Why Master Data Matters: Fail to prepare and miss out on SAP’s Innovations

AI, automation, and analytics are the ‘three A’s’ dominating every conversation in the SAP world. Rightly so. Every business wants to be “data-driven”, and we have all agreed that’s a no-brainer. But whilst many organisations and, indeed, SAP partners talk about them in isolation, in the cold light of day, the truth is none of these innovations will deliver real value if your master data isn’t properly managed and governed. Crucially, this is the part many are turning a blind eye to, and it’s the reason we need to talk about why master data matters.

The risks of ignoring master data are very real. Businesses can run smoothly for years, hitting targets and scaling operations, until one day they’re blindsided by financial losses, compliance breaches, or reputational damage. We’ve seen it happen to organisations as large as Public Health England and the UK’s biggest dairy producer. Both believed they had safeguards in place. Both were wrong.

The good news? Master Data Management and Governance doesn’t have to be a massive, costly project. With the right solutions and automation, data management and governance can be simplified, ensuring your business avoids the stress and expense of fixing problems later. Once again, this is why master data matters: it helps you avoid costly errors before they happen.

In this blog, our experts explain why companies should take it seriously. Uncovering what emerging SAP innovations mean for your business, why master data matters, and how to take practical steps to protect your organisation from the risks of bad data. Along the way, we’ll share insights from our experts and real-world stories that highlight the cost of ignoring Master Data Governance.

No Innovation Without Foundation: The Data Behind SAP’s Latest Tech

SAP’s latest innovations are designed to transform the way businesses operate, from Benchmark engineering to the new era of AI Bots, Joule Agents. These innovations, as advanced as they are, will not deliver what it says on the tin if businesses’ master data is incorrect, incomplete and unmanaged. Yes, they will produce results, but not accurate ones that businesses can depend on for growth. In essence, the return on investment quickly diminishes when the foundation of master data is weak. We recommend that if a business is to take a shine to any of these innovations, it should firstly address the accuracy of the master data they will be feeding into them. This is another example of why master data matters before you adopt new technology. We also need to mention that getting data right isn’t a one-off cleansing exercise, as many think. It’s a change in the way of working and approaching data altogether; otherwise, real change will never happen.

Firstly, let’s dive into the latest trends and innovations SAP has been pushing:

Joule Agents and Benchmark Engineering – The New Era of AI Bots

The most critical AI announcement made by SAP at the latest Sapphire, was the introduction of Joule Agents. These ‘agents’ are essentially AI chatbots, but not the ones we know today. Unlike a traditional chatbot, where we ask it a question and converse with it to gain answers, Joule agents are a series of contextual AI bots running in the background of your systems. The bots analyse what you’re trying to tell the system and then coordinate a unified answer with a reason.

The theory behind this is to introduce an ‘Act’ kind of methodology, rather than the standard conversational AI methodology. This will strengthen users’ adoption of their systems, while capturing users’ struggles before they escalate and delay processes.

Prompt Engineering is no longer present in the SAP world. Benchmark engineering is taking its place, and here’s why we’re here for it. This new form of engineering stores references to already conversed information between the AI Joule Agents and the user for future reference. The theory is that the agents use this information as a benchmark to promote the user, should they face the same complication in the future. The aim is to provide a minimum level of adoption and scale response. But all of this depends on clean, consistent data, which is exactly why master data matters.

Generative AI – The Hot New Topic

Optimised decision making, enhanced productivity and cost reductions are just a few benefits your business can obtain by investing in AI. “Go ahead, bring AI into every innovation” would be the reaction of every software developer. But, from a business perspective, our experts would recommend firstly weighing out the risks and opportunities of bringing AI into your business. And this is another reminder of why master data matters: if the foundation is flawed, the output of AI will be flawed too.

Consider these steps, curated by our consultants, when considering AI for your business: 

1 – Start with a  Pilot Project – Avoid going full-fledged on bringing AI into your business solutions. Start with pilot projects and realise the value of what those projects bring to your day-to-day business processes. For example, adoption or the consumption of your services. 

