For almost 15 years we have been helping manufacturers grapple with disconnected, duplicated, dirty, and distrusted data. With the explosion of AI, the need for clean, context-aware data has never been more urgent. Here is what we have learned about why the old tools failed, and what actually works.
Here is a real thing that happens in manufacturing ERP systems. You open the customer master and you find a record that looks something like this: ACME, INC (Do Not Use).
Here is the thing: that record is not broken. It is actually doing its job. It is hard to accidentally create a Sales Order when the customer name on screen says "Do Not Use." Someone, at some point, was solving a real operational problem by naming it that way. It works. Kind of.
But when you sit down to understand your business holistically, to analyze revenue by customer, to track account relationships, to power an AI model with your own data, records like that start crumbling the walls. You cannot roll up revenue to "ACME" when half the orders live under "Acme Corp," a quarter live under "ACME, INC (Do Not Use)," and the rest were entered as "ACME INCORPORATED" by three different people in three different plants.
This is not an edge case. This is every manufacturing company we have ever worked with.
A single ERP with dirty data is a problem. Two ERPs with dirty data is a structural problem. The duplication is not accidental. It is built in.
When you acquire a company, you inherit their ERP. Their customers have their own item numbers, their own naming conventions, their own ways of entering an address. Your customers may already exist in their system under completely different records. Neither ERP knows. Neither ERP cares. They are not designed to.
So now you have the same customer in two systems with two different names, two different account numbers, and two different histories. Every report you run sees two customers. Every analysis splits the account. And when your new PE sponsor asks for total revenue by customer across the portfolio, you are handing them a number assembled by hand.
The standard answer to this problem is ERP consolidation. Get everyone on the same system. One ERP, one customer master, one truth. It is a reasonable idea that costs millions of dollars, disrupts your key people for years, and still leaves you with years of historical data in the old systems that need to go somewhere.
We have a different answer.
Before we get to the right answer, let us talk about the answer every company already has: Excel.
Every business has that person. You know the one. They have a spreadsheet that maps the old customer names to the new ones, adds the segment codes the ERP never captured, flags the accounts that were acquired with the business in 2019, and notes which ones have changed parent companies. Their spreadsheet is, in a meaningful sense, the most accurate customer master in the company.
When that person leaves, or gets sick, or goes on vacation the week the board wants a customer roll-up, the knowledge walks out the door with them. What looked like a solution was actually a dependency.
We have seen this exact situation dozens of times. The spreadsheet grows over years, becomes impossible to maintain, and eventually gets replaced by a slightly different spreadsheet owned by a slightly different person. The cycle repeats.
The enterprise software industry noticed this problem a long time ago and invented Master Data Management, or MDM. The pitch is compelling: a centralized system for managing customer, supplier, and product master data with governance workflows, golden record management, and data stewardship tools.
The problem is that MDM is built for organizations with dedicated data governance teams, multi-year implementation budgets, and the organizational maturity to enforce data policies across every system and every user. That is not most manufacturers.
And even for the ones who can afford it, MDM has an Achilles heel: it addresses new data going forward. Every record created from implementation date follows the golden record process. Every record created before implementation date sits in the old system, untouched, getting queried by reports that never knew about the MDM project.
Historical data does not clean itself. You still have to deal with years of it regardless of what you decide going forward.
Enrich is the data management layer at the heart of Marquis IQ. It is not an add-on. It is not a premium tier. It comes with every deployment because you cannot run meaningful analytics on data you do not trust.
Under the hood, Enrich draws on three layers: third-party data services for external verification and enrichment, our own proprietary technology built specifically for multi-ERP manufacturing environments, and large language models across every major provider that bring natural language understanding to entity resolution, classification, and attribute extraction.
Here is what it handles:
Here is the kind of structure Enrich makes possible. One real customer, acquired by a private equity firm, with two operating subsidiaries running across four different ERP systems. Before Enrich, these were eight separate records with no connection between them. After Enrich, they are one family.
The four raw ERP names ("LAKESIDE PREC MFG," "Lakeside Precision Manufacturing, Inc.," "LFS INC," "LAKESIDE FLUID SYSTEMS") are not changed in their source ERP systems. The source systems continue to work exactly as they always have. Enrich creates the unified view above them, at the analytics layer, where decision-makers need it.
Revenue rolls up to the family. Custom attributes like Customer Type (Strategic, Key, Standard) and End Market (Automotive Tier 1) are managed once, at the family level, and inherited by every record underneath. When the CRM account manager updates the account strategy for Lakeside, that update is visible to the finance team's view of the same customer.
Every AI tool your business is evaluating, every large language model you want to query against your data, every analytics assistant you want to deploy, starts from the same assumption: the data it reasons over is consistent, trustworthy, and contextually rich.
"ACME, INC (Do Not Use)" is not consistent. Four names for the same customer is not trustworthy. NAICS codes that do not match how your business actually segments its markets are not contextually rich.
The companies that will get the most out of AI in the next few years are not the ones with the most AI tools. They are the ones with the cleanest data foundation. The enrichment work is not prep work for something else. It is the thing.
Enrich is included with every Marquis IQ deployment. Connect your ERP systems, resolve duplicate customers and suppliers automatically, build your own family hierarchies, and add the segmentation and attribution your business actually uses.
FAQ
What we hear most often from operations leaders and data teams when they start looking seriously at master data.
ERP data enrichment is the process of cleansing, standardizing, deduplicating, and extending the master data that lives in your ERP: customer records, supplier records, item master data, and location data. ERPs are built to process transactions, not to serve as decision-making platforms. When you try to analyze your business across inconsistent records, the dirty data undermines every answer. Enrichment creates a clean, consistent, extended data layer above your ERP that decision-makers can actually trust.
Traditional MDM systems are enterprise-grade platforms built for large organizations with dedicated data governance teams. They require long implementation timelines, significant IT involvement, and ongoing administration. Most manufacturers do not have those resources. Marquis Enrich is embedded directly in the analytics platform and designed for business users, not IT teams. It automates the work that MDM would require humans to configure manually, and it handles historical data natively rather than requiring a clean-slate migration.
This is one of the most underestimated problems in master data work. Even if you decide to roll forward with a clean data policy from today, you still have years of historical transactions tied to old, inconsistent, or duplicate records. Marquis Enrich handles historical data by applying enrichment at the analytics layer rather than modifying the source ERP. Every historical transaction is automatically surfaced under the correct enriched record, so your trend analysis is clean from day one without touching the underlying ERP data.
Yes, and this is one of the highest-value capabilities. When a manufacturer grows by acquisition, each plant brings its own ERP with its own customer master. The same end customer often appears under different names, different account numbers, and different formats in each system. Enrich creates a unified customer identity that spans every ERP in the portfolio. Revenue, orders, and margin from every plant roll up to that single identity without requiring ERP consolidation or any changes to the source systems.
Enrich is a core feature included with every Marquis IQ deployment. The reason is simple: analytics built on dirty, inconsistent, or duplicated master data are not analytics, they are a liability. Every Marquis customer gets customer deduplication, address standardization, family grouping, CRM-to-ERP linking, historical data enrichment, and self-service attribute management as part of the base platform. You cannot run meaningful IQ analytics without a clean data foundation, so we do not sell the foundation separately.
Enrich gives your team one golden record per customer, supplier, and item across every ERP, with the custom segmentation and family hierarchies your business actually uses.