Reference Data Management
SAP customizing: tackling issues in reference data maintenance
and reference data distribution
Why Reference Data Is Mission-Critical
Reference data management holds significant importance for businesses, as many data objects and attributes rely on it. Consequently, effectively managed reference data, with a focus on standardization, boosts both business and system efficiency and streamlines business operations. In the SAP software environment, reference data is commonly known as SAP customizing.
The impact of reference data on data quality is profound, spanning various data types and business processes. Its influence extends to the accuracy of reports and the dependability of reporting. Additionally, the introduction of new reference data values can reshape or create business processes, such as introducing new customer account groups or countries. Thus, reference data synchronization across the entire company is imperative, including its business units and IT systems.
Camelot’s tailored solutions help you tackle all issues in the maintenance and distribution of reference data.
Reference Data Management
Solutions by Camelot
SAP Customizing Simplified
Camelot’s Reference Data Management solutions offer a multitude of benefits and assist in seamlessly maintaining data across multiple systems with its extensive object coverage.
It provides various solution options for addressing the maintenance and distribution challenges of reference data, tailored to their level of criticality.
Tailored Solutions for Reference Data Management
Camelot provides a variety of reference data management tools designed to assist you in addressing all your reference data and SAP customizing challenges.
Camelot Reference Data Governance
Camelot’s Reference Data Governance solution leverages the extensibility of SAP Master Data Governance (SAP MDG) with a versatile reference data maintenance framework. This application tackles the four primary challenges of reference data management: standardization, process transparency, governance, and data distribution. Reference data objects are categorized into structural, organizational, and functional objects, all of which are included in the standard delivery.
Camelot Reference Data Synchronization
Camelot Reference Data Synchronization serves as a global reference data maintenance and distribution platform. Reference data (customizing) is centrally maintained once in the existing maintenance transactions such as SPRO. An application for distribution offers all the necessary functions to distribute the maintained data comprehensively or selectively to designated target systems. Camelot’s Reference Data Synchronization can be operated from any SAP system and doesn’t require an SAP Master Data Governance installation.
Camelot Reference Data Analyzer
With the Camelot Reference Data Analyzer, you can pinpoint gaps in your utilization of reference data throughout your landscape. Our solution assists in identifying reference data that isn’t utilized in any of your other data objects, such as Material or Business Partner master data. Conversely, it flags inconsistent reference data that no longer exists but is still being used in other data objects, like Material or Business Partner master data.
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How You Benefit from Reference Data Management
Discover how our reference data management software transforms the way businesses manage reference data, delivering a wealth of advantages. From simplifying data maintenance workflows to boosting system efficiency, Camelot Reference Data Management ensures data integrity and dependability throughout your organization
Prevent breakdowns in your distributed business processes
Camelot Reference Data Management empowers organizations to establish a clear, company-wide process for reference data maintenance.
Improve overall data quality
Our solution introduces an efficient and transparent change process for reference data, slashing the costs associated with correcting inaccurately maintained data.
Establish end-to-end data governance
You only have to maintain reference data centrally once, giving you total control over any changes.
Cut down on rework cost
Identify information owners responsible for specific data effortlessly, including analyzing system usage.
Enhancing Cost Efficiency
Avoiding the Risk of Financial Loss
Inconsistent customizing incurs costs in the long and short term. The more incidents and inconsistencies you have in your systems the bigger the risk and of losing money. A synchronized system landscapes with a dedicated reference data management platform helps you to be the most cost efficient. We show you how to get there. Read more in our blog post.
FAQ
A comprehensive data management strategy encompasses all data types. Implementing specific reference data management practices can help mitigate disruptions in your business processes across your entire system landscape.
Contact us today to schedule a demonstration of Camelot Reference Data Management.
Data can be sorted into various types, including transactional, conditional, master, and reference data. While the basic concept of reference data is generally understood, there can be variations in terms and comprehension. Reference data is data that classifies or categorizes other data, typically remaining static or changing slowly over time. In contrast to master data, reference data typically boasts a simpler structure, lower volume, and less frequent changes. However, it tends to have significantly greater distribution and criticality levels.
Read more in this blog post.
Reference data define and classify other data. It typically changes only slowly over time. Examples of reference data include Currency, Payment Terms, Account Groups, or Measurement Units.
Reference Data Management encompasses the following key components:
- Easy maintenance: It should be easy to maintain and synchronize data across the system landscape.
- Data Integration: Distributing data and interacting with enterprise, analytical, and governance applications should be effortless.
- Controlled replication: Not every data is needed by every system, therefore a functionality to provide the receiver system with specific values is needed.
- Key and value mapping: This helps in managing complex mappings between different data domains and reference data representation across the enterprise landscape.
Reference Data Management (RDM) is the process of managing data that references other data elements, providing context for business transactions, and facilitating classification and categorization. It serves all kinds of business processes.
On the other hand, master data management (MDM) is specifically concerned with managing core business entities. MDM focuses on creating a centralized source solely for master data, effectively breaking down silos. In contrast, RDM places its emphasis on ensuring consistency and interoperability, ultimately enhancing the overall data quality, particularly within master data sets.
The main challenges associated with reference data management are:
- Data quality: Many organizations struggle with maintaining consistent data quality across the enterprise, primarily due to a lack of comprehensive understanding of their data requirements.
- Absence of a centralized reference data management system: This leads to siloed maintenance of reference data, lacking synchronization across the enterprise. Consequently, this scenario fosters the potential for data duplicates and poor quality.
- Distribution considering heterogeneous keys: Since the same reference data can have varied meanings across different organizational entities, failing to map them properly could result in disruptions to business processes.
Establishing a golden copy or single source of truth for reference data management is a foundational step. Similar to master data management, the goal is to centralize data maintenance and distribution within a single system. Key mapping is crucial for supporting seamless distribution and integration across the system landscape, ensuring consistently high data quality. Therefore, integrating reference data management into an organization’s data management strategy is essential for success.