2 – Measure the Results 

3 – Once you have proved it – Start to scale! If a team can prove the business value of the pilot project, the opportunity to scale it to their entire workforce is achievable.  

Digital Transformation – The Push from SAP  

SAP and partners are pushing for businesses to go 100% Clean Core (a strategy and set of principles focused on keeping the core of SAP S/4 HANA systems as close to the standard, vendor-provided version as possible. The goal is to achieve a more agile, efficient, and maintainable ERP system with lower technical debt and easier upgrades.)  

Our experts advise firstly, to consider ‘Does your business really need it? Do you want to reuse, recycle or repair your custom code, and/or potentially lose the competitive advantage you have?”

In summary, it comes down to what your current landscape is. What is your innovation appetite? Is it to move to the cloud going forward, and not just be pushed by SAP and partners?  

The impact of ungoverned data: Why the foundation matters

All the innovations we’ve explored rely on one critical thing: reliable, well-governed master data. Without it, even the most advanced tools can produce flawed results, leading to inefficiencies and poor business decisions. When master data isn’t properly managed, the consequences ripple across the organisation. Duplicate records multiply, errors go unnoticed, and business rules are inconsistently applied, turning what should be a single source of truth into something subjective and unreliable.

These aren’t hypothetical issues. Companies that overlook master data governance often face delays across finance, supply chains, and customer service, and risk regulatory breaches or slow decision-making. Master Data Management ensures that when innovations like AI are deployed, they’re working with trustworthy data, allowing businesses to truly benefit from the technology rather than being hampered by a weak foundation.

In other words, the more advanced your innovation, the greater the dependence on accurate master data. Addressing data quality first isn’t just a best practice, it’s the prerequisite for achieving the full potential of digital transformation initiatives. Here are a few specific examples of what happens when it goes wrong:

One of the largest UK Dairy Producers – A Costly Unit Error

Imagine you’re working at a leading dairy company and have just fulfilled a major set of customer orders. Multiple truckloads of cheese have been packed, loaded, and departed the distribution centre. Everything appears to be running smoothly until a small but critical mistake surfaces. A material master was created with the wrong product EAN number at the source. At first glance, this might sound like a minor typo. But for one of the UK’s largest dairies, this really happened and had dramatic consequences — a clear illustration of why master data matters.

Upon the delivery arriving at the customers’ sites, every pallet scan failed validation, leading to the entire shipment being rejected. What followed? Doubled transport cost, consumer safety concerns due to handling dairy products, resulting in wasted perishable goods and strained supplier relationships as orders had to be reprocessed with delivery timelines renegotiated. And the most frustrating part? This crisis was entirely preventable.

If automated master data governance tools had been in place, this incident would likely never have occurred.

Approval processes would have flagged the inaccurate master data creation to the wider business before it reached downstream systems. Business rules would run checks on all the data being input into the company’s database. The rules functionality, predefined by the business, would reject any incorrect unit of measurement which did not comply with the business’s official units of measurement. Data stewardship would have ensured that any incorrect or suspicious data entry was flagged and corrected before it could go live. Clean, consistent master data would have prevented the underlying system from being a “ticking time bomb” waiting for a trigger. This reinforces yet again why master data matters.

Instead, the company had to deal with a very costly issue, all because of a gap in their data processes. This case underscores why investing in MDM isn’t just about efficiency — it’s about protecting your brand, your customers, and your bottom line. Why master data matters becomes obvious when you see the real-world consequences of data failure.

This experience was the catalyst for one of the UK’s largest dairy producers to realise the importance of prioritising proper master data management and governance policies.

An employee of this company entered the wrong unit of measurement into a system polluted with bad data. There were no governance policies in place or pre-defined rules, all of which would have highlighted this error early on. The mishap resulted in multiple truckloads of cheese, all allocated to different suppliers, being recalled. This was not a system failure but a data failure. The aftermath included huge financial loss, consumer safety implications, and strained supplier relationships, highlighting once more why master data matters.

