Automating decision making with big data: How to make it work

While many companies struggle to adopt big data, a number of industry leaders are leapfrogging big data adoption by going straight to automating core business processes. Andreas Schmidt presents examples from leading European companies that have overcome cultural, technical, and scientific challenges and unlocked the potential of big data in an entirely different way.

Conventional wisdom maps a progression from descriptive analytics (what has happened), via predictive analytics (what will happen), to prescriptive analytics (what should we do). This development mirrors the increasing creation and availability of data, through the waves of computerized and networked business computing. It follows the money (CRM and ERP) to business software dealing with behavioral data (digital marketing software and HR software) and the next wave of autonomous devices, connected sensors, and the Internet of Things. Despite the amount of data that must be processed, which is growing by multiple orders of magnitude from wave to wave, the success of approaches in democratizing data access and visualization has been timid. If our strategy in dealing with big data concludes by simply giving everyone access to visualizations, we are going to fail. Andreas Schmidt presents examples from leading European companies that realize this problem and have unlocked the potential of big data in an entirely different way. They have automated their most critical, most decision-heavy, and most impactful business processes, including pricing, replenishment, and staffing, based on data that is extracted, processed, turned into predictions, and informs decisions that are put into action in minimal roundtrip time. We call these businesses predictive enterprises. For companies bent on following this example to become predictive enterprises, a set of tough challenges must be addressed: Drawing on his experience in running a data science practice and operating a customer’s predictive applications on a predictive application platform as a service, Andreas answers these challenges by demonstrating what a predictive application platform as a service looks like and how its applications are built and operated.