Gone are the days when real-time data processing was optional for businesses. In order to excel in today’s highly competitive landscape, it’s imperative businesses leverage data processing in real time.
You may know that already, but be unsure of why exactly data processing is non-negotiable. Today, we’re going to walk you through why exactly data processing is a must-have.
What is Real-Time Data Processing?
Real-time data processing refers to the collection, processing, and analysis of data as it is generated. Sometimes, you’ll also hear it referred to as ‘stream processing.’ Regardless of what you call it, there’s a few things you’ll need to achieve this:
- A constant flow of information: To succeed with stream processing, you’ll need a constant flow of information starting with your sources and ending with your targets.
- Sub-second latency: High latency is the enemy of real-time data processing. Prioritize a platform that boasts sub-second latency to ensure success.
Real-time data processing is often compared to batch processing. Whereas batch processing includes the collection, processing, and analysis of information at a predetermined time, real-time data processing is instantaneous and constant. This freshness factor is the pivotal difference in why data processing in real time is successful in highly dynamic, ever-evolving environments.
How Does Real-Time Data Processing Occur?
Real-time data processing, or stream processing, begins with continuous data ingestion from sources like IoT devices, sensors, and transaction systems. This raw data from sources gets immediately processed without buffering, involving operations such as filtering, aggregation, and transformation.
Concurrently, streaming analytics algorithms are applied in real time to analyze data streams, detecting patterns, anomalies, correlations, and trends. Processed data is swiftly delivered with minimal latency, typically in milliseconds or microseconds, ensuring timely actionability. Integrated directly into decision-making systems, real-time processing enables rapid response to dynamic conditions, informed decision-making, and automated actions.
This technical capability is crucial in sectors requiring agility and responsiveness, such as finance, healthcare, telecommunications, and manufacturing, where real-time insights result in operational efficiency and a competitive advantage.
Why is Real-Time Data Processing Critical for Modern Businesses to Succeed?
Today’s businesses operate in an unprecedented landscape. As a result, it’s more critical than ever before for organizations to tap into the transformative power of real-time data to ensure success.
1. To Supercharge Decision-Making Processes.
Ever been on the precipice of making a decision and found yourself frustrated by a lack of real-time data available? If so, this one’s for you. One of the best ways to leverage real-time data is to streamline the decision-making process.
By integrating real-time machine learning (ML) analytics, organizations rapidly glean insights from data and can make informed business decisions faster than ever before. This capability allows businesses to react to dynamic market conditions with agility. Unlike traditional batch processing, which involves delays in data collection and analysis, real-time analytics ensures continuous data processing and analysis as it arrives from the source. Thanks to this immediate analysis, actionable insights are more accessible than ever before. And these insights drive better decisions.
In the healthcare industry, for instance, deciding the best course of action for patient care is often difficult. With the assistance of real-time data fueled predictive analytics, doing so becomes easier.
Another example of real-time data processing eliminating the stress of decision-making includes Ciena, a networking systems, services, and software organization. Striim played a pivotal role in enhancing Ciena’s decision-making processes by providing real-time data integration and analytics capabilities.
By leveraging Striim, Ciena gained instant visibility into its data across multiple platforms, allowing it to monitor network performance in real-time and detect anomalies as they occurred. This proactive approach enabled Ciena to respond to issues quickly, ensuring uninterrupted service and significantly improving customer satisfaction. With up-to-the-minute insights and actionable data at their fingertips, Ciena’s decision-makers were empowered to make informed and timely decisions, positioning the organization as agile and responsive in a competitive market landscape.
2. To Enhance Operational Efficiency.
Another use case for stream processing is to enhance operational efficiency. Traditional batch processing is inefficient in comparison to stream processing’s timeliness. Whereas traditional batch processing refers to the collection and processing of data at a scheduled time in bulk, stream processing enables these processes (as well as analysis) to unfold as data flows. This reduces latency and results in the availability of actionable insights right away.By enabling data to be processed and analyzed as it is generated, businesses can significantly reduce the time it takes to respond to changes and make informed decisions. This immediacy is critical for modern businesses in several operational contexts. For instance, in inventory management, real-time data allows for continuous monitoring of stock levels, leading to more accurate demand forecasting and just-in-time replenishment. This minimizes overstock and stockouts, reducing storage costs and lost sales.
Operational efficiency is also improved through real-time monitoring and alerts. Systems can be set up to automatically detect anomalies or performance issues, triggering immediate corrective actions. This proactive approach prevents minor issues from escalating into major problems, reducing downtime and maintenance costs.
