Although reference data are critical to numerous business processes, they often receive little attention. Few companies have a strategy for systematically harmonizing and maintaining these data. The result: clear responsibilities are lacking, necessary adjustments are delayed, and the risk of inconsistent or erroneous datasets increases. To overcome these challenges and ensure data quality, a holistic approach is required—beyond just tool-based solutions.

When it comes to master data, most companies have understood the value of systematic data management: complete, well-maintained, and optimally coordinated material, customer, supplier or financial master data make companies more efficient and successful. Many of them have therefore already established a central master data organization and corresponding governance processes in recent years.

Another type of data is often overlooked: Reference data forms the basis for a variety of master and transaction data. It provides important information for structuring, classifying and categorizing the respective data object and is often distributed in the form of a key across the entire system landscape in a large number of SAP and non-SAP subsystems. If the reference data of the respective subsystems does not match, data objects may not be assigned - there is a risk of process interruptions.

Reference Data: The Overlooked Complexity

Reference data consists of a defined set of values that are referenced by other data objects as part of business processes.This includes data such as country codes and currencies, units of measurement, temperature conditions or defined organizational units.Behind each of these data types, there are usually additional parameters that determine, for example, the format in which controlling information is coded in the respective country.The resulting complexity is often underestimated.

This results in two challenges for companies:Firstly, the large amount of information linked to a data object, such as a country, means that reference data cannot usually be clearly localized, but is used in a variety of systems or has an impact there, but it is not clear in which system leading maintenance is carried out. On the other hand, parameters such as country settings are not always clearly defined, can vary depending on local use or purpose and therefore often require special technical or local expertise to determine and interpret the data.

Challenges of Managing Reference Data

Due to the general conditions described above, maintaining reference data is often very time-consuming. Although reference data is generally largely stable, changes may be required over time.On the one hand, these adjustments become necessary due to external influences, such as when country information changes, new countries are added or regulatory authorities such as the ISO adapt their systems, as in the case of Brexit.On the other hand, the integration of third-party systems or the acquisition of business units can lead to reference data having to be adapted or harmonized. In this context, companies are typically confronted with the following challenges:

• Lack of Awareness
  One of the main reasons why reference data management is neglected in many organizations is a lack of understanding of the importance and complexity of this type of data. The effort and impact associated with incorrect reference data is often underestimated. This leads to a lack of willingness within the organization to engage in reference data harmonization initiatives.
• Unclear Responsibilities
  In contrast to typical master data objects such as customers or suppliers, which can often be assigned to individual departments, reference data is used by almost all organizational areas in different ways. This makes it difficult to define clear responsibilities for certain objects of this data type. At the same time, teams such as the central master data organization often do not have the organizational clout or the technical expertise to define reference data centrally.
• High Efforts for Management and Harmonization
  Particularly in historically grown systems with a large number of connected processes, adjustments to the reference data are associated with a great deal of effort, as they have to be maintained in different subsystems by different departments. This often means that only absolutely necessary changes are made in the systems directly affected, without taking a holistic view.
  
  

• Complex Impacts of Data Adjustments
  Many reference data, such as country assignments or units of measurement, are so fundamental that they are used in numerous places in the SAP data model. This ramification makes it extremely difficult to understand or predict the effects of changes to reference data on the entire system chain. At the same time, the lack of transparency increases the risk of misconfigurations, which could potentially lead to serious disruptions in central business processes.
  
  

As a result, many organizations are reluctant to intervene in the reference data, postpone necessary changes and resort to data mapping instead of carrying out genuine data harmonization.

Benefits of Harmonized Reference Data

Although the harmonization of reference data is a more complex process than mapping, it offers great advantages in terms of sustainable data management. Organizations benefit in several ways: on the one hand, consistent reference data is the prerequisite for an overall higher (master) data quality and improves the reporting and analysis capabilities of organizations as well as the exchange of data with external ecosystem partners such as customers or regulatory authorities. Secondly, harmonized reference data reduces the integration effort when connecting new systems as part of acquisitions or when integrating third-party systems through the central provision of consistent operational standards. And finally, centrally maintained, up-to-date reference data serves as a central knowledge repository that supports the smooth implementation of cross-border business processes and reduces coordination efforts when setting up new interfaces or establishing new processes.

Approach to Establishing Sustainable Reference Data Management

The harmonization of reference data is much more than a purely technical challenge. To ensure effective and continuous reference data management in the organization, a holistic approach is required that clearly defines the responsibility for reference data in the organization and establishes effective governance structures and processes for the organization-wide management and synchronization of reference data.

The first step is to define an organization-wide standard that clearly defines responsibilities for data maintenance. It is important to ensure that the respective unit has the mandate from management to enforce reference data harmonization throughout the entire organization and also has the necessary technical expertise. Once the responsibilities for data management and maintenance have been defined, the organization should create transparency across all systems used as to where in the existing system landscape the reference data deviates from the target standard and where adjustments need to be made. On this basis, the necessary measures for synchronizing the reference data can then be planned and implemented. In order not to overburden the affected units and to avoid process interruptions, it is advisable to examine the effects of these adjustments as part of a change impact analysis. Data harmonization can also be carried out in stages, with only new systems initially adopting the target standard and existing subsystems being mapped. Once the adjustments have been implemented, robust governance processes must be introduced for the continuous management and maintenance of the reference data. In this phase, appropriate software tools can also help to maintain and provide the reference data centrally.

Conclusion

In the discussion about data quality in companies, reference data is not usually the focus of attention. A lack of knowledge about the complexity and effects of inconsistent and poorly maintained reference data means that many organizations do not yet have a systematic approach to reference data management. Trying to close this gap at the tool level alone is not enough. Instead, responsibilities and processes must be established to ensure that reference data is consistent and up-to-date across all systems in the long term. If implemented correctly, integration and coordination efforts relating to the associated business processes can be effectively reduced - and reference data management becomes an enabler for sustainably good data quality as the basis for better decision-making and analysis capabilities within the company.

Companies that are already looking at their master data strategy, replacing their master data tool or setting up new systems as part of the S/4HANA transformation should therefore also take a look at reference data and take the opportunity to define overarching standards and establish processes in order to track them effectively.