Understanding the Role of Data in Artificial Intelligence-Based Personalization

The arrival of big data capabilities has fundamentally transformed how organizations function, making data ubiquitous and inimitable for decision-making. Although this transformation has created massive opportunities for organizations to engage customers, personalize recommendations, and generate more revenue, this has also led to the increased complexity of data (structured, unstructured, and semi-structured), resulting in newer challenges in utilizing it for decision-making. Manually collecting and analyzing such huge and varied data is nearly impossible. The end-to-end data life cycle consists of several interrelated processes: data collection, cleaning, exploration, analysis, and implementation. Although all of these methods are critical for an AI project’s success, data collection has recently become one of the bottlenecks in the process. Apart from data collection, data labeling and analysis are also pertinent issues in machine learning. However, the most critical of these tasks is to draw actionable insights from data and implement them to improve customer engagement and service. This chapter discusses a conceptual framework to describe how data is fundamentally changing the business landscape and the methods through which data could be collected, analyzed, and reported to ensure maximum customer engagement and personalization.