Master data is the set of core records your business depends on every day: products, customers, suppliers, materials. Managing master data means keeping those records accurate, complete and consistent across every system that touches them, and that turns out to be far harder than creating them in the first place. A product record does not stay correct on its own. Prices change, suppliers merge, someone types the wrong unit into a spreadsheet, and the same article ends up with three slightly different descriptions in ERP, shop and catalogue. This article looks at why master data degrades, the challenges that make it hard to manage, and the practices and tools that keep it under control.
What Master Data Actually Is
Master data describes the long-lived business objects that several departments and systems share: product and material master data, customer and supplier records, and organisational data such as cost centres. It changes slowly, which is exactly what separates it from transactional data like orders or invoices that are created constantly and then rarely touched again. Because master data is shared, an error in it does not stay contained. A wrong dimension on a product record surfaces in the online shop, on the picking slip and in the customs declaration at the same time.
That shared, propagating nature is what makes managing master data a discipline of its own rather than a data-entry task. If you want the underlying concept and the case for a dedicated system, our guide to Master Data Management (MDM) covers it in depth. Here the focus is narrower and more practical: the day-to-day work of keeping that data clean.
Why Master Data Degrades
Data quality is rarely lost in one dramatic event. It erodes. The most common cause is mundane: the same object is created and maintained in several applications, each with its own formats and rules, so the versions slowly drift apart. Manual entry adds a steady stream of typos, blank fields and personal shortcuts. Then reality keeps moving. Products are relaunched, assortments are trimmed, two suppliers become one after an acquisition, and the records that described the old world are quietly wrong.
None of this is visible until someone downstream trips over it. Surveys of master data repeatedly put the share of records that are duplicated, incomplete or simply wrong at around a third, and that figure tends to climb wherever no one owns the data. Understanding the mechanism matters, because you cannot fix a moving target with a one-off clean-up.
The Main Challenges in Managing Master Data
Most organisations run into the same handful of problems:
Duplicates: the same customer or article recorded several times, so reports double-count and mailings go out twice.
Incomplete records: mandatory attributes left blank, which breaks marketplace listings and downstream automation.
Inconsistent formats: units, naming and classifications that differ between systems, so nothing matches cleanly.
Unclear ownership: when everyone can edit and no one is accountable, quality has no defender.
The cost of leaving this unmanaged is not abstract. Gartner estimated in 2020 that poor data quality costs the average organisation 12.9 million US dollars a year, a figure drawn from 154 companies already investing in data quality tools. In manufacturing and distribution the bill shows up concretely: returns, failed listings, stalled ERP migrations, and hours lost reconciling numbers that should have agreed in the first place.
Best Practices for Maintaining Master Data
Managing master data well is less about a single tool and more about a repeatable operating model. A few practices do most of the work.
Start with governance. Data governance gives you the rules: who may create a record, which fields are mandatory, how a new supplier is approved, what “valid” means for each attribute. Without those rules, every other measure is improvised.
Assign clear ownership. Each master data domain, whether products, customers or suppliers, needs a named owner accountable for its quality, supported by data stewards who do the hands-on maintenance.
Standardise definitions and validate at the source. Agree common formats and business rules, then enforce them at the point of entry instead of cleaning up afterwards. Prevention is cheaper than remediation every single time.
Measure continuously. Treat quality as a number, not a feeling: completeness rate, duplicate rate, the share of records passing validation. What you measure, you can improve, a point we develop in our article on data quality.
Automate the routine. Matching, deduplication and validation at scale are jobs for software, not for people working through spreadsheets by hand.
The Role of the Data Steward
Governance rules only work if someone applies them, and that someone is usually the data steward. The steward is not the person who owns the data politically; that is the data owner, typically a business lead. The steward is the operational role that keeps a domain clean day to day: checking new records against the rules, resolving duplicates, chasing missing attributes, and flagging where a process keeps producing bad data. In a mid-sized manufacturer this can sit within an existing role; in a large group it is often a dedicated function. Either way, naming it is what turns “everyone is responsible”, which in practice means no one is, into a workable, accountable arrangement.
Managing Master Data with MDM and PIM
At some point spreadsheets and good intentions stop scaling, and the practices above need a system to enforce them. This is where a master data management (MDM) system earns its place. An MDM hub consolidates records from every source system, matches and merges duplicates, and produces one trusted version of each object, the so-called golden record, which then flows back to the connected systems. That single trusted version is the single source of truth that keeps shop, ERP and catalogue in agreement. VIA/MDM does exactly this across the product, customer and supplier domains.
For product data specifically, a PIM system is the sharpest instrument. It applies mandatory fields, validation and approval workflows at the source, enriches records for every channel, and reports completeness and quality scores as you work. Our PIM system VIA/PIM360° operationalises the practices in this article for product master data, so the rules run automatically instead of sitting in a policy document no one reads.
Five Steps to Better-Managed Master Data
- Take stock. Profile your existing master data to see how bad, or good, it really is: duplicates, gaps, inconsistencies.
- Define governance. Set ownership, mandatory fields and approval rules per domain before touching the data.
- Clean and consolidate. Deduplicate, standardise and build golden records, ideally in an MDM or PIM hub rather than by hand.
- Enforce at the source. Move validation to the point of entry so the same problems do not return next quarter.
- Monitor and improve. Track quality KPIs and review them regularly; treat maintenance as a cycle, not a project.
Frequently Asked Questions
What is master data?
Master data is the core, long-lived business data shared across departments and systems: products, customers, suppliers, materials and organisational data such as cost centres. It changes rarely and forms the basis for transactions and analysis, which is why an error in it propagates everywhere at once.
What is the difference between master data and transactional data?
Master data describes stable business objects, such as a product or a customer. Transactional data records the events that involve them, such as an order, an invoice or a delivery. Transactional data is created constantly and references master data, while master data changes slowly but is shared far more widely.
How do you improve master data quality?
Through clear ownership and governance rules, standardised definitions, validation at the point of entry, continuous measurement of quality KPIs, and automation of matching and deduplication. A one-off clean-up alone does not hold, because master data degrades continuously as products, suppliers and processes change.
What role does an MDM system play in managing master data?
An MDM system consolidates records from all source systems, matches and merges duplicates into a single golden record, and distributes that trusted version back to the connected systems. The result is that every application works from the same accurate, up-to-date data instead of its own local copy.
Managing master data comes down to accepting that it decays and building a routine that counteracts the decay: clear ownership, rules enforced at the source, continuous measurement, and a system that does the heavy lifting. Do that, and master data stops being a recurring fire drill and becomes the reliable foundation the rest of the business is built on.
