Looking to the future

The future of digital technology is here. 2017 saw incredible progression for things like data science, artificial intelligence, and machine learning. Where will they go in 2018? In this article, Maria Thomas explores the future of data science and how well it can be combined with predictive analysis.

With the onset of digital technology, data is developing aggressively. Data science is one such concept which is emerging at a rapid scale. It is an integrative blend of scientific methods, designs, and systems to grasp knowledge from data. As a result, qualitative data is overlapping quantitative data gradually. On the other hand, Machine Learning is a composition of algorithms that focus on a data set to make forecasts or implement actions in order to enhance some systems. Once these algorithms are automated with no human interruption and only mechanized control, these kinds of algorithms are more popularly known as artificial intelligence (AI).

In 2017, we witnessed data science making a pathway for AI and machine learning at center stage of the technology cycle. Artificial Intelligence and machine learning (ML) had been trending topics for the whole year. AI applications have been increasingly used in numerous industries, including security, financial services, entertainment, automobiles, and more. There was a drastic growth in a variety of platforms like cloud machine learning and data science platforms.

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In 2018, AI is gaining a momentum over various process developments. More practitioners are rising to the challenge of implementing the positive benefits of AI to the dubious lot. This is the year where we are likely to see a new focus of big data, AI, and ML on various areas like customer service, machine intelligence, process automation, workforce transformations, and more. In previous years, it was essential for data scientists and analysts to have extensive knowledge about which algorithm fits the bill. But now, machine learning and automation processes have facilitated analysts to consider different algorithms. This year we are likely to observe advancements in IoT like improved security features, commutable platforms, and edge analytics.

Emergence of data science, AI and ML as a practice

Utilizing data science, AI, and ML as a process is increasingly popular and being embraced across a wide range of industries and applications. Most businesses are inclined to use open source applications and data management software for resolving critical system neural networks, expediting their supply chain procedures or determining customer expectations.

According to McAfee Labs’ 2018 Threats Report, in the future machine learning will be enforced for cyber-intrusion detection, scam, and spam detection. It can also be used to detect malware in the field of cybersecurity for high-intensity machine speeds in serverless environments. With the growing number of cyber-attacks, AI and ML are both helping companies to improve security approaches. Developers might be able to implement Blockchain as a feasible way to counter network intrusion and ensure safe data keeping.

The benefits of combining data science with predictive analytics

The combination of predictive analytics with data science allows enterprises to reap all sorts of benefits. For example, an organization can adopt a prognostic approach while recruiting to preserve millions in turnover and attrition. What used to take days to execute can be easily done in a matter of seconds using AI and machine learning techniques.

Big data, artificial intelligence, and machine learning are likely to generate new job opportunities in 2018. This year we are supposed to witness a steep upward trend in demand for specialists with professional competency in emerging technologies such as big data, artificial intelligence, and machine learning. Although big data and analytics are relatively trending and the most sought-after professional skill set desired by companies across different sectors, AI and ML are not far behind.

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AI and ML hold a lot of promise for many companies in the current year. It is assumed that almost one in five companies will use AI for decision-making purposes this year. It will assist companies in offering personalized solutions and guidance to employees in real-time. With deep learning in AI, it will be easier for companies to analyze both structured and unstructured data in the text analytics platform.

Conclusion

We can fondly remember the year 2017 as the emerging year of new automated analytics platform primarily focusing on combining sophisticated and automated capabilities rejuvenating every facet of data science. With the evolution of the digitization, the analytics industry witnessed the emanation of artificial intelligence and machine learning in 2017. In the coming years, it is eminent that this new automated technology would continue to seamlessly grow and deliver on the promise of offering the best sophisticated and intelligent automated analytics solutions in the digital era.

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