March 25, 2021

What Real-Time Data Analytics Really Means and Why It’s So Important

Today, our world changes so fast that it can be hard to comprehend at times. We’re constantly bombarded with millions (billions?) of data points that inform decisions—whether made consciously or not. Scientists estimate that the average human makes more than 35,000 decisions per day.

While that may seem implausible, think about this: from the moment you woke up this morning, your brain has analyzed incoming information in real time to make decisions. These range from getting out of bed to what you ate for breakfast, to what blog you read as you sip your coffee.

Modern business intelligence systems are not unlike our brains in the way they continually collect, process, and analyze incoming data. This is a big departure from a time when data was processed in batches. The continual process is the foundation of real-time data processing and analytics.

You’ve probably heard the term ‘real-time analytics.’ The phrase gets thrown around all the time. But what does it mean, and why are so many companies looking to adopt a real-time data analytics strategy today?

In this post, I dig into the details, help you understand the business benefits, and share how teams use real-time analytics to inform better decision making.

What is Real-Time Analytics?

Put simply, real-time analytics means that you can immediately process and query new data as it is created to inform decisions in the moment and guide your business decision making.

Gartner defines real-time analytics as a discipline that applies logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real time means the analytics is completed within a few seconds or minutes after the arrival of new data.

On-demand real-time analytics waits for users or systems to request a query and then delivers the analytic results. Continuous real-time analytics is more proactive and alerts users or triggers responses as events happen.

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Real-Time Analytics vs. Near-Real-Time Analytics

Many people often confuse or conflate real-time and near-real-time analytics. Near-real-time (or near-time) data processing and analytics is, as the label suggests, quick but not instant.

While near-real-time processing is no doubt fast, many companies require real-time analytics to understand what’s happening across their business units. Typical industries that rely on real-time data analytics include information technology, financial services, transportation, healthcare, and advertising. In these scenarios, data must be understood immediately to steer decisions, and in some cases, even deliver products or services to customers.

Why Real-Time Analytics are on The Rise

We’re amidst a shift in business intelligence as companies modernize their data infrastructure to meet the real-time demands of business. Despite our always-on world, and the ubiquity of smartphones and other mobile devices that function in real time, many companies still operate on historical data that is analyzed in batches—meaning they can’t get instant insights. This can have a significant impact on their ability to compete, understand customer trends, and address market changes in a timely fashion.

Gartner suggests that by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.

This radical change in the way companies operate requires new data infrastructures to manage data collection, processing, and analysis at scale. Older, on-premises data storage can cost companies a fortune to manage these workloads—which is why so many companies are moving to the cloud. Cloud data warehouses and data lakes offer companies centralized cloud data stores, near-infinite computing power, and the flexibility needed to analyze data in real-time.

80% of enterprise workloads will run in the cloud by 2020.

Benefits of Real-Time Data

Spend

A real-time analytics strategy requires a robust data infrastructure to collect, store, and analyze data as it’s created.

A recent study from Amazon Web Services found that it would generally cost you between $19,000 and $25,000 per terabyte per year at list prices to build and run a good-sized data warehouse on your own. But switching to a modern cloud data warehouse could save your company as much as 96% on data warehousing costs, all without any of the operational headaches associated with building and running your own data infrastructure.

You should also consider the on-going costs associated with data warehouse maintenance and security. On-prem data storage requires an IT team to continually manage and maintain the hardware, update security patches, and deploy changes. Depending on your data needs and company requirements, this can quickly add up.

Accuracy

Like most things in this world, data expires. And with the rate that new data gets created today, it’s not only necessary but imperative that teams utilize the latest information to make decisions. Otherwise, they risk operating on outdated assumptions. If you think your data doesn’t have an expiration date, think again. The CGOC estimates that 60% of data collected today has lost some—or even all— its business, legal or regulatory value.

Real-time data provides the latest insights. Trends can change quickly, and if your team is using last week’s or last month’s data to diagnose a current problem or inform the next big decision, they can miss opportunities. Even worse, it could cost you additional spend downstream.

Timing

The ability to react in real time to issues or trends can’t be understated. Real-time analytics can help you pinpoint issues the moment they arise, and in some cases, catch them before they occur. And in an age where the consequences of data compliance and regulations failures increase by the day, real-time analytics is an investment that can protect your company and its customers. Not only saving you from paying fines but also from losing customers’ trust, should you miss a security or operations problem when it pops up.

Common Real-Time Analytics Examples

While there is a myriad of industry use cases where real-time analytics are paramount, here are some of the more common examples we see every day.

Real-Time Analytics for Information Security

As companies grapple with more significant data security risk—and data compliance regulations such as GDPR—they’ve turned to Security Information and Event Management Software (SIEM). These solutions rely on real-time data to aggregate and analyze activity from data sources across the entire IT infrastructure.

Read how the Snowflake security team uses its data warehouse and Sigma to monitor security threats.

Real-Time Analytics for Marketing

Real-time customer analytics are essential for improving experiences across marketing touchpoints. They can also ensure marketers serve the right information to the right customer at the right time. Customers increasingly expect personalized interactions with brands, and that’s a big reason why 44% of enterprises gain new customers and increase revenue as a result of adopting and integrating customer analytics into their operations.

Real-Time Analytics for Logistics

The supply chain has seen dramatic improvements in recent years, thanks to the application of real-time insights gathered by logistics providers. Freight providers use real-time information to understand shipping trends, cut costs by eliminating inefficient routes, and deliver improved customer experiences.

Real-Time Analytics for Finance

Real-time analytics is critical in the financial services industry. Not only do financial institutions use real-time data to improve customer offerings, but it’s also part of modern fraud detection capabilities and trading strategies—allowing firms react to the latest market trends.