Demystifying Quality Attributes in the World of Data Science

Data Science – A world of data, data history which uses various algorithms, statistical approaches, tools and techniques to reveal what the data articulates, has changed the current software industry scenario.

Today the industry is more inclined towards the increased development of software systems having components that are more data-driven. Use of machine learning, artificial intelligence, predictive analytics is widespread.

There are set standards, protocols, process and methodologies to design, develop and manage any software-intensive system. In the phase of writing Software Architecture one such set standard which is referred is – Quality Model and the listed Quality Attributes. Quality Attributes plays a vital role in defining the Architectural Goals. It is helpful in specifying, measuring and evaluating the product quality of any software / software-intensive systems.

With the emerging Data Science trend, while working on Software Architecture for such data-driven software-intensive systems, there is a need to interpret existing Quality Attributes and its relevant applicability in the right context.

In this session we will look at:
• Quality Attributes / Characteristics that are relevant for systems that are more data-driven and developed with the use of ML, AI or PA concepts.