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Big data trends to watch out for in 2017

Suvro Banerjee
11th Jan 2017
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Within a significantly short period of time, big data has evolved from being a hot topic of discussion at science symposiums to playing a most pivotal role across industries. The ability to mine big data for deep insights has radically altered the dynamics of the way businesses function, drive sales, and attract customers and has become an essential component of business strategy today. As businesses taste success by uncovering insights locked inside data, we foresee these seven major trends in analytics in 2017:

Credit : Shutterstock
Credit: Shutterstock

Insight-driven organisations:

Understanding, managing, and manipulating big data has now become fairly ubiquitous and something most companies have the ability to deal with. 2017 will be the year when successful businesses will move beyond just data handling to leveraging the insights big data uncovers. The focus will now be on mining data effectively to meet organisational requirements and for precise targeting of products and services. An insight-driven approach will facilitate an evolved customer experience, competitiveness, advanced security (which typically requires fast storage and high storage capacity), and operational efficiency.

An electronics company, for example, spent most of its marketing budget on conventional marketing channels including print, television, and display advertising. However, while analysing the consumer decision journey it was observed that most of the customers browsed through retailers’ websites and less than nine percent visited the brand’s platform. As soon as the company shifted away from general advertising to distributor website content, its e-commerce sales sharply jumped by 21 percent.

Analytics across the enterprise:

Analytics will not remain isolated to a few departments like marketing and risk management — it will pervade the entire enterprise. Data analytics will be utilised to understand the dynamics of business operations and will reveal ways and means to increase efficiency. Insights from multiple departments will be integrated to derive overall business strategy and to eliminate redundant processes, and even within departments to increase efficiency, growth, and productivity.

Applications in cyber security:

As our dependence on digital technology has reached an unprecedented level, cyber attacks have also become much more rampant. Apart from attacking global financial institutions, cyber criminals are now also targeting personal data and devices and increased occurrences of ransomware have been registered across the globe. Being a prime industry concern, cyber security — only possible through big data analytics — will become a major area of investment and will grow rapidly. Enterprises and government agencies will upgrade their security systems to next-generation software that addresses ultra-modern security threats. According to the ‘Cybersecurity Market Report’ by Cybersecurity Ventures, cyber security spending will top $1 trillion from 2017 to 2021.

Internet of Things and People:

A transition will be noticed from Internet of Things (IoT) to Internet of People (IoP). Predictive analytics around human behaviour, interactions, and other cognitive areas will grow and start to percolate all industry verticals. For example, hospitals will increasingly deploy machine learning techniques to predict the likelihood of the relapse of a disease. This will enable them to work out a patient’s readmission precisely at the time of the initial discharge.

Bridging the talent gap:

It is very obvious that the talent gap in data analytics will soar further as demand expands. Organisations and academic institutions are expected to collaborate closely to generate skills and talent to meet the demand for data engineers. Employees from all corporate functions will be expected to understand, appreciate, and work with analytics since it emerges as one of the key instruments in every organisation. The data science talent gap is alive and well today. McKinsey estimates the shortage of personnel in 2017 at about 200,000 for the US alone; this figure can easily double on a global scale.

While academic institutions are scrambling to put together degree programs in data science, boot camp-style schools are mushrooming everywhere to provide 12–14 weeks of training in marketable skills. In March 2016, ‘Anaconda Skills Accelerator Program’ (ASAP), a 12-month data science course, was launched by Continuum Analytics (creator of open-source analytics platform Anaconda). Such programmes impart essential data science skills to their participants and equip them with cutting-edge methods to better market themselves. As per the industry estimate, data scientists receive as much as twice the remuneration received by a programmer.

Business-science collaboration:

Businesses will have to learn and deploy traditional, scientific techniques of pattern-matching and artificial intelligence for analytics use cases. For example, techniques to analyse gene sequences in DNA are being used in text-matching algorithms to process bulk emails. Expect to see very close collaboration between data scientists and the scientific community, especially belonging to disciplines such as neuroscience, molecular biology, astrophysics, particle physics, and organic chemistry. For example, image processing is widely used in ‘tagging’ in social media, while voice recognition is used in apps like Siri.

Technology:

Organisations will rapidly move away from on-premise platforms to cloud and hybrid environments. According to an IDG survey, 44 percent of applications used by Fortune 500 organisations are already on the cloud, and by the end of 2017, more than 50 percent of IT applications will move to the cloud. There will be a rise in demand for analytics tools that are simple, flexible, and capable of handling a variety of data sources. These nimble tools will also be required to gracefully handle massive volumes and velocity of data.

Hadoop will continue to become increasingly popular, as it enables us to store an extremely large volume of data at a significantly lower price point. Share of unstructured data in the data warehouse will continue to increase, which will further the cause for Hadoop. Hadoop, now, is past the business relevance and scalability assessment phase and its adoption is expected to accelerate sharply. According to a survey comprising 2,100 CXOs, business, and IT executives from 1,300 companies across the globe, 49 percent of the respondents agreed they obtained tangible value through Hadoop. Also, 45 percent of the rest were anticipating considerable benefit within a short period of time.

We are advancing towards a completely new era in the domain of analytics. Big data, disruptive as it is, has paved the way for pioneering ideas and innovative techniques across industry verticals. It is now expanding the scope of opportunities for previously untouched market segments and is driving the stream of disruption towards them. In order to retain innovation and growth, organisations will have no choice but to leverage big data analytics to its full capacity to make 2017 a success.

(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)

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