Five Big Data and Data Science trends to expect in 2018
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According to an IDC Digital Universe Study, by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. Uncovering insights from this humungous amount of information will require the seamless adoption of big data technologies, stronger data security, and integration of AI, machine learning and cognitive technologies applications with business operations. This calls for investments in right infrastructure as well as skilled talent that enable the right use of big data and analytics platforms. It is worth noting that India is among the top 10 big data analytics markets and the big data analytics sector is expected to witness an eight-fold growth by 2025 - from the current $2 billion to $16 billion.
Here are top five big data and data science trends to watch out for in 2018:
Prescriptive analytics is poised to drive proactive decision-making:
For companies looking to transform HR processes, perceptive analytics helps access and analyze huge amounts of data for making smarter workforce decisions. For instance, Google uses analytics for improving hiring decisions, and Amway uses assessment tools such as a predictive index to take more informed hiring decisions. Training in HR analytics can help your functional experts take advantage of this trend to efficiently perform various activities such as analyzing attrition, employee performance and predicting appraisal.
Data scientists will be in high demand:
With the data science market growing at tremendous pace, data specialists are in high demand. According to IBM, by 2020, the demand for data scientists will increase by 28%. In India, the number of analytics jobs doubled between April 2016 and 2017. This means; finding data scientists will be an arduous task. About 50,000 jobs remain open due to lack of skilled professionals. HR teams that train their mathematicians and analysts in data science course will be better placed to keep pace with the competition.
Cognitive technologies and artificial intelligence (AI) are reshaping business processes:
According to Deloitte University Press, it is now possible to automate the tasks that require human perception skills. Cognitive systems such as IBM Watson, Stanford’s Deepdive and Google’s Deepmind enable enterprises to make sense of unstructured data through natural language processing (NLP). Bangalore based Talview leverages IBM Watson to expedite the process of hiring for its clients. To take advantage of such cognitive technologies HR and L&D leaders need to reskill their workforce and invest in adaptive learning strategy for practical application of cognitive technologies.
Machine learning is fast becoming the pillar of big data platforms and analytics:
Machine learning (ML) is finding wide ranging applications across functions and industries. Pinterest uses machine learning (ML) to enhance content discovery, whereas Belong - an Indian recruitment startup, uses AI for scanning candidates. Lesson to learn: integrating ML with data analytics enables companies to access more accurate insights into real-time decision making quickly. Providing access to learning initiatives that help in up-skilling your employees in big data and data science is a great way to gain more value from analytics in real-time scenarios.
Enterprises are increasingly adopting a cloud-first strategy and cloud-based platforms for big data analytics:
By 2020, at least a third of all data will pass through the cloud. Business leaders that can efficiently analyze multiple sources of data can tap into various opportunities to boost outcomes across functions. For instance, Xerox leveraged a cloud-first strategy to efficiently analyze data and reduce attrition rate at its call center by 20%.
Cloud, Artificial Intelligence, and ML are poised to drive significant changes to the Big Data landscape in 2018. Whether you are in retail, healthcare, banking or education, you can benefit from these changes by re-skilling and training your employees in big data technologies and data science. The result: enhanced operational performance, improved customer experience, and sustained growth.