How to Build a Business Case for Manufacturing Master Data Management Software

Most people who work in manufacturing data management already know they would benefit from better governance. The problem isn’t conviction. It’s the conversation with the people who control the budget.

A CFO or COO doesn’t think in terms of duplicate material records or ungoverned change workflows. They think in terms of cost, risk, and return. If you can’t translate a master data problem into those terms, the investment doesn’t get prioritised. It gets noted, agreed with in principle, and quietly deprioritised in favour of something with a clearer number attached to it.

This article is about building the case that gets the decision made. Not a technical justification. A business one.

Start with the cost of what you have now

The most common mistake in building an MDM business case is leading with the solution. Don’t open with what your chosen solution costs or what it does. Open with what your current situation is costing you.

This requires some honest calculation. Most of the cost of poor master data governance isn’t visible as a line item. It shows up as time, errors, and missed opportunities. None of which are labelled “master data problem” on a P&L. Your job is to make them visible.

Here’s a framework for doing that across the areas where the cost is typically highest.

Manual processing time

Start with the most tangible cost: the hours your team spends creating, changing, and correcting master data manually.

Count the number of material master creation requests your business processes in a month. Add change requests. Add the time spent on corrections when something goes wrong. Multiply by average hourly cost. Then ask yourself how much of that time is genuinely value-adding versus administrative overhead that exists because you don’t have a governed, automated process.

Princes, one of the UK’s leading food and beverage manufacturers, calculated that managing material master for finished goods alone was consuming approximately 51 working days of non-value-added processing time across departments every year. That’s one data object, at one manufacturer. When they implemented MDM software, they improved that by 75%.

Put your own version of that number on a slide and the conversation changes immediately.

Error correction costs

Every master data error that makes it into your ERP system creates downstream work. Someone has to find it. Someone has to fix it. Someone has to deal with whatever operational problem it caused before it was found.

Think about the last time a production run was disrupted by a data error. Or a supplier payment went to the wrong account. Or a procurement order came back wrong because the material record had an incorrect unit of measure. What did that cost in time, in materials, in relationships?

You don’t need a precise figure. You need a credible estimate, backed by real examples from your own operation, that a finance director can look at and say “yes, that sounds about right.” Anecdote plus estimate is more persuasive than either alone.

The cost of slow new product introduction

Every week a new product is delayed getting to market is lost revenue. If master data is a bottleneck in your NPI process, and in most manufacturers it is, quantify it.

How long does it currently take from a new product being approved to it being ready in your ERP? How much of that time is spent waiting for data to be created, corrected, or approved? If you could compress that by 30%, what would that mean in revenue terms for your next three product launches?

You don’t need to be precise here either. A directional estimate that your commercial team can validate is enough to make the point.

AI and digital transformation readiness

This one is increasingly relevant and increasingly understood at board level. Most manufacturers are investing in AI, advanced analytics, or broader digital transformation programmes. Most of those programmes will underperform or fail if the master data underpinning them is ungoverned and unreliable.

Research from MIT Sloan found that bad data costs most companies between 15% and 25% of revenue. That’s not a manufacturing-specific stat, but it’s a credible third-party reference that puts the scale of the problem in terms a CFO can anchor to.

More specifically: if your business is investing in demand forecasting, predictive maintenance, or supply chain optimisation, all of which depend on clean, consistent master data, the cost of not governing that data isn’t just operational. It’s the return on those other investments being diminished or lost entirely.

Work out your own numbers

If you’re not sure where to start with quantifying the cost of your current situation, our MDM ROI calculator can do the heavy lifting. Enter your current hours spent on master data tasks each week, the percentage of those processes that are manual, your estimated data error rate, and how you’d rate your current user experience. It takes about a minute and gives you a personalised estimate of what better governance could save your business in time, money and errors.

Calculate your MDM ROI

Step 2: Quantify the operational gains

Once you’ve established what the current situation costs, build the other side of the equation: what changes when you fix it.

The numbers that tend to land best with finance audiences are time-based, because they convert directly into headcount cost or capacity freed for higher-value work.

Processing time reduction. A 70% reduction in manual data entry is a consistent outcome for Maextro customers. For a business running hundreds of data requests per month across multiple objects and plants, that’s a meaningful release of capacity. Translate it into hours, then into cost.

Error reduction. Governed creation and change workflows with validation rules catch errors before they reach SAP. In practice this means fewer corrections, fewer production disruptions, and fewer of the downstream operational failures that trace back to bad data. Estimate the current cost of data-related errors, even conservatively, and apply a realistic reduction factor.

