What is Master Data Management? 


Master data management (MDM) is the collection of processes and controls that help an organisation to manage their data, control data quality and ensure it is consistent across all users who need it.  
It is common for the same data to be created and managed in many different systems across an organisation which leads to inconsistent and duplicate information, and so a key aspect of MDM is to create a common data platform when data can be brought together into a unified dataset. This becomes the most accurate and most trusted version of data, that can be distributed back to all systems and teams that need it. This also becomes a data asset across the whole organisation, driving report requirements and data insights. 
There are multiple ways to implement MDM Data Management Platforms that fit many needs, in this blog we will detail these and explain the process in implementing an MDM solution.

Types of MDM 



This is where sets of master data are pulled from various source systems and are then consolidated in the MDM hub. This creates a single view of consolidated, accurate data from these sources. This approach is used for analytics purposes as it is a direct feed from source to a hub meaning there is no impact on the source data making it unintrusive. 



The coexistence method is set up in a similar way to the consolidation method explained earlier however the key difference with coexistence is with this approach, data is now fed back to source once consolidated. This approach makes data quality much better as well as making reporting and access far quicker as master attributes are all in one place. 



The registry approach to MDM works best for systems where there are multiple sources for data with their own rules and structure, in this method the MDM hub assigns global identifiers across all sources and uses them to create a single, reliable version of the data. The benefit of this approach is that the result gives you organised and mastered data for reporting without having to actually touch the source itself. 



Transaction is an approach to MDM where you are bringing in the source data to an MDM hub, creating all of the matching and cleansing rules as per the other approaches however the final part of this process is then pushing back out the freshly mastered data back into source systems. This means your data is now accurate and complete at all times however due to needing to feed data back into source systems this is the most intrusive form of MDM. 

What is the MDM process? 

To get started with implementing an MDM solution, you will initially need to undertake what is known as the data discovery phase. This is where you look into the source systems you are intending on using for MDM and look for common shared data across all systems that can be used for matching in the creation of a single version of the truth. This is one of the more time intensive processes in the implementation of an MDM solution however it is key to making sure everything is accurate going forward. 
Once you have gone through the discovery phase you will then need to integrate your source into your chosen MDM platform, some of these may take solutions and some may not. For an example at Sentinel our Hub solution is a code free integration with source data. 
This then takes you to using MDM to cleanse, validate and standardise your data to achieve a high level of data quality. This is done by first looking at the pool of data that has been imported into the MDM software and cleansing any duplicates as well as duplicate records. 
Once this has been completed and you are happy that a consistent data set has been formed, you then need to create matching rules between your data sources to help create a single view of truth from across all data sources. 
 Thanks to the MDM management and cleansing process, you will now have a more intuitive and insightful way of viewing your data. 

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