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Big Data Analytics (in-house) – Oman
1 May @ 08:30 - 2 May @ 17:00
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.
During the Masterclass and after successfully completing it, participants will:
- Study the business rationale for using Big Data
- Review innovative use cases, such as combining Emotional and Artificial Intelligence (E + A)*I, underpinned by Big Data
- Appreciate Big Data characteristics and types
- Understand various Big Data sources and the mechanisms to access them:
- Learn the foundations of Open-source Intelligence (OSINT)
- Examine four (4) modern Big Data technologies and be able to:
- Understand the applications and functionality of each
- Evaluate the benefits and weaknesses of each
- Review real-life case studies
- Design and run different types of Big Data analyses and visualisations, including:
- How-to create meaningful visualisations with high impact
- Understand what is Artificial Intelligence (AI) and how to use it
- Apply AI to a specific task, such as estimating real estate prices
- Create a business case blueprint for Big Data projects and understand success factors and potential roadblocks
- Take away a fully functional Big Data environment, with four (4) Big Data technologies, Artificial Intelligence tools and datasets used for analysis.