What is Agile Analytics?
Agile Analytics is not a rigid or prescriptive methodology; rather it is a style of building a data warehouse, data marts, business intelligence applications, and analytics applications that focuses on the early and continuous delivery of business value throughout the development lifecycle.
The Agile Manifesto is adapted to make it more appropriate to Agile Analytics by Ken Collier:
Manifesto for Agile Analytics Development:
Individuals and interactions over processes and tools
Working DW/BI systems over comprehensive documentation
End-user and stakeholder collaboration over contract negotiation
Responding to change over following a plan
The items on the left are valued more than the items on the right.
How does Agile Analytics work?
- Iterative, incremental, evolutionary: Agile is an iterative, incremental, and evolutionary style of development. The system is built in short iterations that are generally one to three weeks long by adapting to frequent user feedback.
- Value-driven development: Every iteration must produce at least one new user-valued feature.
- Production quality: Each newly developed feature must be fully tested and debugged during the development iteration and acceptable by the user.
- Barely enough processes: Traditional styles of DW/BI development require “signing off” formalities in transition from requirements analysis to design. Primary objective is the production of high-quality, high value, working systems, the amount of ceremony required for other activities are minimized.
- Automation: The only way to be truly Agile is to automate as many routine processes as possible. The more automation is done, the more focus is on developing user features.
- Collaboration: In traditional projects, the development team solely bears the burden of meeting timelines, budgets and quality. In Agile methodology, the project community includes the sub communities of users, business owners, stakeholders, executive sponsors, technical experts, project managers, and others. Frequent interaction between the technical and user communities is critical to success.
- Self-organizing, self-managing teams: The Agile project manager’s role is to enable team members to facilitate a high degree of collaboration with users another members of the project community. The Agile project team itself decides how much work it can complete during an iteration and then holds itself accountable to honor those commitments.
Why is Agile Analytics better than the Waterfall model?
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Agile methodology stresses on ensuring that the end customer is highly satisfied with the developed product and is flexible to accommodate the changes as the requirements of customers change even at a later stage.
References: Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing by Ken Collier