Agile software development has seen a lot of success, but applying Agile to business intelligence (BI) has been a struggle. This is also one of the key reasons that organizations have not been able to address the continuum of self-service needs.
With the right set of tools in place, you’ll want to adapt your processes to adopt key Agile practices to make your organization more effective at all aspects of BI.
Tools and process should mirror. Agile focuses on a high-level of business user involvement, and iterative communication of requirements. For instance, data discovery is your R&D / Rapid Prototyping, and can drive requirements for managed reporting. Guided Self-Service provide input for refining existing requirements. And managed reporting becomes the highly scalable, fully validated releases of those requirements. This approach enables you to avoid the implicit water-fall of traditional BI and respond more rapidly to changing requirements.
Engage key users with the data warehouse team. Get people engaged. The data warehouse team should be most focused on providing the data sets that the organization needs across the entire continuum. Key analysts, who are on the leading edge of business requirements, can help shape that roadmap. And, the warehouse team can help educate the analysts on what data is available, and the nuances involved in any particular data set.
Deploy new data sets to your power-users via data discovery first. Don’t try to get data into the warehouse first. Push it into data discovery applications and deploy it to your key analysts. They will iterate through several potential KPIs, dashboard presentations, and other analytics quickly. As those requirements stabilize, the technical team can start the work of automating and governing that data set. This approach avoids costly rework on the data side while requirements are still volatile.
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