How Real-Time Data Analytics Can Lead to Better Decisions

How FedEx Gets Ahead of Logistics Problems

Great American Ballpark delivers a vivid example of a shift taking place across industries: Organizations are moving beyond analytics that explain what happened yesterday and toward systems that help employees decide what to do next. Real-time data analytics matters because the value of information can decline quickly when decisions cannot wait.

“Real-time data analytics enables businesses to process and act on data as it is generated, leading to faster decision-making, greater operational efficiency and improved customer experience,” says Houbing Herbert Song, a fellow with the technical professional organization IEEE. Latency reduction is particularly important for applications such as financial fraud detection, behavior monitoring and dynamic pricing, he says.

The distinction is between information that merely describes the business and information that can actively influence it. The technologies and use cases differ depending on the industry, but the underlying goal is similar: Put trusted information in front of the people and systems that can act on it quickly.

At package delivery giant FedEx, it’s all about using data and artificial intelligence to anticipate disruptions across one of the world's largest logistics networks. “We’re helping customers and team members anticipate potential disruptions, make more informed decisions and take action earlier,” says Arvind Rajagopalan, vice president of data and AI at FedEx.

The company is using Microsoft Azure to deliver real-time analytics across its network, with the goal of improving visibility and operational efficiency. Data and AI have become increasingly important competitive differentiators, Rajagopalan says, helping FedEx provide better service, operate more efficiently and create new value for customers.

WATCH: How to overcome the top five data challenges businesses are facing today.

The difference between traditional reporting and real-time intelligence is particularly important in a logistics environment. Rather than simply reporting what happened, predictive insights allow FedEx to help employees, and even customers, make faster, more informed decisions. AI can push that capability further by analyzing operational and delivery data in near real time, identifying potential service issues, recommending actions and enabling intervention before a customer is negatively affected.

At the same time, the company is simplifying its technology environment to support those ambitions, with plans to retire legacy systems and integrate AI into 50% of its operational workflows by 2028, Rajagopalan says.

“Our future technology platform will be an agile, cloud-first system that seamlessly integrates AI, data and modern engineering,” he says. “This modern technology stack will accelerate innovation, elevate customer experiences and power every part of the business.”

At FedEx, that means embedding AI directly into operational workflows rather than treating it as a stand-alone technology. The objective, Rajagopalan says, is to build the data, infrastructure, workflows and capabilities required to integrate AI across operations and deliver measurable business outcomes at scale.

That requires discipline. “It is easy to get distracted by the latest AI hype cycle,” Rajagopalan says. Instead, IT leaders should keep the broader business objective in view. “When the IT team focuses on improving outcomes for the customer, we’re more likely to make a greater impact across the enterprise as well,” he says.

How Indeed More Efficiently Matches People With Jobs

The scale of the data challenge looks different for national job search website Indeed, but the need for timely insight is just as pronounced. The platform has more than 665 million profiles of job seekers, with 31 people being hired every minute, according to Madhu Kurup, vice president of engineering. Timely data analysis helps Indeed understand changing employer needs, job seeker behavior and shifts in supply and demand so it can continually improve its matching capabilities.

Indeed uses Snowflake as the foundation for its analytics platform, with centralized security controls and expanded self-service access. The goal is to make trusted data available throughout the business without sacrificing governance or security.

The architecture also avoids creating separate data silos for every new analytics, reporting, AI or operational use case. Using Snowflake, Indeed has built what Kurup describes as a shared, governed data foundation that gives teams access to consistent information across the organization.

That approach has improved accessibility while lowering infrastructure costs by 45%, he says. The payoff ultimately comes through the company’s core business of matching people with jobs.

“We’ve evolved from a search-based job board into an AI-powered matching platform,” Kurup says. “That scale allows us to continuously improve the quality and relevance of our recommendations, helping job seekers discover better opportunities while enabling employers to connect with qualified candidates more efficiently.”

AI helps the company identify patterns in large data sets, improve recommendations and surface insights more quickly. It also helps automate repetitive, high-friction tasks, freeing employees to spend more time applying judgment to important decisions.

“Our goal is to use AI to augment people, not replace them,” Kurup says.

For all three organizations, real-time analytics is increasingly intertwined with AI. But the technology works best when it is built on a strong data and technology foundation rather than treated as a separate layer, says Song. “In the age of AI, such an architecture should be extended with AI models to enable intelligent, low-latency, explainable and trustworthy decision-making while preserving privacy and resilience,” he says.

At Sportservice, the human element remains central to turning data into useful action. The company's analysts work directly with the people running its venues, bringing business context to the numbers.

“Models are created by human beings,” Dash says. That means organizations need people who understand the nuances of the business and can determine which numbers actually matter.