Friday, March 17, 2017

New Course - Agile Analytics

Summary: Recently, I had a presentation on Agile Analytics with one of my clients. Post the session, I had this realizaton - values, principles and practices that we interacted on can be put into a course so that it enables professionals to know better on Agile Analytics and apply them in Business (BI) Intelligence and Data Warehousing (DW) system development. This post elaborates on the course and topic of Agile Analytics.

Agile Manifesto has been available since 2001. There have been many frameworks or methods on Agile development and different bodies who promote them. Agile development has seen wide acceptance in software community. To be more specific, the highest use has been in application software development.  In addition, many lean approaches such as JIT/pull, visualization, continuous flow etc. have gone into, making it more of Lean/Agile development. 

Business intelligence (BI) and data warehouse (DW) systems have been available for years. It is based on a tiered structure – data source tier, integration tier, presentation tier and finally the analysis or analytics tier.  Development in traditional way takes enormous amount time. Many times, the systems are not used or even it used, customers tend to use few functions in-place of the entire set of functionalities delivered.  Professionals in BI/DW domain say big design upfront have to there, otherwise you will have problems later, e.g., once the data models have been developed it becomes very difficult to change.  It need not be the case with Agile Analytics. 

Agile Analytics is not a framework or method such as XP or Scrum. Rather it is a development approach. The main objective is to have valuable and working software frequently. It has a set of practices and guidelines, which helps us in doing so. 

Agile Analytics is based on a set of values and principles, mostly adapted from the Agile Manifesto. The practices used in here, have their roots in many other Agile and Lean frameworks. The practices are with respect to project management such planning, estimating, tracking, reporting and adapting as well various engineering practices such as database refactoring, ETL refactoring, adaptive data modelling among many others. 

This 2-day course on Agile Analytics covers both the management and engineering (which includes design, development, testing, release) practices in BI/DW domain. The practices are primarily derived from a gamut of Agile frameworks such as XP, Kanban, Scrum, Lean, FDD, TDD, ASD, Crystal, DSDM, but adapted for Agile Analytics.  

More details about the course: 
  • Agile Analytics - Course Overview (Link)
  • Agile Analytics - Course Agenda (Link)
  • Agile Analytics - Course Benefits (Link)
  • Agile Analytics - Who Can Attend (Link)

The course details are available in the Agile page. The agenda of the course is embedded into the post. In total, 10 modules will be covered exhaustively in 2 days.

For a detailed breakdown of the course content and coverage, send a mail to

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