DIGITALISATION
50 vast to administrate manually . Once analytical software began collating information from multiple areas , algorithms developed for machine learning programmes liberated companies by allowing them to take action on the collected data , as opposed to simply managing it . Automating the collection and management process means that procedures can be faster and more accurate . With businesses able to pinpoint problems and resolve them in real-time , the possibilities are nothing short of transformational .
This evolution is a necessity both for supply chains and modern business generally . Data no longer comes from standard sources ; in an increasingly digital world , it is woven into practically every facet , including customer interaction . Jonathan Clarke , Manager of Statistical Modelling at LexisNexis Risk Solutions , believes any supply chain without strong analytical capabilities will fail to be competitive . “ Analytics has , therefore , become a key necessity in any business process to sufficiently review data in order to make informed business decisions ,” he explains . “ Using bespoke Big Data architecture that can process vast data assets , as well as leverage machine learning tools , will empower a business to be able to highlight risk quickly and efficiently .” However , before these advantages can properly manifest themselves , companies must employ an intelligent and well-thought-out strategy to make the most out of the data collected .
Big Data represents a double-edged sword : more information is available , yet it is amorphous and representative of nothing if not sufficiently harnessed . “ As data generation continues to grow , the amount of ‘ useful ’ data
MARCH 2020