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 business systems and facilitate computing across system architectures that contain a variety of platforms and applications. In addition, effective master data management helps make data used in business intelligence (BI) and analytics applications more reliable.

Master data management software provides a single view by consolidating data from multiple source systems into a standard format. In the case of customer data, MDM harmonises it to create a unified set of master data for use across all applicable systems. This allows organisations to eliminate duplicate customer records with mismatched data, giving operational workers, business executives and data analysts a complete picture of individual customers without having to piece together different entries.

How it works

MDM solutions comprise a wide range of data cleansing, transformation and integration practices. As data sources are added to the system, MDM initiates processes to identify, collect, transform and repair the data. Once data meets quality thresholds, schemas and taxonomies are created to help maintain a high-quality master reference. This allows enterprise-wide data to be accurate, up-to-date and consistent.

How to implement MDM

For companies that want to implement an MDM system in their organisation, a number of steps are recommended. The first step should be an assessment of the organisation's enterprise data management situation.

In addition, it is time to define the strategy and have a clear vision of what the objectives to be achieved with MDM are.

Based on this assessment, the rest of the steps to implement MDM can be taken.

• Document and model data and business processes. Data sources must be identified and metadata defined. An IT architect should design the MDM architecture according to the organisation's focus and objectives.
• Analysis. Once the data sources for the chosen area have been identified, analysis must be performed to evaluate the data flow and transformation rules. It is also time to refine metadata definitions and define master data quality requirements.
• Construction. Build the MDM database, according to the architecture that has been created. At this point, collaboration between different departments and professionals is very important, so that the development takes into account all points of view.
• Implementation. 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 to the success of the MDM strategy.
• Data governance. It is important to determine access rights and assess data quality levels for MDM. This is the best way to determine rules and policies to analyse, secure and manage data well.
• Maintenance. Once the previous steps have been completed, it is time to carry out the internal changes for the first iteration. This is the time to plan and deploy the next iteration, 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 that the smooth running, progress and scalability of the project can be guaranteed, ironing out any stumbling blocks that may be encountered along the way.