From restaurants and FMCG brands to Swiggy, Zomato, quick commerce and AI, India’s food economy is generating an enormous new asset: information. But who controls it, who monetises it, and who gets left behind?
A customer orders a biryani on a Friday night.
The restaurant knows it sold one biryani.
The customer knows they ate one biryani.
But the digital platform sitting between them may be able to understand something far more valuable.
It can potentially see what the customer searched for, what they clicked on, what they considered, what they eventually ordered, how much they spent, whether they used a discount, when they ordered, where the order was delivered and whether they returned the following week.
Now multiply that transaction by millions.
Then add grocery purchases, quick-commerce baskets, restaurant searches, product discovery, advertisements, payments, location signals and repeat behaviour.
Suddenly, this is no longer just a food transaction.
It is a data point.
And millions of such data points can become something much bigger: a map of what India eats, when it eats, where it shops and what it is likely to buy next.
That raises an uncomfortable question for India's food industry:
Is India's food business slowly becoming a data business disguised as a food business?
The answer is not as simple as yes or no.
But one thing is increasingly clear: the companies sitting between food businesses and consumers are acquiring an informational advantage that traditional restaurants and FMCG companies never had.
The restaurant knows the order. The platform can see the pattern.
For decades, a restaurant's relationship with its customer was relatively straightforward.
A customer walked in, ordered food, paid and left.
The restaurant knew:
what was ordered
how much was paid
when the order happened
perhaps the customer's phone number or loyalty information
But digital food delivery has changed the equation.
Platforms can sit at the centre of thousands or millions of such transactions.
That can create visibility into broader patterns:
Which cuisines are rising?
Which dishes are declining?
What price points are working?
When do customers order?
Which restaurants are getting repeat business?
Which promotions actually convert?
Which neighbourhoods are showing demand?
Which menu items are performing?
Swiggy's recently launched Guru, for example, is explicitly positioned as a 24×7 AI business partner for restaurant operators. Swiggy says Guru can analyse restaurant sales, orders, average order value and more than 30 other metrics, provide funnel analysis and recommendations, offer menu insights, and allow restaurants to manage advertising and discount campaigns through conversation. The service is being rolled out across more than 720 cities and is aimed at more than 2.7 lakh restaurant partners.
The significance goes beyond one AI assistant.
It demonstrates where the industry is heading:
transaction data → analytics → AI → business decisions.
The platform isn't merely delivering the food anymore.
It is increasingly helping businesses understand the market around the food.
And then quick commerce entered the picture
If food delivery provides a window into what India eats outside the home, quick commerce provides a rapidly expanding window into what India buys for consumption at home.
The scale is already significant.
Eternal says Blinkit now has more than 31 million monthly customers, over 2,400 stores across 300+ cities, and a delivery network of more than 500,000 delivery partners.
Meanwhile, Eternal reported that 109 million Indians completed transactions worth more than $10 billion through Blinkit, District and Zomato during FY26.
This is important because quick commerce is not simply another grocery channel.
It can generate an incredibly detailed picture of consumer demand.
Consider a simple basket:
Milk + protein bar + Greek yogurt + bananas + peanut butter.
The purchase of any one product tells a story.
The basket tells a much richer one.
It can reveal product combinations, purchasing frequency, price sensitivity and emerging consumption patterns.
And unlike traditional retail data, digital commerce can potentially observe these signals at a much greater speed and granularity.
The digital shelf is becoming as important as the physical shelf
For decades, FMCG companies fought for shelf space.
Where will the biscuit sit?
Which brand gets eye-level placement?
Which packet gets the refrigerator's prime position?
Which product gets the retailer's recommendation?
Quick commerce has not eliminated that battle.
It has digitised it.
Now the question becomes:
Which product appears first when a consumer searches for it?
Imagine a consumer opens a quick-commerce app and searches:
“protein bar.”
There may be dozens of products.
But consumers don't treat every product equally.
The products appearing prominently have a major advantage.
That makes the algorithm increasingly similar to the supermarket shelf.
And the company controlling that digital shelf can influence product discovery, visibility and potentially conversion.
