Traditionally, historical data (or batch data) was used for decision-making. However, lately, there’s a lot of focus on real-time data, which provides more business value. According to a survey by McKinsey, high-performing businesses are almost five times more likely to use real-time data, as compared to their counterparts.
Real-time data is gaining prominence because it can help end-users to make decisions on the fly, allowing for more accurate and faster decision-making. A survey found that 92% of businesses plan to invest more in real-time analysis, whereas 79% planned to cut investment in batch processing that powers historical data.
What is real-time data?
Real-time data refers to delivering information to end-users instantly after data is collected and is mainly used to perform real-time analytics. There’s minimal delay in the collection, processing, and analysis of data by the business. This data can be collected from a wide range of sources, such as cameras, social media feeds, sensors, operational systems (e.g., ERP systems), and databases.
Real-time data can be categorized into the following types:
Event data: a collection of data points that’s generated based on well-defined conditions within a system.
Stream data: a large amount of data that’s generated continuously and doesn’t have any discrete beginning or end.
Real-time data examples
Some common examples of real-time data within an organization include notifications of malfunctioning systems, locations of moving objects, and telemetry measurements. Real-time data can come from both applications and sensors.
Applications: User behavior actions (e.g., clicks, form input), page view events, auction bids (e.g., Amazon bidding), IT monitoring systems
Sensors: Proximity alerts, equipment temperature, river flood gauges, system overload
For example, companies can use real-time data to learn how customers are reacting to their product release by collecting real-time social media feeds from their posts and comments. Similarly, companies can use real-time geolocation data to personalize their services based on the customer’s location.
Real-time data vs. historical data
Real-time data doesn’t make you wait for data analysis and provides you with relevant insights quickly, so you can make decisions on the fly. What makes it unique is that you can use it to know about both past and present trends for any department (e.g., sales). Also, real-time data is unbounded, which means that data streams have a start but no end, as they are continuously generated and analyzed.
Historical data is the more traditional form of data, which is collected, processed, and stored in an organization’s data repository. Unlike real-time data, it’s not analyzed immediately and isn’t made available to users immediately. It can take days, weeks, or even months for end-users to access historical data and make decisions based on it. You can use it to learn about past trends, but due to its inherent limitations, you can’t use it to learn about the latest trends. Another thing to note is that historical data deals with bounded data, i.e., it has a fixed beginning and end, which means it’s analyzed for a specific period.
For example, consider the use of data to plan your route. Historical data can go through past traffic trends to suggest how much time it can take to get from one place to another. However, if there’s a closed lane or an unexpected event causing a traffic jam, your estimate can go wrong, and you might be late since historical data can’t collect and process this data instantly. On the contrary, traffic apps that collect real-time data, such as Google Maps, can offer instant course corrections, so you can take the most efficient route.
Benefits of real-time data
Real-time data allows companies to adopt a proactive approach. Access to real-time data means they can make decisions based on the latest data to address their inefficiencies and empower their end-users to be well informed.
Improve customer experience
According to a study, 93% of consumers prefer to stay informed about how their delivery process is progressing. Nearly 50% of consumers don’t even want to buy again from a company that provides poor visibility into delivery. Real-time data can provide this visibility to help improve customer experience and customer retention.
You can use this availability of real-time data to support customer requests and respond to customer questions with real-time information from drivers, deliveries, and operations. Real-time insights can help you provide real-time tracking capabilities to customers that let them view the position of their goods at each stage, from order confirmation to doorstep delivery. Customers can also use instant communication to chat with their delivery driver to get delivery on their preferred schedule.
With historical data, you might have to deal with information silos where your teams act independently without being aware of each other’s latest operations. When data isn’t collected and processed in real time, departments within the same organization might be unaware of a problem.
For instance, if a customer reaches out to your customer representative to ask about delivery of a product, the representative might tell them it will arrive on time based on their system’s data, which is one day old. However, the product might have gone out of stock a few hours earlier, leading to a late delivery and resulting in poor customer experience. Meanwhile, if real-time data was available, the representative could have provided a more accurate answer to the customer.
Real-time data allows you to adopt a proactive approach to how you manage your organization’s assets. You can use it to predict, prevent, and identify machinery and components that are failing, service spikes, and other infrastructural issues. By detecting these issues earlier, your teams can address them smoothly and quickly before they snowball into problems of epic proportions.
Use Striim to power your real-time data architecture
In the past, there was some hesitation toward adopting real-time systems unless they were absolutely necessary. That’s because these systems were expensive. Over time, improvements in the price and performance of memory and CPUs have made real-time data processing significantly more affordable for large-scale adoption. Therefore, you should consider using real-time data to improve your processes. One way to do this is to use a real-time data integration platform like Striim. It can:
Integrate your data in real time
Move data between a wide range of systems, including sensors, heterogeneous databases, log files, data warehouses, and message queues
Pre-process and enrich real-time data with reference to streaming analytics
Get a free trial today to learn more about how Striim can bolster your data architecture.