For over three decades now, data modeling has been the leading discipline for understanding business data requirements and representing them in a precise, understandable structure. Today, more than ever, businesses rely on data for their decision making, sometimes even vast amounts of data. To those in data management, data modeling has proven its business value and needs no further justification. They have seen the tangible value of the model and the tangible danger of omitting it. To others, because these benefits are not so clear, data modeling requires systematic economic justification.
We can accomplish this by showing the economic value of real data modeling benefits, such as improved requirements definition, reduced maintenance, accelerated development, improved data quality and reuse of existing data assets. Client experiences are available that show the benefit of data modeling in each of these areas. We can express these economic benefits in different units of measure, such as dollars saved, human resource costs saved, or a percentage saving on different development expenditures. We can also collect and aggregate these benefits at different levels of detail, such as by project or by development phase. Maintenance remains the largest expense in most development budgets, accounting for 50% to 80% of the budget. Reduced maintenance is the big-ticket item in savings because of data modeling.
To maximize these benefits, we must perform data modeling well. It must be iterative, incremental, and collaborative. The day of monolithic projects is over. Modeling must progress through different levels, such as from the conceptual level of planning, to the logical level of business detail, to the physical level of the implemented database.
Challenges exist, and new ones surface. New technologies and methods, such as agile development, column-oriented databases, NoSQL, and big data, put data modeling under fire. To survive and sustain its momentum, data modeling is adapting, redefining its role in these trends, but will continue to play a key role in each of these innovations.
Read the 23-page whitepaper “The ROI of Data Modeling” by Tom Haughey to learn more about the basic definitions, levels of data modeling, and scoping data modeling projects. Also explore enterprise standards, three types of systems, placement of data modeling, data modeling benefits, and three methods to calculate ROI.