With the shift from boxed products to services, rich data is available from all stages of the Software Development Life Cycle.

By leveraging this data, AI can assist software engineers, break down organizational boundaries and make our products more robust.

This video from a recent Microsoft Research event demonstrates several AI powered features like reviewer recommendation, test load reduction and automated root causing for boosting developer and infrastructure productivity.

Here’s an interesting article from CodeProject defining the cycles of data science and how it relates to business cycles and the fairly well established framework of SDLC. Although some will argue that data science is “pure science” and this cycle belongs to the “data engineering” label, organizations that fail to move innovations efficiently from “the lab” to production are not going to be competitive.

By its simple definition, Data Science is a multi-disciplinary field that contains multiple processes to extract knowledge or useful output from Input Data. The output may be Predictive or Descriptive analysis, Report, Business Intelligence, etc. Data Science has well-defined lifecycles similar to any other projects and CRISP-DM and TDSP are some of the proven standards.