Agile methodology helps organizations to adjust change, compete in the market and build high quality products. It is seen that organizations develop with agile methodology, with expanding change in requirements from customers. Compiling and synchronizing data with agile teams of organization is critical in rolling up data across as per the necessary portfolio.
The standardized agile performance exclusively depends on the plan. The ordered data-schema empowers productivity, quality and responsiveness of the organization’s progress. The level of data consistency is maintained with historical and real time scenarios.
Consider the following diagram to comprehend the data science experiment cycle −
Data science includes the analysis of requirements followed by the creation of algorithms dependent on the same. Once the algorithms are planned along with the environmental setup, a user can create experiments and collect data for better analysis.
This ideology computes the last sprint of agile, which is designated “actions”.
Actions includes all the mandatory tasks for the last sprint or level of agile methodology. The track of data science phases (with respect to life cycle) can be maintained with story cards as things to do.
The future of planning completely lies in the customization of data reports with the data collected from analysis. It will also include manipulation with big data analysis. With the assistance of big data, discrete pieces of information can be analyzed, effectively with slicing and dicing the metrics of the organization. Analysis is constantly considered as a better solution.