Proper management of reference data is an increasingly important success factor in complex system landscapes. Thus, it often concerns organizations as a whole, not just their IT-departments. This article explains the relevance of reference data and shows an approach to realization.

What Is Reference Data?

Data can be categorized based on the different data types like transactional data, conditional data, master data and reference data. Reference data (RD) is a relatively new term and while the basic concept is mostly known, terms and understanding vary. Hence, the first step to managing reference data is to understand what reference data is and identify the relevant data objects.

So, what is reference data? Reference data is data used to classify or categorize other data. Typically, they are static or only slowly changing over time.[1]

Reference Data builds the basis for all other data types
Figure 1: Different data categories with examples

Reference data defines and categorizes permissible master and transaction data records. Compared to master data, reference data usually has an easier structure, less volume and lower change frequency, but much higher degrees of distribution and criticality.

Reference data management (RDM) references to all strategic, organizational, methodical, and technological activities regarding a company’s reference data. Besides, reference data builds the basis for all other data types.

Why Is Reference Data Business-Critical?

Reference data has a high relevance for the business as a considerable amount of data objects and data attributes are based on reference data. Therefore, well-governed reference data with a high standardization enhances business and system efficiency and accelerates business processes.

Reference data has a high impact on data quality as it is used across several types and levels of data and business processes. Reference data affects the integrity of reports and the reliability of reporting. New values of reference data can even (re-)shape business processes (e.g., new customer account groups or new countries). Therefore, it is crucial to harmonize reference data across the company, its business units and IT systems.

Furthermore, reference data management simplifies group consolidation by all means, and the use of the unified reference data objects reduce IT project costs (e.g., reduced migration efforts).

What Are Typical Reference Data Objects in a SAP Environment?

The relevant domains using reference data could be identified based on the business processes and their core segments, e.g., logistics, sales & distribution, and finance & controlling. The table below shows examples of reference data objects assigned to different domains.

Examples of reference data objects assigned to different domains
Figure 2: Examples of reference data objects assigned to different domains

Why Is Reference Data Not the Same as SAP Customizing?

SAP recognized the importance of reference data objects and the necessity of having a structured and guided way for their creation and maintenance. Because those objects were so critical for operations, SAP integrated them into a special area known as “Customizing”. However, part of the SAP Customizing are only reference data objects seen as crucial for operations by SAP and not everything in the Customizing is reference data. Also, reference data objects are not a fixed canon but vary greatly from industry to industry:

 

Customizing Non-Customizing
Reference data Transaction Type, Plant Vehicle Equipment (Industry-specific RD)
Non-Reference data Change Request types, CO-PA Master data, e.g. Material

 

However, SAP recognized that not only master data objects are owned by business, but also the reference data objects. Not only do they spring into existence closely aligned with the business, more and more RD objects are also moved into the maintenance of the business, for example the data objects of intercompany trading partner or transaction type within MDG-F, the data governance solution of SAP, where the finance business unit maintains their finance master data.

Reference Data Challenges in SAP Landscapes

Challenge 1: Customizing

Access Restrictions: With a restricted space for critical data objects in place, rules and configurations were integrated in the SAP Customizing as well. This  makes it practically impossible to execute proper business guidance for reference data objects within standard SAP ERP systems – and rightfully so, since IT and operations cannot allow widespread access to crucial system settings.

Timeliness: Still, the only way for business to manage their reference data objects within SAP Customizing is by tasking the managing IT, which often means having to adhere to release cycles as well.

Challenge 2: Distribution

Reference data objects recognized by SAP are maintained in SAP Customizing (e.g. country, plant, functional area). In a landscape with multiple SAP ERP systems, Customizing is often very individual per system, which makes (master) data transfers often complicated due to different check rules. Also, the synchronization of Customizing itself is a challenge of its own. SAP provides a toolset different from master data transfers, usually called transports, which are again highly regulated by IT departments and not easily deployable.

Hence, a lot of our customers are facing reference data distribution and synchronization issues. As a project example, the assessment result of one of our RDM projects is illustrated below. The group-central MDG is set up with a clear SPoT philosophy for master data and reference data. As the group has a complex system landscape where a lot of additional source systems are involved for data objects as well as target systems of subsidiaries and their transactional systems, automatic data distribution to the various target systems is required.

Exemplary assessment result from one of our RDM projects
Figure 3: Exemplary assessment result from one of our RDM projects

How to Solve These Challenges

Customers who want to tackle these challenges should look for an approach for RDM projects that is independent of the technical toolset and setup. It should support evaluating and supporting the existing reference data landscape and end-to-end processes, and additionally typical data tasks and processes like data cleansing, mapping, and loads. Find out more in our upcoming paper.

Customer Benefits

  • The tailored reference data management solution fulfills the business requirements of reference data utilization and data distribution needs.
  • Short term development and consultant capacities ensure successful implementation of an immediate reference data management solution for E2E business processes.
  • The preparation of a mid-term co-innovation concept helps to build a future-proof industry reference platform for reference data management.
  • Direct collaboration with SAP brings the reference data management industry framework into SAP standard.

[1] DAMA-DMBOK: Data Management Body of Knowledge (2nd ed.). Data Management Association. 2017. ISBN 978-1634622349

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