Master Data Management – The First Steps in Data Consolidation

Master Data Management

Master data management provides a consistent view of key business information. This includes data on customers, products and locations. It also includes reference data, such as postal codes, transaction codes, financial hierarchies and state/country codes.

MDM helps ensure data accuracy by implementing processes for data validation and data quality checks. It can help reduce operational errors and optimize business processes.

Data Retention

It’s easy to see why master data management became a necessity for businesses — inaccurate or inconsistent data can negatively impact customer satisfaction, operational efficiency and decision-making. Even minor errors can cause major headaches like a wrong email address sent to a customer or a product that appears in one database but not another.

This is particularly true in organizations with multiple departments and systems that handle the same data. Without the right people, processes and technology in place to manage a single version of truth, different versions of master data about a business entity are likely to exist, creating inefficiencies in operations and limiting analytics capabilities.

When choosing a master data management implementation style, consider the type of data you will be managing and how your organization will evolve. You don’t want to make design decisions that will preclude you from incorporating new types of data into your MDM program later. For example, if you start with a consolidation style hub for customer master data and begin to collect more information about your web customers, you’ll need to be able to accommodate this new data source.

Data Cleansing

The first step in master data management is to clean up messy information. This might include converting all measurements to metric, standardizing all formats for part numbers or addresses, and making sure that numeric values add up correctly. It’s also important to remove duplicate records and reconcile them. Finally, a cleansed set of data should be able to be understood by everyone involved in the project.

Once you have a master data hub, you can use it to synchronize your applications with it and offer a single version of the truth for different domains within your business. Coexistence style master data management requires more work than the Consolidation model, however, as it involves multiple copies of the same data in your applications, which have to be updated each time the hub synchronizes with them.

Data Migration

Whether you’re upgrading your current MDM system, moving to a new one, or implementing SAP S/4HANA, successful data migration requires solid planning and proactive management of master data interdependencies. With the right tools, you can reduce risks and deliver on time and budget.

Many companies experience large-scale data issues as a result of mergers and acquisitions, creating duplicate master databases with distinct attributes. The resulting data-reconciliation process is expensive, difficult to manage, and often results in unreliable information.

Ideally, a single version of truth should exist across all copies to ensure that data values are aligned. This requires an enterprise-wide infrastructure that standardizes and integrates the authoritative source from diverse sources of data. This transactional style of MDM reduces latency through direct coordination between master and sources, but it requires a great deal of expertise to implement correctly. It also comes with significant overhead in terms of processing, storage, and reporting. Data governance policies must be enforced throughout the process.

Data Consolidation

Data consolidation is one of the most important steps in master data management. It unifies your critical business assets into a single managed database that improves information quality, fast-tracks process execution and simplifies information access.

The data consolidation process involves identifying your source systems, cleansing and matching them, transforming the data, and loading it into a central repository. This could be a warehouse, a database or a spreadsheet.

The next step is to integrate the data in a manner that allows for easy reporting and analytics. Finally, it is crucial to maintain the consolidated data by updating and correcting errors as they are discovered. It is also essential to establish processes that ensure that the consolidated data meets the needs of all stakeholders in your organization. If the “single version of the truth” is not embraced by all users, it will be impossible to align metrics and achieve successful business outcomes.