How Real-Time Analytics Helps You Get Value from Your Data Faster

Learn how real-time analytics fits into your data strategy, explore use cases, and understand technologies that power it.

What You Should Know About Real-Time Analytics:

  • Real-time analytics turns data into insights immediately after it’s collected, allowing you to act on that data in the moment.

  • Real-time streaming analytics processes data while in transit, providing a steady flow of insights before data is stored.

  • Companies choose a variety of analytics strategies based on business needs. Predictive, prescriptive, and cognitive analytics all rely on real-time analytics.

  • Scalable processors and in-memory technology lay the foundation to support a robust real-time analytics strategy.



Organizations are collecting more data, faster than ever before. And with the number of connected IoT devices set to reach 24.1 billion by 20301, there’s no sign of slowing. Yet many companies are struggling to turn these piles of data into insights they can use to grow their business.

This is where real-time analytics can help. In this article, you’ll learn how organizations use real-time analytics, how to fit it into your data strategy, and the infrastructure needed to successfully implement it within your business.

What Is Real-Time Analytics?

Real-time analytics turns data into insights immediately after it’s collected. These kinds of insights are used when time is of the essence. Otherwise known as operational intelligence, real-time analytics can predict when a device is about to fail, warning your operations team before it happens. Prompt retailers to send mobile promotions to customers when they’re in a store vicinity. Or detect credit card fraud before a transaction is completed.

To better understand how real-time analytics works, let’s compare it to traditional analytics, or batch processing. With the traditional approach, limited sets of historical data are stored and indexed. When business users need insights, they query the system. Batch processing is typically used for routine tasks like generating monthly sales reports or running payroll.

While batch reporting is appropriate for tasks that aren’t time sensitive, others require immediate insights, such as patient safety monitoring or fraud detection. This is where real-time analytics comes in.

Real-Time Data Analytics vs. Streaming Analytics

There are different types of real-time analytics, including on-demand and continuous—or streaming—analytics. Gartner clarifies how they’re related with the following definition: “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.”2

As edge computing and the Internet of Things (IoT) push more data to businesses at higher speeds, the need to process that data while it’s in motion—before it’s stored—has increased demand for streaming analytics. In addition, more businesses are relying on streaming analytics to provide real-time business analytics that allow them to make split-second decisions and gain a competitive edge.

91% of CIOs say streaming analytics can help them boost their bottom line.3

Real-Time Analytics Use Cases

From retailers and manufacturers to financial services firms and healthcare organizations, businesses are struggling to keep up with the fast pace of data. Because the value of this data can evaporate within days, hours, minutes, or even seconds, near-real-time processing is critical to gaining the most valuable business intelligence.

For example, IoT data that directs a driverless truck becomes worthless—and even dangerous—if the data is stale. Likewise, data that indicates fatigue in a machine on a manufacturing line comes too late once the machine fails.

Real-time analytics addresses many organizational pain points. Online retailers are blending transactional and web browsing activity to determine the next best offer to serve up to a customer. Banks are analyzing behaviors to determine fraudulent activity or detect signs that a customer who works with one of their departments is ready for a pitch from another department. Dynamic pricing, risk management, call center optimization, and security are just a few of the processes that can be optimized with real-time analytics.

Even sports teams make use of streaming analytics to better manage ticketing, concessions, retail sales, and on-field performance. For example, if a gate gets too crowded, the organization can immediately send more ticketing and security staff to that location to help keep wait times down and ensure crowd safety.

In these cases, near-real-time data allows companies to deliver value-added services and products at the very moment the customer wants them and to defend against potentially dangerous situations before they happen.

How Do Real-Time Analytics Fit in with an Overall Analytics Strategy?

Analytics is a spectrum, with most companies adopting a mix of analytic approaches based on data types, workloads, and the type of business problems they are trying to solve. Analytics now spans five categories:

  • Descriptive analytics answers questions about what happened in the past.
  • Diagnostic analytics offers insights about why those events happened.
  • Predictive analytics analyzes current and historical data to provide insight on what might happen in the future.
  • Prescriptive analytics suggests actions an organization could take based on those predictions.
  • Cognitive analytics automates or augments human decisions.

These five categories build on each other in a stepwise manner, moving an organization toward an on-demand enterprise where decisions become faster and better.

