Whitepaper : ER/Studio Data Architect

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.

This can be done 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. These economic benefits can be expressed in different units of measure, such as dollars saved, human resource costs saved, or a percentage saving on different development expenditures. They 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, they must do 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.

Data Modeling is the activity of defining the information needs of an organization by classifying the objects of interest and their interrelationships. A simple return on investment (ROI) formula expresses the desirability of an investment in terms of a percentage of benefit on the original investment outlay.

This paper addresses measuring the ROI of using data modeling within an organization. Reaping the full benefits of data modeling is achievable only by using data modeling. The rest of this section explains proper use of data modeling.

Register to read the full whitepaper.

See Also:

• Whitepaper: Agile Data Modeling: Not an Option, but Essential
• Whitepaper: Enterprise Data Modeling for Business Intelligence Applications
• Whitepaper: Is your Data Modeling Workflow Agile or Fragile?
• Whitepaper: Mastering Data Modeling for Master Data Domains
• Whitepaper: Model Behavior: An Introduction to Data Models
• Whitepaper: The ROI of Data Modeling
• Webcast: A Perfect Ten: The Data Model
• Webcast: A Photographer and a Data Modeler Walk Into a Bar…
• Webcast: Agile Data Management vs. Agile Data Modeling
• Webcast: Becoming a Better Data Modeler: Part 1 (Data Modeling Certification)
• Webcast: Becoming a Better Data Modeler: Part 2 (Faulty Design Patterns)
• Webcast: Data Modeling and Blockchain
• Webcast: The Importance of Data Model Change Management
• Webcast: You’ve Just Inherited a Data Model – Now What?
• Webcast: Principles of Data Modeling
• Webcast: Put Together the Pieces of your Data Model Puzzle
• Webcast: SQL Server Data Modeling Best Practices
• Infographic: Improve Your Data Modeling Skills
• Infographic: Data Modeling and Collaboration with ER/Studio