Efficient data management (MDM: Definition and how to implement it in your company)

Importance of MDM

When done correctly, master data management can also streamline the exchange of data between different enterprise systems and facilitate computing in system architectures containing a variety of platforms and applications. In addition, effective master data management helps to make data used in business intelligence (BI) and analytics applications more reliable.

Master data management programmes provide a single view by consolidating data from multiple source systems into a standard format. In the case of customer data, MDM harmonises them to create a unified set of master data for use in all applicable systems. This allows organisations to delete duplicate customer records with non-matching data, which provides operating workers, business executives and data analysts with a complete picture of individual customers without having to combine different entries.

How they work

MDM solutions comprise a wide range of cleaning, processing and data integration practices. As data sources are added to the system, MDM initiates processes to identify, collect, transform and repair data. Once data meet quality thresholds, schemas and taxonomies are created to help maintain a high quality master reference. This allows the company's data to be accurate, updated and consistent.

How to implement MDM

For those companies that want to implement an MDM system in their organisation, it is recommended to follow a series of preliminary steps. The first of these should be an assessment of the situation of the organisation's business data management.

It is also time to define the strategy and have a clear vision of what the objectives to be achieved with MDM are.

From this assessment, the rest of the steps to implement a MDM can be taken.

• Documenting and modelling data and business processes. Data sources must be identified and metadata defined. An IT architect must design the MDM architecture according to the approach and objectives of the organisation.
• Analysis. Once the data sources for the chosen area have been identified, an analysis must be carried out to assess the data flow and the transformation rules. It is also time to refine metadata definitions and define master data quality requirements.
• Construction. Build the MDM database, in accordance with the architecture created. In this regard, collaboration between different departments and professionals is very important, so development takes into account all the points of view.
• Implementing. It is time to complete the database with the master data of the first thematic area and the associated metadata. Of course, choosing the right technology is part of this step and will be critical for the proper functioning of the MDM strategy.
• Data governance. It is important to determine access rights and assess the quality levels of data for the MDM. This is the best way to determine rules and policies to analyse, secure and manage data properly.
• Maintenance. Once the previous steps have been completed, it is time to carry out the internal changes for the first iteration. This time is used to plan and deploy the next one, until the MDM programme is fully deployed.

It should be noted that, on this last point, it is essential to have previously developed a data culture in the company so that all the actors involved are aware of the changes that are going to take place and assume them as their own in order to achieve the objectives set.

As we can see, the trick is to start small and grow in a modular way, so as to guarantee the smooth running, progress and scalability of the project, ironing out any pitfalls that may be encountered along the way.