A big data PR strategy uses real-time operational and behavioral data, not just survey results, to prove a brand's claims and catch problems before they become crises. Uber relies on signals from 200 million monthly users completing more than 40 million trips a day, per its fourth-quarter 2025 earnings, to manage pricing and safety in real time. Toyota Connected and IBM's own breach-cost research show what happens when that same kind of data is mishandled instead.
What is a big data PR strategy?
A big data PR strategy is the practice of using an organization's own operational, behavioral, and sensor data, rather than surveys or anecdotes, to shape communications, back up public claims, and spot reputational risk before it surfaces. The discipline rests on three properties of the underlying data: volume, the sheer scale of records generated; velocity, how fast new data arrives and must be acted on; and variety, the mix of structured transaction records, unstructured text, and sensor streams a modern company produces.
A fourth property, veracity, is the one communications teams most often overlook. Data that is fast and abundant is not automatically trustworthy. A company that cannot verify where a number came from, or when it was last checked, cannot use it in a public claim without risking a correction later.
The distinction between big data and the analytics most communications teams already use is scale and speed, not the presence of numbers. A quarterly customer-satisfaction survey is data. A stream of millions of transactions, sensor readings, or trip records updating every second is a different discipline entirely, one that requires its own storage, processing, and governance decisions before a single number reaches a press release.
Three organizations illustrate the range this discipline covers. Uber Technologies uses trip, pricing, and fraud-detection data to run a marketplace spanning multiple continents. Toyota Connected uses vehicle telemetry to power features while publishing an explicit Toyota Connected privacy policy. McKinsey & Company studies how retailers and telecoms turn customer data into personalized marketing, and its personalized marketing research is one of the clearest records of what that data can and cannot do for a brand's relationship with its customers.
Communications and business teams that treat big data only as an IT concern miss the point. The same pipelines that route a ride request or a personalized offer also generate the evidence a company needs for reputation management strategy, the exposure it needs to plan for in a crisis communications plan, and the infrastructure decisions that determine how fast it can respond when something goes wrong.
How does big data help brands earn public trust?
Big data earns public trust when a brand uses it to make and keep specific, verifiable promises rather than general claims. Trust, in this context, is not a feeling a campaign creates. It is a measurable outcome of customers repeatedly getting the experience a company said they would get.
McKinsey's research on personalized marketing found that 71 percent of consumers expect companies to deliver personalized interactions, and 76 percent get frustrated when a brand fails to do so. One North American retailer that McKinsey studied restructured its promotions around customer data and produced $400 million in value from pricing improvements, plus another $150 million from generative-AI-enabled targeted offers, over a single year. A separate European telecom in the same study saw customers engage with gen AI-personalized messages 10 percent more often than with standard campaigns.
Why it works: personalization systems match an individual customer's transaction and browsing history against a decision model in real time, so the offer a customer sees reflects their actual behavior rather than a broad segment guess. McKinsey's analysis found this kind of data-driven targeting, backed by a "4D" technology stack covering data, decisioning, design, and distribution, is what let the retailer above convert incremental sales into measured margin, not just brand sentiment.
For communications teams, the implication is direct: a promise a brand makes in a press release or an ad campaign is only as strong as the data pipeline standing behind it. If a company claims it "knows its customers," the underlying data architecture is what makes that claim checkable, and therefore credible, to reporters, regulators, and the customers themselves.
How does Uber use big data to run a real-time global marketplace?
Uber uses big data to balance a marketplace of drivers and riders across many cities at once, adjusting prices, routes, and fraud checks in the seconds between a ride request and a match. The company's fourth-quarter 2025 earnings release put the current scale of that operation in concrete terms.
Uber CEO Dara Khosrowshahi said the company "accelerated into another record-breaking quarter, with more than 200 million monthly users completing more than 40 million trips every day." Full-year 2025 revenue reached $52.017 billion, up 18 percent year over year, on 13.6 billion total trips across the year. Gross bookings for the fourth quarter alone broke down to $27.4 billion in Mobility, $25.4 billion in Delivery, and $1.3 billion in Freight, according to the same release.
Each of those trips and orders generates location, pricing, timing, and safety data that Uber's systems process to solve three problems simultaneously: setting a price that reflects real-time supply and demand, predicting arrival times against live traffic conditions, and flagging patterns consistent with fraud or safety risk before a trip is completed.
Why it works: a marketplace this size cannot be run on periodic reporting. Decisions have to be made inside the same window a rider is waiting for a match, which means the data pipeline itself, not a quarterly dashboard, is the operational and reputational infrastructure. When that pipeline works, the public story is reliability at scale. When it fails, whether through a pricing error or a safety lapse, the same data trail becomes the record regulators and reporters use to reconstruct what happened.
What big data mistakes put brand reputation at risk?
Big data puts brand reputation at risk when a company collects more personal data than it can secure or govern, since the resulting breach or privacy failure becomes the story instead of the product. The two most common failure points are weak governance around who can access sensitive data, and unclear boundaries around what data a company shares with third parties.
IBM's Cost of a Data Breach Report 2026, produced with the Ponemon Institute, found the global average cost of a data breach reached $4.99 million, a 12 percent increase over the prior year, driven by higher detection, escalation, and lost-business costs. In the prior year's edition of the same report, healthcare breaches carried the highest average cost of any industry, at $7.42 million, for the fifteenth consecutive year, largely because of the extra scrutiny and regulatory penalties sensitive patient data attracts.
