Twice a month, we bring you headlines from around the industry to keep you informed of the latest trends and conversations. Whether you are in IT, finance, sales or marketing, we are your go-to source for how the intersection of predictive analytics, machine learning and AI is transforming business planning.
As countries and businesses across the world become more interconnected, so goes the advancement, complexities and sheer amount of data associated with the global supply chain. Having access to big data will not be enough to compete on the highest levels. Savvy executives know that gaining value from this data via actionable insights is what’s most important, and actionable insights are the results of analytics.
Analytics not only tell executives what’s happening to their businesses, why it’s happening and what course of actions are at their disposal. To take it a step further, many have turned to predictive analytics to see what’s ahead of the curve. Supply chain executives who can quantify and anticipate future performance can better plan their materials and inventory.
See these six steps to find out how coupling big data with advanced analytics can transform your supply chain, here.
With the Internet of Things (IoT) and predictive analytics software continuing to rise in popularity, predictive engineering has become a top priority for over 15% of manufacturing executives, according to a recent survey.
Predictive analytics technology has been proven to increase efficiencies and significantly reduce costs in the manufacturing industry, with 44.8% of companies seeing an increase in the speed of product development. However, 54% of manufacturing managers believe their business is not effectively using predictive analytics technology, with many also feeling that a lack of integrated technology across departments is a main reason for the delay in implementation.
Read this article from Global Manufacturing for details.
Industry interest in advanced and predictive analytics grew sharply in 2017, with business intelligence experts, business analysts and statisticians/data scientists being the most prevalent early adopters. However, less than 20% of citizen data scientists, financial analysts and market analysts use advanced and predictive analytics frequently, and executives and third-party consultants use the technology the least.
This Forbes article features the 2017 Advanced and Predictive Analytics Market Study by Dresner Advisory Services. In the report, find trends on dashboards, data virtualization and how in-memory analytics and more are considered the most critical predictive analytics and BI platforms in enterprises today.
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