To Engage Business, Focus on Information Management rather than Data Management
IT professionals have been pushing an Enterprise Data Management agenda for decades rather than Information Management and are frustrated with the lack of business engagement. So what exactly is the difference between Data Management and Information Management and why does it matter?
Before getting into Information Management, First, let me start with a few definitions. As stated by Integration Law #4, “Information = Data + Context”. Further, to define data and content, content is anything that is intended to read by a human while data is content read by a computer. For example, data is what is in the files on your computer hard drive. When you open the file with Word, the application translates the data that the computer can read (bits and bytes) into content that a human can read (letters and words). Similarly, Excel turns raw data into information by adding context (rows and columns with headers) and SAP turns data into products ordered by customers. To put this in the context of information modeling, what is the difference between an Enterprise Information Model (EIM) and an Enterprise Data Model (EDM)?
An Enterprise Data Model is “A conceptual data model or logical data model providing a common consistent view of shared data across the enterprise, however that is defined, at a point in time. It is common to use the term to mean a high-level, simplified data model, but that is a question of abstraction for presentation.”
A Conceptual Data Model is “A data model that is presented at a high level of abstraction, hiding the underlying details, and making it easier for people to comprehend. A conceptual model should reflect the phenomena in the users’ world being modeled as directly as possible, as close to the way the users think. For example, many-to-many relationships are common in conceptual models.” 1
A close look at these definitions shows that for Enterprise Data Management the emphasis is on starting with a data model, and then generalizing it within a broader context. In other words, it’s still a data model, but with some of the details hidden. Furthermore, the “people” that are referred to in the second definition are data or IT professionals, not business users, for one simple reason – the process context is missing.
The above sets the foundation for my definition of an Enterprise Information Model (EIM). The EIM does not model data entities and relationships, regardless of the level of abstraction. Instead, an Enterprise Information Model describes information exchanges in the context of business activities. This is fundamentally different than an Enterprise Data Model. We start by modeling how the enterprise operates as a set of business functions and the information that is created and used by each function. We can represent the EIM at various levels of detail by organizing the business functions and information in hierarchies. The key difference is that regardless of the level of abstraction, in an EIM we are focusing on information exchanges in the context of business activities rather than in an EDM where the focus is on data structures for storing data. In short, data in motion versus data at rest.
So which is better – EIM or EDM? Both are valid and needed. But if we’re trying to engage the business to take ownership and accountability for data governance and information management, we are better off starting with an Enterprise Information Model which include the business context rather than an Enterprise Data Model.