By Anil Kanwar, India Regional Director, Confluent India
India is scaling. Our economy has become the fourth largest in the world, and the digital economy, from startups to smart cities to smart citizens, is moving at an extraordinary pace.
This growth has attracted massive investments and bold promises from businesses eager to capture market share. Yet for all the ambitious roadmaps and grand strategies, many are finding that execution in India’s complex market reality is far more challenging than anticipated.
In this pursuit of scale, a new truth is emerging: businesses can’t win on ambition alone. They need infrastructure that thinks and moves in real time.
In India’s hyper-diverse, high-volume markets, success hinges on precision, timing, and context. Whether you are delivering groceries to a Tier-2 town or optimising logistics in a metro city, the biggest differentiator is not just data—it’s when that data becomes available on-the-go.
Today, the challenge is that most organisations are struggling with data latency and are operating on yesterday’s insights. They’re collecting vast amounts of data such as customer behavior, supply chain movements and market signals, but by the time this information reaches decision-makers, the moment has passed. Reports are generated after decisions are already made. AI models are fed outdated context. Customers receive reactive, not predictive, experiences. This timing gap isn’t just an inefficiency; in India’s fast-moving markets, it’s becoming a competitive liability.
The solution lies in fundamentally changing how data moves through organisations. Instead of the traditional approach of storing data first and analysing later, forward-thinking companies are embracing architectures where data flows continuously, enabling decisions to be made as events unfold rather than after they’ve concluded.
Complexity is India’s Competitive Pressure
India’s markets aren’t linear. They’re multilingual, multi-regional, multi-device, and digitally uneven. Consumer behavior shifts not just by season but by region, language, and even local events. Business leaders navigating this terrain must make decisions based on thousands of micro-signals, from shifting consumer patterns to supply chain disruptions, credit scoring, fraud detection, and beyond.
The sheer volume and variety of these signals creates three critical data requirements that traditional systems struggle to meet.
First, data must be fresh because markets change with startling speed. What’s relevant at 9 AM might be obsolete by noon, especially during high-velocity periods like festival seasons or monsoons. Organisations operating on yesterday’s insights will constantly find themselves behind the curve.
Second, data must be contextual because India defies one-size-fits-all approaches. Customer behavior in tier-one cities differs from tier-two towns. Regional preferences, local regulations, and cultural nuances all influence business outcomes. Without proper context, even accurate data leads to misguided decisions.
Third, data must be instantly available across systems because modern business operations are interconnected. When disruptions occur, multiple teams need simultaneous visibility to coordinate effective responses. Siloed information creates blind spots that aggravate problems rather than solve them.
Traditional batch-based infrastructure can’t meet this need. It was built for an era of simpler, slower markets where decisions could wait for overnight processing runs. Real-time data processing flips this model, making information available to applications, teams, and AI systems as events unfold rather than minutes, hours or days later.
The Business Case: From Reaction to Real-Time
Real-time data is reshaping how businesses operate, enabling them to respond to market conditions as they unfold. It’s not just a technical enhancement, but a strategic edge that empowers companies to act while the moment still matters.
The applications span every industry. Retailers adjust promotions based on live footfall and inventory levels, optimising both customer satisfaction and margins. Fintech companies issue loans using current behavioral data, improving approval rates while managing risk effectively. Logistics firms reroute deliveries mid-transit when delays emerge, preserving service levels despite disruptions. Healthcare providers can monitor patient vitals continuously, triggering interventions when required.
The shift from reactive to predictive operations has become a competitive differentiator. Sectors requiring agility, flexibility, and speed find themselves depending on immediate access to actionable insights.
AI + Real-Time = Sustainable Scale
Another key shift is in how AI is being deployed. Many Indian companies are racing to adopt generative AI, machine learning, and advanced analytics. But they often overlook a fundamental dependency: the quality and timeliness of the data powering AI models.
Without this real-time input, AI risks being ineffective, or worse, inaccurate. India’s digital leaders are increasingly realising that to scale AI responsibly, they must prioritise data that is trustworthy, governed from the start, and fresh and in motion.
Trustworthy data ensures AI models make reliable decisions rather than perpetuating errors. Governance from the source prevents compliance issues and maintains data integrity across systems. Fresh, continuously flowing data keeps AI models current with the changing conditions.
Streaming architectures also optimise resource usage by eliminating redundant data copies and reducing storage overhead, making AI implementations both more scalable and cost-effective, while reducing the carbon footprint.
What Business Leaders Can Think Next
Business leaders need practical steps to harness the advantages of real-time data. The key is to build a strategy that addresses immediate pain points while laying the groundwork for long-term competitive advantage.
Start by identifying high-friction areas where slow decisions, delayed insights, or recurring rework indicate opportunities for real-time transformation. Focus on solving business pain points like faster turnaround, better CX, and lower risk rather than pursuing technical perfection.
Don’t replace everything. For new capabilities and systems, assume data will need to move in real time from the outset.
Empower cross-functional teams to work with live insights rather than static reports. Finally, invest with the future in mind, recognising that real-time capabilities form the foundation for digital maturity, AI-readiness, and national