Poor data quality impairs business processes, leads to incorrect decisions and makes it difficult to comply with laws and guidelines. Companies are therefore regularly advised to set up a professional master data management system. Master data management (MDM) is the goal. However, not everyone is going down this path yet. Why?
Master data quality influences business processes
Master data, i.e. the static basic data or reference data on products, materials, suppliers, customers, employees and finances, is one of the business-relevant data that every company has to deal with. Quite a few companies have problems with the quality of their master data. This costs money and therefore raises the question of "return on investment". The success of master data management in relation to the capital invested is not so easy to answer in "hard" figures, but there are empirical values from projects and assessments by those involved in the process that make it possible to make statements about the return on investment.
Initial situation with inadequate master data maintenance
Let's first take a look at the typical problems that companies have to contend with. Creating customer and supplier master data is usually a lengthy process, and media breaks in the processes are the rule rather than the exception. A high error rate in data creation and maintenance results in frequent changes, especially to address data and bank details. Duplicate checking often does not take place. Since an overview of "supplier groups" and "customer groups" is regularly missing, the negotiating position in purchasing and sales is weakened. Poor data quality leads to incorrect deliveries and returned mail (mailings, invoices, etc.).
With material master data, the mere fact that a material master data record in SAP MM (Material Master) can have up to 1000 individual fields is a particular challenge. Of course, 1000 fields are not always filled, but 150 to 200 can be (depending on the company and material). In addition to the global master data, which is the same for everyone, there is plant-dependent data that increases the amount of data. In addition, many departments are involved in the master data process for materials (purchasing, sales, materials planning, etc.). This means that there are a large number of people involved in the process, and communication and coordination take up a lot of time - without a master data system and without data governance. Many companies also allow hundreds of "occasional users" to create material master data; for them, the creation and maintenance is extensive and confusing. Professional master data management therefore aims to reduce the number of users - and therefore also the number of potential sources of error. With master data management, an attempt is made to authorize more specifically and thus allow who creates, enriches, changes and releases master data at all.
A closer look at the costs of poor data quality and the consequences of long throughput times when creating master data makes it clear where the "adjusting screws" are and what potential master data management holds.
Costs of poor data quality
Experience shows that hardly any companies are able to prove the costs of poor data quality with hard facts, i.e. with figures. What they do know, however, is that incorrect customer and supplier master data results in costs for incorrect deliveries and orders, postage and labor costs for returned mailings and a high workload for correcting errors. In addition, they often do not have an overview of the order volume from the same supplier, which results in excessively high prices in purchasing. Inadequate material master data generates costs, for example through understated invoice items due to incorrect parts lists and excessive logistics costs due to incorrect weights. Increased complaints due to incorrect deliveries, incorrect material orders and production downtime due to missing materials are further cost drivers.
Consequences of long throughput times when creating master data
Long lead times when creating customer and supplier master data lead to delayed orders, and when creating material master data to a delayed "go-to-market". If a company does not know when a material is "ready" in the system to start production, the market launch will ultimately be delayed. Regardless of the master data domain, long process runtimes lead to delayed deliveries if a product cannot be ordered on time. Employee dissatisfaction is added to this if they do not know when "their" material is ready for them to continue working, or if they have to constantly correct errors; employee productivity decreases. Finally, customer dissatisfaction threatens if no reliable statement can be made as to when what will be delivered if it is not known where the business process currently stands.
Benefits through master data management solution
With a view to the typical master data problems in companies, the cost drivers due to poor data quality and the consequences of lengthy internal processes, the benefits of a master data management solution for the individual domains can be described as follows:
- For customer master data: Cost efficiency in mailing campaigns through better data up-to-dateness and quality, reduction of incorrect deliveries, increased efficiency in data maintenance and better compliance (e.g. for embargo lists, VAT ID checks).
- For supplier master data: higher cost efficiency in procurement and supplier management through identification of redundancies, better negotiation options for volume contracts, faster creation of supplier master data and thus onboarding of "replacement suppliers", faster ordering process, avoidance of costly errors in delivery acceptance and payment.
