It’s Monday morning. Your controlling department has released the quarterly figures, and it quickly becomes clear that the sales analysis by product group differs from the sales report by 15%. This isn’t due to calculation errors, but because 40% of the item master data lacks a clear product group assignment.
You know that the problem runs deeper—namely, in data maintenance. But as soon as you bring up this unpopular topic at work, all you hear is, “We don’t have time for that.”
Does this scenario sound familiar? If so, you’re probably familiar with the paradox that many companies face: All employees want reliable analytics, but hardly anyone is willing to do anything to make them happen. So how do you solve this problem? How do you effectively integrate data maintenance into your daily work routine? In this article, we’ll take a closer look at this challenge.
What is data maintenance?
Data maintenance is the ongoing process by which companies keep their data up-to-date, complete, and accurate. A key part of this is master data maintenance, which involves basic company information as well as data on products, customers, and suppliers. This includes, for example, bank account information, prices, addresses, and purchasing terms.
Data quality is the result of data maintenance. The more consistently master data is maintained, the better the data quality—and the more reliably processes, reports, and automations function within the ERP system.
Why Does Data Maintenance Fail in Companies?
To get a handle on the dilemma posed by the data maintenance paradox, you first need to understand its causes. Essentially, the problem can be viewed from two perspectives: the organizational perspective and the human perspective.
1. Organizational Perspective
From an organizational perspective, poor data quality can be attributed to the fact that master data maintenance is often not an integral part of the process:
- There are usually no guidelines regarding complete data entries.
- There are no audit cycles in place to ensure that the data is reviewed on a regular basis.
- And there are no data owners to verify such requirements.
In many companies, data maintenance is therefore optional. And that has serious consequences for the accuracy of data entry.
In most cases, the reason for this situation is quite simple: For a long time, data maintenance wasn’t considered particularly important because business was going well and sales were strong. Many companies simply didn’t see a need for optimization.
But today, in an era of intense international competitive pressure, the situation looks different in many companies: With orders drying up, process optimization is becoming increasingly important. Without adequate data quality, however, this goal is difficult to achieve.
2. The Human Perspective
Even when data maintenance is firmly integrated into a company’s processes, employees often don’t take great care when entering data. And let’s be honest: Who can really blame them? After all, data maintenance is just an extra step that makes their day-to-day work more complicated.
The problem: In most cases, employees do not benefit directly from data maintenance. The impact only becomes apparent when you look at the company as a whole. It is not uncommon for just a single department to reap the benefits of the diligence shown by the rest of the organization. That is precisely what makes it so difficult to get employees on board with master data maintenance.
A real-world example:
A colleague in production planning doesn’t enter her setup times into the system. She thinks to herself: “Why should I spend my time on data maintenance if I don’t benefit from it? I might end up suffering—despite my extra effort—because of another department’s lack of diligence. If I only deal with data maintenance sporadically, I’m making my life easier.” A classic case from game theory.
This approach works for the employee because she keeps track of the times in her head. The detailed planning, on the other hand, does not factor these times into its calculations—and thus fails to ensure an efficient project workflow.
Here’s How Much Poor Data Costs Your Company
Inadequate master data management is not just an internal nuisance. It has a direct negative impact on your operating costs. This is because incorrect or incomplete data records affect the entire organization.
The following overview highlights a few areas of business where neglecting data maintenance can end up costing you dearly:
| Area | Role in the Process | Consequences of Poor Data Maintenance | Economic Impact |
| Detailed planning | Detailed planning of work processes, sequences, personnel, materials, and machinery | Delays, unrealistic planning, limited system support | Downtime, longer lead times, inefficient use of resources |
| Disposition | Management of inventory levels, receipts, and shipments, as well as the availability of items and components | Excess inventory, missing parts, production delays, delivery problems | Rising inventory costs, tied-up capital, emergency purchases, contractual penalties |
| Distribution | Using Customer Data from the CRM for Targeted Marketing and Sales Management | Wasted reach, inappropriate messaging, missed sales opportunities | Loss of revenue, inefficient use of the budget |
| Purchasing | Procurement of Materials and Components | Incorrect order quantities, missing or incorrect MRP data, and price information | Higher purchase prices, rush orders, unfavorable terms, supply shortages |
| Management Accounting | Evaluation of Key Performance Indicators and Decision- -Making Based on KPIs | Unreliable analyses, lack of transparency | Poor decisions, bad investments, delayed management |
As you can see:
Poor data quality causes not only operational problems but also massive financial problems. It simply costs your company money—every single day.
The international Forrester study “Data Culture and Literacy Survey, 2023” reveals just how alarming the consequences actually are : More than a quarter of the data and analytics leaders surveyed worldwide estimate that annual losses due to poor data quality exceed $5 million. 7% even report losses totaling $25 million or more.
