Since it was first defined in 2001, Big Data has made major advances into consumer lives and virtually every organization. According to IDG, a global market intelligence leader, “From 2005 to 2020, the digital universe will grow by a factor of 300 to 40 trillion gigabytes”, approximately the same as 625 Billion iPhone units.

Big Data benefits are becoming increasingly visible due to this growth and the growing organisational acceptance and maturity in the area. According to the National Bureau for Economic Research demand forecast in a retail environment improves as more and more data becomes available. The research found that the firm’s forecast performance, the subject of this study was Amazon, improved over time as a result of gradual improvements due to the introduction of “new models and improved technologies” [1].

According to IBM by 2020 up to 85% of customer services interactions will not involve a human. Growing machine learning and NLP capabilities will allow for chatbots, phone support and self-service interfaces to deliver customer service at an acceptable level. These technologies will reduce errors, increase the speed of resolving customer issues and remove some of the bias present in customer service interactions. The UBS bank has already deployed, using IBM Watson, an avatar based on economist Daniel Kalt to interact with clients. The avatar was trained by Kalt himself and allows customers to speak to Kalt as if he were present physically [2].

While the corporate sector was one of the early adopters, Big Data is making substantial inroads in other areas. Medical science is one of these. The presence of adverse effects in a drug, after clinical trials are completed, is an important consideration for pharmaceutical companies. Pharmacovigilance is an activity with the goal of discovering and understanding harmful side-effects, called adverse events (AE). Natural Language Processing (NLP), heavily reliant on Big Data, was used to enhance the existing pharmacovigilance process by using analysing user comments on various health-related sites and MEDLINE abstracts in addition to the structured information already in use. The results showed that using NLP allowed unreported AEs to be identified, thus improving the existing pharmacovigilance process [3].

At the Masterclass delegates will study how Big Data can help their organisations, review real-life success stories, learn about Big Data sources and Open-source Intelligence (OSINT), examine and evaluate major technologies, start using Artificial Intelligence and prepare a business case for a Big Data project.