Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains

Abstract

Digital transformation accelerates the use of technology to meet changing business and market needs. Increasingly, industry executives see cloud as the enabler to digital transformation. Industry executives are adopting IoT to gain real-time insights from their business processes. These business insights require the handling of enormous amounts of data, which results in longer time-to market. Keeping up with handling growth in demand and analytics by means of manual coding requires effort and time because demand is increasing exponentially and the skillset is limited. As a result, the speed of digital transformation is slow. AI-driven data engineering enables the handling of growing data complexity, which helps align data engineering needs with business demands, thereby reducing the manual effort involved. Key technologies that enable AI-driven data engineering include machine-learning-enabled data engineering, data integration, data security and privacy, data engineering pipelines, and big data analytics. Data complexity is the crux of digital transformation and is growing because of the IoT technology, as more devices and sensors are connected to form the Internet of Things. The complex data received from connected devices is fast-changing and applies to various industries. Digital transformation is poorly served if data complexity in these digital solutions is not addressed. Cloud is frequently used as a platform to implement digital transformation because of its ondemand scalability. An industry case study of an AI-driven data engineering architecture implemented for retail cloud solutions clearly depicts the need for AI-driven data engineering.