Looking at these figures, it quickly becomes clear: data maintenance is not a process you can neglect. To ensure your long-term viability, you must approach this task with the necessary seriousness. So what specific steps can you take to permanently integrate master data maintenance into your daily workflow? It’s simple: Build a strong foundation—consisting of five pillars.
The 5 Pillars of Sustainable Data Management in ERP
Simply urging employees to maintain data is generally of little use. Instead, clear rules and useful functions are needed within the individual processes—supported by the ERP system. The following five pillars have proven effective in practice.
Daniel Bartetzko, Asseco Solutions
Only by integrating systematic data management into your processes can you respond to a crisis at any time with accurate analyses.
1. Enable required fields and validity checks in the ERP system
The best tool for improving data quality is an ERP system. For example, you can enable required fields and validation rules in the system to ensure that important information is entered reliably and consistently. This way, data entered directly can no longer be incomplete.
Example:
A new item cannot be released until the product group and the planning parameters have been fully maintained. This ensures that subsequent analysis in Controlling is reliable from the outset.
But be careful:
Also remember to meticulously review your existing database. Only by adding the data that has been missing so far, deleting duplicate entries, and updating outdated records will you be able to sustainably improve your previously poor data quality.
2. Designate responsible data owners for each area
Proper master data management requires clear responsibilities. So-called “data owners” ensure that relevant data is created, maintained, and regularly reviewed within their area of responsibility. In addition, they define standards and serve as points of contact for questions. Without these clear responsibilities, data maintenance remains a vague collective task that often gets overlooked in day-to-day operations.
Example:
In production planning, data owners are responsible for maintaining work plans and setup times. In this way, they ensure that changes from the production floor are promptly reflected in the system.
3. Conduct a data review once a quarter
Even if you have achieved good data quality, that doesn’t mean it will stay that way in the future. Especially when processes change or the product lineup expands, errors and inconsistencies can quickly creep back in.
Setting a fixed schedule for the systematic review and evaluation of your data sets therefore ensures that the database is regularly cleaned up . Without such a fixed schedule, duplicates and outdated records will accumulate over time.
The ERP system also plays a key role in data reviews. With the help of reports, anomalies can be specifically identified and addressed.
Example:
Once a quarter, a team in the purchasing department checks whether the lead times for purchased parts are still up to date!
4. Take Advantage of Automation and AI Capabilities
Modern ERP solutions offer the ability to automatically analyze master data using AI, taking into account the associated transaction data, and to proactively flag necessary changes to master data.
It is also possible for the ERP system to perform certain tasks fully automatically. This also has a positive effect on user acceptance: Employees often are much more receptive to data maintenance guidelines when they do not require any additional effort on their part.
Example:
The system regularly reconciles lead times and lot sizes in Purchasing with actual order and delivery data. It actively and periodically indicates which master data should be updated.
5. Conduct a cross-departmental workshop
Many employees have no idea what the data they enter will be used for later on. As a result, the workforce often lacks an understanding of the implications for the rest of the process, and master data maintenance automatically ends up being given a lower priority.
A company-wide workshop can provide the necessary transparency at this stage. During the workshop, you’ll demonstrate to each department the drastic consequences that incomplete or incorrect data can have for the organization. This knowledge usually brings about a fundamental shift in how data maintenance is perceived.
Example:
During the workshop, the work planning department realized that missing setup times are skewing the detailed schedule. In the future, this department will certainly pay more attention to data maintenance.
Real-World Use Case:
PALME Benefits from Accurate Data Maintenance in ERP
Since PALME Duschabtrennungen GmbH began using the APplus ERP system, data quality has improved significantly. While the old system did not require systematic master data maintenance, APplus now requires users to enter existing information in a structured manner.
On the one hand, this does mean a noticeable increase in workload for PALME. On the other hand, the company is now able to generate comprehensive analyses and reliable key metrics from the ERP solution. This, in turn, enables better decision-making.
Conclusion: Data maintenance requires clear processes and responsibilities.
Data maintenance is not purely an IT issue; rather, it is primarily a matter of organization, communication, and accountability across all departments. It must be firmly integrated into all business processes. This includes careful data entry as part of operational workflows, as well as clear guidelines and responsibilities. Furthermore, master data maintenance can only be successful if you communicate the necessity across departments. Only then will your colleagues be willing to go the extra mile with you.
Always keep in mind: Optimal data quality is by no means a peripheral issue that you can address at some point in the future. If you don’t act now, even the next crisis could threaten your company’s very existence. As soon as revenue drops rapidly, you’ll need a precise analysis of the causes based on high-quality master data.
ERP systems provide the tools needed to solve the data maintenance problem—but it is the process owners who provide the decisive impetus.
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