The Data Driven Intelligence Innovation Lifecycle: From Experimentation to Real-World Value

About the Session

Data-driven Intelligence innovation is realized by employing proven techniques to identify valuable patterns, conducting experiments through a structured and methodical approach, swiftly terminating those that don't yield results, and nurturing successful ideas into tangible business value. Uncovering value within the vast amounts of data available today can open new business opportunities, enhance quality and efficiency, elevate customer service, and create differentiation to maintain a competitive edge. Imagine discovering value by applying human behavior analytics to proactively assist a customer in need within an aircraft cabin, automating quality assurance in airline engine production using cameras and vision intelligence, or utilizing a digital twin of your airline systems to predict and prevent maintenance issues. With the immense volume of data generated by IoT sensors and IT systems, many companies struggle to identify the critical data and determine when to apply solutions like Artificial Intelligence (AI), vision intelligence, or real-time digital twins. Some find their experiments fall short of achieving desired objectives because ideas remain stuck in the experimental phase. Today's data doesn't always fit neatly into traditional tables and databases; much of it is unstructured, such as visual data from cameras, and often stored in disparate silos, complicating the task of correlating and identifying relationships. This data demands a different analytical approach than what has been traditionally applied. By implementing a focused data-driven innovation strategy aligned with your corporate goals, you can unlock the latent value within your data.

Speaker

Dippu Kumar Singh

Senior Solutions Architect (For Emerging Data & Analytics)