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The Functions of EIM

These EIM functions have similar design patterns around data and provide the context for process areas such sales and marketing, finance, and production. For the purpose of this text, each is defined as follows:

  • The transaction processing function—Centers on the creation and maintenance of the core business transactions in the business. This function is still 60% to 70% of all information technology (IT) budgets and defines what the transactions mean and how it is used within the organization.
  • The master data management (MDM) function—Concentrates on the creation and maintenance of the core domain definitional information of an organization. It provides the context for our transactional and analytic data. For example, it provides the definition of what a customer is and what a product is. These definition and instantiated data elements are used in creating transactions and determining the measures needed to analyze what is a customer or how much of a product is used.
  • The business intelligence (BI) function—Focuses use of data for different types of information analysis. A BI environment is the most data-centric of all EIM functions. It captures, collates, and conforms data from many disparate sources into a set of repositories in various structures for the many different types of reporting, descriptive, and predictive analytics used by disparate end users. A BI environment now offers their organizations a centralized environment to provide financial and marketing reporting and analytics.

Other authors and organizations may have different perspectives of what EIM consists of and the functional processes that it covers. This book, though, focuses on how to perform information governance activities and tasks within the development and ongoing operations in these three EIM functions.

Data Management: EIM’s Technical Development and Management Discipline

To understand how information governance interacts in EIM functions, it is important to understand how EIM functions are developed and maintained. This section discusses the technical discipline of data management. Common patterns exist in the data-driven aspects of the three EIM functions. They have similar requirements and patterns in the blueprints, development life cycles, and maintenance of the applications. Over the past 30 years, IT has evolved a technical discipline known as data management.

Data management is the development and maintenance of architectures, best practices, and procedures that manage the full data life cycle of an organization. It is within data management that data architecture artifacts such as data models, data integration models, and information access patterns are developed and maintained.

The best example of a well-known data management process is data modeling. The systems development life cycle (SDLC) details how data models capture business requirements of an organization. It determines how to best structure those requirements into the different types of technical structures that are available: transactional, operational, and analytic (data warehouse, dimensional), as shown in Figure 1.2.

Figure 1.2

Figure 1.2 Data modeling in SDLC and maintenance tasks

The Relationship Between Data Management and Information Governance

A very tight relationship exists between data management and information governance. Often, the two are confused as the same discipline or overlap in areas such as metadata and data quality management. Within the development of the data management artifacts such as data models, there are information governance tasks such as business definitions of the entities, attributes, and relationships. Chapter 4, “Performing Information Governance Tasks in Transactional Projects,” explores these relationships in much greater detail. For this section, you just need to understand that a data management artifact is the blueprint for a database or data integration process and that the information governance aspects give it business context.

What Is Information Governance?

There are many definitions and points of view on what information governance is and what it is not. For this book, the formal definition of information governance is as follows:

  • Information governance is the orchestration of people, process, and technology to enable an organization to leverage data as an enterprise asset.

Although information governance spans both business and technology (as shown in Figure I.1 in the Introduction), it is truly a business function with its primary directive to establish the policies for the creation and usage of data with an organization. It is an integral aspect of the understanding of an organization, which leads to the position that information governance should be considered an ongoing organizational function on par with accounting or marketing.

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