This is why quick commerce is becoming more than a logistics business.
It is becoming a consumer interface.
The real gold may not be the purchase. It may be the intent.
There is an important difference between knowing what somebody bought and understanding what they were considering buying.
Imagine a consumer searches for:
“sugar-free biscuits.”
They click on five products.
They compare prices.
They abandon the basket.
Three days later, they purchase one.
Each step creates a signal.
The purchase is only the final event.
The journey before the purchase can potentially reveal:
interest
consideration
price sensitivity
brand preference
product discovery
conversion behaviour
This is extremely valuable to advertisers and FMCG companies.
A traditional billboard can tell a brand that its advertisement was displayed.
A commerce platform can potentially connect advertising exposure much closer to actual shopping behaviour.
That is why retail media is becoming such an important business model.
Food platforms are increasingly becoming advertising platforms
This is another major shift that consumers may barely notice.
The food and grocery platforms increasingly don't just make money when a consumer places an order.
They can also monetise the consumer's attention before that order.
Brands can pay for:
sponsored placements
search visibility
promotional campaigns
display advertising
targeted communication
Swiggy itself has disclosed that it uses data analytics to formulate digital marketing campaigns, including targeted advertisements, notifications, pop-ups and messages. Its latest disclosures also point to advertising becoming an increasingly important monetisation lever across parts of its ecosystem.
That creates a powerful loop:
Consumer behaviour → data → targeting → advertising → purchase → more data.
The platform isn't just facilitating commerce.
It is potentially monetising the intelligence generated by commerce.
The biggest question: Who actually owns the customer?
This is where the story becomes uncomfortable for restaurants.
Ask a restaurant owner:
“How many customers did you serve through food-delivery platforms last year?”
The answer could be thousands.
Now ask:
“How many of those customers could you directly contact tomorrow?”
The number could be dramatically smaller.
This creates a distinction that the food industry needs to take seriously:
Having customers is not the same as owning the customer relationship.
A restaurant may prepare the meal.
But the platform may control:
discovery
ordering
payment interface
communication
reviews
promotions
advertising
delivery
analytics
That gives the intermediary enormous strategic importance.
The restaurant supplies the food.
The platform can potentially supply the intelligence around the transaction.
Restaurants generate data too
It would be wrong to portray restaurants simply as victims.
Restaurants generate enormous amounts of valuable information themselves.
Their POS systems can contain:
order history
item-level sales
peak hours
repeat customers
average ticket size
payment information
inventory movement
discounts
cancellations
table turnover
menu performance
The problem is that this information is often fragmented across multiple systems.
A restaurant may have:
POS data in one place,
delivery-platform data somewhere else,
Google reviews elsewhere,
Instagram engagement elsewhere,
and customer WhatsApp conversations somewhere else.
The platform, by contrast, can potentially have a broader view of the digital transaction occurring within its own ecosystem.
That asymmetry matters.
The same thing is happening to FMCG
Traditional FMCG companies historically depended heavily on layers of distributors, wholesalers and retailers.
The manufacturer could know how much product entered the distribution chain.
But knowing exactly why individual consumers bought it was harder.
Digital commerce changes that.
A quick-commerce platform can potentially observe:
which SKU gets searched
which product gets clicked
which brand gets purchased
how frequently it gets reordered
what it is purchased alongside
how discounts affect conversion
which neighbourhoods show demand
which products are substituted when another is unavailable
This creates something traditional FMCG companies have always wanted:
closer visibility into consumer behaviour.
The basket may be more valuable than the product
Suppose 10,000 people buy a packet of oats.
Useful information.
But suppose the platform knows that a particular cohort frequently buys:
oats + almond milk + protein powder + bananas.
That is much more valuable.
Because now the platform can begin to understand the consumption occasion rather than simply the product.
The same principle applies everywhere.
Pasta + pasta sauce + cheese
could indicate one kind of household behaviour.
Baby food + diapers + wipes
another.
Ice cream + soft drinks + snacks
another.
The individual products are ordinary.
The combinations are intelligence.
And AI changes everything
Data has always been valuable.
