How are leaders using real-time data?

Exploring real-time data

Real-time data is no longer a future ambition, it’s a present-day priority. 

Across industries, leaders are moving beyond batch-based analysis to embrace streaming architectures, enabling faster decisions, sharper customer experiences and more adaptive operations. But how are they doing it in practice?

This article, in partnership with Confluent and the Data Visionaries Community, surfaces insights directly from those leading the charge. By spotlighting real-world examples and frontline perspectives, we’re exploring how real-time data is being harnessed for impact today.

Meet the contributors:

• A senior data leader in advertising
• A Chief Data Officer in the energy sector
• Aldwin Tito, Chief Data Officer, former Shell
• Sarah Gadd, Chief Data Officer, Julius Baer
• Phil Goldstein, Senior Product Marketing Manager, Confluent

Real-time data is becoming the default

The shift from retrospective analytics to event-driven, real-time systems is reshaping how businesses operate. In finance, it enables fraud detection in milliseconds. In retail, it powers personalised offers. In manufacturing, it prevents downtime through predictive maintenance. In advertising, it optimises campaigns in-flight, reallocating spend and adjusting creative based on what’s working (or not) in that very moment.

“The ability to see campaign performance as it happens means you can shift budgets, swap out creative, and tailor targeting before opportunities are lost,” said the advertising leader, speaking anonymously. “It’s the difference between being proactive and being too late.”

Phil Goldstein, Senior Product Marketing Manager at Confluent, agrees:

“Leaders are recognising that to truly capitalise on real-time data, they must move beyond static, batch-based thinking. The most impactful use cases—from fraud detection to dynamic logistics—are built on the principle of ‘data in motion.’ This means architecting a system where data is not just stored, but continuously streamed, processed, and acted upon as events happen.”

In private wealth management, the most time-sensitive data is market-related.

“Our biggest real-time use cases are related to market data and therefore include product, pricing and transactional types of information,” says Gadd. “Changes in this regard need to be reflected immediately.”

For the energy sector, urgency often means safety and operational continuity.

“If there’s a valve that doesn’t perform the way it should, you send someone to investigate before it becomes a serious safety issue. You don’t wait for equipment to break, you replace it before that happens, because downtime can mean millions lost per day,” said the energy leader.

From an industrial perspective, Tito emphasises that the question is not whether real-time data is important (it clearly is) but how it’s integrated and used:

“Technologies like sensors and drones now provide more accurate and timely data, enabling stronger predictive and prescriptive analytics,” said Tito. “But without strong foundational controls over metadata and master data quality, integrating real-time data into AI or predictive models becomes unreliable.”

Embedding real-time into the data stack

Leaders are embedding real-time capabilities in different ways, whether for customer experience, operational intelligence, or AI/ML systems that adapt on the fly.

In advertising, real-time streaming underpins almost every stage of the funnel, from media buying in milliseconds to dynamic creative optimisation. “If you want a personalised ad to show up at the exact right time for the right customer, you can’t rely on yesterday’s data,” notes the advertising leader. “You have to process, decide, and deliver in seconds.”

In the energy sector, continuous sensor streams are standard, but filtering is essential:

“You stream millions of points per second, but you don’t need every single one. You identify the key data points that matter for the process you’re running,” says the energy leader.

Tito points out that real-time architectures are increasingly integrated into digital twins, creating live, continuously updated replicas of physical assets to improve reliability management and predictive maintenance.

Goldstein highlights the architectural significance of this shift:

“This architectural shift is what enables businesses to close the loop between customer actions and business decisions, unlocking the next wave of efficiency and personalisation.”

Where the biggest opportunities lie

In finance, Gadd argued that the coming $8 trillion wealth transfer to Millennials and Generation Z will reshape expectations.

“The next generation is mobile-first, expects instant access to information, and will want to customise scenarios on the spot,” says Gadd. “When they’re speaking with a relationship manager and ask, ‘What if I did this?’ They will want to see the impact immediately.”

In advertising, the most valuable opportunities are in closed-loop optimisation: where data from impressions, clicks, and conversions flows straight back into bidding and creative decisions. “The combination of real-time streaming and AI means we can test, learn, and adjust continuously, not just between campaigns but during them,” says the advertising leader.

Goldstein reinforces this point:

“The examples in this article—from optimising ad spend in milliseconds to predicting equipment failure—show that real-time data’s true value lies in its ability to drive immediate action. It’s no longer enough to simply analyse historical data. The most forward-thinking leaders are leveraging data streaming platforms to connect those real-time insights directly to the systems that can act on them.”

In energy, predictive maintenance, emissions monitoring, and AI-assisted workflow automation are top priorities. Tito sees similar themes across industrial sectors:

“Real-time data can greatly enhance environmental reporting and compliance, streamline and automate workflows, and enable faster detection and response to security incidents. But the push to expand capabilities should be aligned to business strategy, not just driven by hype or fear of missing out.”

Overcoming the challenges

While real-time technology is mature, the biggest obstacles are often organisational.

“One of the biggest challenges is that people think they need real time when they actually don’t,” said Gadd. “Low-latency trading still takes microseconds. Many use cases are really near real-time (and rightly so).”

For advertising, the challenge is often about signal quality and fragmentation. “You might have live data from dozens of platforms, but if it’s inconsistent, incomplete, or delayed by even a few seconds, you risk making poor optimisation decisions,” notes the advertising leader.

Tito stresses that data quality is the cornerstone of trust:

“Without completeness, consistency, and accuracy, models risk becoming biased, autocatalytic systems that degrade over time. Trust has to be established at the source process and system, and maintained through every layer of aggregation and analysis.”

And for global energy operators, regulation and local laws can restrict what’s possible:

“In some countries, you cannot move data out at all, even to the cloud, so you have to run on-premises. That affects how you apply modern real-time technologies,” notes the energy leader.

Goldstein also points out the AI connection:

“As organisations rush to embed AI and machine learning into their operations, they are quickly realising that these models are only as good as the data they are fed. A real-time data streaming platform acts as the critical foundational layer, ensuring that data is not only delivered with low latency but also with the quality and governance necessary to build trust.”

What leaders can do now

From all four leaders, several lessons stand out:

• Start with the process, not the technology. “Why do I need to change it? Do I understand the data I need to execute it? Everything else comes afterwards,” says the energy leader.
• Invest in foundational data quality. “Don’t assume technology alone will solve your challenges,” says Tito. “You need the right mix of business process knowledge, data management skills, AI expertise, and technical proficiency.”
• Embed governance into streaming architectures. “Capture metadata, define standards, and ensure you can run data quality checks as information flows,” says Gadd.
• Ensure signal reliability in high-speed optimisation. “Real-time decisions are only as good as the data feeding them,” says the advertising leader.
• Prioritise innovation through ROI. Avoid upgrading systems just for the sake of it; ensure a clear business case.

The future of real-time data is not about chasing speed for its own sake. It is about knowing when immediacy changes the outcome, and designing the architecture, processes, and governance to make that speed sustainable.

Across all industries, it means starting with the business problem, and letting the technology serve the strategy, not the other way around.

As Goldstein concludes: “This moves the organisation from being reactive to being proactive, creating a continuous feedback loop that powers everything from hyper-personalised customer experiences to mission-critical operational intelligence.”

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