I have had an identity crisis for years. My resume is a trail of analyst, modeler, architect, engineer, designer job titles. A person unfamiliar with the data community would deduce that I am one dynamic person to work in so many varied positions. Those familiar with these job titles know they all boil down to data modeling and related activities.
Maybe I am naive, but accountants are accountants and chemists are chemists. It is as if the data modeling community is looking for more glamourous job titles. I can recall some rabid discussions when data modelers began using the title, architect (maybe mid-late 90s). The brick and mortar architects were not happy with this misuse of their job title. Of course, we now have architects of many flavors in IT beyond the data species.
I am not going to list an expansive list of data modeling job titles. The normal candidates prefix the title with data, database or information and end with analyst, modeler, architect, designer or engineer. I took a look at my DAMA Data Management Body of Knowledge (DMBOK). The typical titles were glossary entries with definitions that were somewhat light but pretty much on target. These entries allow for plenty of room for argument and interpretation within the data modeling community.
What led us to this plethora of job titles? It’s evolutionary: data modeling roles expanded as did their artifacts leading to titles more closely aligned with their work. It’s organizational: IT management has a history of merging roles and creating roles with more responsibilities and hands in IT projects. It’s cultural: IT embraces new titles as they become more common and frequent in social media, at industry events and referenced in IT publications. It fits the pattern: IT job titles including data folk must often fit a job naming patterns across the organization that does not respect the naming conventions of professional communities.
Can there ever be agreement within the data modeling community? The evolutionary, organizational, cultural and fits the pattern statements speak to why there will not be universal agreement. Historically, data modeler appeared first when the data model was the artifact produced by our role. The other variations came as the role expanded and became not so tied to the ERD. Our role will continue to become more diverse. For many, it will move away from the data model. This is a healthy thing that demonstrates he need for the work we do. Don’t lament the diminishing titles of data modeler and data architect.
In August of 2014 I explained in a post why I moved to using data architect rather than data modeler when talking about the modelers of data. That still holds for me a year and a half later. It certainly does not apply to all data folk. There certainly is an identity crisis if we look at our community as not being as diverse as it really is.
Data Architect (I am pretty sure…)