3 ways to keep pace with the real-time data revolution

From global businesses to individual consumers, we have all come to rely on real-time information. "Modern data", or immediately available information about everything from credit card fraud to taxi availability, empowers businesses, users and consumers to make fast, informed decisions. Poor data can cause huge inconveniences, and even cost lives. That storm that just made landfall? That’s actually a Category 3 hurricane. The train that hasn’t arrived? The transit system was never updated to reflect a delay.

Now, real-time information is at a critical juncture, with the potential to be used in a variety of high-impact ways beyond simple day-to-day applications, including telecommunications, healthcare, data privacy, accessibility and information access.

It's already essential to financial technology and fraud detection, at a time when these crimes are on the rise: 46% of global organizations have reported experiencing fraud, corruption or economic crimes in the last 24 months. The fraud detection market is estimated to grow to $34 billion in 2024. The immediate detection of these crimes and hacking attempts is critical in safeguarding not only businesses, but the sensitive customer data they often store.

Fraud detection is powered by real-time analytics and insights, particularly with the ability to apply data through anomaly detection, predictive capabilities, artificial intelligence and machine learning. For financial organizations and businesses, fraud detection needs to happen "on the swipe", and not a millisecond later.

The cybersecurity market is expected to grow to $300 billion by 2024, with global spend having already exceeded $1 trillion in 2021. Cyber attacks happen every 39 seconds, putting personal, sensitive information at risk for millions of users. The instant detection and prevention of these attacks is critical in stopping a domino effect of additional security breaches. This means that the only answer is a real time one.

Real-time information is both abundant and rapidly growing. This has many benefits but also heightens the risk of false information and inaccurate reports. Studies have shown that false news spreads more rapidly on Twitter than real news does, and by an alarming margin: false news stories are 70% more likely to be retweeted than true stories are.

In most online environments, machine learning algorithms are in place to monitor, observe and distribute information immediately as readers consume it. And while algorithms are often designed to quickly display relevant content, they can also help to disseminate false, misleading or outright incorrect information that continues to circulate through wider networks.

Many organizations run into problems with data flow, or how quickly data is made available in vital instances. If it's unavailable, or two slow to become available, it's likely to lead to a less effective response.

As organizations continue to grapple with a rise in data and the need for real-time decision making, they are also forced to reevaluate their data processes and procedures. Many global businesses have multiple systems, databases and platforms, all designed to move information. But too many tools can lead to greater confusion when it comes to the accuracy and freshness of data, causing duplicate, incorrect and outdated information to make its way to users.

Real-time applications and analytics don’t need a complex array of technologies to be functional. Organizations must untangle their data webs and infrastructures, building a foundation that relies on three key pillars: speed, scale and simplicity. This will allow for greater collaboration and impact. Real-time data can be of great value to businesses, but it needs to come from a system designed to quickly move and make sense of data – a system that is fast, unified, scalable and resilient. The organizations providing that data will have an important role in keeping the world informed.