Professional master data management as a success factor
Data management needs order and discipline.
Master data management - even as MDM or master data management - doesn't sound very exciting. At a time when knowledge doubles every five years or so, is it still necessary to deal with this actually "trivial" topic?
Business practice shows that correct data is still a complex issue: marketing campaigns do not bring the desired success because email addresses are not properly maintained, contracts are returned as undeliverable and duplicates cause unnecessarily high costs. System migrations often fail because fields in the old system have been misused due to a lack of flexibility in the data model, which makes it difficult to assign data correctly. [1] The formula applies:
Overall quality (GQ) =
System quality (SQ) x User quality (AQ)
It illustrates that although master data management can be supported by qualitative software, user quality, i.e. awareness of the importance of correct master data and the fatal causes of incorrect master data, is just as crucial for overall quality. Only when SQ and AQ reach a value as close as possible to 1 can an appealing overall quality be achieved (target: GQ = 1).
Data management requires order and discipline [2]
Information is only useful if it is up to date, consistent and uniformly presented. Ensuring data quality is therefore an organizational challenge that must be mastered at the point of origin and use of the master data in the specialist departments.
The costs of good data quality include costs for error detection and prevention. In addition to the hardware and software costs for supporting data quality management, this also includes the personnel costs for developing data requirements. On the other hand, there are the costs of poor data quality, i.e. costs caused by data quality problems. These include, among other things
Rework costs,
Costs of problem management,
costs of unused business intelligence reports. [3]
A functional architecture that addresses the levels of strategy, organization and systems is ideal for professional master data management.
Management consultants Roland Berger cite the following aspects as key success factors:
Strategy - management buy-in: master data management and the associated data harmonization are long-term programmes that often require procedural and organizational changes. Strong management buy-in and support is a fundamental prerequisite for long-term and sustainable success.
Organization - Master data management is not just an IT project: the project is a cross-cutting issue within the company. The activities, processes, functionality and structures of master data management must therefore be coordinated across the various business areas. As master data is always managed by the business and not by IT, master data management is primarily a task for the specialist departments and all users accessing their systems at the point of sale (POS). If necessary, a separate management system and a specific process and organizational structure must be set up for this purpose.
Systems - acceptance through simple workflows and IT services: The better the usability of a system, the higher the level of responsible data entry. If the person entering the data has to collect data unnecessarily or is not supported by tools such as "address helpers" or direct plausibility checks, the initial data entry will not provide sufficient data quality for the smooth handling of subsequent processes such as rating. Irrespective of this, comprehensive IT support with regard to system interfaces is necessary and services, e.g. for the provision of duplicate reports, are desirable. [4]
Ongoing progress and quality checks are also essential: In order to measure overall progress and sustainably improve data quality, key performance indicator systems and controls should be established. Practice shows that simply setting rules is often not enough. The desired effects can only be achieved with the use of control mechanisms. Simple metrics can be developed quickly, such as the fill rate for optional fields. The general rule is: turn those affected into participants. If ideas from the specialist departments are taken into account, this in turn contributes to increased compliance with the input rules.
Data quality is volatile
When companies are surveyed, they realistically estimate the efforts to maintain data quality to be rather high.
It is important to set up precise processes for recording data changes (e.g. customer relocation) and to provide employees with simple options for checking, correcting and adding to the databases for which they are responsible. This is the only way to ensure that the efforts invested in data quality using resources and budget are not in vain.
Why the effort?
The following motives can be cited for successful master data management: [5]
More effective business processes by reducing process costs due to lower error rates, less exception handling / escalations due to inadequate master data quality;
Fulfillment of governance, risk and compliance (GRC) requirements, particularly against the backdrop of increasing regulation for financial service providers;
greater flexibility for new business models, new markets and/or new systems.
How afb successfully supports you in master data management
For many years, we have set ourselves the goal of effectively supporting our customers in the complex process of procuring and qualitatively preparing the database required to process credit applications - from the offer to the customer to the application with fully automated credit decision and processing through to contract management.
This is made possible on the one hand by helpful functionalities in our Credit Management Solution (afb-CMS) and on the other hand by our comprehensive data services.
Selected functionalities for efficient data management workflows:
Data capture / validation: supplementing the already familiar data plausibility checks with functions for data evaluation and completion. Data evaluation and completion takes place directly at the POS, i.e. on the one hand directly in the sales conversation (highest probability of correction) and decentralized by the employee at the POS (reduction of correction effort for the financial service provider). Indirectly, this also increases the quality of the (automatic) initial decision.
Interfaces to external data pools: Static data pools (e.g. address data, account data) or dynamic services from third-party providers that are directly connected to the afb-CMS can serve as data sources.
Simple object entry: The employee at the POS can efficiently search for or specify an object to be financed using the object search by connecting to an integrated vehicle catalog or a transfer option from the Wholesale finance inventory. Furthermore, the afb-CMS offers the possibility of quick entry of objects during application entry (e.g. make and type designation for vehicles).
Benefit from these advantages!
Support at the POS: User-friendly mandatory field design, automatic data formatting during input with immediate feedback and high-quality object information support the business partner at the POS during consultation and sales.
Flexible search: Convenient search mechanisms with predefined filters in combination with individual search and sorting criteria support the user in the search and selection of objects and help to avoid duplicates.
Process guidance: Workflow support by highlighting primary and secondary functions and providing immediate feedback (e.g. on the progress of extensive entries).
Error handling: Possible errors are already identified by the user during input and can be easily corrected.
Easier entry of object data: Data from the Catalog Data Management solution can be transferred directly to Retail Finance, Sales Leasing and Wholesale Finance. The data is then available for the subsequent financing and leasing calculation. This reduces the need to enter data to a minimum.
Data maintenance as required: On request, we offer our customers individual additional data maintenance according to individual requirements.
1 The complexity of this topic can be increased if not only company-wide but also inter-company master data management is considered.
