloads needs the lake. Newco that primarily needs operational reporting can stay simpler. The work pairs with TSA Snowflake and Databricks separation.
Newco needs more than just the current snapshot of data. Trend analysis, year over year comparisons, customer lifetime value, churn modeling, and forecasting all require historical depth. The standard policy is that Newco gets the historical data corresponding to its perimeter, going back the number of years required by regulation plus the depth required for normal business analytics.
The lineage challenge is harder than the volume. Tables in the warehouse were built by ETL jobs that reshaped data from operational systems. Some of that lineage is documented. Some is in long lost notebooks. The Newco team needs to reconstruct enough lineage that they can rebuild the pipelines if needed and explain the data to auditors. The buyer-side advisor pushes for a lineage discovery sprint in month three so this work is not deferred until exit pressure.
The legal review of the historical extract has to happen before the data physically moves. The data protection officer signs off on the population, the masking rules, and the retention schedule. Without this approval, the transfer creates compliance exposure that surfaces in the post close audit.
The pipelines that feed the warehouse have to be rebuilt against Newco source systems. Some sources are the same systems Newco inherits from the seller. Some are new applications selected during the rationalization. Each source needs a connector, an extraction schedule, and a load process targeting the Newco warehouse.
The temptation is to copy the seller's pipeline code and point it at the Newco warehouse. This rarely works cleanly because the source systems and the target schema have both changed. The cleaner approach is to rebuild the pipelines in the Newco preferred ETL tool against the new architecture, using the seller's code as reference rather than starting point. The migration is a chance to retire technical debt at the data layer.
Consumer side, the dashboards and reports that the business runs every day need to migrate too. The buyer-side advisor inventories every active dashboard, identifies the owner, and prioritizes the migration based on business criticality. Critical dashboards migrate first with rebuilt data definitions. Lower priority dashboards may not migrate at all. The work pairs with TSA Tableau and Power BI separation.
Data warehouse cutover succeeds or fails on reconciliation. The numbers in the Newco warehouse have to match the numbers in the seller warehouse for every important metric, every reporting period, every domain. Mismatches trigger an audit response that consumes weeks. The buyer-side advisor designs the reconciliation framework before any production cutover, with a documented tolerance for known differences and a clear remediation path for unexpected gaps.
The cutover itself runs as a parallel period. Newco warehouse runs alongside the seller warehouse for a defined window, both fed from the same operational sources where possible. Daily reconciliation reports compare totals, distributions, and dimension counts. Variances are investigated, the root cause is documented, and either the Newco pipeline is fixed or the variance is accepted with a known cause.
When reconciliation runs clean for several consecutive periods, the seller warehouse can be retired from Newco's perspective and the dashboards re pointed to the Newco warehouse only. The seller retains its own warehouse for the seller's own data. Both parties sign off the data separation as complete, which is a documented exit milestone in the TSA. The work pairs with carve-out application portfolio rationalization and carve-out network separation.
How Newco decides which inherited applications to keep, replace, retire, or defer.
Read the article →How the dashboard and reporting layer transitions during the TSA period.
Read the article →How Newco rebuilds the middleware layer that feeds the new warehouse.
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