Big Data Analytics in Supply Chain

Early adopters of Big Data and advanced analytics spent a significant percent of IT budgets modernizing analytics capabilities. This involved scalable cloud-based architectures, integration frameworks, tuned compute and storage, Hadoop constructs, and visualizations. Let’s not exclude the necessity of the team of domain experts to construct intelligence around domain areas. Of the many firms looking to implement big data analytics, a startling few have the ability to invest the capital required to accomplish such an ambitious project.Make no mistake, the value delivered by successful projects is significant, but very few companies ever come close to meeting this goal. In reality, many do-it-yourself big data analytics projects fail to deliver on the expected target. In fact, according to one Gartner report*, up to 70% of BI projects fail to meet end-user expectations. The biggest obstacles are cost, lack of technology expertise, best practices, and domain knowledge to unlock the value in data.  For the fast followers, these massive upfront investments are no longer necessary to achieve equal or better results. Big Data and cloud companies, like FusionOps, are delivering innovative platforms at a significantly lower cost and faster time to value. At the end of the day, creating enterprise grade supply chain data analytics is not a core competency for most companies. CIOs are finding that partnering with a cloud analytics provider is the best strategy for success. These partnerships help drive innovation by delivering powerful new analytics to business users with an enterprise-grade cloud. Studies show that customers eliminate the overhead and lengthy timetables associated with achieving real analytics value.