What is master data management?

Master data management or MDM is a method that defines and handles the critical data of an organization, from products, vendors, customers etc, with data integration into a single point of reference.

More specifically this means removing duplicates, standardizing and mapping data and implement rules to eliminate incorrect data from entering the system. This will result in the creation of an authoritative source of master data.

Organizations have this problem from business unit and product line segmentation, in which the customer will be serviced by different contact points, with redundant data being entered about the customer and account.

Master data management has the purpose of incorporating processes for collecting, aggregating, matching, consolidating, persisting, verifying, persisting and distributing such data throughout the organization to ensure alignment, common understanding, consistency, accuracy and control, in the ongoing maintenance and application of this information.

What should an organization expect from master data management implementations:

  • Optimizing the workflow by allowing internal and external stakeholders join in your business processes quickly
  • Ensure that your data quality needs are met by having defined rules that reflect your organization’s standards
  • Attach digital assets and rich content with redefined layout to your structured data for the simplification of the user experience
  • Have a traceability trail and maintain different versions of your structured and unstructured data in one place for better data governance
  • Integration choices to connect with business enterprise systems (ERP, CRM, BI, ESB etc) or third party applications

The above benefits should solve your master data management issues in record time no matter the volume an variety of critical business data. Our implementations on top of open-source data modeling software will make it easier fo you to model data, business rules and workflows that requires configuration not coding. Using our solutions ill allow you to expand to new data domains easily.

What is the goal of master data management?

It basically enables an organization to link all of its critical data into on “file” called a master file, that provides a common point of reference. MDM streamlines data sharing among personnel and departments. Moreover, it can facilitate computing in multiple computer types, platforms and application.

The ultimate goal is to be able to provide the end user with a trusted singe version of the truth.

Poor master data management can occur in a large organization

  1. A customer of a bank has taken out a mortgage and the bank begins to send mortgage solicitations to that customer, ignoring the fact that there is a previous relationship. This occurs when the marketing section within the bank lacks integration with the customer services at the bank. The two groups remain unaware of the existing record link that does not exist consistently.
  2. Mergers and acquisitions usually create an entity with duplicate master data. Ideally, database administrators will resolve the deduplication of the master data. However, this can present difficulties because of the dependencies that existing applications have on the master databases. As a result, the systems do not fully merge and remain separate with a special reconciliation process defined that should ensure consistency between the data stored between the two systems. Because of this, you find organizations with a large number of poorly integrated databases which can cause operational problems in areas of customer satisfaction, operational efficiency, decision support and regulatory, GDPR compliance.
  3. In a federated HR environment, the organization may focus on storing a shallow detail of people data as current status, date of hire, last promotion etc. This simplification can introduce business impacting errors into dependent systems used for planning and forecasting. The stakeholders might be forced to build a parallel network of new interfaces to track on-boardings, planned retirements and divestment which works against the goals of master data management.
  4. One generic issue is with the quality of data, consistent classification and identification plus data-reconciliation. Master data management requires data transformation because the data extracted from the source system is transformed, loaded and updated into the master data management hub.

How does master data get transmitted?

There are several ways to collate and distribute master data to other systems:

  1. Data consolidation - the process of capturing master data from multiple sources and integrating it into a single hub for replication to other destination systems
  2. Data federation - provides a single virtual overview of master data from one or more sources to one or more destination systems
  3. Data propagation - the process of copying master data from one system to another through point to point interfaces in legacy systems

Our service offering includes but is not limited to:

  • Data source identification
  • Data collection
  • Data transformation
  • Normalization
  • Rule and access administration
  • Error detection and correction
  • Data consolidation
  • Data storage
  • Data distribution
  • Data classification
  • GDPR user data management and control
  • Schema and data mapping
  • Data enrichment
  • Product codification

The tools we build and adapt include:

  • Data networks
  • File systems
  • Data warehouses
  • Data mining
  • Data visualization
  • Data federation
  • Data virtualization