Public Health England – Underreported Covid Cases

At the height of the COVID-19 pandemic, every number mattered. Each case reported shaped government decisions, lockdown measures, and public health advice. For millions across the UK, these figures dictated whether they could visit loved ones, whether hospitals would have enough capacity, and how quickly the virus could spread unchecked.

But behind the scenes, Public Health England (PHE) was relying on Microsoft Excel to store and manage this critical data, a tool never designed to handle such scale or importance.

And then, the spreadsheet failed them.

A data error occurred within the Excel file, resulting in around 16,000 positive COVID-19 cases going unreported. Sixteen thousand people, and everyone they had been in contact with, were missing from the national data set. This is another stark example of why master data matters in high-stakes situations.

The consequences were immediate and far-reaching. Contact tracing efforts may have been delayed, meaning thousands of individuals continued unknowingly spreading the virus. Government decisions on lockdowns, testing capacity, and healthcare resources may have been made on incomplete data. Even worse, the underreporting created a false sense of security for some, as the official case numbers appeared lower than they were.

The root cause? Relying on Excel as a Master Data Management solution. Excel cannot apply business rules to validate data before it’s stored. It cannot automatically flag duplicates or highlight anomalies. And it lacks data ownership controls, meaning no one was formally accountable for reviewing or rejecting the data before it went live. Without these safeguards, there was no way to catch the corrupted data until it had already impacted national reporting — reinforcing why master data matters for governance and decision-making.

Had a robust MDM platform been in place, the outcome would have been very different. Business rules would have ensured all records were complete and accurate before they entered the system. Anomalies would have been detected instantly, allowing teams to correct errors before they caused damage. Clear data stewardship would have made someone responsible for validating the information before it was used to make critical decisions.

In moments of crisis, the integrity of your data is everything. Without the right systems in place, even one spreadsheet error can change the course of events for an entire nation — highlighting once again why master data matters.

NASA Mars Orbiter – $125 Million Lost in Translation

Disclaimer: Due to the costly example, it must be noted that NASA is not an SAP customer. This may not happen to SAP customers, but it is a relevant example of poor master data management.

It was supposed to be a moment of celebration. On mission day, engineers, scientists, and NASA teams around the world were gathered, ready to witness history as the Orbiter entered Mars’ orbit. Years of planning, millions of dollars, and the hopes of countless researchers all built up to this single moment.

But the celebration turned to shock.

Instead of smoothly entering orbit, the spacecraft burned up in Mars’ atmosphere and broke apart. In an instant, the mission was lost.

A root cause analysis followed, and the findings were as simple as they were devastating. The measurements used in the spacecraft’s process build had not been converted into the correct unit of measurement. NASA had a standardised metric norm that required all data to be submitted in kilograms, but the figures were delivered in pounds. Somewhere along the way, the team assumed the conversion had already been made, but it hadn’t.

This oversight caused the Orbiter’s trajectory to be miscalculated, sending it fatally off course. The consequences were immense. Years of work from hundreds of engineers were undone in a matter of seconds. Millions of dollars in investment literally went up in smoke as the Orbiter disintegrated. A major scientific opportunity to study Mars was lost, setting back research and delaying vital discoveries. Another lesson in why master data matters: even small errors can have catastrophic results.

With robust master data management and governance tools in place, the outcome would have been a celebration. Data Quality Checks would have flagged the incorrect unit of measurement instantly. Rule-based engines would have applied the correct business rules. These measures are exactly why investing in MDM shows you why master data matters.

Real-World Data Stories – Summary

As extreme as certain cases may appear, there is one key factor which would have rewritten the outcomes for all these stories: the utilisation of a Master Data Management and Governance solution. This clearly demonstrates why master data matters in every organisation, across every sector.

Master Data Governance sets the policies and rules for managing a business’s data, while Master Data Management is the practice of enforcing those rules to create and maintain a single, trusted source of critical data. Understanding this is the essence of why master data matters for operational resilience, compliance, and business growth.

