Real-Time Data vs. Near-Time Data

Delays in business operations and decision-making cause companies to miss opportunities and expose them to risk. Real-time data empowers organizations to act quickly based on insights that reveal where problems and opportunities lie.

This post explores what real-time data is, how it differs from near-time data, and how your company can benefit from the powerful insights that real-time data delivers. We’ll wrap up with examples of how real-time data improves business operations, with near-time data offering a glimpse of the impact that real-time insights deliver in improving efficiency, driving profitability, and fortifying digital defenses.

What is real-time data?

Real-time data represents the pinnacle of information processing, providing instantaneous insights the moment data is generated. While no industry-standard definition exists for exactly how quickly data must be processed, the general rule is that real-time data is measured in seconds. 

In contrast, traditional reporting methods can leave data days old by the time a BI team completes their analysis, and spreadsheet extracts can be even more outdated when they reach decision-makers. Powered by modern cloud data platforms with centralized data stores and nearly unlimited computing power, there's simply no reason to settle for batched or delayed data when conducting analyses.

What is near-time data?

Near-time data (aka near-real-time data) is a compromise for organizations not yet equipped for full real-time processing. Typically measured in minutes or hours, this approach provides a recent snapshot with a slight, acceptable delay. While it offers some improvements over traditional batch processing, near-time data falls short of the game-changing capabilities of real-time analytics.

Why real-time data is superior

Like pizza, data is best served fresh. Near-time data doesn’t provide an on-demand snapshot of what’s happening right now. Even a brief lag can result in missed business opportunities. 

Real-time data sources provide teams with actionable information on important business processes and consumer behavior. Departments across an organization, including sales, marketing, finance, and operations, have come to rely on the in-the-moment insights that real-time data provides.

Benefits of using real-time data

Access to real-time analytics gives your business a competitive edge, powering more agile decision-making. These benefits can be realized no matter what industry you’re in. With advances in modern cloud data technology, incoming data streams from your website, SaaS solutions, mobile apps, social channels, and IoT devices business are available to be queried as they’re generated. Here are five benefits of real-time data.

Relieves burdens on technical resources

When business users across your organization have access to real-time data, your IT or BI team isn't bombarded with requests for ad-hoc reports; they can focus on more strategic tasks.

See how Payload introduced real-time self-service analytics to see a 50% savings and free their BI team to identify a new revenue stream.

Capitalizes on emerging opportunities

With the competitive edge hinging on immediacy, relying on data gathered even a short time ago can result in missed opportunities. Whether you’re a merchandiser capitalizing on trending sales data or a financial services organization optimizing its trading strategies, real-time data offers a competitive edge in a fast-moving business environment.

Increases operational efficiency

Real-time data makes it possible to achieve gains in efficiency. For example, manufacturers can utilize this information to optimize production plans, use production equipment more strategically, and make more accurate predictions about when individual machines will require repairs.

See how Metrikus increased operational efficiency by 25% with Sigma.

Increases customer satisfaction

Analyzing consumer behavior and identifying where buyers are falling out of the funnel or where bottlenecks are occurring can help simplify the path to purchase.  Retail and e-commerce businesses can accurately anticipate the need for complementary products and serve up highly targeted offers.

Addresses potential problems early on

When it comes to a security breach or an issue on the production line, minutes matter. Access to real-time data allows companies to react immediately to resolve issues when they arrive. Moving fast can help save a company’s reputation or prevent wasting significant time and money.

Real-time data examples

A variety of industries have pivoted to using real-time data for a range of applications. Here are a couple of examples showing how businesses are using dynamic data.

Dynamic deals: Real-time flash sales for instant conversion

To maximize impulse purchases, a retailer could leverage real-time location and browsing data to instantly trigger a personalized flash sale notification for items in a specific department as a customer walks through their physical store or browses their mobile app. For example, a retailer might use this approach to create an instant, targeted offer based on the customer's precise location and current interest. This immediate, targeted offer drives impulse purchases and maximizes conversion rates within a limited time window. Real-time inventory data ensures the offered items are in stock, preventing disappointment and enhancing the overall customer experience. This use of real-time data transforms fleeting interest into immediate sales, demonstrating the power of contextual personalization.

See how a leading food service distributor uses real-time, self-service analytics to find root causes of issues, find solutions, and take action to ensure fulfillments are achieved and SLAs are met.  

Instant alert: Real-time fraud prevention in online transactions

When a customer initiates an online purchase, financial institutions can use real-time transaction analysis to instantly assess risk factors. For instance, by comparing the transaction details against historical patterns, location data, and known fraud indicators, the system identifies anomalies such as unusually high purchase amounts or suspicious geographical locations. 

If a potential fraud is detected, the transaction is immediately flagged, and the customer is prompted for verification. This real-time intervention prevents unauthorized transactions, protects customer funds, and minimizes the risk of chargebacks for the financial institution. This immediate response, enabled by real-time data, is key to maintaining trust in online payment systems.

‍See how Cowen has created a culture of self-service analytics and eliminated ad hoc requests with Sigma.

Near-time data examples

Near-time data represents a compromise between traditional reporting and cutting-edge real-time analytics. Let's explore how different industries are utilizing near-time data to enhance their operational capabilities.

Predictive maintenance: near-time equipment health monitoring

To minimize downtime and maximize operational efficiency, a manufacturing facility could employ near-time sensor data analysis to monitor the health of integral machinery. For example, sensors continuously transmit data on vibration, temperature, and pressure, which is analyzed every few minutes. When the system detects anomalies or deviations from established baselines, maintenance teams receive immediate alerts. 

This proactive approach allows for timely intervention, preventing costly equipment failures and ensuring uninterrupted production. Near-time historical data is used to train machine learning models that improve the accuracy of predictions over time.

Public transportation: Near-time bus/train arrival updates

To improve the passenger experience and reduce wait times, public transit systems can utilize near-time vehicle location data to provide accurate arrival estimates. For instance, GPS and sensor data from buses and trains are transmitted every few minutes, which is then used to update digital displays and mobile apps. This near-time information empowers passengers to make informed decisions about their travel plans.

These near-time data applications demonstrate the growing need for faster, more responsive data analytics, a requirement that pushes businesses toward true real-time data processing capabilities.

Real-time data requires a real-time analytics tool

Real-time data can reshape how companies operate, making them more profitable and better able to accommodate rapidly changing business conditions. But to benefit from real-time data, you need an analytics tool to work with it in real-time. Many analytics tools rely on data extracts, creating near-time data. Modern tools like Sigma sit on top of your cloud data platform, ensuring that data stays centralized and always up-to-date.

‍