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analytical decision management

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Published By: Oracle     Published Date: Jan 28, 2015
Retailers continue to collect this data and many have made good use of it, segmenting and targeting customers and rewarding loyal behavior with discounts and offers. Still, many sense that there’s untapped potential. They’re right. With the cost of data storage plummeting and the capabilities of analytical tools on the rise, this data’s value is set to skyrocket. John Bible, Senior Director of Retail Data Science and Insight at Oracle Retail shares his view on how insights from these vast data storehouses can scientifically inform retailers’ decision-making in critical strategic, tactical and operational areas, including category management, shelf space allocation and new product introductions.
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Oracle
Published By: IBM     Published Date: Aug 08, 2012
IBM Analytical Decision Management revolutionizes how organizations make decisions by optimizing and automating high-volume, operational decisions at the point of impact to drive better business outcomes. In this technical white paper, learn about the architecture, deisgn and execution environments for this leading-edge technology, as well as recommendations on how to manage an implementation of this decision management solution.
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ibm, technology, optimized decision-making, analytical decision management, decision management, automation, technology, decision management solution
    
IBM
Published By: SAS     Published Date: Mar 06, 2018
For data scientists and business analysts who prepare data for analytics, data management technology from SAS acts like a data filter – providing a single platform that lets them access, cleanse, transform and structure data for any analytical purpose. As it removes the drudgery of routine data preparation, it reveals sparkling clean data and adds value along the way. And that can lead to higher productivity, better decisions and greater agility. SAS adheres to five data management best practices that support advanced analytics and deeper insights: • Simplify access to traditional and emerging data. • Strengthen the data scientist’s arsenal with advanced analytics techniques. • Scrub data to build quality into existing processes. • Shape data using flexible manipulation techniques. • Share metadata across data management and analytics domains.
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SAS
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