Paper: Standards and Techniques for Data-Driven, Decision-Centric Process Innovation
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Standards and Techniques for Data-Driven, Decision-Centric Process Innovation
This chapter was originally published in BPM Everywhere
James Taylor, Decision Management Solutions, USA
In an era of Big Data, organizations are applying analytics so they can become data-driven. Cookie cutter treatment of customers is being replaced with personalized, targeted communication and offers. Pay and chase fraud recovery is giving way to the prevention of fraud before it gets into the system. And post-transaction risk monitoring is being replaced with dynamic, transaction by transaction risk-based pricing and management. New data sources, better management of corporate data and the growing power of analytics are combining to create a new generation of data-centric decision-making.
Process-centric organizations need to adopt new standards and techniques so they can use data-driven decision-making to radically innovate their business processes. Proven techniques allow organizations to discover the decisions in their processes. Adopting the new Decision Model and Notation standard lets them clearly model this decision making, simplifying their process models and identifying clear and compelling analytic opportunities. Focusing on decisions and processes as peers creates innovative decision-centric processes with higher rates of straight through processing, more customer-centricity and improved operational effectiveness.
This paper introduces operational decisions, discusses how to find them in a business process and shows how they can be modeled effectively in the new Decision Model and Notation (DMN) standard. How to use these models to frame analytic requirements is covered as is the opportunity for process innovation created by changing the role of decisions in business processes.
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