Master Data Management: how to extract value from your assets

During the current Information Age, based on a huge impact of digital technologies and on the proliferation of data, connecting business strategies to a correct data collection and administration is the key to success. It is now necessary to take a data-driven perspective and to focus on data management systems and analytics, which hold a primary position.

One recurring problem is that during the implementation of a data management methodology managers often realize that sometimes it happens to lose sight of some crucial factors for the accuracy of the entire process.

Above all, the ability to manage Master Data embodies a decisive challenge. The term is referred to those data that describe the core entities of business, such as products, customers, locations and suppliers. Although it might seem irrelevant, the management of Master Data has a great impact on business and a real problem concerns the eventuality that those data, which cross different corporate areas, turn out to be fragmented, duplicated or out-of-date.

Things can become even more difficult since a single and a generally shared view of data should not be restricted to a company level, but it should also consider company’s relationships with external stakeholders: consumers, retailers, brands and wholesalers ought to follow a common standard in the comprehension of the information.

However, an integration effort made by all supply chain players, as well as being too expensive, in many cases turns out to become a really difficult matter because of a conflict of interests between all the parties involved. A correct data integration process does not necessarily focus only on the possession of the same data, but it is most of all about aggregating them and making them visible to all the players even if through different ways and formats, respecting any needs and requests.

For this reason, what companies are now searching for is an external party who stands as a ‘trust mediator’ to whom entrust their own data, so that it is possible to define a vision of data which is unique, accepted and generally shared by all the participants involved. This information trust mediator should handle all the interests related to those data, and it becomes essential for the management of both supply chain members’ expertise and systems operating costs.

It often happens that these people are not completely aware of the opportunity to achieve excellent results starting from data that they already own, since they usually miss some critical points in the application of their method. It is therefore essential to build a Master Data Management process that should not be aimed at generating revenue, but which should be intended to analyze data and to obtain some relevant insights from this analysis.

The Master Data Management procedure consists of some key passages:

Data categorization – Every Master Data is composed by a series of related fields that are filled by specific information. To guarantee data reliability, each field is associated with a single information. The data categorization aims at assuring this uniqueness and at searching for as many data definition variables as possible, in order to make use of algorithms for automatizing these activities and adopting a predictive logic.

Data integration – In the next step, those fields need to be integrated with a series of additional information, since data are not only Master Data referred to products, clients and other entities, but there is even more info associated to them. It regards for example behavioral profiles, stock data or sell-out data; analyzing these data’s relation to Master Data is fundamental for business.

Data cleaning – Lastly, it is necessary to avoid the occurrence of obsolete, duplicate or out-of-date information. The cleaning of all data and their comparison, useful to solve some potential problems, is a human labor which can be improved by artificial intelligence. It is in fact possible to use algorithms capable of identifying these problems and to automatize the clean-up process. Once the Master Data has been cleaned and adjusted, it would be possible to gain several insights which can direct future business decisions.

For the achievement of the entire process it should be useful to prior define some KPIs which could be used as a wake-up call for the accuracy of every informational content in the fields. This is due to the fact that not only the systems’ architecture is relevant, but also the content reliability and quality of data is.

In conclusion, the implementation of a Master Data Management structure given by a third party may offer several advantages to the business. However, before dealing with the execution of this procedure, people should understand that it is impossible to start with knowing before at what level of accuracy the process will end up; thus, companies cannot begin with a certainty of a profitable consequent result. Then it makes sense to prior define what error level people are willing to endure for the possibility to extract value starting from the optimization of certain assets which are daily available, their data.

Online sources:

SAS Institute Inc. (2012), Tutto quello che è necessario sapere sulla gestione strategica dei dati,

Ferguson M. (2019), The importance of Data Architecture in a Data-driven enterprise,

Boldrini N. (2011), il Master Data: invasivo ma con vantaggi diretti sul business,

Barriolade C. (2019), Cos’è il master data management e perché garantisce il controllo dei dati,

Haselden K., Wolter R. (2006 – updated), The What, Why, and How of master data management,