For instance, consider how retail giant Macy’s leveraged real-time data to enhance operational efficiency. Macy’s was able to utilize real-time data to streamline inventory and order management processes, which reduced the amount of time required for these tasks. This enabled the company to focus more on its strategic initiatives and business growth.
3. To Gain an Edge Over the Competition.
Yet another reason why modern businesses must use stream processing is to gain an edge over the competition. Real-time data empowers companies to make decisions swiftly and accurately, outpacing competitors that rely on traditional batch processing. This is because brands that use real-time data processing are privy to a myriad of helpful information that adds invaluable context, guiding decisions. Moreover, real-time data can be used to train ML models, allowing businesses to adapt quickly to dynamic environments.
Real-time data streams can be fed directly into ML models, providing them with the latest data for training and inference. This continuous learning process allows models to adapt to new patterns and trends with minimal latency. For instance, in fraud detection systems, streaming data can help identify fraudulent activities as they occur, enabling immediate action to prevent financial losses. This contrasts with batch processing, where delays in data processing can result in missed opportunities and slower response times.
Moreover, Kramp, a leading distributor in the agricultural sector, adopted Striim for its real-time analytics needs. This transition enabled Kramp to move from traditional, batch-load dependent data warehousing to a flexible, cloud-based infrastructure. With Striim, Kramp improved data quality and operational efficiency, which were crucial for its machine learning applications. These enhancements allowed Kramp to provide better customer service and streamline operations, which translates to maintaining a competitive edge in the market.
4. To Give Customer Experience a Boost.
According to Gartner research, 80% of businesses expect to compete primarily on customer experience. Moreover, according to PwC, a majority of customers (73%) point to experience as an important factor in regards to purchasing decisions, behind price and product quality. If your business isn’t already centering customer experience, it’s behind.
Another popular use case for real-time data processing is to uplevel the customer experience. Companies can utilize real-time data to collect, process, and analyze customer information in a more timely manner than ever before. Consequently, businesses can leverage these immediate insights to offer up personalized customer experiences, share custom messages, and give customers the feeling that the business truly understands their needs.
American Airlines, for example, has committed to using real-time data to elevate customer experience. The team deployed a real-time data hub consisting of Striim, MongoDB, Azure, and Databricks to guarantee a seamless, real-time operation.
The architecture uses CDC from sources including MongoDB to garner operational data in real time, as well as process and model the data for downstream usage. Then, it is streamed to consumers — again, all in real time. This enables a delightful customer experience where flyers can rest assured they’ll arrive safely and reliably on every American Airlines flight.
5. To Mitigate Risk.
Finally, businesses leverage real-time data processing to mitigate risk. Real-time data processing is an excellent tool to reduce risks due to its timely nature. For use cases such as fraud detection, batch processing is insufficient — financial organizations require data updated-to-the-second in order to detect anomalies and take swift action.
The manufacturing industry can also utilize stream processing to mitigate risks. Instead of taking a responsive approach to equipment maintenance, utilizing real-time data allows businesses to gain insights into predictive analytics that can foresee breakdowns, share insight into when maintenance is required to avoid a breakdown, and more.
UPS Capital also leveraged stream processing to mitigate the risk associated with porch piracy, or package theft. An increase of online shopping outpaced traditional security measures, exposing major operational vulnerabilities and resulting in a surge of package theft. Moreover, its existing systems were unable to handle the tremendous quantity of data resulting from more package deliveries. Finally, UPS Capital was struggling with a lack of real-time data processing available, which made rapid-response impossible.
The team at UPS Capital integrated Striim’s real-time data streaming technology with Google BigQuery’s analytics capabilities. This enhanced delivery security as Striim enabled immediate data ingestion from several sources, making real-time decision-making feasible. From there, data flowed into Google BigQuery, where advanced analytics and ML predicted delivery risks. As a result, each delivery location received a confidence score which improved predictive accuracy and enabled the business to manage delivery risks more proactively than ever before.
Take Advantage of Real-Time Data Processing to Simplify Decision Making
The best way to take advantage of real-time data processing is whatever makes the most sense for your organization. When you begin to leverage stream processing, you’ll likely realize its myriad of use cases for your business which enable you to run your business more seamlessly and drive massive growth.
Ready to tap into the full potential of real-time data processing? Book a demo with us today to explore how Striim can help you make your data useful the moment it is born.