AI makes it dramatically more useful.
A spreadsheet can tell a restaurant:
“Orders fell 8%.”
An AI system can potentially ask:
Why?
And then connect multiple signals.
Perhaps:
lunchtime orders are stable
dinner orders have declined
one high-performing dish was removed
competitor discounts increased
average delivery time increased
customers in a particular locality are ordering less
Suddenly the data isn't just describing the business.
It is recommending what the business should do.
That is precisely the direction Swiggy is taking with Guru.
Swiggy says Guru can analyse the restaurant's sales funnel, identify what isn't working and recommend fixes, while also helping restaurants manage advertising and discounts.
The bigger question is:
Who has the data needed to build the smartest AI?
Because AI without data is a tool.
AI with years of behavioural data can become a competitive moat.
The data flywheel
This is perhaps the most important concept in the entire story.
A platform gets more consumers.
↓
More consumers generate more transactions.
↓
More transactions generate more data.
↓
More data improves recommendations, forecasting and advertising.
↓
Better experiences attract more consumers and merchants.
↓
More consumers generate even more data.
This is a data flywheel.
And it can make scale increasingly important.
A new competitor can buy delivery riders.
It can buy servers.
It can spend money on discounts.
But it cannot instantly recreate years of behavioural history.
That is why data can become a strategic asset even when it isn't directly sold.
But is this “ownership”?
Here is where the debate needs nuance.
It would be misleading to simply say:
“Swiggy owns your data.”
Or:
“Zomato owns your food preferences.”
Personal data is governed by India's evolving data-protection framework.
The Digital Personal Data Protection Act, 2023 defines personal data as data about an identifiable individual and regulates the processing of digital personal data.
More importantly, India's regulatory framework has moved forward since the Act was passed.
The government notified the Digital Personal Data Protection Rules, 2025 in November 2025, along with an enforcement timeline for the Act's provisions.
So the better question isn't simply:
Who owns the data?
It is:
Who can collect it, process it, combine it, analyse it, derive insights from it and commercially benefit from those insights — and under what legal basis and contractual terms?
That is a far more important question.
Because the real value may be in “derived intelligence”
Consider two pieces of information.
Raw data
Customer bought a protein bar.
Derived intelligence
A particular consumer cohort is showing increasing willingness to pay for high-protein snacks between 7 PM and 10 PM.
The second piece can be far more commercially valuable.
It can influence:
inventory
advertising
pricing
product development
store locations
restaurant menus
promotions
brand strategy
This is why the debate cannot stop at personal data.
There is another asset emerging:
Commercial intelligence derived from millions of transactions.
Could a platform know more about a restaurant's market than the restaurant itself?
Potentially, yes.
A restaurant knows:
“My biryani sales increased 15%.”
A large platform may potentially observe:
“Biryani demand across this city increased 9%, while premium biryani grew 18% and your restaurant gained or lost share relative to comparable restaurants.”
Those are very different levels of information.
This is the fundamental advantage of a marketplace sitting in the middle.
The restaurant sees itself.
The platform can potentially see the ecosystem.
And now platforms are giving some of that intelligence back
This is where the story becomes more nuanced.
Platforms aren't necessarily hiding all their intelligence from merchants.
In fact, they're increasingly productising it.
Eternal says one of its goals is to support small businesses through data-based insights that can help partners grow income and optimise costs.
Swiggy's Guru takes the same broad idea further with conversational AI.
This can genuinely help smaller restaurants that could never afford sophisticated data analysts.
A neighbourhood restaurant may suddenly have access to:
sales analysis
campaign performance
menu insights
AOV analysis
recommendations
operational information
That is a genuine benefit.
But it raises a second question:
How much of the intelligence can the restaurant access — and how much remains exclusively inside the platform?
The private-label question
This is where regulators and industry participants should pay attention.
Suppose a platform knows that:
Category X is growing extremely fast.
And suppose that platform also has commercial interests in products, brands or categories competing for the same consumer demand.
The natural question becomes:
Can a marketplace use marketplace-wide intelligence to strengthen businesses that compete within the marketplace?
