Agile Data

The Agile Data Method: A Philosophy-Based Methodology for Effective Data-Oriented IT Activities

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The goal of the Agile Data method is to describe a philosophical foundation on which an effective approach to the data-oriented activities on software development projects, and within IT departments, can be based. First and foremost, the Agile Data Method subscribes to the values and principles of the Agile Alliance. Although this advice is a very good start, I believe that it needs to be extended with philosophies that address the issues pertinent to the data community.  The Agile Data method is defined by its six philosophies:

  1. Data. Data is one of several important aspects of software-based systems.

  2. Enterprise issues.  Development teams must consider, and then act appropriately, regarding enterprise issues. They need to take the bigger picture into account if they're to avoid building yet another silo application.

  3. Enterprise groups. Enterprise groups (such as enterprise architects, data management, ...) exist to nurture enterprise assets and to support other groups, such as development teams, within your organization.  These enterprise groups should act in an agile manner that reflects the expectations of their customers and the ways in which their customers work.

  4. Uniqueness. Each development project is unique, as is each organization, requiring a flexible approach tailored to its needs.  One software process does not fit all and therefore the relative importance of data varies based on the nature of the problem being addressed.

  5. Teamwork. IT professionals must work together effectively, actively striving to overcome the challenges that make it difficult to do so.

  6. Sweet spot. You should actively strive to find the 'sweet spot' for any issue, avoiding the black and white extremes to find the gray that works best for your overall situation.

An interesting observation is that most of these philosophies aren’t specific to data, instead they are applicable to information technology efforts in general. As the first principle implies you need to look at the overall picture and not just data, therefore data-specific principles very likely won’t serve you very well.  Heresy? No, just common sense.