Growth of Big Data Analytics in India

R. Chandran, Chief Technology Officer, Bahwan CyberTek

A consequence of the ‘new digital normal’ is the explosion of data. Big data is no longer a buzzword. It has come of age. Customer-centric enterprises are deploying big data analytics, AI, and ML to process gigabytes of data for improved efficiency, targeted selling and better user experiences. But what does this mean for India?

India’s digital edge

McKinsey report identified India as the second-fastest digital adopter when compared to 17 other economies including the US, UK, Brazil and China. According to a brand evaluation firm, six Indian brands feature among the top 10 fastest-growing IT services brands. Competitive data prices, accessible smartphones and low-cost, high-speed internet catalyzed the ‘Digital India’ movement much before the pandemic, giving us a head start. The pandemic, however, paved the way for customer-centric enterprises to expand deeper into geographies and cater to demands from tier 2 and tier 3 cities. According to this 2021 Deloitte report, the share of e-commerce sales volumes of India’s tier 2 and 3 cities increased to 46%. Increased sales also led to the increased usage of digital payment platforms. 

Increased digitization and proliferation of new platforms are creating avenues for data to flow in from various touchpoints like mobiles, websites, digital apps, location data, credit card transactions and videos to name a few. This pile of unknown data is increasingly becoming a valuable asset for enterprises – helping them make precise decisions and staying a step ahead of customer needs. 

How big data unlocks value

The hype cycles for big data analytics have finally come to an end. Even enterprises that were dragging their feet are now accelerating at a rapid pace to realize their analytics goals. According to an IDC report, global spending on big data and analytics solutions was forecasted to reach $215.7 billion in 2021. There are staggering amounts of data being accumulated as you read this paragraph. A single minute packs over 100,000 Microsoft Team connects, 6 million shoppers online, and 65,000 photos on Instagram. How can B2B firms capitalize on the data flowing in from the touchpoints? Why is it imperative to invest in big data analytics? Here are a few benefits:

  • Better customer experience – Big data can be used to take your products and services where your customers are using their preferred channel. Targeted, result-oriented campaigns have replaced expensive ambiguous marketing campaigns. Big data analytics uses customers’ past purchase history and predicts behaviour by analysing browsing behaviour and point-of-sale transactions. These insights offer personalized shopping experiences making customer journeys engaging and effective.
  • Create new products/services – A report in 2019 said 47% of companies felt big data has changed the nature of competition. Predictive and prescriptive analytics will help enterprises sift through vast amounts of data and track the customer journey, feedback, behaviour, product success, to evaluate the progress of existing products and create new revenue streams.
  • Risk aversion – Risk management is an essential core competency and a differentiator. Threats can be in any form – financial liabilities, management errors, accidents, operational crisis, natural disasters, or like the pandemic – a healthcare crisis. Big data analytics helps enterprises identify, evaluate, assess, and control the above liabilities that threaten business continuity and affect earnings. 

Unlocking value across sectors

In the current data-intensive ecosystem, intelligent data curation and evaluation promote effective impactful decision making. Across industries, big data is making enterprises smarter and more efficient. 

  • Energy - Big data analytics, AI, ML, and Deep learning provide 360 views of remote assets for energy firms. Sensors across these assets gather data points to sense disruptions, predict performance, and sustain deliverables. Remote monitoring and big data analytics have optimized performance and saved costs across energy parks, facilities, and geographies.
  • Banking – AI-based predictive analytics provides immunity for financial institutions. Big data-based early warning systems help banks identify potential threats much before the default, preventing huge losses. Using analytics, banks can manage entire risk portfolio that includes credit risk, capital allocation, pricing risk, liquidity risk, model risk, and operational risk.
  • Telecom – There is an exponential pace of growth in the telecom space, thanks to the onset of 5G. Communication Service Providers use big data analytics to optimize the network, enhance security, and respond to customers in real-time. Sophisticated data analytics simplifies the data infrastructure to collect and analyze data from millions of users in real-time.  

Big data analytics is playing an important role in making enterprises customer-centric. Across sectors across different sizes, enterprises are striving towards data simplification – a key business priority in 2022. Intelligent data extraction will determine leaders and laggards in the coming months.