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), to 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 is concluded in giving everyone access to visualizations, and simplifying visualizations so much that they can be consumed by everybody, we are going to fail.
We present an example of 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, or staffing, based on data that is extracted, processed, or turned into predictions. These predictions are turned into decisions that are put into action in minimal roundtrip time. We call these businesses predictive enterprises.
Is there an analyst in this process? Only to provide oversight and make sure the machine is running smoothly? Are there data scientists in this process? Plenty.
Understanding the core business, modeling the data and the decisions to prepare, are some of the most challenging tasks for data scientists in the world. With each small decision being automated, an aggregate of more intelligent, data-driven decisions leads to tremendous impact on the operational efficiency of the business.
For companies bent on following this example to become predictive enterprises, a set of tough challenges must be addressed:
- Cultural challenges: How can we trust our core business to a black-box algorithm? What if something goes wrong? How do you reflect the biases of our team in the model?
- Technical challenges: How to get access to the data in the first place – and how to execute decisions? How can we turn data scientists from system explorers into system builders, crafting fine-tuned automation processes?
- Scientific challenges: How can we communicate decision quality, objectively and understandably? How can we keep models learning, accurate, and flexible at the same time?
This presentation is based on our experience in running a data science practice, and operating a customer’s predictive applications on our predictive application platform as a service. In the final section of the presentation we will show what a predictive application platform as a service looks like, and how applications are built and operated.
Lars Trieloff is director of product management at Blue Yonder, one of the leading European companies providing platforms for predictive applications. He is responsible for product roadmap, strategy, and marketing. Prior to Blue Yonder, Lars was responsible for product management for Adobe Marketing Cloud and Day Software’s content management platform.
Passionate about creating products that make work easier and help companies run better, Lars combines technological and business thinking into an outcome-oriented approach toward product and innovation management.
After a stint in San Francisco, Lars lives with his family in Potsdam, Germany.
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