Agile Analytics is a set of principles and techniques to enable agility for the development and deployment of business intelligence, advanced analytics, and machine learning projects in a rapidly changing business environment. In this article we'll discuss the significance of Agile Analytics in a business strategy and how it can help.
Agile Analytics is a new way of defining and building high-quality analytics systems to maximize the business value in the respective functional area like marketing, operations and supply chain.
The fundamental approach is to consider all the data insights and analytics requirements holistically and recommend an agile development methodology. It focuses on highly disciplined practices and techniques tailored to customer demand and adjusts to the culture of your organization for an early and continuous delivery of business value.
Agile analytics methodology is a way for all analytics systems to allow for the highest level of flexibility, iterative delivery of insightful metrics, and accountability. This allows for the successful and timely completion of agreed-upon end-to-end work, from setting up the data sources and feeds, ETL scripts, data wrangling and metrics development, building the visualization layer, and analyzing the insights.
The agile analytics approach also allows for a fast ramp-up or ramp-down of resources to align the analytics project timeline. Agile provides the opportunity to assess the project path throughout the development life cycle. This is achieved through regular Sprints, or iterations, for which teams must present a potentially deliverable product increment, along with daily Scrum meetings to update activity and status. The agile approach includes wide, cross-functional participation to provide an avenue to change the project direction as needed.
The five phases of the analytics system development and deployment will be delivered using the agile analytics execution model:
This is the first phase of the project where the team will establish the engagement governance model, define the success criteria, create a road map of the entire project, define best practices and data governance models, and create an As-Is Assessment around the people, process, technology, and strategy involved.
In this phase, the team will complete the following activities:
This is the readiness and baselining phase for infrastructure, data sources, data integration and ETL process, data extraction, security, data quality, training, and support. In this phase, the team will add more team members to enable and prepare for scaling out agility without sacrificing data quality and governance.
The team will focus on scaling out the production version of a Data Visualization Dashboard by different functional organizations within organization in the order of priority, project champions, and strongest prototype.
During the post-production support phase, the team will be responsible for supporting the production version of the Data Visualization Dashboard and gradually transition over support to the client’s application or IT support team, based on the mutual agreed upon timelines and transition methodology.
The development and launch of a successful BI and Advanced Analytics system is hard and complex. Studies show that 50% percent of projects were unsuccessful in on-time delivery as well as meeting the requirements and satisfaction of the users.
These issues could easily be resolved by better defining the scope. Most of the failures are caused by incorrect requirements, the quality and quantity of the data, ineffective processes, failure to manage change requests, scope creep, and schedule management. If correctly deployed, the adaption of Agile Analytics for the BI and Analytics system will provide great business benefits.