- For material master data: optimized headcount through better planning, automation, increased productivity. The following applies to all domains: professional master data management creates the basis for making better decisions, ensures smooth processes and optimizes adherence to laws and guidelines (compliance). All of this increases competitiveness. Good data quality also ensures the satisfaction of customers, suppliers and employees.
Return on investment - experience in master data management
If you want to express the benefits of master data management in figures, you have to rely on empirical values. The main factors influencing the ROI for both customer and supplier master data as well as material master data include the number of master data systems and updates, the duration of throughput times and the manual coordination effort as well as the follow-up costs for incorrect data and duplicates. In the case of material master data, the number of production sites with plant-specific material master views is also taken into account.
Those involved in master data management projects estimate that an MDM tool can reduce processing or throughput times by up to 50 percent. A significantly lower coordination effort contributes to this. A faster go-to-market is difficult to specify exactly, but the following example gives an idea of the potential: If a company reports that it takes 70 days for a new material to be fully created in the system and you extrapolate this to the number of parts that are installed in a product, then you get an idea of what can be achieved with a system that reduces the material creation time from 70 days to a few days (e.g. 2 to 3 days in a specific case).
Another empirical value states that error costs caused by process intransparency can be reduced by 80 to 90 percent. The reason: master data management ensures complete transparency along the entire process chain, so that it is always known where the master data process stands. According to estimates, 53 percent of data can be found more quickly and easily.
KPMG AG Wirtschaftsprüfungsgesellschaft (KPMG) also provides the following figures on specific key business indicators in response to the question of the ROI of master data management:
- 2 to 5 percent lower spending volume efficient bundling of orders, better negotiating position for purchases, analyzable vendor structure and master trees; transparency across product groups to know what is being purchased from whom; enough information to evaluate suppliers; faster onboarding of suppliers when process for master data is in place.
- 4 to 7 percent lower IT project costs less data migration effort, as fewer problems occur during M&A activities or system harmonization because data first enters the ERP via the master data system; less data cleansing effort; less system complexity because a master data system transfers the master data, and reduced maintenance effort.
- 1 to 2 percent improved sales growth better negotiating position, transparent price conditions, analyzable customer master data and family trees, identification of cross-selling potential.
- 5 to 10 percent less working capital efficient warehouse management and lower inventory costs, for example, if it is known that ten materials with only a slight difference in the master data are actually one material and consequently the required stock only needs to be held for one material; lower storage costs, efficient invoice management & revenue management.
In addition to these quantitative benefits of master data management, KPMG also identifies a number of qualitative advantages. These include reduced risks due to fewer data protection breaches, lower customs and tax risks (compliance) and reduced credit risks. Master data management not only offers advantages in analytics, but is also essential. Anyone offering predictive maintenance, for example, must already have their master data under control so that they can find the object that is currently in need of maintenance. KPMG sees further advantages in leveraging automation potential if you know what can be derived from the master data and in an improved reputation through smooth deliveries, fewer complaints and a correct customer approach.
Recommendations for master data projects
If you want to exploit the potential that lies in professional master data management, you need to set up an MDM project. The following seven steps have emerged as a best practice approach:
- Deriving the master data strategy from the corporate strategy
- Identification of data objects and allocation to business processes and information requirements
- Development of master data models, definition of structures and relationships for these objects
- Documentation of the master data life cycle from creation to archiving
- Establishment of quality management to improve and maintain master data quality (reliability) 6... Establishment of a central master data organization
- Analysis and selection of a professional IT solution
Even if the return on investment from master data projects is difficult to quantify, the benefits are tangible based on numerous factors. Master data projects are certainly time-consuming, but the effort is worth it. On average, master data management projects reach the break-even point after one and a half to two years.
Find out more about the benefits of standardized data and centralized data management in our blog post "How data management leads to better business results".
