How Startups & Enterprises Harness AI-Powered Data Insights To Drive Business Growth

Many Indian companies now use artificial intelligence (AI) and machine learning (ML) to manage and analyse their data assets, deriving valuable insights for business success. In fact, big data analytics has emerged as a game-changer across industry segments and organisations of all sizes, as data-driven decisions lead to timely innovations, higher efficiency and better customer experience. When backed by cutting-edge AI/ML tools, such data analyses enable both startups and enterprise-scale businesses to develop robust operational frameworks and forward-thinking strategies.  

Consider this. According to Data Streaming in APAC: Powering the Future of Business report by Confluent, nearly half of Asia Pacific organisations (48%) are actively democratising the use of AI/ML across their operations, while an additional 43% are considering doing so. More interestingly, 77% of startups invest in AI/ML and other advanced technologies to realise their full potential, per a SAP India-Dun & Bradstreet study. In essence, harnessing big data and the power of AI will result in improved business models and a fast-evolving industrial landscape, optimising value delivery and sustainable growth.      

To explore the transformative power of real-time data insights, Inc42, in collaboration with the data streaming platform Confluent, recently organised a roundtable in Bengaluru as part of its ongoing knowledge series titled Executive Boardroom: Harnessing Data For Growth. The first roundtable brought together 12 technology leaders from diverse industries such as insurance, ecommerce, automotive, software development, artificial intelligence and more. Their discussions centred around the theme – How Startups & Enterprises Harness AI-Powered Data Insights To Drive Business Growth. 

Sameer Dhanrajani, Founding Partner MIRAI Ventures & CEO, 3AI, moderated the session. Among the participants were:

• Subhash Choudhary, cofounder & CTO, Dukaan
• Harish Rama Rao, senior VP (product engineering), Acko
• Vivek Pandey, CTO, Simpl
• Ankur Sharma, cofounder & chief product officer, Instamojo
• Chandra Sekhar Reddivari, technical architect & head of AI, Vymo
• Anoop Kumar Mishra, senior director (data & AI), Ace Turtle
• Naveen Budda, cofounder, KarmaLifeAI
• Ananthakrishnan Gopal, cofounder & CTO, DaveAI
• Ramakrishna R, cofounder & CTO, CureSkin
• Alok Dubey, chief architect, Embitel
• Kishore Gopalakrishna, cofounder & CEO, StarTree
• Sheshanth Bhambore, regional sales director (digital natives), Confluent

Rule-Based Data Analysis Vs AI-Driven Insights: Why Timeframe Matters

During the session, the speakers underlined the critical importance of real-time data insights and the growing complexity of navigating the information explosion. With cloud computing on the rise, easy access to massive data sets and AI-driven data analyses are taking centre stage. However, differentiating between a human-centric and an AI-driven approach is crucial for analysing data insights and making data-driven decisions. 

Alok Dubey from Embitel compared these two to understand their suitability and advantages better. “We mostly see two aspects of data analysis. First, how soon do I want the data to be analysed? That is the fundamental question. If you are in an ecommerce or quick commerce business, you need to analyse certain data sets/patterns quickly. It is often rule-based analysis [uses a set of rules to classify data under predefined categories]. But adding ML tools or AI engines on top of it will not add any value,” he explained. 

“Then there is another aspect for deriving AI/ML insights. It requires bigger datasets to be generated over a longer period, and it will take time to analyse the data. If we are willing to spend that time, it will churn out something meaningful.”

How The Flywheel Effect Drives Business Performance 

A well-crafted data strategy roadmap – a plan that details how organisations can manage, analyse and execute real-time data-driven insights – is also essential to achieve business goals. Understandably, this will require resources to build the data infrastructure and an implementation timeline before businesses can optimise data usage for decision-making on all fronts.      

StarTree’s Kishore Gopalakrishna further detailed how data could drive business performance and enhance consumer experience.

“Take, for instance, Uber Eats, DoorDash or Zomato. Their customers choose restaurants based on the data provided [what’s on offer, reviews and more], while the companies get access to primary user data. But businesses today have moved beyond that; they want to use new datasets well. That’s when the experience part comes in and your end users start generating more data. Think of data as a flywheel that can influence your customers and churn out data so that you can build more products and refine them based on data insights,” he said.

This ‘flywheel effect’ is becoming crucial for businesses looking to innovate products. It also creates a feedback loop, resulting in a better user experience.

Sheshanth Bhambore from Confluent shed light on another strategic component – converting raw data into coveted products using AI-driven analysis.

“We are in the business of keeping this data available to every user, every data scientist, every decision-maker, so that they know what data is flowing, how much data is flowing [volume], if datasets are integrating and what the outcomes will be. We also evaluate our analytics and operations to simplify things for our customers.”

Access to vast consumer data also allows Confluent to extract deeper insights and equip businesses to build products based on the use cases popping up during the data-to-insights journey.

Emerging Job Roles In The Data Analytics Continuum

As AI-GenAI continues to evolve and gain traction across industries, new job roles are emerging to replace routine tasks and traditional functions. Experts think this will transform work as we know it and open many new opportunities. 

Ankur Sharma, cofounder and CPO at Instamojo, is particularly enthusiastic about the talent he prospects in the data analytics continuum.

“I also think a new and probably a very specialised role is likely to emerge – the role of a person [I call him the interviewer] who can ask the right questions at the end of it all [to ascertain value]. Yes, we have very powerful AI assistants and loads of data at our fingertips. But unless we know how to ask the right questions, sharp and insightful queries, it will be a classic case of ‘garbage in garbage out’. The value of the data will always hinge on the questions we pose.”        

He said that the emerging talent pool is evolving in sync with the rapid technological shift. But more new roles will arise, especially those like the ‘interviewer’, who can ask GenAI the right kind of questions.

Overcoming The Challenges Of Data Analytics

As companies increasingly navigate the complex journey, there are challenges galore. Naveen Budda of KarmaLifeAI mentioned the challenges one would face in monetising the data. Although data remains a critical asset and a powerful growth driver, leveraging its value from every aspect can contribute to business bottom lines.

Sharma of Instamojo also cited poor documentation as a major barrier. According to him, the sheer volume of data generated is difficult to manage and segregate based on metadata.

In spite of these challenges, the benefits of data processing, analytics and insights are obvious. Both startups and enterprises across the country have incorporated AI-powered data analytics into their core strategies for growth-oriented decision-making and long-term success. It is perhaps the most cohesive way to quickly capture market trends, changes in consumer behaviour, technology shifts and all other challenges plaguing business growth.