So why Waterfall?

  • Easy linear planning and implementation
  • Tangible results at the end of each stage – producing better visibility to others and provides a baseline to move forward on.
  • Able to visually see and communicate a target delivery / end date based on scope agreed.
  • Removes the possibility to scope creep.

What are the Facts?

DM Review magazine estimate the average deployment time of a business intelligence project is 17 months, with a failure rate of almost 80% according to research by Gartner. Although the waterfall methodology cannot be proven to cause projects to fail, it does allow scope to analyse the suitability of the waterfall method for business intelligence projects. Statistics taken from a 2011-2015 survey found the results of 50,000 projects showed 11% of Waterfall projects were successfully completed whereas 39% of Agile projects were successful, making the Agile business intelligence strategy 4 times more viable for successful BI projects.

Moss (2013) dedicates an entire chapter explaining why traditional methods no longer work, specifically waterfall with Business Intelligence projects. Moss argues that waterfall methodologies were created in the 1970’s to help developers successfully turn requirements from individual users or departments into customized solutions. She goes on to say that the concept of data governance and data integration did not exist in those day and were not designed to deliver enterprise-wide standardized solutions for everyone in the business to use.

Breakdown of Waterfall

The main concern with waterfall is the length of time it takes to complete the project. A successful business intelligence project should be allowed to integrate itself with the current business needs and the changing needs of the business going forward whilst the project is being carried out. Due to the linear design of waterfall the business needs are only collected at the beginning in the first stages of planning, however the more complex the project becomes the harder it is to get this right the first time resulting in business intelligence failure.

The Risks to your Business Intelligence Project?

  • The risk of misinterpreting the requirements by the business intelligence developers
  • The risk that requirements might have changed during the lifecycle of the project
  • The risk of incorrectly or incompletely expressed requirements
  • The risk of needless requirements
  • The risk of incomplete tasks before the project gets cancelled.
  • The risk that business users did not know exactly what they wanted or could expect from the business intelligence solution in the first place.

The results of this methodology is disappointed users who become frustrated by the impact of their inability to express or interpret their earlier requirements, teamed up with the new requirements as the business moves on during the frozen state of waterfall.

What’s next?

Agile BI is a project approach which can rapidly and cost effectively adapt to meet changing business needs through true business-IT collaboration in a self-organising manner and as such create business value through the early and frequent delivery of working business intelligence software.


September 2, 2015