Master Data Management
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5 tips for relaxed master data management


Master data forms the basis for successful business partner relationships - it is essential for addressing business partners in a targeted manner and forms the backbone for business and sales processes. That is why many specialists and executives rate the value contribution of master data management for their company as high and critical to success.


The dynamics of digitization make master data management, which is so important for business processes, more difficult

The technical implementation of an MDM does not have to be complex, though. It is true that the dynamics of digitization confront organizations with a rapid pace of development: infrastructures, system landscapes and application environments often change so quickly that IT can only keep pace with agile methods.

A master data management system (MDM) can map and integrate different systems - without disrupting ongoing operations

In order for IT departments to be able to implement an project to manage, maintain and improve reference data without stress, the MDM solution must therefore be able to be implemented "on the fly" and integrate different system landscapes. Uniserv, a specialized provider of solutions for the management of business partner data, has summarized the preparations that IT departments must make for this in 5 tips:

Tip 1: Uniform data set instead of IT proliferation and data silos

The biggest problems that make master data management difficult for IT departments are the heterogeneity of evolved system landscapes and the parallelism of sales, marketing and CRM processes. The result of both is that data from business partners is located in different places. As a consequence, this data can only be used to a limited extent in individual departmental solutions or not at all, because it does not converge and harmonize in a central platform. Complicating matters further is the fact that data not only comes from different sources, its structures also vary. It can be in records, arrays or lists, but also in batches, graphs or hash tables. To make it usable for all departments, these silos must be broken down and data sets must be standardized. A 360-degree view of customers and business partners is only possible by integrating all this distributed and differently structured information into a single data set across the board. The MDM must also be able to map and integrate the most diverse system worlds, regardless of manufacturer.

Tip 2: Ensure data quality

Another major challenge for MDM is the quality of master data. In environments that are insufficiently harmonized and characterized by a pronounced silo structure, for example, the number of duplicate or multiple master data is significantly higher. Incompleteness is another issue.  Due to the fact (as an example) that, redundancies arise across different, distributed systems because of different branches and clerks, multiple online profiles and separate databases that are not synchronized with each other, the matching of clean data to resolve these duplications is hardly possible. Often, matches are then displayed that are incorrect and confront the IT department with virtually unsolvable challenges. But if the quality of the master data basis is insufficient, operational business processes based on it are also not resilient. The result: business departments work with incorrect, outdated or redundant master data. The data must therefore first be quality-cleaned, i.e., checked in particular postally, duplicates must be eliminated, and all data must be brought up to the latest - and same - status.

Tip 3: Establish data governance strategy

In order to ensure that the quality of data in this process is not only produced once, but can be maintained permanently, continuous monitoring and the corresponding reporting must be implemented. Due to the numerous systems distributed throughout the company, the information does not correspond to a uniform standard. Either specifications in accordance with a data governance strategy are missing altogether or they are simply not observed by employees. Different source systems therefore host different data. This phenomenon results in considerable additional effort when it comes to making customer and business partner data centrally usable.

In addition, it is not even possible to record or check the quality of the data at all due to a lack of IT harmonization. Monitoring measures and analyses have to be carried out manually due to data incompatibility, which often happens only sporadically. In addition, comprehensive, (partially) automated monitoring of data quality is virtually impossible in heterogeneous, non-integrated data landscapes. Strategies for optimizing master data management should therefore definitely also take into account the integration of monitoring functions and a data quality firewall (DQ firewall) to support enterprise-wide data governance.

Tip 4: Integrate metadata management

Metadata is equally important for master data management and thus also for master data management. It is impossible to imagine the value creation of Big Data without metadata. They give data records a context and show when and in which systems they were generated. However, if these valuable context clues are not kept centrally - for example, in an overarching data catalog - it becomes very difficult to perform a metadata-based contextualization of information and thus gain profound governance insights. Another consequence of inadequate metadata management, which occurs in combination with distributed, non-harmonized master data inventories: The resulting database is hardly usable for the operational business processes based on it and represents a quality- and security-critical factor.

Tip 5: Comply with GDPR

Last but not least, when setting up a master data management, data protection laws such as the European Union's General Data Protection Regulation are of course important and extremely critical to security. In order to implement its contents, which strengthen the rights of business partners and customers, companies must be able to quickly communicate all collected data to them in a structured form. In many companies today, neither processes nor systems are still designed with this in mind. This should change if master data management is to be successful. If you follow the tips already mentioned, you are well on the way. Now it is a matter of establishing information processes and assigning responsibilities. For example, it must be clearly documented who entered or deleted which information, when, and why. To ensure that all processes relating to the processing of customer and business partner data remain transparent and the highest level of data integrity is guaranteed, the definition of roles and access rights is of fundamental importance for any master data management strategy.The central task for the realization of intelligent management of customer and business partner master data is therefore to maintain consistent and quality-assured master data in the system throughout the company, which can be synchronized back into source systems as required.

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