Getting Master Data up to code

The stories above show the very real consequences of poor master data management. Across industries and scenarios, one common thread emerges: these crises could have been prevented with proper Master Data governance in place.

For businesses looking to innovate and scale, this is a wake-up call. Technology and automation, no matter how advanced, can only perform as well as the data that underpins them.

That said, building this foundation doesn’t have to be overwhelming and, luckily, we’re here to help. The we have written the next section of this guide to explore common misconceptions and inpart some actionable advice to get your data up to code,

The Myths of ETL

“We already have an ETL tool — that’s data governance.” “We cleansed our data, ran it through ETL, and uploaded it — we don’t need data governance.” 

Our experts hear statements like these regularly from companies that are frustrated that their data initiatives aren’t delivering results. The truth is, these are common myths and here’s why they’re holding businesses back: 

• ETL (Extract, Transform, Load) tools are designed to pull data from various sources, reformat it into a usable structure, and then deliver it to a target system. Their primary purpose is data movement and transformation, not the holistic management of data quality, security, and compliance that true data governance requires. 

Unlike a proper MDM solution, ETL tools do not define or enforce business policies for data usage, nor do they provide the collaborative workflows needed to ensure consistency across departments. They also have limited data auditing capabilities, which makes it difficult to trace information back to its source, a critical requirement for regulatory compliance and accountability. 

In short, ETL tools are valuable for moving and transforming data, but they are not built to manage, govern, and maintain the integrity of that data across the enterprise. For that, you need a dedicated MDM solution. 

‘”ETL tools are data governance tools.’” “We have cleansed our data, got an ETL tool in place and have uploaded the data. We didn’t need data governance to do that.” Our experts have heard this being said time and time again, from companies frustrated that their efforts are failing. Here is why these are myths: 

• ETL tools are not data governance tools. You need a proper MDM tool to do the data governance for you. ELT tools are only going to support your business with uploading data and for data transformations. 

• Regardless of how much effort your business puts into cleansing the data, without data governance, the data could be corrupted in as little as one week.  

An expert’s checklist for building a scalable data governance strategy  

• Educate businesses on key data attributes​

• Translate SAP jargon into easy-to-understand business language​

• Identify Active / Inactive objects​

• Standardise object names​

• Identify Duplicates & Archive​

• Identify some basic rules at the early stage​

• Data completeness – Don’t overlook missing data​

• And most important – Start thinking long-term data governance strategy…

An expert’s advice:  on how to master data, before it masters you 

For any organisation looking to innovate, grow, and stay competitive, one truth stands out: SAP’s newest innovations will only ever be as powerful as the data you feed into them. The risks of poor master data management aren’t theoretical; they are real, costly, and, as we’ve seen, capable of derailing everything from supply chains to compliance and even brand reputation. 

Why put yourself in a position where you are directly risking a costly business crisis? The late month-end close, the recall of a product shipment, and the regulatory fine are the bottlenecks that keep business leaders awake at night. With MDM and MDG, these are completely preventable. 

The good news is that building a scalable data governance strategy doesn’t have to be a massive transformation project. Start with the data objects that matter most, educate your teams on why they matter, assign clear ownership, and let automation carry the heavy lifting. The result? You’ll shift from crisis management to proactive growth, freeing your team to focus on innovation, customer experience, and delivering the outcomes your business cares about. 

And if you’re wondering where to even begin with Master Data Management solutions, you’re not alone. With so many tools and platforms on the market, it can be hard to know what’s right for your business. 

The decisions you make today about how you manage and govern your data will define the speed, accuracy, and resilience of your business tomorrow. We advise, don’t wait until the next crisis to act. Build the foundation now and give your innovations the fuel they need to truly deliver. 

If you’re looking to get the most from SAP’s upcoming innovations, Maextro can help unlock the value in your master data that makes these innovations truly usable. Explore how it can support your SAP data strategy today.