The Resurgence of Data Modeling: Part 2

The dichotomy in the evolution of data modeling tools (described in my previous blog) is of more than historical interest; it goes to the heart of how IT shops manage information modeling and software design. It's also a key differentiator in corporate IT philosophy and approach, with potential impact on capabilities and effectiveness.

Analyst Jeffrey Hammond, co-author of a recent Forrester Research report on the data modeling market, finds that organizations aligned more traditionally -- with clearly delineated database administrator and data modeling/architect roles -- tend to favor classic data modeling tools like CA ERwin. Organizations with an emphasis on integrated enterprise architecture are more likely to favor tools like Sybase PowerDesigner, with its integrated modeling platform that goes beyond data. For organizations looking to empower developers, Hammond recommends tools like Embarcadero ER/Studio and Microsoft Visual Studio.With recent trends and developments in interrelated areas such as data warehousing, business intelligence, master data management, data integration (e.g. for ERP reporting) and data governance, the future of data modeling tools is beginning to look very different than their past. Organizations need to take a long, hard look at their IT strategy and requirements before committing to any tool (or a tool vendor), Hammond advises. Selecting a data modeling tool begins with closely examining the evolutionary path of the vendor as well as the architectural leanings of the tool, and then determining how that aligns with your IT strategy and goals.

For example:

Optional capabilities are leading to wider price ranges for data modeling tools, from a few thousand dollars for the base product up to the low five-figure range for full suites. Look for community editions -- free versions of data modeling tools with limited capability -- to test the waters before fully wading in.

As organizations become increasingly data aware and data driven, the ability to understand and define data -- to effectively deploy data for operational efficiency and competitive advantage and as a precursor to data governance -- is becoming increasingly critical. As such, tools that help us understand, define and integrate data will only gain in importance and ubiquity.

Relational data modeling is a very mature science and I am seriously concerned that, left to themselves, vendors will be tempted to invest minimally in enhancing data modeling tools, treating them as cash cows kept on a starvation diet. However, I'm also hopeful that business demands and competitive forces will continue to spur innovation in data modeling.The future of data modeling tools is beginning to look very different than their past. Organizations need to take a long, hard look at their IT strategy and requirements before committing to any tool (or a tool vendor)...