I’m a fan of agile development. Prior to focusing on business intelligence and data warehousing, I architected and developed client server and n-tier applications, and I found that agile development techniques delivered better end results. I believe that, in large part, this came about because of the focus on smaller, functional iterations over a more traditional waterfall approach. However, this approach is still not regularly used on BI projects. Since it can have a big impact on the success of your BI initiatives, I’d like to list some of the challenges that prevent adoption of this approach. I’ll go into more detail on each of these in my next few posts, and also cover some of the benefits you can see if you overcome the challenges.
First, though, let me define what I mean by an iteration1. An iteration is a short development cycle that takes a specific set of requirements and delivers working, useful functionality. Generally, I think the duration of an iteration should be 2 weeks to a month, but I don’t consider that an absolute rule. More than 2 months for an iteration, though, really impacts the agility of the project, so I prefer to keep them shorter. Delivering working functionality in an iteration doesn’t necessarily mean that it has to go to production. It does mean that it could go to production, if the project stakeholders choose to do that. Delivering useful functionality means that what’s been developed actually meets a stakeholder’s need.
There are a number of challenges that you might encounter in doing iterative development for BI. Fortunately, you can work around these, though it’s not always easy.
Many BI/DW initiatives are large and involve multiple systems and departments. Even smaller BI projects often have multiple audiences with differing needs and wants. Managing the scope of the effort appropriately can be challenging in that environment. This means that defining and managing scope is critical to successful iterative development.
- Foundation development
Particularly when BI projects are getting off the ground, they need a lot of foundational setup – environments, software installations, data profiling, and data cleansing. This poses definite problems for the first couple of iterations, especially when you take into account the guideline of delivering working and useful functionality with each iteration. The foundation needs to be built in conjunction with delivering useful functionality.
- Existing infrastructure and architecture
Iterative BI development requires an infrastructure for development that is flexible and adaptive, and it makes it much easier if existing solutions were architected to be easily modified. Sadly, this is not the case in many organizations. Existing process and infrastructure tends to be rigid and not support or encourage rapid development. Existing data warehouses tend to be monolithic applications that are difficult to modify to address changing business needs. And many BI development tools do not provide adequate support for rapidly changing BI applications.
- Changing requirements2
Changing requirements is a fact of life in most IT projects, and it’s definitely the case in BI/DW projects. While agile and iterative development can help address changing requirements, it still poses a challenge. As requirements shift, they can have a ripple effect on the BI infrastructure – adding a new field to a report may require changes to the database and ETL processes in addition to the report itself. This can make seemingly inconsequential changes take much longer than expected, and lower the adaptability of the project.
- Design 2
Due to the scope and complexity of many BI/DW initiatives, there is a tendency to get bogged down in design. This is particularly true when you consider that the database is a key part of BI/DW projects, and many BI developers feel most comfortable having a complete and stable data model before beginning development (which is perfectly reasonable). However, design by itself does not produce working functionality, so an iteration that involves nothing but design doesn’t really meet my definition of iterative.
After looking at all these challenges, you may feel that it’s pointless to try iterative development on BI projects. However, I’m happy to say that all these items can be addressed (granted, some of them are much more easily fixed than others). If you do make some changes, you can get a number of benefits, including the ability to rapidly accommodate change, deliver working functionality more quickly, and facilitate emergent requirements and design. The end result? Happy project stakeholders who are more satisfied with the results of their projects, and less wear and tear on the BI developers. Everybody wins!
Over the next couple of months, I’ll be posting more information about the challenges, and how you can work around them. Please keep reading, and if you have questions or comments, don’t hesitate to get in touch with me.
1. If you are familiar with Scrum, you will notice some similarities between what I describe as an iteration and a sprint. Scrum has influenced my thinking on agile methodologies quite a bit, and I’m a big fan of it. However, because I don’t follow the definitions in Scrum rigidly, I’ve found it better to not use the same terminology to avoid confusion. If I do refer to a sprint, though, I will be referring to the Scrum definition (a 30-day increment, resulting in potentially shippable product).
2. Please don’t take the above to mean that I don’t believe in requirements or design – I feel that both are vital to iterative development. However, the approach that many BI practitioners take to requirements and design does not lend itself to iterative development.