Increased understanding of master data management

Master data management has long been a misunderstood and difficult area of data management. At the same time, the needs related to digitalisation and tackling the incompatibility of systems are growing rapidly. How are these matters related to each other, and why should MDM be taken into account in planning new investments?

Master data management (MDM) is the bedrock of information management. Master data are formed within the conceptual and reference structures of the large information systems used in business and administration. MDM concerns process configuration, organisation, systems, and integration models that are an essential part of an organisation’s operations.

MDM has long been seen as various things: as the silo-specific management of sub-areas of data, or as being synonymous with harmonisation and one-time repair of registers, or as a support function for reporting. Few of these procedures have proved to be sufficient to understanding MDM, and some have even impeded understanding of it. As a result, the whole value of master data management has been unjustifiably treated with suspicion.

But to see the true value of MDM, it’s it is useful to keep in mind the analogy that nothing lasting can be built on sand; a solid foundation is vital. The same is true for information management. MDM must be understood correctly if data-related matters are to be properly tackled in organisations. Only in this way is it possible to create the real conditions for success.

Information management is becoming more significant and valuable

Regardless of the field, the prevailing trend is towards a higher level of integration and automation. This is also an indispensable part of safeguarding the wellbeing of high-cost societies in the coming decades. And investments in productivity and competitiveness are indeed largely focused on information management. Dealings with banks or the tax authorities about 10 years ago is a good example of a situation in which a manual and labour-intensive process has been replaced by immaterial functions. Seen broadly, the development has benefited both the producer and the customer. The foundation for this complete transformation of services is a sustainable data management model.

Such examples clearly illustrate how MDM and data management are a structural part of business development. However, very few MDM initiatives are in fact launched specifically as MDM initiatives. What is much more typical is that the need to develop master data management comes about as a reflection of a business-driven investment or a developmental need. Therefore, it is of the utmost importance to identify master data management in the right way. Otherwise the management of master data becomes more fragmented, and the silo effect worsens. Scattered and fragmented master data remains master data – it just does not serve the relevant activities very well.

Transaction creation requires more context than the “Golden Record”

Management through data has long been discussed. Leadership with correct and up-to-date information is likely to produce better results. Data warehousing and various reporting solutions are mainstream solutions for achieving transparency. What is characteristic of these is that the data generated in the process gets put into intermediate and post-processing systems to meet the management’s need for information.

Very few have turned to the process to resolve the matter. A structurally complete transaction that has been derived from master data also behaves predictably and consistently in reporting. The degree of transaction management that is directly based on data from MDM is thus directly reflected in the performance and transparency of the management system.

Another common belief is that is possible to find a single truth. The term “Golden record” is often used nowadays with a hint of irony. This is because in reality one very rarely comes across a situation in which a single record can serve the widely differing information needs of an entire process. Often, there has been no need to consider whose perception of the concept of a customer is the truest. All viewpoints are real, and require master data that is in accordance with their processing needs. Part of the data relates to the same role – and can be harmonised. Other parts of the data do not relate to this role, and so cannot be harmonised. However, the deep-rooted assumption that there is a single truth to be found is not justified, nor is it justified from the point of view of realistic integration.

Automation, integration and digitalisation are built on master data

A useful definition of digitalisation is that it is an organisation’s ability to act in automated processes with the aid of data management. The starting point for development cannot be assumed to be that the preconditions for digitalisation could arise on their own from e-commerce software, user interfaces, or new self-service operations model descriptions. Partly moving the business process outside the organisation is only possible through a detailed understanding of the process, and of the content of the information used in it. Since what is at issue is the highest degree of process automation and administrative automation, it’s not surprising that the requirements placed on master data, business regulations, and integration ability are under the most pressure in these situations.

Because the matter is of central importance in many organisations – and is also important nationally – one would hope to hear more discussion of how to handle the background work involved in information management. In a new and extremely competitive digital environment, it is much more realistic to create the conditions for success from a good, system-independent data management platform.

Wholes are needed, not partial solutions

We have lived for a long time in a supplier-driven world, where MDM was spoken of in a technology-centric way, as customer- and product data management. Separated markets, system features, technology solutions, and governance models evolved for these concepts. It was a well-established way of handling MDM through subsets, and very few saw any other dimensions in this matter.

But looked at from the point of view of enterprise resource planning, this is not the conclusion you should arrive at. Depending on the structure of the process and of the system, the formation of a logically complete transaction demands consistent interaction between several data sets. Product data, distribution data, organisation data, and customer data form the core starting points for the normal order-delivery process. These rules of the process are not new – it is just that the conception of MDM has been overwhelmingly general in nature.

Gartner announced in March 2016, that it will discontinue separate market quadrants for product data and customer / party data. The new MDM market-model reflects much more clearly the market/customer needs (instead of supply/vendors), and states that MDM is comprehensive and integrated, and should reach an architectural role that extends to transactional systems. Single-domain systems and analytical MDMs, which support reporting only are no longer recognised as MDM solutions in their own right. It must be said that this development is very welcome. The new quadrant qualification criteria form a much more meaningful starting point for placing master data management within the architecture and within the development model.

A particularly valuable message can be found in the preface, as is evident from the following short passage: ”It is clear that a slow and steady shift in the MDM market is underway. Gartner believes this will continue and likely gain momentum as awareness increases that the benefits of MDM are most transformational at the level of business process.”

Invest in genuine master data management

In accordance with this, the ultimate role and mission of master data management has always been to direct and safeguard the formation of transactions. When properly used, therefore, MDM is a powerful driver for improving process performance and for achieving transparency. This is, after all, precisely why investments into information management are made.

Tomas Stenlund has worked at Ineo for 17 years in information management tasks. Today he participates as a leading architect in data management and integration development for customers. He is also a member of the 3MDM solution concept management team.
This article was previously published in the 3/2016 issue of Sytyke’s membership magazine.