It is becoming increasingly apparent that supply chains that learn to harness the power of the data sources benefit significantly; leveraging the advantages of advanced analytics, supply chains can become more responsive, demand-driven and customer centric.
The nature of big data calls for a multi-disciplinary approach, spanning skills and expertise in computer science, applied mathematics, statistical analysis and economics to facilitate the analysis of large and various sets of data. Current tools and techniques developed for this practice are abundant and some even context-specific.
Big data analytical tools originate from various sources including businesses and academics developing these tools for personal and internal use, or as a product or service to other businesses. Moreover, there are some tools originally designed for analysing smaller datasets that are adaptable to large volumes of data applications.
In this second paper of our four part series, we consider these tools, platforms and methods currently used to analyse large portions of data depending on the type and form of data available and the problems to be solved.