Analytics is by far the biggest influencer in IT industry – a phenomena evident by the rise of next-gen technology Cognitive Computing, Blockchain and Virtual Reality which has at its core a valuable asset “data”, and analytics quite irrefutably is the essence of it. After all, it’s the whole mix of technology, data and analytics that is revolutionizing the way we work.
In keeping with our annual tradition, we present the much-researched and a carefully thought-out study carried out in association with AnalytixLab, a premier analytics training institute in India. We invited nominations from various organization to identify the analytics trends that will shape the future of our industry in India for 2017. After a lot of fact-finding from the industry insiders, we bring to you the top 10 analytic trends that have had the highest impact on the analytics industry today and potentially going forward in 2017.
This year, we received an astounding 36 submissions. A lot of new trends have hit the space, a few faded away and there are others that endured and will definitely stay. This study, now in its third year presents neatly sorted out viewpoint of the industry leaders and veterans on Top 10 Analytics Trends in India to watch out for in 2017.
Artificial intelligence becomes pervasive in business
The Cognitive age is clearly upon us— it is indicated by the fact that more than $1 billion in venture capital funding went into cognitive science in 2014 and 2015, and further supported by fact that various analysts project the overall market revenue for cognitive sciences to exceed $60 billion by 2025. As the cognitive era evolves, it will likely become another key decision making tool in the toolbox of CXOs; vital for the right applications but not entirely replacing traditional business & advanced analytics capabilities that complement the human thought process. In a nutshell, the man-machine dichotomy is not “either-or”, it is unequivocally “both-and”.
Debashish Banerjee, Managing Director at Deloitte Consulting
There is a lot of hype around “artificial intelligence,” but it will often serve best as an augmentation rather than replacement of human analysis because it’s equally important to keep asking the right questions as it is to provide the answers.
Souma Das, Managing Director, India, Qlik
Analytics of Things continues to be a game changer
Internet of Things and Analytics of Things: The Indian Internet of Things (IoT) market is set to grow to $15 billion by 2020 from the current $5.6 billion. Just about every type of company seemed to have an IoT strategy in 2016. However, today, IoT is more about Data Than It Is Things. The original description of “Internet of Things,” was describing a network of connected physical objects. But in 2016, it was apparent that this initial description didn’t consider the importance of data or cloud computing. So now, IoT isn’t about connecting billions of objects to the Internet, it is really going to be all about the data and the ability of organizations to gain insights out of all of this data. This means that getting value from data goes beyond devices, sensors and machines and includes all data including that produced by server logs, geo location and data from the Internet.
Sunil Jose, Managing Director, Teradata India
The “Internet of Things” is exploding. It is predicted that the number of device connected would reach 50 billion by 2020. Most of these smart devices would be in factories, energy sector, health care systems, home appliances and wearable devices. The vital data so generated would enable us to track health parameters, optimise machine performance, and reduce response time for breakdowns and also save lives.
In order to create real value of IoT/IIoT, the Sensors & Communications node needs to integrate with the Analytics infrastructure, else it will be a simple data collection exercise. The technologies & skills related to IoT protocols, edge analytics & real time sensor analytics will be the key differentiators for its success & adoption in the market.
Vinay Gupta, Head of Analytics at Suzlon[divider size=”1″]
Enabling real-time automated decisioning systems
There is a visible shift across many big data and analytics users to streaming and real time analytics. Businesses particularly digital advertising, ecommerce, logistics & transportation are looking to leverage ream time analytics and are heavily invested in this space. This is also apparent from the elevated adoption levels of Apache Spark Streaming, Apache Storm or Twitter’s Heron.
Srikanth Sundarrajan, Principal Architect at InMobi
Enterprises are increasingly enabling real-time automated decisioning systems whether to streamline operations or mitigate risk. The older rule-based systems are now being replaced by a new generation of systems powered by online machine learning and artificial intelligence – these are self-learning systems that can recalibrate in an automated manner and can be deployed on a large-scale.
Pradeep Gulipalli, Co-founder at Tiger Analytics[divider size=”1″]
Analytics is made invisible, embedded within the system
Analytics works best when it’s a natural part of people’s workflow. In 2017, analytics will become pervasive and the market will expect analytics to enrich every business process. This will often put analytics into the hands of people who’ve never consumed data, like store clerks, call-center workers, and truck drivers.
Deepak Ghodke, Country Manager, Tableau
Technologies that nudge us to drink water, take regular walk breaks, or inform us that our cab has arrived have become commonplace. This is not restricted to consumer facing decisions. In fact, businesses in 2016 are beginning to realize the value of this kind of data and deploy on-demand analytics to drive better decisions. They are capturing and streaming unstructured data, blending it with other data sources, deploying analytical models to unearth insights, and are using rules engines to drive applicable “nudges”.
Mihir Kittur, Co-founder, Ugam[divider size=”1″]
Fintech is growing and so is Fintech Analytics
Going by the events of the last year (2016), Fintech will clearly emerge as the most challenging as well as beneficial. The linkage of various identity proofs to uniquely identify a person and their financial footprint would be the key to the mission to drive out corruption and black money.
