It’s simple really. Since the exponential explosion of data continues to blaze forward; data modeling techniques, and indeed most aspects of data management, have been forced to evolve at a frantic pace in order to try and keep up. As a result, just about everything associated with data modeling has begun a momentous shift forward. Mostly due to the advent of supersmart analytics technologies which offer excellent opportunities to automate processes and reduce complexities.
However, it’s beyond reckless to interpret these advances as a means to disregard the discipline of data modeling. This basic exercise should never be an afterthought. If data scientists, database analysts, application developers and a growing gaggle of novices (I.E. business users, executives, administrators and the like) intend to use the foundation of data modeling to extract more value from their cache of divergent snips of knowledge, there’s a fundamental set of requirements that absolutely must be sustained. They are:
- Be clear regarding what information exists and what it’s about.
- Be able to extract portions of the information suitable for a particular purpose.
- Be able to efficiently exchange data between organizations and systems.
- Be able to integrate information from various sources, and know what data is about information that already exists, and what is about something new.
- Be successful at sharing data between applications and users with different views.
- Be well versed at overall management of the data, including the history, for life.
These are the core of outstanding data management and must occur with or without the new highly-advanced sets of tooling. Either way, someone (more than one someone) in your organization must have a solid sense of the ‘relationships’ between data.
As the need for pure dimensional data warehouses lessens, and traditional methods of data modeling fall from favor, one thing remains crystal clear; the requirement to understand the contextual implications of data relationships is an ongoing imperative. This essential element won’t change anytime soon. If ever. Our position is unambiguous. Savvy organizations should approach data modeling as a business process benefit, unrelated to the specific details. The key word, harkening back to Nijssen’s Information Analysis Methodology, is
‘relationships.’ There will always be data, (and brilliant, glitzy, point-and-shoot tools notwithstanding) there will always be a need to model it.