This is not an accusation against any particular company.
It is a structural question about digital marketplaces.
The more businesses a platform operates across the value chain, the more important data governance and competitive safeguards become.
ONDC offers a different philosophy
India's Open Network for Digital Commerce was designed around a very different architectural idea.
Its strategy documents describe an open, interoperable network intended to reduce dependence on closed platforms and shift some power toward consumers, merchants and service providers. The architecture is explicitly intended to work across areas including retail, food delivery and logistics.
Whether open networks ultimately produce a better data balance is still an evolving question.
But the philosophy is important.
Because it asks:
Should one company control the entire customer interface?
Or can commerce be structured so that multiple participants can interact across an interoperable network?
That distinction could become increasingly important as AI begins sitting on top of commerce.
The next battle may not be for your attention
It may be for your AI assistant.
Today the consumer does the searching.
Tomorrow an AI agent may do it.
Instead of:
“Open the grocery app and search for milk.”
You might simply say:
“Order the groceries we normally buy.”
The AI could potentially decide:
which brand
which store
what quantity
what price
when to reorder
The consumer's attention becomes less important.
The algorithm's preference becomes more important.
And that could completely transform FMCG competition.
Swiggy is already moving toward agentic commerce
In 2026, Swiggy announced integrations around AI-native commerce, including access for developers and enterprises to capabilities across Food, Instamart and Dineout. It has also partnered with Sarvam for multilingual voice-led commerce, allowing consumers to discover and order food and groceries through conversational AI.
This is a significant shift.
The platform isn't merely becoming a place where consumers browse.
It is increasingly becoming infrastructure that AI systems can use to transact.
That raises an entirely new question:
If an AI chooses what we eat and what we buy, who controls the data that teaches that AI our preferences?
The food company of the future may not look like a food company
Imagine a company that controls:
Consumer interface
Marketplace
Payments
Logistics
Advertising
Consumer intelligence
AI
Such a company doesn't necessarily need to manufacture food.
It can potentially sit above the food economy and understand it.
That is the strategic shift investors, restaurants and FMCG companies should be watching.
The uncomfortable asymmetry
There are three players in the transaction.
The consumer
Generates behavioural signals.
The restaurant or FMCG company
Generates the product.
The platform
Connects the two.
But the platform can potentially observe the interaction at scale.
That creates an informational asymmetry that traditional food businesses rarely had to deal with.
And information, in digital markets, can become power.
So, who owns India's food data?
There isn't one simple answer.
Consumers have rights over their personal data under India's emerging data-protection framework.
Restaurants retain their own business information and operate under contracts with platforms.
FMCG companies have their own sales and consumer datasets.
Platforms collect and process significant amounts of information generated through their services, subject to their policies, contracts and applicable law.
And aggregated or de-identified information can create another layer of commercial intelligence.
So perhaps the better question isn't:
Who owns India's food data?
It is:
Who has the greatest ability to turn India's food data into economic power?
And right now, that may increasingly be the companies controlling the digital rails through which food is discovered, ordered, delivered and purchased.
The food industry is still about food. But something else is being built underneath it.
The restaurant industry is not becoming a technology industry.
FMCG companies are not becoming software companies.
Quick-commerce companies are not simply becoming grocery stores.
Something more interesting is happening.
A data economy is being layered on top of the food economy.
Every search.
Every basket.
Every reorder.
Every discount.
Every abandoned cart.
Every delivery.
Every restaurant campaign.
Every product impression.
Every AI recommendation.
adds another signal.
And those signals are becoming increasingly useful for predicting what India will eat tomorrow.
That is the real strategic asset.
The restaurant may own the kitchen.
The FMCG company may own the brand.
The consumer may own their personal choices and rights.
But the platform increasingly sits at the intersection of all three.
And that leaves India with a question it has barely begun to ask:
When millions of Indians order dinner, buy groceries and discover new food through a handful of digital platforms, who gets to learn the most from what we eat?
Because in the next phase of India's food economy, the most valuable thing on the menu may not be the food.
It may be the data generated by the person ordering it.