Dr Nupur Pavan Bang, Associate Director, Thomas Schmidheiny Centre for Family Enterprise, Indian School of Business
Financial institutions are moving rapidly towards “digitization” and educating their customers to adopt digital channels for day-to-day activities. With eroding revenue streams, intensifying competition, and ever-increasing customers’ expectation financial institutions need to explore new way of doing business. Measuring customer relationship, evaluating the customer journey and recommending right bundle of product & services at the right price in real time through technology enabled digital platform will be the vital enablers to improve customers’ banking experience.
Suman Singh, Chief Analytics Officer, ZAFIN[divider size=”1″]
Rise of Self-Service Analytics
The realization that what delivers impact is not automated MLR or one-size fits all solutions, but context driven customized solutions that leverage business know-how (that probably exists deep within a company) and domain knowledge (of expert consultants with rich industry and applied analytics experience) will dawn on most business leaders in 2017.
Randhir Hebbar, Cofounder at Convergytics
Technological advancement has led to tools for Self Service Business Intelligence or Self Analytics. This approach meets the needs of data producers and consumers alike, adding speed and agility to the process while protecting organizational data and the system overall with a single version of the truth.
Tejinderpal Singh Miglani, CEO, Incedo Inc
There has been a paradigm shift in deriving business value from analytics owing to the exponential growth in the volume, variety and complexity of data. Today’s competitive business environment asks for democratisation of analytics with self-service capabilities to meet the time-to-insight demands.
Self-service work benches, packaged analytics, dashboards, visualization frameworks and collaboration tools for data scientists (built right into the analytics frameworks) are gaining popularity. The spotlight has now shifted from IT-led reporting to business-led self-service analytics. Self-service allowing “regular” users to derive value from large data assets owned by enterprises is the way forward.
Having said that, the specialists will not be redundant. In fact, there will an even greater demand so as to ensure that they manage and provide the entire infrastructure and a strong footing for decision making across the organisation.
Jobin Wilson, Principal R&D Architect-Data Sciences, Flytxt[divider size=”1″]
Democratization and consumerization of analytics
More organizations are “democratizing” Business Intelligence (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 such as drag-and- drop dashboards.
Anil Chawla, Managing Director, Customer Engagement Solutions, Verint Systems
More data is now available to companies of all sizes. So more easy access to data within a company & sources to find external data will be a trend that I see. Companies can partner each other & leverage each other’s data. A DTH company knows when you move residences & that data can help a Retailer who sells furniture or is in that catchment. I believe that in year 2017 more marketers will leverage each other’s data to build more effective analytics solutions.
Ajay Kelkar, Co- founder of Hansa Cequity[divider size=”1″]
Mobile first Omni channel strategy
With increasing penetration of mobile phones, the number of mobile apps have sky rocketed. Due to the limited space on these mobile phones, consumers are engaging with apps that create value for them. To stay relevant, the apps developers are using app analytics to understand their users’ profile and transaction behaviour to fine tune their product features and offering to increase user experience and engagement. We will continue to see analytics playing a larger role to declutter the space.
Debasmit Mohanty, CEO & Founder, StratLytics
With increased penetration of mobile, companies are taking a Mobile First approach to engage consumers, leading to progress in Mobile Analytics. With the available location and motion sensing capabilities, significant progress was made in data collection in a privacy compliant fashion to determine what, when, where, and why of the activities that consumers engage in. This data is enabling improved insight and reach for business growth and consumer experience improvement.
Amit Deshpande, Vice President, Analytic Consulting Group, Epsilon[divider size=”1″]
Leverage GeoSpatial analytics in improving business models
With a massive adoption of mobile devices across India in the past year and businesses getting more digitized, there are lots of “time and place” data that is being collected today. Besides mobile devices, emergence of sensors with respect to smart cities in India, drones being evaluated in agricultural and construction sectors, emergence of social media in real time, we are seeing tremendous business potential in terms of leveraging Geo spatial analytics in improving business models and this trend will only continue to grow.
Sunil Shirguppi, Senior Vice-President – Big Data and Analytics at Happiest Minds Technologies
In 2016, businesses found value in understanding the ‘where’ factor of data and the ability to query location based information or location analytics into their existing analytics. Business intelligence solutions along with geographic analysis brought forth insights that helped companies better communicate with their customers, create more targeted promotions and pursue previously unrecognized cross-selling opportunities.
Manish Choudhary, SVP, Global Innovation and MD, Pitney Bowes India[divider size=”1″]
Analytics Governance Platforms
The use of analytical models is significantly increasing across business functions (marketing, sales, risk, pricing, etc.) and business & product lines of the enterprise. They are at different stages of deployment and being used continuously by various business users simultaneously. However post implementation of such models, businesses are not necessarily being able to track how these models are performing and not sure if they are delivering the promised value. Neither do the businesses know the interlinkage effect of all these models working together.
With the increasing use of analytical models in business decisions, consolidating them in a single technology console, monitoring the health of analytics implementations and model performance is becoming a crucial need for business leadership and regulators. This will help track the analytics performance, demonstrated ROI and avoid the risk of incorrect decisions made because of analytics. ‘Analytics governance platforms’ will gain prominence across all large enterprises and will become mainstream to monitor the analytics deployment through workflows. The ROI of the analytics governance platforms will be seen in the long-term and would reap the benefits of governing complex analytics environments in a single platform with more visibility to the analytics ROI.
Prithvijit Roy, CEO, and Co-founder, BRIDGEi2i Analytics Solutions
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