Data collection is a key component in today's business world. Predictive analytics can now be used to evaluate information, extract patterns and make predictions about future outcomes, needs and challenges, explained CRM Magazine. Many companies are turning to data collection software to amass information that can be used in this way to inform a variety of decisions, from sales to distribution.
"The creation of the social Web has led to a new wave of unstructured data and, subsequently, to technologies that have sprouted up to decipher it," explained Destination CRM's Kelly Liyakasa. "According to Gartner, analytical systems are expanding their focus on capturing and correlating structured data to include unstructured data as well."
As this trend develops, supply chain managers should consider how predictive analytics is being used by companies throughout the decision-making process. PC Advisor recently described how eBay has launched big data initiatives to improve its competitive edge in the e-commerce market. The company collects more than 100,000 data elements and has 90 petabytes of stored data and tables that contain 3.5 trillion rows of information.
To track and manage supply chain information, the company uses its warehouse data collection software to better understand stocking, shipping and delivery trends. It can then take steps to streamline the process and deliver a higher rate of accuracy and speed to customers - both critical as the number of e-commerce options available to consumers grows. This kind of activity will only become more common, according to eBay, as data continues to collect over time.
"To be competitive, companies must be able to have visibility into their supply-chain data and make informed decisions based on the intelligent correlation of requirements, design, simulation, test results, and yield data," advised Brian Haacke, High Tech Industry Sales Director at Dassault Systemes, in an interview with JB's Circuit. "Connecting data sets is a start. Yet it is the marriage of operational and design intelligence that enables effective analytics to improve traceability, root-cause analysis, and time-to-yield ramp-up."