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10 data science trends you need to be aware of in 2017


2016 was an amazing year for data science, but the industry never slows down. We have hit the beginning of the year, so it’s time to start thinking about how the industry’s going to change in 2017, and how you can prepare accordingly.

Data science is a field dominated by those with the foresight to plan ahead, anticipate changes, and adopt and ace trends before their competitors do. So take note of these trends to come, and prepare for them. You don’t need to use all of them, but you should be aware of their existence if you want to continue being competitive in your industry.

Applied AI and Advanced Machine Learning

AI and Machine Learning (ML) have been a constant on tech trend lists for years. In 2017, you will see real world applications of AI and ML technologies, like deep learning, neural networks and Natural Language Processing across various industries such as banking, travel and healthcare.

Most of the concepts are based on unsupervised machine learning techniques and don’t require prior training on data sets. The combination of extensive parallel processing power, advanced algorithms and massive data sets to feed the algorithms has unleashed this new era.

Demand for data scientists will increase significantly as data science usage expands beyond large companies, tech start-ups and developed markets

In addition to industries and markets where data science is more established, there are other industries like Fintech, health care and transportation where data science as a discipline is young and will grow rapidly. Healthcare companies are using data science to examine breast cancer and help patients avoid biopsies. In transportation, traffic management agencies are able to use real-time traffic and weather data to predict traffic flows and manage emergency response. The utilization of data science in these industries will increase the demand for data scientists.

Business managers who can analyse data to take decisions will be highly valued.

The demand for data science savvy business managers will increase in 2017, as more and more businesses will start taking higher number of data-driven decisions. These managers have to understand the fundamental principles of data science well enough to envision and/or appreciate data science opportunities to supply the appropriate resources to the data science teams, and to be willing to invest in data and experimentation. They must have the ability to steer the data science team carefully to make sure that they stay on track towards an eventually useful business solution.

Analytics will continue to move towards being real-time

In 2017, you will see a widespread adoption and implementation of streaming analytics in enterprises.  Open source streaming engines, such as Spark Streaming and Flink, will be used with enterprise Hadoop Data Lake to enable companies to analyse data in real-time and deliver personalized services to their customers.  Due to this, the rate of adoption of these technologies will ultimately take half the time it has taken Hadoop to rise as the default big data platform over the past six years.

Increased focus on ROI generated from data science activities

The report, from the Economist Intelligence Unit (EIU), found that although 70% of business executives rated analytics as “very” or “extremely important”, just 2% are ready to say they have achieved “broad positive results”. In the next year, companies will focus more on getting return on investment from their data science initiatives.

Companies will look to create more data science “teams” rather than look for data science supermen

Data science is a team sport that requires the right blending of people with different skills, expertise, and experiences. Previously, I used an analogy of Avengers and Superman to build a data science team.

In 2017, companies will build more data science “teams”, rather than hiring one data scientist who can perform all the roles independently.

Data collection and analysis from mobile devices will increase

Smart mobile devices and connections — which Cisco defines as those “with advanced computing/multimedia capabilities and a minimum of 3G connectivity” — will account for 72 percent of all mobile devices by 2020, according to Cisco, up from 36 percent in 2015. And they are expected to drive 98 percent of mobile data traffic by that time.

In fact, the worldwide phone penetration rate is increasing so quickly that 5.4 billion people will have handsets by 2020, which is more than the number of people who will have electricity (5.3 billion), running water (3.5 billion) or cars (2.8 billion).

Cisco predicts mobile data traffic will see a 53 percent compound annual growth rate through 2020. Companies will start to analyse these data sets to deliver better services to their customers.


For many organizations, the IOT will be a cornerstone of their digital business strategies. However, it will also be very disruptive, requiring them to master many new technologies and capabilities. The technologies and concepts of IOT will have a huge impact on organizations’ business strategy, risk management and security. Vendors will offer a dizzying array of wireless tech as well, to support IOT field use cases.


Chatbots are computer programs that can have automated text conversations with users using Artificial Intelligence and Natural Language Processing. They can range from simple, informal, bidirectional text or voice conversations such as an answer to “What time is it?” to more complex interactions such as collecting oral testimony from crime witnesses to generate a sketch of a suspect.

Here is an article that talks about the real-world application of Chatbots.

Machine learning and big data in cloud

The big four cloud computing giants – Amazon, IBM, Google and Microsoft – all are working on analytics, big data and machine learning suite of services.  These suite of cloud services will take care of everything from data ingestion through prediction, which will make data analysis on cloud much more affordable.

What’s your data science trend for 2017? Share it in the comments section below

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