The concept of enterprise information management includes all the disciplines that allow an organization to manage data and information as assets for a variety of purposes.
Enterprise information management is one of the hottest topics in information technology (IT) today. Many metadata management efforts have gone well beyond the scope of data warehousing / business intelligence and instead are targeting the entire enterprise. This expansion is vital as these organizations have realized that in order to compete in the “Information Age” that they had better manage their information a whole lot more effectively than they have up to this point. These organizations will really have a definitive advantage over their competition in the coming years.
Enterprise Information Management Fundamentals
Enterprise information management (EIM) leverages many disciplines like metadata management, data management, data governance, master data management, service oriented architectures, process management, information security, IT portfolio management, data delivery and data architecture. Following are the fundamental concepts of EIM.
Enterprise Information Management (EIM)is the systematic processes and governance procedures for applications, processes, data, and technology from a holistic enterprise perspective. The purpose of enterprise information management is to bring enterprise order, purpose, structure, efficiency, and performance to applications, processes, data, metadata and technology.
As we read this definition, there are several key words. “Systematic” is a vital concept in this definition since you want to automate as much of the EIM process as is possible. If this is omitted, then your EIM initiative will just be a bunch of documents and spreadsheets that will be so difficult to manage and grow that eventually they will become neglected and stale. Another important concept in this definition is “holistic.” This implies that the EIM process must not be just a point solution. It is acceptable if it starts as a point solution but it MUST be built to be expanded and grown over time from an enterprise perspective.
Metadata & Data
Metadata, data, and information are three of the most important concepts in EIM. All three of them are linked together and are the foundation of any EIM effort.
Metadata: Metadata contains the knowledge (context) that a piece of data is called “Customer_Name”, is 40 characters in length, and exists in systems A, B, and C. In addition, metadata would represent that our company has 3 systems which contain customer master data.
Data: Data would be a specific instance (content) of “Customer_Name.” For example, Customer_Name equals “David Marco.”
Information: Data that is meaningful to a business user, meaning that they understand it, trust it and they know what to do with it.
Information = Data + Metadata
(content) + (context)
Why “Information” Management and Not “Data” Management
Data on its own has little to no value. I could give you some data “24”, “37” and “42”. OK are you ready to make some key decisions in your company? Of course not, because I only gave you data (content). What I didn’t give you was the metadata (context) relating to the data. If I provided you metadata that stated that these data values are annual net sales revenues, in millions of US dollars for your region over the last 3 years, now you can make decisions and actually utilize this data. Simply put, information is the merger of data (content) and metadata (context).
Why Is Enterprise Information Management (EIM) Valuable?
It is important to understand that most companies and large government agencies never planned their IT enterprise; rather, it “just grew” over time. As the IT environment grew as we built “stovepipe” solutions all over the enterprise, nobody (of authority) ever stopped and said, “Wait a minute – we need to optimize and tune this environment so that we can reuse that which we’ve already done.” As a result, our IT environments have grown like weeds that go untreated in a garden. After some time the flowers (applications) can’t grow or survive anymore and the weeds (redundancy and needless dependencies) are ruling the garden.
Most current IT environments are plagued by excessive levels of data redundancy, process redundancy, technology (software/hardware/middleware) redundancy, massive data quality problems, extended IT development life-cycles, high project failure rates and applications that are so convoluted that they are almost impossible to adapt to changing business needs.
Many IT environments have a budget in the billions of dollars annually. Yet these same companies just assume it will just take care of itself. Any quality information management professional knows that Data Does Not Manage Itself.
Consolidation vs. Integration
Many organizations confuse consolidation with integration when in reality these two concepts could not be more different. Consolidation endeavors to take ten tables, each with a million rows, and simply merge them into one table with ten million rows. If that ten-million-row table has 20% data redundancy and 15% data quality problems then that is acceptable, since consolidation does not address this problem. Integration looks to take the ten tables and integrate them into one table (maybe more) with 8 million unique rows and data quality problems at 0%.
Every organization, of any size, in every industry, needs to practice enterprise information management, so it can use the data and metadata, the processes and technologies it has invested in, to create information and knowledge that will enable effective operations, optimal decisions, and competitive advantages.