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Data Reengineering and Meta Data Management Strategy

  Article published in DM Direct Newsletter
February 25, 2005 Issue
  By Soumendra Mohanty

Recently, I have been involved in many engagements where managing the ever-changing meta data invariably was a source of concern. These concerns become a show stopper when there are hundreds of downstream systems and thousands of users who depend on the meta data to perform meaningful analysis.

One of my clients is facing a major data management crisis as there are approximately 2,000 tables, and every time there are small changes in the source systems, there is a major initiative to do impact analysis and subsequent fixes.

In this context, a centralized meta data management strategy is very much needed, but before even we get down to meta data management, a data reengineering needs to be done to align the client's business process and external dependencies.

Data Reengineering

What does data reengineering mean? In fact, data reengineering is nothing but a combination of reverse engineering and forward engineering, primarily focused on data as opposed to systems or applications.

When a system is reverse engineered, it is analyzed, documented, modeled and understood in order to better perform subsequent efforts or initiatives. Such exercises focus heavily documenting the system, the applications within and the external influence on the system. Readers might argue saying that documents generated out of this exercise are also valuable to the organization and can serve as meta data as well. Absolutely, there is no doubt this information is immensely valuable. However, there still is a void about data propagation, data management and a single version of the truth across the enterprise about the same data element.

In the subsequent discussion points we will see how we can effectively use the same concept of reverse engineering focusing primarily on data management.

Figure 1: Reverse Engineering Taxonomy Focusing on Data Assets

Figure 1 depicts reverse engineering taxonomy focusing solely on data assets. The journey from as-is data implementation assets phase to as-is information requirement assets ensures that data propagation lineage and traceability is documented as well as monitored and managed.

At the end of data reengineering phase enough information has been obtained about the "as-is" data in the organization; which could be effectively used as a platform to address meta data management challenges.

Meta Data Management Strategy

The data reengineering phase enables the organization to look deep into the existing data usage and build a base from which the organization can rebuild itself to realign with the future. In other words, by understanding the details of the data usage within the organization as it is happening today, organizations can formulate strategies for better management of the same data for future initiatives and corporate directives.

In this context, meta data management takes a pivotal role and ensures that business pain points are addressed proactively. Data reengineering provides a structure, permitting consultants to reconstitute specific organizational data requirements and implement processes (meta data management strategies) guiding their resolution and ongoing management.

Following the Zachman Framework for enterprise integration, the data reengineering phase can drive the enterprise-wide meta data management strategy.

Figure 2

Figure 2 shows an example of data reengineering targeted at each of the rows and primarily focusing on the purpose and stakeholders would result in deliverables and methodologies to drive the meta data management strategies.

Data reengineering often results in a deeper understanding of data assets of the organization which, in turn, leads to further data consolidation and data acquisition strategies.

Figure 3

The convergence of data reengineering and meta data management should be treated as an ongoing journey management and hence requires well-defined data governance and data stewardship programs.

Organizations wishing to have increased business values should invest in and get buy-in from the stakeholders (in this case the data users) to adapt to an enterprise-wide framework and provide a closed-loop feedback for further process improvements.

Figure 4

The data reengineering phase enables organizations to probe deep into the data usage as it is happening across the enterprise. The knowledge gained can be used effectively to weed out undesirable practices and patterns and promote a centralized approach of managing the data assets aligned with the organization's vision.

A data governance framework as in Figure 5 would add value to the as-is data assets and, at the same time, would constitute an enterprise-wide meta data management strategy.

Figure 5: Enterprise Meta Data Strategy


For more information on related topics visit the following related portals...
DW Administration, Mgmt., Performance and Meta Data.

Soumendra Mohanty is a program manager of Accenture, India where he leads the Data Warehousing/Business Intelligence Capability Group providing architectural solutions on various industry domains and DW/BI technology platforms. He has worked with several fortune 500 clients and executed projects in various industry domains as well as technology platform areas. He can be reached at Soumendra.Mohanty@accenture.com.


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