Data is about Detail - An Empirical Investigation for Software Systems with NLP at Core

Businesses continue to operate under increasingly complex demands such as ever-evolving regulatory landscape, personalization requirements from software apps, and stricter governance with respect to security and privacy. In response to these challenges, large enterprises have been emphasizing automation across a wide range, starting with business processes all the way to customer experience. As AI continues to be a core component of software systems being developed, data assumes a predominant role. AI-centric software systems of industrial scale need large amounts of training data, that in our experience, has introduced several challenges. In this paper, through an empirical study based on interviews with AI practitioners, we present current challenges that need to be addressed in ‘data requirements’ of Software Systems with NLP at the Core (SSNLPCore). We further discuss the impact of the challenges and techniques currently employed by practitioners for addressing them. Our findings reveal that a focus on details pertaining to data is required early into the project lifecycle, which include aspects such as how we may select, process, and annotate data. This can ensure that the AI component is effective in meeting business goals of software systems. CCS CONCEPTS • Computing methodologies → Natural language processing.