Predictive analytics are the beginning point of “advanced analytics,” where decision-making may be fueled by real-time information. Therefore, predictive, prescriptive, and cognitive analytics are use cases that benefit from the capability of real-time data analytics.

No matter what type of analytics companies use, they need to adopt a comprehensive data strategy built upon a real-time analytics architecture that breaks down both data and organizational silos. The common theme is the ability to capture, store, analyze, and secure data so that insights can scale rapidly across the organization to allow timely business decisions.

Infrastructure Needs for Real-Time Analytics

The analytics solution stack is made up of four layers—infrastructure, data, analytics, and application. Intel® technologies span every important part of a company’s infrastructure, across the network, storage, and compute, allowing data to be efficiently managed and rapidly harnessed for competitive advantage. A consistent architecture—for example, one based on Intel® Xeon® Scalable processors across an organization—provides a predictable path to rapidly scale analytics initiatives without the need to support multiple architectures.

Traditional big data solutions, focused on data warehouses, are not suited for most real-time processing. Increasingly, cloud vendors are providing Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) offerings that can be used in support of real-time analytics. Brokered solutions across clouds allow companies to run workloads wherever they wish depending on the volume, variety, and velocity of the information.

As companies generate huge amounts of data in the cloud, they must determine which data needs to be moved back to the enterprise to make intelligent decisions. Real-time data may be processed on the edge, with data analysis occurring at or close to the collection point. However, real-time analytics in the data center requires rapid access to and analysis of increasingly large amounts of data. This means it’s essential to optimize every level of your infrastructure—from the CPU to memory and storage subsystems.

Persistent memory technologies keep more data closer to the CPU and retained in memory during power outage cycles, eliminating the latencies caused by I/O bottlenecks, fetching data from slower SSDs, and speeding up restarts.

Real-time analytics requires taking data from anywhere, in any format, and getting it into the right record form so that it can be processed as a whole. The key is to understand where data is created and how it will be used to improve business processes and decision-making.

Who Are the Key Players in Real-Time Data Analytics?

As an analytics technology partner, Intel provides the flexibility to choose from industry-leading analytics software solutions that are either open source or proprietary.

SAP HANA is a single database that combines a database with advanced data processing, application services, and flexible data integration services. HANA leverages in-memory database software—an approach to querying data when it resides in the system’s memory (today called RAM)—rather than querying data that is stored on physical disks.

This allows customers to process data in many new ways, much faster, building a series of what-if scenarios to help exploit opportunities or avoid problems. Other traditional technology vendors, such as IBM and Oracle, have also enabled real-time operations in their platform with new technology.

Open source solutions, centered on Apache Spark base code, bring real-time analytics to unstructured data such as social media, images, and video. Spark uses in-memory analytics scaled across numerous systems so that large amounts of data can be processed in parallel.

Many of these solutions can be offered in the cloud, allowing analytics to be run where data from social media and IoT is being generated. As a result, companies can query transactional and online data to shed light on patterns and trends in real time, moving as quickly as the world and their customers do.

New solutions and providers are constantly entering the market. This provides a rich ecosystem of solutions primed to take advantage of the compute, network, and storage capabilities that Intel provides to enable higher agility in enterprise analytics and decision-making.

Intel Real-Time Analytics Technology

From scalable processors to in-memory technology, Intel provides solutions that speed performance of the compute-intensive applications that power real-time analytics and swift decision-making.

Intel® Xeon® Scalable Processors

Intel® Xeon® Scalable processors provide high-speed performance for real-time analytics, AI, and other data-demanding workloads.

Intel® Optane™ Technology

Intel® Optane™ technology includes Intel® 3D XPoint™ memory media, Intel® memory and storage controllers, Intel® interconnect IP, and Intel® software. They work together to provide lower latency and accelerated systems for analytics workloads that require large capacity and fast storage.4

Intel® Memory Drive Technology

Intel® Memory Drive Technology expands system memory for faster analytics insights. The technology transparently integrates Intel® Optane™ Solid State Drive (SSD) into the memory subsystem, increasing capacity past DRAM limitations.

Real-Time Insights for Real-Time Value

Real-time streaming analytics can help you get more value from your data, faster. From improving inventory modeling to heading off network security threats, businesses in every industry are putting in place the infrastructure needed to make more-accurate predictions and more-confident decisions.