The Agile Data (AD) Method
The increasing pace of change, increasing complexity, and increasing volume of data demands nothing less than complete data agility.
The mission of this site is to share proven agile and lean strategies for data initiatives in the form of the Agile Data (AD) method. AD defines a collection of strategies that IT professionals can apply in their context to work together effectively on the data aspects of software systems. This isn’t to say that AD is a one-size-fits-all methodology. Instead, consider AD as a collection of ways of working (WoW) and ways of thinking (WoT) that will enable IT professionals and their stakeholders to work together effectively when it comes to the data aspects of their initiatives.
Data has been an important aspect of every single system which I have ever built. Then again, so have business rules, user interfaces, telecommunications, and a slew of other issues. My experience is that:
- Software developers will usually struggle to get the data stuff right, and will often make questionable decisions from an enterprise data point of view.
- Data professionals will usually struggle to work in an agile manner, which to say is collaborative, evolutionary, and quality-focused in nature.
- There is a cultural impedance mismatch between the two groups which exacerbates these problems.
Important Topics for Agile Data Practitioners
Data Quality (DQ)
- Continuous Database Integration
- Data Normalization
- Data Quality in an Agile World
- Data Technical Debt
- Database Refactoring: Fix Production DBs
- Data Repair: Fix Production Data
- Database Testing
- How to Assess DQ Techniques
- How to Choose the Right DQ Techniques
- Metaphor: Data is the New Water
- Test-Driven Development (TDD)