3 Trends to Foray in Data Analytics Industry in India

The revolutionary impact of data analytics on businesses is inviolable. 2021 witnessed the data analytics industry grow by a sizable 26.5% YoY (year on year) and is expected to reach a market size of $40.88 bn in 2022. With India being a thriving IT (Information Technology) talent-hub, we can capitalise this opportunity to emerge as the global destination for advanced analytics. According to Indian Brand Equity Foundation (IBESF), nearly $2 bn was estimatedly spent by Indian companies on data analytics, marking an 11.5% growth in the year.

In recent times, India has witnessed an explosive surge in the adoption of technology in diverse business practices. The COVID-19 pandemic was instrumental in accelerating the pace of digitisation. India witnessed the trend of businesses moving to online models from an offline structure. Amidst this change, concepts like data generation, data storage, data engineering, big data, IoT (Internet of Things), hyper personalisation, etc. became the new norm. This has also led to the pressing demand for digital preparedness, driving businesses to swiftly adopt advanced technologies including big data, AI (Artificial Intelligence) and ML (Machine Learning). Data analytics has, consequently, emerged as the most critical component of the digital transformation processes. Firms that have adopted the technologies rank higher in the digital adoption index, giving them a greater competitive advantage.

Following are three exciting trends that we think will have a sweeping impact on the data analytics industry in India.

Digitisation of Businesses – Data Engineering to the Fore: The pandemic has accelerated the digital consumption of services across industries manifesting from the increased internet penetration and local language compatibility in India. The explosion of data being produced and collected, in the wake of this consumption, will need to be landed, curated and stored in a manner that makes it effective for use in driving further business and operational efficiency. This is especially pertinent for the vast amounts of unstructured data being generated from people and machines as businesses are realising the power of combining all this data into centralised datamarts to get insights at scale. Additionally, on the rise are scalable and rapid deployment/refresh of models – critical to keep up with the face of inherent diversity, legacy systems, changing data and dynamic behaviour patterns. Explainable AI to Hyper-personalise Services: Centralised and engineered data will allow the use of advanced ML and AI algorithms to enable hyper-personalisation of services, obsessively focussed on engagement and experience.

For example, rules-driven customer journeys can now be augmented with sophisticated recommendations, which consider far more detail than just age, income and location. Orchestrating the delivery of personalised content requires understanding of customer ‘moment of truth’ and breaking organisational silos such as marketing, risk, service, installation, product development, etc. We have seen almost all sectors in India making significant strides in this segment with regard to their customer-base during the pandemic.

Translation and Amplification of Insights: There is a tangible movement away from static dashboards towards bite-sized, actionable insights that can be democratised and scaled. There is rising demand for the analytics ecosystem to be able to translate stakeholder requirements into scalable, pointed insights on growth, risk and cost that can augment decisions. These need to be wrangled from the centrally engineered data systems, using predictive strategies and embedded efficiently into internal systems and workflows for seamless action. To truly monetise data, insights need to be automated, simplified and amplified.

The past decade has witnessed the generation of an explosive amount of data, from 1.2 trillion gigabytes to 59 trillion gigabytes. Unfortunately, only a handful of enterprises have the ability to make the optimum use of the available wealth of data. It is obvious that in the present times, the need for businesses to stay data-driven and agile has become critical. Data, from that perspective, has become a business imperative for firms to stay competitive, even relevant. It is where the future rests.