FEATURE
THE ART OF DATA SCIENCE HOW DATA SCIENCE CAN POWER YOUR BUSINESS
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or the uninitiated, data science is the process of gleaning business insights from structured and unstructured. It collects, analyses, and interprets large volumes of data, using various methods ranging from statistical analysis to machine learning, to improve business operations, reduce costs and enhance customer experience. Though the term has been in use for decades, there is a sudden surge in demand for data science platforms as enterprises continue to amass enormous volumes of data in both structured and unstructured formats. This has created opportunities to transform data into value by gaining actionable insights into business challenges. “The abundance of big data originating from web applications, mobile, and Internet of Things (IoT) has brought opportunities and challenges for business. Companies have the opportunity to get insights from this data to optimise processes, foster
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CXO INSIGHT ME
MAY 2022
innovation, and create new business opportunities,” says Hadj Batatia, Director of B.Sc. Data Sciences, Mathematical and Computer Sciences, Heriot-Watt University Dubai He says the science behind working with data is becoming more accessible. Until recently, limited numbers of people graduating from some universities were able to master the needed mathematics, statistics, and computational models; but they were coming from different programmes, making knowledge sharing and cooperation difficult. Today, universities offer integrated data science programmes; various tools, platforms and technologies are available on the cloud. On-line courses ad onthe-job training are offered to re-skill employees. These factors lead to the democratisation of data science, with the aim of allowing companies of any size to benefit from this revolution. Celal Kavuklu, Customer Advisory Director for Middle East and Africa at
SAS, says the need to operationalise and realise the value of data science is now booming, underpinned by the need to manage and deploy models effectively. “Gartner reported that less than 40% of models created, with the intention of productionalising them, are ever put into production. Bain & Company reported that 70% of enterprises view analytics as a critical strategy, but less than 10% of enterprises are realising the benefits. Managing all models, no matter the language, in one place is key. This allows organisations to take advantage of automation to create repeatable deployment processes and monitor models once they’re in production to ensure the highest level of performance is maintained.” What is the difference between data science and AI/ML? Data science and AI are frequently used interchangeably. Data science is the discipline that aims at scaling machine
