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Blog Post

August 13, 2018

Balancing Enterprise Data Management Models

By John Desborough

Establishing the right data model is the key to successfully framing enterprise data management (EDM). You have to keep enough structure to capture current business processes with enough flexibility to accommodate the ever-changing requirements.

Some may say that EDM hardly needs a data model - just map data items from input sources to downstream system and add in validation across sources for each item. What's the point of considering the data and what it means to the business? Why not just keep adding more data sources, more mappings and more end points?

It doesn't scale well - seriously. The complexity of the mapping increase with the number of input sources multiplied by the number of downstream systems - you end up with a tangled mess. By not having a centralized body of information about the fundamental relationships and dependencies between the different data sets and business objects, you will have tremendous difficulty in building out any business logic.

To capture the relationships and dependencies between different data items, you need a structured data model. Having the right data model means understanding the complex relationships - especially in the financial data world. All of these relationships and dependencies can be built into a centralized data model to provide structure for business-aware EDM processes. By developing a common framework of data, what it means and its relationships with other data and business objects, you avoid the tangled mess of individual mappings.

However, if you employ a classical relational data model, where the relationships have to be defined before they are needed, it makes it challenging to add or modify new attributes without extensive design changes. This inflexibility can cause suffering unless your design is perfect from the start.

My view: there are benefits of scale and efficiency from having a data model to build upon but you have to look towards the more "modern technologies" that offer data model approaches that build and manage structure, but that are designed from the outset for change. This approach copes easily with new data sources and new data requirements but they are able to achieve this in such a way as to remove the need for costly database re-engineering or for continual and expensive consultations with your vendor partner.

John Desborough is a Director, Consulting and Technology Solutions at MNP. He is an accomplished business solutions program manager and business transformation architect with 30+ years in the information and technology consulting domain. John has extensive background in information management and governance with both public and private sector clients on a global scale. Drop John a line to discuss this topic in more detail: [email protected]