That same prior-year report found 63 percent of breached organizations had no formal AI governance policy in place, and breaches involving unauthorized, unapproved AI tools carried an average premium of $670,000 on top of the baseline breach cost. Neither figure is a technology footnote. Both describe a communications exposure: a company cannot credibly claim disciplined data practices in a press statement while its own internal governance has no policy behind it.
Toyota Connected offers a contrasting example of how a connected-vehicle company can manage the same underlying risk. Its published Toyota Connected privacy policy states plainly that the company does not sell customer data, encrypts data in transit and at rest, and lets customers opt out of personalized services through their regional call center.
Why it works: stating a specific, falsifiable commitment, such as "we do not sell customer data," rather than "we value your privacy," gives reporters, regulators, and customers something concrete to hold the company to. IBM's data shows what the absence of that discipline costs in dollar terms; Toyota Connected's page shows what stating the commitment in public looks like when a company treats data governance as part of its public communications rather than a legal disclaimer buried in a policy document.
For a PR or communications lead, the mistake to avoid is treating data governance as purely a legal or security function. The moment a breach happens, governance failures become communications failures, and the company's prior public statements about its data practices become the standard the company is judged against.
Where should a company's data live, and why does it matter for PR risk?
A company's data should live across three distinct storage tiers, matched to how fast and how often that data needs to be retrieved, because using the wrong tier for sensitive or fast-moving data is itself a source of PR and security risk. The three tiers are object storage for a data lake, high-performance storage for active computation, and operational databases for real-time application access.
| Tier | Purpose | Example Use Case |
|---|
| Object storage / data lake | Low-cost, high-volume storage for raw and historical data that is not accessed constantly | Years of archived trip logs or vehicle telemetry kept for analysis and compliance |
| High-performance storage | Fast, expensive storage for data actively being processed or modeled | Training data for a fraud-detection or pricing model |
| Operational databases | Low-latency storage that serves live application requests | The record a system checks the instant a ride request or account login happens |
The scale of this problem is growing quickly in any industry built on connected devices. Visual Capitalist's analysis estimated that a single connected vehicle, depending on its sensor load, could generate between 380 and 5,100 terabytes of data over a year, driven mostly by camera and LiDAR sensor output rather than basic diagnostics. The Automotive Edge Computing Consortium, a group founded by AT&T, Intel, Ericsson, NTT, KDDI, and Sumitomo Electric, has separately projected that connected-vehicle data traffic could exceed 10 exabytes a month industry-wide as autonomous features spread. The same analysis put total global data storage capacity at roughly 8 zettabytes in 2021, on pace to double to 16 zettabytes by 2025, which is the scale problem underneath every connected-device privacy policy a company publishes.
Why it works: matching data to the correct storage tier is not just a cost decision. Sensitive personal data sitting in the wrong tier, whether over-retained in a high-performance system or under-secured in a data lake, is exactly the kind of governance gap IBM's breach research links to higher incident costs. A company that can show a reporter or regulator that its data architecture matches its stated privacy policy has a materially stronger position than one whose policy and infrastructure do not match.
How can companies prove their big data claims to readers and AI search tools?
Companies prove their big data claims to readers and AI search tools the same way: by naming specific numbers, dated sources, and named entities instead of general assurances, since that is the language both human readers and AI answer engines look for when deciding whether a claim is trustworthy. A statement like "we protect customer data" is not verifiable. A statement like "we encrypt data in transit and at rest, and do not sell customer data," sourced to a named policy page, is.
This is the same underlying discipline covered in generative engine optimization for PR: AI systems retrieve and quote specific, sourced blocks of text rather than ranking entire pages, so a data claim written as a vague summary is far less likely to be surfaced, correctly or at all, than one written as a precise, attributable statement.
Consider how this plays out in practice. A company page that states "Uber's fourth-quarter 2025 revenue reached $14.4 billion, up 20 percent year over year" gives an AI engine and a human reader the same checkable fact, with a source and a date attached. A page that instead says "Uber had a strong quarter" gives neither an engine nor a skeptical reporter anything to verify, and a claim that cannot be verified is a claim that is easy to dismiss.
For a communications team, this means the underlying data strategy and the public-facing writing strategy are no longer separate functions. The number a data team can defend in an audit is the same number a communications team should be putting in a press release, a policy page, or a statement to a reporter.
What should communications and business leaders do next?
Communications and business leaders should treat their organization's data architecture as a communications asset, not only a technical one, and audit it against three questions before making public claims about data use. First, can the company state, in one sentence, exactly what customer data it collects and why. Second, does its storage architecture actually match that stated policy, tier by tier. Third, is there a named person or team accountable for data governance who can answer a reporter's question without deferring the question indefinitely.
Uber's scale and Toyota Connected's public privacy commitments both work because the underlying data infrastructure was built to support the public claim, not bolted on afterward. IBM's breach-cost data shows the fastest-rising cost is not the breach itself, but the detection, escalation, and lost-business cost that follows when a company cannot quickly and credibly explain what happened.
The through-line across Uber's marketplace, Toyota Connected's privacy program, and IBM's breach-cost research is the same: big data becomes a communications liability the moment a company cannot explain its own data practices as clearly and specifically as it explains its product.
CONCLUSION
Big data is not a technical footnote to a company's PR strategy; it is the evidence base the strategy stands or falls on. Uber's real-time marketplace, Toyota Connected's public privacy commitments, and IBM's 2026 breach-cost data all point to the same conclusion: brands that can explain, in specific and checkable terms, what data they collect and how it is protected earn public trust, and brands that cannot are the ones making headlines for the wrong reasons. Communications and business teams that want a stronger position going into 2026 should start by auditing whether their own data architecture matches the promises already in their public messaging.
