What is MDM?
MDM stands for Master of Data Management and is a model for maintaining operational information in a quality-enhancing and cost-effective approach.
The main purpose of master data management is to maintain a so-called Single version of the truth for each entity, thereby obtaining an easily accessible and accurate source for all information and avoiding conflicting and ambiguous data for any one entity.
MDM includes all the information needed for a business to function and be effective. This includes customer and supplier information, personnel information, article and product, logistics data, etc.
Why is MDM needed?
When each entity is maintained in several different systems and by different roles within an organization, the same information needs to be added and enriched individually. This creates a high risk that the same entity contains different, and in many cases conflicting, information for different parts of the business. To ensure the quality of the information, costly and in many cases complex synchronization between different systems is necessary, often without common identifiers with a high risk of duplication. An implemented MDM strategy ensures that an entity has one and only one current version within the organization and that all parts of the business can take advantage of it whenever they need it.
Mergers and acquisitions
When companies merge, for example in a takeover, a situation arises where you have at least two or more “versions of the truth”. Without a defined and implemented process for master data, cost and risk increase during the merger effort.
These types of mergers are often complex due to the fact that within each organization there are own models of data and entities and unique dependencies between different systems. Without an implemented strategy for MDM, the complexity increases further. Not infrequently one is forced to settle for the fact that the merger cannot be fully implemented. Therefore, these projects often end in the implementation of unique processes in order for different data models to coexist. This, in turn, entails increased and persistent maintenance costs.
A well-defined MDM strategy facilitates merger work by providing a clear and uniform definition for each entity type. If this is also defined for the organization with which the merger is made, the conditions for a more successful outcome are also increased.
Reduce the risk of data silos with MDM
A data silo means that you have a subsystem that does not share its data with anyone else. There is no benefit in the rest of the operations from what is supplied in the silo. A typical case for silos is that on one side you have a marketing department running customer-specific and targeted campaigns, and on the other side a sales support system (CRM), but no information exchange between them. Therefore, the market does not fully understand the current prospects. The sales department, on the other hand, finds it more difficult to synchronize completed campaigns with their efforts towards customers. A target-oriented marketing campaign can then go completely wrong because it has not been able to take into account current customer relationship information even though it is available in the CRM system.
MDM reduces the risk of data silos by enabling efficient and quality-assured synchronization of data between systems. With MDM applied to the example in the paragraph above, one gets a uniform definition of what is “customer” and where is “prospect”. This makes it possible, without the risk of duplication, to provide the marketing department with up-to-date information on new prospects, customer status, etc. The sales organization receives reliable data on which customers entered into various marketing efforts, when they were carried out and in the best of cases — even the results of them. For example, if a customer has clicked on a certain CTA (Call To Action) in a promotional email — a good opportunity for the responsible salesperson to pick up the phone and book a customer visit.
MDM and GDPR
The still fairly new data protection regulation, GDPR (General Data Protection Regulation), increasing the need to have control over one's data, and especially where within the organization it occurs. Every individual has the “right to be forgotten” through GDPR. It is then the responsibility of the business to ensure that all instances of that person's data are deleted. Without MDM, this is made more difficult because there are no clear definitions about what data exists in the business, where it exists, or to what extent it exists.
If MDM has a “single version of truth”, and through it a uniform approach to clearing out singled out information from all consuming systems, you better guard against the heavy fines that you face in connection with a GDPR audit.
MDM system for managing important data
Master of Data Management is built around the principles a business has for handling important data. These principles include data models, tools used to manage data, roles and responsibilities, standardization, and technologies.
To maintain this, IT support is often needed whose purpose and functions support the MDM work. These include tools for data models, processes, enrichment, data quality, etc. needed for an effective MDM strategy.
There are a variety of MDM systems on the market, each with its own niche. It is therefore not possible to single out a single system as the optimal one for all activities, one should evaluate them on the basis of their own activities and their needs before making an investment decision.
Gartner periodically evaluates MDM systems available on the market and presents them in its Magic Quadrant for Master Data Management Solutions. The square is divided into four parts into which the systems are distributed – Niche Players, Challengers, Visionaries and Leaders.
Fiwe works with solutions from both Informatica which is the leader in the quadrant and is aimed at the enterprise segment and Centric (Formerly Contentserv) targeted at SMEs defined as challengers in the quadrant.
