During the past five years, data analytics have been transitioning from mainly scientific and academic applications to a much wider commercial/business setting with complex integration points, such as data, technology, business processes, other analytics, and security. These previous academic/scientific “clean-room” approaches for analyzing data do not translate well to a government setting, where an agency might have several thousand analytics, working against a large number of data sets, and tightly integrated with dozens of mission-critical process/workflows. Also, academic analytical techniques typically are not concerned with addressing the data V-forces (volume, variety, velocity, veracity, variability, and visibility) that affect Big Data situations, nor with security (of varying dimensions).
Our experience has shown that data analytics is a different class of software. As software, data analytics require a different development lifecycle and corresponding methodology. There are some unique aspects of data analytics that require refinement of generic agile practices to address the unique requirements of data analytics.
At Axiologic Solutions, we have created a Data Analytics Development Methodology – ANALYZEit™ – as a way to build useful data analytics the right way. ANALYZEit extends existing systems engineering and software development practices and explicitly focuses on data analytics, specifically pitfalls to avoid, and important issues on which to focus.
ANALYZEit features
