Enterprise Information Management (EIM) is a very large, complex and diversified field. One company may be working on master data management while another may be embarking on a data quality initiative. Both of these organizations are doing EIM. One is focusing on master data management and the other on data quality. What is critical is that both realize that these are EIM initiatives and that they need to adhere to the best practices of EIM. There are eight EIM Focus Areas that are important to understand.

• Data Management
• Process Management
• Data Architecture
• Information Quality
• IT Portfolio Management
• Master Data Management
• Information Management
• Information Security

Figure 1: EIM Focus Areas

To some degree each Focus Area can exist without the other Focus Areas with the exception of Data Management. For example, your IT Portfolio Management area may not be concerned with the data quality. On the other hand, when you begin implementing your EIM program there will be a great deal of overlap between Focus Areas. For example, most Information Delivery implementations will have some Data Quality elements to them even if Data Quality is not the focus of the corporation.

Over this eight part series I will examine each one of these Focus Areas in depth so that we have a good understanding of what they are and how to be successful in implementing them.

Data Management

Function: The Data Management Focus Area looks to holistically manage the data within an organization at an enterprise level.

Data Management looks to answer the following key data questions in a company:

• What is the data’s source (data heritage)?
• What does the data mean (data lineage)?
• What are the data’s valid values?
• What formulas were used in the data’s calculations?
• What are its business rules?
• What are its technical rules?

What Is The Data’s Source?

This question revolves around the heritage of the data; meaning, where or what system was the data created in and what was its original structure, meaning and value. For example, the heritage of the Customer_Name field could be that a sales person types in the customer name in the Sales system from the application that the customer filled out or off of the customer’s driver’s license.

Understanding the source for the data is important as it contains the answers to many of the common EIM questions. For example, most data quality issues tend to occur at the data’s originating source so if you are trying to improve your company’s data quality then its heritage is critical to understand. Also when business users are looking at data to make strategic decisions they typically need to know the data’s origin point so that they truly understand its meaning. This deeper understanding leads to better strategic decisions by these knowledge workers.

What Does The Data Mean?

Data has a life-cycle. The data is created (heritage) and it is moved from system to system and even merged with other data, changed, truncated, calculated, reformatted, transformed, aggregated and even deleted. This data lineage describes all of the events that have “happened” to the data. The data lineage should reflect this information from the origin of the data and every step of the downstream data life-cycle including its final resting place(s).

Understanding the lineage of the data helps answer the most fundamental EIM question…What Does Our Data Mean? So many times when business users are interacting with their company’s data they don’t truly understand the data as they don’t have the data’s meaning (meta data) that they can review to gain a full insight into the data. Instead these knowledge workers are forced to make assumptions about the data. These assumptions can pose significant problems for the decision making process.

What Are The Data’s Valid Values?

This question relates to the valid domain values of a data field. For example, the valid values for the Customer_Type field may be a single alphanumeric letter between A and E, or social security number could be any valid, positive numeric value of 9 numbers between 0 and 9.

What Formulas Were Used In The Data’s Calculations?

Most companies utilize complex calculations in their organizations. Customer profitability and product profitability can be very elaborate algorithms comprised of many fields. A senior executive looking at customer profitability to make strategic decisions about a particular customer or group of customers would probably want to know exactly how the customer’s profitability is being calculated.

What Are Its Business Rules?

Business rules is a very broad topic, with entire conferences dedicated to it. Essentially they are the key information (rules) around the data. They define such key items as the error thresholds, who is allowed to use the data, in what ways are they allowed to use it, data meanings, valid values, etc. What is important to understand is that business rules are designed for business users and knowledge workers. They are written in prose and not in technical vernacular like pseudo code or SQL statement syntax.

What Are Its Technical Rules?

The technical rules are merely the business rules that are designed for the IT department of an organization. These rules can be stated in a technically friendly vernacular like SQL statements or syntax, along with standard prose.

Data Management Meta Data Capture

There is a host of valuable meta data that needs to be gathered, retained and disseminated in order for the Data Management function of EIM to be properly executed. This list includes:

• Logical data entities and elements names, definitions, domains, business rules and technical rules
• Physical data tables and attributes names, definitions, domains, business rules and technical rules
• Transformation rules and relationships
• Application listing names & definitions
• Data stewards
• Subject areas
• Business entity listing and definition
• Relationships between meta data objects
• Plenty more!

Best Practices

Data Management is the foundation for all of the other EIM Focus Areas. Regardless of which Focus Area you wish to target first, you will have to address the Data Management Focus Area too. For example, it is impossible to implement an enterprise Information Quality initiative without understanding what the data means, what systems does the data exist in and a host of other key Data Management issues.

Next month I will continue to walk through the eight EIM Focus Areas.

About the Author

Mr. Marco is an internationally recognized expert in the fields of enterprise information management, data warehousing and business intelligence, and is the world’s foremost authority on meta data management.  He is the author of several widely acclaimed books including “Universal Meta Data Models” and “Building and Managing the Meta Data Repository: A Full Life-Cycle Guide”.  Mr. Marco has taught at the University of Chicago, DePaul University, and in 2004 he was selected to the prestigious Crain’s Chicago Business “Top 40 Under 40” and is the chairman of the Enterprise Information Management Institute (www.EIMInstitute.org). He is the founder and President of EWSolutions, a GSA schedule and Chicago-headquartered strategic partner and systems integrator dedicated to providing companies and large government agencies with best-in-class solutions using data warehousing, enterprise architecture, data governance and managed meta data environment technologies (www.EWSolutions.com).  He may be reached directly via email at DMarco@EWSolutions.com