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” .
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 .
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 .
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.
 Bajari, P., Chernozhukov, V., Hortaçsu, A., & Suzuki, J. (2018). The Impact of Big Data on Firm Performance: An Empirical Investigation. IDEAS Working Paper Series from RePEc, IDEAS Working Paper Series from RePEc, 2018.
 Moore, M. (2018). Upgrading the Call Center. Fortune, 178(5), 104.
 Yeleswarapu, S., Rao, A., Joseph, T., Saipradeep, V., & Srinivasan, R. (2014). A pipeline to extract drug-adverse event pairs from multiple data sources. BMC Medical Informatics and Decision Making, 14(1), 13.
Upon completion of this program, participants will:
Program Level and Pre-requisites for Attending
Foundation level, no pre-requisites
Number of Attendees
Maximum 17 attendees
A distinguishing feature of this program is its practical aspect. At the end of each module there is a practical exercise designed to reinforce the concepts of the module and to give participants a chance to “get their hands dirty”. The exercises are either scenarios where participants work in groups or individual tasks executed within a specially prepared software environment.
A specific module of the program is devoted to creating a Big Data analytics pilot project, which the participants will be able to use as a blueprint in their teams for a real-life Big Data analytics initiative.
At the start of the course, each participant will receive a USB with a fully configured Virtual Machine that will be used for the practical work throughout the course. Participants will be allowed to keep the USB after the course and use the software in their work.
- Business Rationale for Big Data
- Big Data Characteristics
- Big Data Case Studies:
- Participant Round-table Discussion
- Group Discussion
- Building on the Wisdom of W. Edwards Deming and Peter Drucker
- Case Study: Using predictive analytics to identify customer behaviors
- Structured Data
- Transactional Systems
- Data Warehouses
- Semi-structured Data
- Unstructured Data
- Open-source Intelligence (OSINT)
- Applications and Techniques
- Practical Examples
- Practical work: Accessing, downloading and pre-processing external datasets
- Overview and Main Characteristics
- Benefits and Weaknesses
- Selection Methodology
- Introduction to Data Storytelling
- Essentials of Impactful Visualizations
- Main Principles
- Big Data Reporting
- Machine Learning Categories
- Types of Machine Learning
- Practical Work
- Building a Machine Learning model of a business process
- Training a model to identify network hacking attempts based on historical data
- Required Skillsets
- Legal and Privacy Considerations
- Delivery Approach and Stages of Experts’ Involvement
- Defining the Business Need
- Discovering Necessary Data-sources
- Establishing Required Resources/Expertise and Costing