Trends 2016: The different layers of Big Data Analytics

By Anil Chawla

Indian Big Data analytics and business intelligence market is forecast by IDC to grow at a 26.4% compound annual growth rate to $ 41.5 billion through 2018. In 2015, the development of information, mobile, social media technologies and cloud has altered the market significantly and brought about a shift toward self-service, cloud and analytics applications tailored for business users and information workers. Data analytics is being used to provide greater insight into the behaviour of customer analysis and this in turn empowers an organisation to deliver a richer and more rewarding customer experience. In turn, this leads to greater customer satisfaction, less customer churn and therefore greater profitability. This current generation of companies will drive the need for predictive, actionable analytics and cognitive-intelligence applications. A few of the current trends in the Big Data market include:

Next Generation Speech Analytics – Speech Analytics has become a force to reckon with and it is predicted to stay in the next year given the role it plays in building digital strategy, customer retention and improving customer satisfaction. Speech Analytics help organisations in transforming audio into actionable insights along with providing visualization power of the same, predicting and hence reducing customer churn, broad base compliance solutions, increasing operational efficiency, loyalty and sales effectively while reducing cost.

Big Data Analytics in the Cloud – Analysts have predicted that as a result of the zettabytes of data that is being churned and stored along with the increasing demand for this across organisations as a whole and individual departments as well, the future state of big data will be a hybrid of on-premises and cloud

 Analytics of Things (AoT): Internet of Things (IoT) is probably the most popular buzzword with regard to data analytics today. Globally, from less than 5 billion devices in 2009, it is predicted to grow to over 25 billion devices by 2020. It has slowly but gradually penetrated into a wide variety of sectors with BFSI, Manufacturing, Healthcare, Retail, Government and Transportation being the ones which it has seen maximum penetration.  Analytics tools and techniques for dealing with the massive amounts of structured and unstructured data generated by IoT are already coming into use,   As the Internet of Things generates massive amounts of structured and unstructured data from new sources, enterprises must be prepared with the new class of big data analytics required to uncover and capture value

Data Security – The importance of the role of analytics in data security has become increasingly important. Analytics are already transforming intrusion detection, differential privacy, digital watermarking and malware countermeasures. Strong security practices, including the use of advanced analytics capabilities to manage privacy and security challenges, can set businesses apart from the competition and create comfort and confidence with customers and consumers.

Predictive analytics – This means applying big data to recognize events before they occur. With newer and sophisticated big data analytics, extracting information from data and using it to predict trends and behaviour pattern is becoming the game changer for organisations.

Renewed Rise of Open Source – Open source is regaining popularity in the big data analytics space. Open source solutions are often free or inexpensive and the communities around them can enable rapid development and iteration. This is makes it the choice of solutions platform for many new and emerging organisations world over, but with Open Source  risk management must be part of the equation.

The democratization and consumerization of analytics: Connected to ease of use, more organizations are “de­mocratizing” BI and analytics to enable a broad range of non-IT users, from the executive level to frontline personnel, to do more on their own with data access and analysis via self-service BI and visual data discovery.

Mobile BI and Analytics: The increasing adoption of mobile devices has opened up new platforms from which users can access data and both initiate and consume analytics. Executives on the go can apply analytics to gain deeper insight into business performance metrics, while frontline sales and service personnel can improve customer engage­ments by consuming data visualizations that integrate relevant data about warranty claims, customer preferences, and more.

Storytelling: As analytics and advanced analytics becomes more main stream, being able to tell the story with ana­lytics is becoming an important skill. A data story—a narrative that includes analysis—can move beyond recounting of facts to weave together pieces of analysis that make an impact and move people to action.

While India has some distance to go with regard to large scale implementation of big data across all sectors and in every sphere, the fact that organisations are sensitised to the need for the same is evident from adoption seen across most sectors. 2016 will see an adoption of big data analytics across sectors, a combination of big data and advance analytics which will definitely prove to be a game changer for organisations irrespective of its size and the sector in which it operates.

Anil-Chawla

 

 

 

 

 

 

 

 

(The author, Anil Chawla, is managing director, EIS, Verint Systems)

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