Data relationship management
Data Relationship Management (DRM) is part of master data management and supports users in the active management of complex operational and master data . The master data includes all basic data that is not changed in the operational environment over a certain period of time (e.g. article master data, customer master data, supplier master data).
Organizations can make changes in their departmental perspective while ensuring compliance with corporate standards. Regardless of whether financial or analytical information is being processed, data relationship management delivers timely, accurate, and consistent data to drive ongoing operational execution, business intelligence, and performance management .
Use in companies
In particular, through data relationship management - the extraction of hidden forecast information from large databases - companies can identify valuable potential, predict future behavior and enable companies to make active, knowledge-based decisions. The automated, forward-looking analysis enabled by DRM goes beyond the analysis of past events typically provided by historical tools such as decision support systems. DRM tools answer operational questions that have historically been too time consuming to pursue. But it is precisely the answers to these questions that make data relationship management possible. There are different techniques in DRM software, each with their own advantages and challenges for different types of applications.
As companies grow and develop, it is becoming increasingly important to manage master data across information silos. Information silos are created, for example, by mergers or acquisitions, the dissolution or merging of departments or simply by inadequate data management. Data consistency, integrity, quality and accuracy suffer as a result, and no one trusts the resulting information and findings. Therefore, the approach of data relationship management is particularly suitable in companies that collect different data from various sources and that are active in a dynamic, rapidly changing industry.
User-friendly interfaces for business and IT
DRM acts like a platform for managing critical corporate data that normally requires human judgment. This platform saves companies time and resources to resolve inconsistencies by optimizing manual, error-prone, and uncoordinated change events.
With DRM, IT administrators can also ensure data integrity and security by aligning data management processes with company policies. IT can codify business rules and configure validations to ensure that users do not compromise the integrity of corporate master data when comparing, exporting, or modifying data relevant to their department.
Hierarchy management to manage complexity
A functioning DRM is also characterized by the fact that, in addition to attribute management, the hierarchies are also managed automatically. In particular, it includes maintaining the drag & drop hierarchy in order to optimize the process of updating hierarchy elements. It also enables head-to-head comparison and navigation across functional perspectives so users can view data and spot inconsistencies between views. Entire data nodes can be copied and used across versions to maintain reference accuracy. Referential integrity is built into the product by enforcing business rules that ensure, for example, that a parent record in alternate hierarchies is always linked to the same child records and that changes to child records are automatically synchronized.
Intra- and cross-dimensional hierarchy support provides the flexibility to manage structures of many types to suit the needs of the business. By supporting balanced and irregular hierarchical structures, users can manage hierarchies regardless of how the data must be stored or represented in a particular target system.
Importing, mixing and exporting to synchronize master data
Systems that enable data relationship management typically have extensive import, mixing and export functions with which changes can be made either in the recording system or in peripheral systems. The import function enables the bulk loading of entire hierarchical structures and their attributes from source systems, creating an import profile that can be configured based on the specifications and format of the source system. With the help of a merge function, users can selectively merge data from an imported hierarchy into an existing hierarchy, or merge the corresponding data across a number of existing hierarchies. The exact specifications of a software vary, of course.
Once a recording system has been set up, users can export data using wizards that can be configured according to the needs of the target system. It is possible to configure an export function to filter, compare, transform, balance hierarchies and avoid duplication of work.
Versioning and what-if modeling
Data relationship management plays an important role in the migration to or introduction of new systems due to organizational changes such as the takeover of a new business area, the reorganization of a regional sales team or the coordination of planning and production systems. The master data versioning and modeling capabilities of DRM software are different from other solutions and enable organizations to conduct what-if scenarios and impact analyzes to determine the impact of such changes before they impact production systems. Hierarchies can be versioned and stored in external files for archiving purposes or used to transfer and share hierarchy elements.
Audits with DRM
Changes to master data using manual processes such as spreadsheets, phone calls and emails are time-consuming and error-prone. To meet auditor requirements, organizations must maintain documentation and manually create a full audit trail for such changes, which often impacts compliance and risk management initiatives. Data Relationship Management provides a framework for querying, comparing, and fully logging hierarchy management activities, including detailed transaction history for full compliance with various audit requirements. In addition, the standard setting can be used to roll back to a specific point in time and provide an insight into what the master data looked like at that point in time.
Security models in DRM
Another characteristic of DRM is that administrators can use a precise security model that not only controls the dimensions and hierarchies to which users have access, but also allows differences in access based on the version in which the data is located. The security model enables adjustments to the functions and actions that users can perform on the hierarchies to which they have access.
Some data relationship management vendors also allow companies to make critical corporate master data available to anyone interested in business by creating public views that can be accessed anonymously. Casual users can reference and download published data and gain a deeper understanding of dimensions and attributes through a browser-based, read-only interface.
Human workflow to promote governance through collaboration
Data Relationship Governance offers a configuration-based approach to delivering collaborative workflows to automate change management and data correction processes. Front-line company users can send enrichment change request approval workflows to subject matter experts and industry approvers for authorization before making changes to production versions of master data. In a similar way, data administrators can assign tasks to members of their work group to synchronize hierarchy changes, correct, complete or enrich master data attributes in order to increase the consistency, quality and correctness of the entire master data repository.
literature
- A. Berson, L. Dubov: Master Data Management and Customer Data Integration for a Global Enterprise. McGraw-Hill, New York 2007, ISBN 978-0-07-151089-9 .
- Ulf Troppens, Rainer Erkens, Wolfgang Müller: Storage networks . dpunkt, 2nd edition, Heidelberg 2008, ISBN 978-3-89864-393-1 .
- H. Krcmar : Information Management. 4th edition. Springer, Berlin 2005.
- Prof. Dr. Freimut Bodendorf : data and knowledge management . 2. updated & exp. Edition, Springer Verlag, 2006. ISBN 978-3540287438 .
- J. Radcliffe, A. White, D Newman: How to Choose the Right Architectural Style for Master Data Management. Gartner , Stamford 2006.
- M. Scheibmayer, E. Naß, M. Birkmeier: Master Data Management - White Paper. RWTH Aachen, Aachen 2011.
Individual evidence
- ↑ Prof Dr Richard Lackes: Definition: master data. Retrieved August 18, 2020 .
- ^ Oracle: Oracle Data Relationship Management. Retrieved on August 25, 2020 .