ERP Data Consolidation After an Acquisition:
The 100-Day Playbook for PE-Backed Manufacturers

The data problem that arrives with the deal

Private equity acquisitions create an immediate information gap. Before close, the deal team had access to three years of financials, a quality of earnings report, and whatever data room materials were available. After close, the operating partner is responsible for a business where the actual numbers are trapped inside an ERP that nobody on the platform team knows well, with cost structures and data schemas that do not match any other entity in the portfolio.

The typical platform company at its third add-on acquisition is running three or four different ERP systems across its sites. Epicor at the original platform. Sage 100 at the first acquisition. Dynamics Business Central at the second. Infor at the third. Each one reflects the culture, history, and configuration decisions of the company that ran it for the past decade. None of them talks to the others.

The instinct is to standardize on one ERP. That instinct is not wrong for the long term. But full ERP consolidation takes 18 to 36 months per entity and costs $1.5M to $5M depending on complexity. In the meantime, the finance team is spending four days every close cycle pulling reports from each system, remapping accounts, eliminating intercompany transactions by hand, and building a consolidated view in Excel that is always slightly wrong and always late.

The question is not which ERP to standardize on. The first question is: how do you get to one version of the truth before your next board meeting?

The four data problems you inherit on day one

Multi-ERP environments share a predictable set of data problems regardless of which ERPs are involved. Understanding all four matters because each one blocks a different set of reports, and fixing them requires different work.

The 100-day data playbook

The 100-day timeline is a PE convention for good reason. The first board meeting with the new sponsor typically falls around day 90 to 120. Having clean, consolidated data in time for that meeting sends a signal about operational control that no narrative substitute can match. The playbook below reflects what the most effective PE-backed operators do in that window.

Data mastering: the work that makes consolidation accurate

Every consolidated report in Phase 3 is only as accurate as the master data normalization completed in Phase 2. This is where most acquisition data projects either hold or break. The underlying logic is simple: if the same supplier appears as four different vendor records across three ERPs, your consolidated spend report will show four separate suppliers with no relationship to each other. Negotiating leverage disappears. PPV by supplier is meaningless. Working capital targets cannot be applied at the vendor level.

The same dynamic applies to customers. A key account purchasing from three of your plants shows up as three separate customers in a raw data consolidation. Consolidated AR aging is wrong. Customer profitability is wrong. The revenue bridge that your board wants to see cannot be produced accurately.

Mastering is not a one-time data cleaning exercise. It is an ongoing enrichment process where new records entering any ERP are automatically evaluated against the master and either matched to an existing golden record or flagged for review. Once the initial enrichment is complete, the process becomes self-maintaining.

The enrichment process works by matching raw records against a reference database of known entities, applying rules-based clustering for near-matches, and using AI-assisted disambiguation for cases where the automated match is uncertain. For most manufacturing portfolios, suppliers are the highest-priority domain: a single large supplier appearing in four vendor records represents a negotiating opportunity that cannot be captured until the records are unified.

For a deeper look at how data mastering works across customer, supplier, and item domains, see the Marquis data quality and mastering overview.

What board-ready reporting looks like at day 100

Once the data layer is running and master data is normalized, the consolidated view that the PE sponsor needs becomes a daily refresh rather than a four-day manual exercise. The operational analytics PE sponsors typically want: gross margin by entity with a period-over-period bridge, working capital metrics by entity, pricing compliance and realization data, and three to five thesis KPIs tracked against the acquisition model.

The entity card below shows what that consolidated view looks like when three plants on three different ERPs report through a single analytical layer. The IQ Insights alert at the bottom is generated automatically when any metric crosses a predefined threshold, pushing the relevant context to the finance team and the operating partner without requiring a manual review cycle.

The IQ Insights layer monitors every entity's key metrics continuously and generates these alerts automatically when thresholds are crossed. For a newly acquired entity, this means the operating partner gets the same quality of early warning on a $4.7M plant as they do on the $8.2M anchor entity, without requiring the smaller plant to have dedicated analytics staff.

Using data consolidation to inform your ERP strategy

One underappreciated benefit of building the data layer before committing to an ERP consolidation path: the data itself tells you which ERP is performing best. Once all entities are reporting through a unified analytical layer, you can see which plants have the cleanest master data, which ERPs produce the most accurate inventory records, and where manual workarounds are most prevalent. That visibility is more useful than any vendor comparison exercise when deciding where to consolidate.

The data layer also reveals where ERP standardization creates the most value for the business. If the biggest working capital opportunity is in a plant running Sage 100 that has 200 days of inventory on certain SKUs, that is a different prioritization signal than if the biggest gross margin problem is in a plant running Epicor. The reports, once running, do the prioritization work that would otherwise require months of consulting engagement.

Many PE-backed platform companies find that once the data layer is running well, the urgency around ERP consolidation actually decreases. The operational processes at each plant continue to run on the ERP those teams know. The consolidated reporting the sponsor needs runs on the data layer. The distinction between the two becomes clearer, and the decision about whether and when to standardize on a single ERP can be made based on operational and commercial logic rather than reporting necessity.

How Marquis IQ handles multi-ERP consolidation

Marquis is purpose-built for PE-owned platform companies running multiple ERPs across multiple sites. Every component of the 100-day playbook described in this article is handled by the platform: certified connectors that pull from each ERP without disruption, a data mastering layer that enriches and deduplicates customers, suppliers, and items across all systems, and IQ modules that surface consolidated operational reporting the day after the master data is ready.