I’ve been making the point in the past several years that master data management (MDM) development projects are different, and are accompanied by unique challenges. Because of the “newness” of MDM and its unique value proposition, MDM development can challenge traditional IT development assumptions.

MDM is very much a transactional processing system; it receives application requests, processes them, and returns a result.  The complexities of transaction management, near real-time processing, and the details associated security, logging, and application interfaces are a handful.  Most OLTP applications assume that the provided data is usable; if the data is unacceptable, the application simply returns an error.  Most OLTP developers are accustomed to addressing these types of functional requirements.  Dealing with imperfect data has traditionally been unacceptable because it slowed down processing; ignoring it or returning an error was a best practice.

The difference about MDM development is the focus on data content (and value-based) processing.  The whole purpose MDM is to deal with all data, including the unacceptable stuff. It assumes that the data is good enough.  MDM code assumes the data is complex and “unacceptable” and focuses on figuring out the values.  The development methods associated with deciphering, interpreting, or decoding unacceptable data to make it usable is very different.  It requires a deep understanding of a different type of business rule – those associated with data content.  Because most business processes have data inputs and data outputs, there can be dozens of data content rules associated with each business process.  Traditionally, OLTP developers didn’t focus on the business content rules; they were focused on automating business processes.

MDM developers need to be comfortable with addressing the various data content processing issues (identification, matching, survivorship, etc.) along with the well understood issues of OLTP development (transaction management, high performance, etc.)  We’ve learned that the best MDM development environments invest heavily in data analysis and data management during the initial design and development stages.  They invest in profiling and analyzing each system of creation.  They also differentiate hub development from source on-boarding and hub administration. The team that focuses on application interfaces, CRUD processing, and transaction & bulk processing requires different skills from those developers focused on match processing rules, application on-boarding, and hub administration. The developers focused on hub construction are different than those team members focused on the data changes and value questions coming from data stewards and application developers.  This isn’t about differentiating development from maintenance; this is about differentiating the skills associated with the various development activities.

If the MDM team does its job right it can dramatically reduce the data errors that cause application processing and reporting problems. They can identify and quantify data problems so that other development teams can recognize them, too.  This is why MDM development is critical to creating the single version of truth.

Evan also blogs at evanjlevy.com.