Big Data and Data Science Trends to Expect in 2023
Big data is the term that is given to large chunks or volume of structured and unstructured data. Though the volume of data plays an important part in determining the business of an organization, what matters more is how the organization operates on the data. This means, analysis of the data for insights that will eventually lead to decisions related to strategic business moves.
Data science is related to big data in the sense that it is an interdisciplinary field that helps extract sensible information from data in various forms. This is done by means of different algorithms, scientific methods, and algorithms.
Big data and Data Science has grown leaps and bounds in the last few years. However, there are trends in the fields that you can expect in the coming year. Some of the trends to look out for are:
1) Perceptive analysis:
The concept of perceptive analysis will be used in order to establish a sense of proactive decision-making. This, in turn, will help companies that are looking for an opportunity to transform their HR processes into a smarter workforce that is capable of analysing huge amounts of data and arriving at smart decisions with respect to the business. Some major activities that will benefit from this kind of analysis include prediction of appraisals, analysis of attrition and employee performance. An example of a global organization that is incorporating this approach includes Google using perceptive analysis for working or their hiring decision.
2) Increased demand for Data Scientists:
The industry for data science is growing at a rapid rate. The result of this is that Data Scientists are scarcely available. According to a study conducted by IBM, it is said that the demand for Data Scientists in India alone, by the year 2022 will increase by well over 28% which means that 50,000 odd job openings will remain open for skilled Data Scientists and professionals. Thus, the HR teams that are capable of training and producing analysts related to Data Science have a chance at making their organization more profitable and keeping pace with the overwhelming competition.
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3) Use of Cognitive technology:
Artificial Intelligence along with Data Science has made it possible to automate those tasks that need human perception skills. This gives rise to cognitive technology developed by large organizations such as IBM and Google. For example, Google’s technology Deepmind makes it possible to interpret unstructured data by means of using cognitive technology and the principle of Natural Language Processing (NLP). In order for these technologies to become a success, organizations need to invest in reskilling their workforce to keep up with the learning strategies of these technologies.
4) Incorporation of Machine Learning:
Machine Learning is nothing but the extension to Data Science Practices. However, Machine Learning is a lot more robust and accurate, thus making it a pillar of large data analysis platforms. Integrating Machine Learning with the already existing processes will enable any organization to provide much more accurate insights into decision making in real-time. It will also help in up-skilling the employees to gain more input and data from the analysis in different scenarios.
5) Adoption of cloud-based platforms:
According to recent studies, it is said that by the year 2022, at least one-third of all bid data will pass through cloud platforms. Using cloud will help business leaders analyse data from different sources and obtain the various business opportunities in the form of functions. Thus, cloud computing along with concepts of Artificial Intelligence is said to change the landscape of Big Data irrespective of the organizational field.
The post is by Nirmal Patel, a digital marketer, freelance enthusiast and ingenious writer who enjoys the challenges of creativity attention to detail at Imarticus Learning and can follow him on LinkedIn.