Faster NPI. A governed NPI workflow that validates all required data before creating records in SAP removes the back-and-forth that slows new product introduction down. If your current NPI process takes four weeks from data request to system readiness and a governed workflow gets that to two, the value is in the product revenue that hits the market sooner.

Audit and compliance efficiency. For manufacturers operating in regulated industries or under retailer compliance frameworks, the audit trail that comes with governed MDM is itself a cost saving. The time spent preparing for audits, tracking down who changed what and when, and rectifying compliance gaps is real overhead. It largely disappears when every change is automatically logged with a full approval history.

Step 3: Factor in deployment speed and time to value

Traditional enterprise MDM implementations are long, expensive, and painful. This is one of the reasons many manufacturers have delayed investment. They’ve seen what a multi-year MDG implementation looks like and concluded that the cure is worse than the disease.

This is where the comparison matters. As an example, Maextro deploys in 2 to 5 weeks on SAP BTP. That speed has two implications for a business case. First, the time to value is short. Operational improvements start accruing within a month of go-live, not after a lengthy implementation and change management programme. Second, the implementation cost is significantly lower than a traditional MDG deployment, which typically requires extensive consulting resource and custom development.

When building your business case, model two scenarios: the cost and timeline of a traditional MDG implementation versus our example, Maextro. The delta in implementation cost, consulting resource, and time-to-value is often enough on its own to make the decision straightforward.

Step 4: Present the risk case

Every business case has two sides. The investment case, which covers what we gain, and the risk case, which covers what happens if we don’t act.

For MDM, the risk case is often more persuasive than the return case, particularly at board level. Finance directors are trained to be sceptical of projected returns. They’re trained to take risk seriously.

The risks worth putting on the table are these.

Data debt compounds. Every month your master data is ungoverned, the problem gets harder and more expensive to fix. Duplicates accumulate. Technical debt builds up in your ERP. The cost of remediation grows. Not acting isn’t a neutral choice. It’s a decision to let the problem get worse.

Digital transformation initiatives underperform. If your business is investing in AI, S/4HANA migration, or supply chain transformation, those investments are sitting on a foundation of ungoverned data. The ROI on those programmes is directly affected by the quality of the master data underneath them.

Regulatory and compliance exposure. In food and beverage, pharmaceuticals, defence, and other regulated manufacturing sectors, data governance isn’t just good practice. Regulatory frameworks are tightening across the EU and UK, and the expectation that manufacturers can demonstrate control over their product and supplier data is increasing. The cost of a compliance failure, whether a recall, an audit finding, or a failed customer inspection, dwarfs the cost of the governance that would have prevented it.

Competitive disadvantage. Your competitors are investing in this. The manufacturers who govern their master data well introduce products faster, manage their supply chains more efficiently, and make commercial decisions on data they trust. The ones who don’t are carrying a structural cost that erodes margins quietly, over time, until the gap becomes impossible to close.

Step 5: Structure the business case document

A CFO or COO doesn’t need a detailed technical specification. They need a one-page summary and a supporting document they can share with their team. Here’s the structure that works.

Executive summary. One paragraph. What’s the problem, what’s the solution, what’s the investment, what’s the return.

Current state cost. Two or three specific, costed examples of what poor master data governance is currently costing the business. Use real examples from your operation. Be conservative rather than optimistic. A finance director who can’t challenge your numbers is more likely to approve them.

Proposed solution. A brief description of what Maextro does, how it works alongside SAP, and why it’s the right fit for your environment. Keep this non-technical. Focus on what changes operationally, not how the software works.

Financial model. A simple table: implementation cost, annual licence, projected savings in year one, year two, year three. Payback period. Don’t overcomplicate it. A clear payback period of under 12 months, which is achievable for most manufacturers given the processing time savings alone, is the most persuasive number in the document.

Risk of inaction. Two or three sentences on what happens if the investment isn’t made. Be direct. Not alarmist, but honest.

Recommendation. A clear ask. Specific investment. Specific next step.

The conversation that gets the decision made

The business case document gets you to the meeting. What gets the decision made is being able to answer one question confidently: what does this cost us today?

If you can walk into that conversation with a credible, specific answer, built on real data from your own operation and translated into the language of time and money, the investment case for MDM stops being abstract and starts being obvious.

If you’re ready to start building that case and want to understand what Maextro would look like in your specific SAP environment, speak to our team or see the manufacturing MDM platform in detail.

And if you want to understand the full cost of what poor data governance is doing to your business before you build the case, our webinar The Price of a Digit: Exposing the Business Impact of Bad Data is a good place to start.