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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 Agile Data Mission

To share proven agile and lean strategies for data initiatives.

What is the Agile Data Method?

The Agile Data (AD) method 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.

Why Should You Adopt the Agile Data Method?

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:

  1. Software developers will usually struggle to get the data stuff right, and will often make questionable decisions from an enterprise data point of view.
  2. Data professionals will usually struggle to work in an agile manner, which to say is collaborative, evolutionary, and quality-focused in nature.
  3. There is a cultural impedance mismatch between the two groups which exacerbates these problems.

For a more detailed discussion, please read Why Agile Data?.

Modern software development processes - such as Disciplined Agile Delivery (DAD), Extreme Programming (XP), and Scrum -- are all evolutionary (iterative and incremental ) in nature. Every single one of them. Some modern approaches, in particular XP and Scrum, are agile in nature (for the sake of simplicity, let's define agile as a highly collaborative, evolutionary, and quality-focused approach). Traditional approaches to data-oriented activities, however, tend not to be evolutionary, are rarely agile, and certainly struggle with quality. Luckily, agile data techniques exist, they're more effective than traditional techniques, and they're proven in practice.

Important Topics for Agile Data Practitioners


Data Quality

Data Warehousing (DW)/Business Intelligence (BI)

Architecture and Design




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