Over the years, data management has grown to include greater portions of requirements analysis and modeling. We realized that good results could not be achieved by simply implementing databases; we also had to understand system data specifications as well as demonstrate our understanding using specialized modeling tools.
Further growth has occurred in the areas of enterprise-wide data coordination, integration, stewardship and use. The focus has evolved from how to best design a database for an application to how to design data structures and organizations to effectively use data in implementing organizational strategy.
Recent developments have incorporated data security and quality. In the post-9/11 world, organizational management must now be equally concerned with security of its data as well as the quality of this asset.
There are a limited number of educational programs geared toward data management education. (DAMA International maintains a list of these at our Web site, www.dama.org. To access this list, click Professional Education, then Academic Programs.) Because of the lack of educational programs, one is less likely to pick data management as a career choice - something else draws us to this profession. The combination of limited program offerings and broader areas of interest produces a much more diverse group of professionals than existed two decades ago when DAMA was founded.
There is more to data management than there was, and much of our learning must be realistically considered on-the-job-training. How does (in general) this affect our desired data management tipping point? The answer is both good news and bad news.
First, the bad news. The increase in data management scope has left us with multiple tipping points that must be reached before the organization can be said to practice "good" data management. Today's practitioners must become better with more areas than ever before. New skills are required, and practitioners determined to be on the leading edge must figure out how (for example) to incorporate XML into their data management operations. Because we have all learned that quality cannot be designed into a system after it has been implemented, we now need to incorporate security concepts into our data development operations. Each organization requires a specialized and often changing mix of skills to meet individual organizational objectives.
The good news is that by drawing a heterogeneous group of practitioners, we are better able to put together effective data management teams that can respond to today's challenges. Approximately 50 percent of data managers come to this profession without formal coursework in information systems, computer science or data engineering. They bring a different and diverse set of knowledge, skills and abilities to our profession, and it is up to individual data management teams to determine how best to access and implement the results of this diversity. Recently, I've witnessed teams of linguists combining their knowledge of taxonomies with software engineers who could spin efficient parsing algorithms. Both groups came together to develop solutions to an organization-specific data quality challenge that couldn't have been solved by either of them individually.
Knowledge of your data management team's "other" abilities can help lead to the resolution of data challenges more rapidly than traditional means. It is up to us to take advantage of the available resources, to understand our capabilities, weaknesses, strengths and limitations. For most organizations, there is no set approach to reaching the data management tipping point and, in reality, there are a number of different tipping points for each organization. One of the ways DAMA International will continue to be the premier organization for data professionals worldwide will be to provide local, regional and international forums where data professionals can exchange stories and teach each other lessons so that all can benefit from our collective experiences.
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