A pragmatic approach to master data management
I have frequently experienced projects that ran into serious problems due to data issues – typically issues caused by differences between various source systems. The requirement is to be able to identify the same object in different systems. The problems could show up in various types of projects like Analytics/BI projects, Big Data projects, projects integrating new frontend systems, projects consolidating or replacing backend systems, etc.
In short all IT related projects.
To my experience, only a very few organizations have successfully solved all MDM issues.
Organizations have approached master data management in numerous ways. Some organizations are drawing upon their existing resources to handle master data management, often calling upon employees to manually clean and migrate data. This method tends to be prone to human error, causing further complications and does not scale well as business needs change. Many organizations have implemented specific data management tools to aid with integration and cleansing. Integration tools, however, do not always support large amounts of data and are limited in the types of files and data sources they can manipulate.
Another strategy implemented by organizations, despite common understanding that it is a poor solution, is point-to-point integration. Point-to-point integration, commonly referred to as custom code, is a method in which skilled developers write custom code and implement it within each specific endpoint in order to create connectivity. This requires extensive knowledge of each endpoint, and as the number of endpoints increases, it becomes a grueling task.
Moreover, as organizations take advantage of mobile, cloud, and SaaS applications to power their business, their IT ecosystem grows in complexity. With more and more endpoints requiring connectivity, point-to-point integration becomes a complex and fragile “spaghetti architecture”.
Why is MDM important?As organizations try to change from being reactive to become proactive the data requirements change from traditional analysis describing events and behavior towards analysis predicting customer behavior and prescribing appropriate business responses.
This change requires data to be identifiable across every system available both internally and externally.
Some organizations use a shift towards “customer centricity” to start working on Customer Data Integration (CDI), which is a subset of Master Data Management for a single data domain, with the purpose of achieving a 360° customer view to increase top-line growth.
Other organizations try to optimize the supply chain by focusing on product master data management and in this way reduce costs and improve profitability.
For most companies, all of these activities need to be completed – fast – to be able to compete with new entrants with no physical assets and other legacies to deprecate.
