Streaming analytics is also known as event stream processing. It is the analysis of huge real-time data through the use of continuous queries, called event streams. An action or a set of actions like a financial transaction, equipment failure, a social post, or a website click, or some other measurable activity directly trigger the streams. The data can be originated through various sources like the Internet of Things (IoT), transactions, cloud applications, web interactions, mobile devices, and machine sensors.
IoT based streaming analytics helps a wide range of industries extracting value from in-motion data and spotting opportunities and risks.
Streaming Analytics allows management, monitoring, and real-time analytics of live streaming data, for example, streams of GPS data from cars calculate each move of the car and driver based on any chosen criteria.
Companies manage their key performance indicators on a daily basis through the visualization of the company’s crucial information. This data is viewed in real-time. Hence, companies can have a granular view at any given time. This data helps improving sales, reducing costs, and identifying errors. Companies can react faster to mitigate risks with the help of provided information. In short, streaming analytics accelerates decision-making, and gives a clear picture of business metrics.
To accelerate business, it’s very important to know customer preferences. Streaming analytics helps companies to visualize what customers are buying or not buying. This data enables companies to generate more profit and retain existing customers. They can rapidly respond to their customers’ needs and increase revenues.
Once a business understands the current trends and benchmarks, it can develop white papers, use cases, and forecast the company and industry. It spread awareness of industry change and reduces internal and external threats. In this way, a company can become a competitive, innovative, and strong brand.
The main difference between streaming analytics and traditional analytics is how data is analyzed in both. In traditional analytics data is first stored and then analyzed for deriving insights. On the contrary, streaming analytics follows analyze-first-then-store-paradigm. In IoT based streaming analytics, data is first analyzed during events and then only relevant data gets stored for batch analysis. It scales the constant flow of information and helps to deliver continuous insights to users across the organization.
Here are a few examples of use cases of real-time analytics performed on streaming data.
Manufacturers embed intelligent sensors in all devices from the production line to the supply chain. They can analyze the data through sensors in real-time. It allows them to spot problems and rectify them before their products leave the production line. It not only improves productivity, and efficiency of operations but also saves money.
Streaming analytics is very helpful in cybersecurity. It can identify suspicious behavior and activities and flag them for investigation. So, the attack can be stopped at the initial stage before it does any damage.
A hotel can use streaming analytics to monitor reservations in real-time. They can text or email special promotions and discounts to their guests constantly.