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October 20, 20173 min read

Data Science in The Coming Years

We are living in a world amidst a digital revolution – new, disruptive technologies are being introduced by the passing day to effectively manage enormous amounts of data generated on a daily basis. Industry leaders and enterprises are struggling in extracting useful insights from the unremitting influx of data. That is why they are dedicating more and more budget in integrating Big Data technology throughout their supply chain.

Data Science is Trendy

The concept of Data Science has been around for a while now, but it has risen in popularity meteorically over the past few years. With rapid advancement in technology, processing and storage of mountains of information is becoming more manageable. Thanks to improvements in Data Science, organizations are able to cope with the ever-increasing, competitive marketing demands. This has also led to a surge in demand for more data scientists who are needed to sanitize and analyze large volumes of data to gaining insights for optimizing their business operations.

data science trends

Innovations are Coming

Here are a few trends and innovations you can expect to see in the coming years in the data science industry and get an idea of where it is heading:

Radical Expansion of the Spark and Hadoop Market

Just in 2017, Big Data technologies, like Spark and Hadoop, saw steady growth throughout the year. More and more organizations are realizing the huge benefits of adopting these platforms for analyzing data and their positive impact in driving down IT budgets. Apache Spark utilizes in-memory computation and has been growing on an unprecedented rate since the past year. Several studies have shown that 61 percent users have opted for Spark for public cloud services, which is higher as compared to 36 percent of Hadoop YARN. However, Hadoop is not far behind. This Big Data platform is known for offering top-of-the-line solutions to help exploit a wide range of technologies, including data mining, data visualization, predictive analytics, clickstream analysis, IoT, and data warehousing. Hadoop is scalable, cost-friendly alternative to a majority of Big Data management systems, and has seen staggering increase in its customer base with the rise in the adoption of Data Science practices. According to the Hadoop Market Forecast 2017-2022, its market share is likely to grow beyond $16 billion by the end of 2020.

A Substantial Increase in the Recruitment of Chief Data Officers

Gartner made this prediction in 2016 in the article, 7 Big Data Trends in 2016, and it is proving to be quite accurate. A Chief Data Officer (CDO) is an executive who looks after data governance, data strategy, and policy management, and is responsible for data security, privacy, and strategy development, as well as lifecycle management. With the increase in dependence on data for process optimization, it is important to create an integrated data-driven organization culture, and for that CDOs will play a vital role. A CDO will eventually become the primary driver of utilizing the capabilities of Data Science within different departments across the enterprise, as they understand the significance of Advanced Analytics and the benefits an enterprise can gain from Data Science integration.


Dominance of Deep Learning Technology in the Corporate Landscape

Deep learning is already being used for a whole host of important applications, including machine translation and other types of language processing, like Facial Recognition, Object Classification, and Detection in Images, Automatic Image Caption Generation, and others. AI is expected to take a more generalized form in the future, and the concept is likely to transform into Artificial General Intelligence (AGI). Data scientists are making breakthroughs in the field of deep learning to come up with more refined systems for cracking machine learning problems. Tech giants, such as Microsoft, Facebook, Google, and Baidu are spending millions on further research on deep learning technology to explore more avenues with their AI R&D teams.

Greater Administrative Control over Data Security Permissions

Data warehouses will be optimized to provide only a single version of the same data to all users, while providing greater control over who can access what kind of data. Companies will be able to put up stronger data security permissions, however, it will require a mass revision to permission policies of assigning access to different users. In addition, technology for detecting and monitoring potential data exfiltration will be implemented for keeping unauthorized personnel from accessing important data.


The future of Data Science is bright, and we can expect to see some major innovations and shifts in trends, may be bigger than discussed above. The applications of machine learning, automation, and AI will go mainstream, and become an integral part of various business areas, providing companies with a major competitive edge to stay ahead of the curve.

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