Future of data analytics
Tharindu is the Applications Management Director at Creative Software. He and his team work with world leading companies helping them gain useful insight from their data using data analytics or as it is popularly referred to, big data.
Speaking on the current status of big data, Tharindu commented, “Data analysis as a concept has already been embraced by the entire enterprise world. The global market for big data-related software and services are projected to increase from US $ 42 billion in 2018 to US $ 103 billion in 2027, attaining a compound annual growth rate of 10.48 percent according to Wikibon.
According to an Accenture study, 79 percent of enterprise executives agree that companies who do not embrace big data will lose their competitive position and could face extinction. Even more, 83 percent have pursued big data projects to gain a competitive edge. The main software vendors in the world, Microsoft, Google, Amazon and IBM are using and have invested big data platform solutions. IBM, for example, has IBM Analytics, Microsoft has Azure and Amazon is going with AWS.”
Outlining the future predictions of big data, Tharindu stated, “Some may think big data is just hype and will and will eventually fall into oblivion. However, there are several reasons to why big data is here to stay. A decade ago, even though there was a demand to combine and leverage data from multiple data sources, systems were not yet ready and available to deliver on their visions. Today, it is a reality via data analytics. Another reason is the present ability to analyse large amounts of data. In search results analysis, for example, multiple inputs can be processed simultaneously and give us an indication of how the human mind functions. Modern analytics systems are also capable of asking ‘why’ and have the capacity to explore by using one end condition as the beginning of another processing and analysing line.
However, the main reason is that analytics is not magic. When an organisation needs insights into a critical aspect of its business, it can be extracted using software engineering tools. There is no rocket science involved or a special method of doing it.
He further explained how data has changed within the last decade, especially with the advancements in technology and due to the four ‘Vs’ that comprised of volume – large volumes, velocity – the speed of data generation, variety – transaction management systems, web services and value – high value.
“This large amount of data of high variety can be used to analyse what people think, talk about, buy and wish they could buy. It can be used to avoid accidents; for example, a flight within an hour of flying time generates 3-4 terabytes of data. If this is analysed properly it could be used to prevent future aircraft accidents,” said Tharindu.
He added, “What people wish they could buy, could be a method to do product planning. Predictions have a high value; every company making a strategic plan would love to know what is going to be “in”, what can generate profits. Even though software used for predictions were developed in the 1960s, the science didn’t take off due to the cost and capacity of the hardware. Today, however, relevant hardware has become an inexpensive paving way to limitless possibilities. “
Citing the opportunities prevailing in the market Tharindu said that data analytics is well on its way to becoming mainstream in large organisations. “Data science engineers or data scientists are needed in any organisation. Data engineers are a mix of statisticians and computer professionals. Students who didn’t follow computer studies or software engineering specifically but followed subjects such as math and statistics with knowledge of using computing tools which are used in data analytics have a good opportunity in this field in the future. This is an ideal opportunity for those who want to work in the IT field but don’t want to be limited to software engineering,” concluded Tharindu.