Streaming analytics links to external data sources, making it possible to integrate data into application flow or use processed data to update an external database. Stream processing, which is the next stage in CEP (Complex Event Processing) development, conducts analyses and carries out actions on real-time data by means of consistent queries. Based on the analysis of a series of recent events, streaming analytics technologies, as well as CEP, enable a set of actions.
Critical to Stream Processing
Streaming analytics are critical to stream processing. They move within the data stream, calculating statistical analytics constantly. They allow monitoring, management, and analysis of live streaming data in real time.
With streaming analytics, a business always knows what is happening, but this doesn’t relieve it of responsibility. They must act on the data swiftly before it loses value.
Data can come from many sources: sensors, market data, mobile devices and phones, or the Internet of Things (IoT).
Data that loses value incurs extra administrative and operational costs. It poses business risks, causes productivity to drop, makes a company less competitive, and may even bring about legal action.
Tapping into Data
What streaming analytics does is tap into a wealth of data, aggregate it, and merge it with the location data of the client in real time. For example, they can follow a car’s every move and establish which vehicles are closest to that car.
Indispensable Insight into Customer Behavior
The benefits of streaming analytics go beyond automotives. You gain indispensable insight into what clients are buying or not buying, their likes and dislikes, and their preferences. This way, you not only keep existing customers but also generate extra profit. By cross-selling and up-selling services and goods, companies can respond to customer needs and grow their income on a consistent basis.
Thanks to the capacity to visualize critical company data, businesses are able to manage their KPIs daily. They view KPI data in real-time. This data can reduce expenses, improve sales, identify errors, and offer reliable information, enabling the company to assess and cope with risk more quickly. Streaming analytics gives access to reporting and business metrics and accelerates the decision-making process.
Companies can develop white papers, identify trends and benchmarks, use cases, and create forecasts of their sector or even industry. This way, internal and external threats are reduced. Companies become more aware of industry changes, which helps them stay competitive, become more innovative, and augment their brand.
Solutions like Imply.io are used to collect activity streams, view streams, clickstreams, and other user-generated data. Users interact with digital devices to produce this data. Imply is used to troubleshoot anomalies in usage patterns and measure user engagement among other applications. Druid, its core engine, is able to compute user metrics to an exact as well as to an approximate extent.
Imply can compute metrics like the number of active daily users in less than a second and with 99% accuracy. The solution can be used to measure how many users took a certain action rather than another as well as for funnel analysis. This can be helpful in tracking the number of users who sign up for a product.
A Move to Proactive Processing in Real-Time
Streaming analytics presents a move from “reactive” to “proactive” real-time processing. Traditionally, an existing set of actions such as a payment, a purchase, or system failure trigger real-time decisions. Real-time response is then determined by a preset instruction. On the other hand, streaming analytics constantly analyzes data in motion prior to its storage. This includes actions like normalization, scoring, and manipulation. The process is focused on changes in or detection of patterns.
Streaming analytics is of paramount importance in areas like cybersecurity and fraud, where people normally have no concrete idea of how someone’s going to attack. What they can do is start looking for different types of behavior and use these pattern changes to draw the relevant conclusions.
Failsafe Risk Assessment
With streaming analytics, companies can grasp risks before these manifest because they can analyze data as soon as it becomes available. They can identify new revenue streams and business opportunities, resulting in better customer service, new customers, and profit increase. Streaming analytics solutions are so powerful that tens of millions of events can be processed per second. Since data is processed before entering a database, streaming analytics technology supports the rapidest decision-making possible. It offers security protection by giving companies an option to connect different events quickly, detect security threat patterns, and perform network and physical asset security monitoring.
Streaming analytics is the go-to solution where data storage has become impractical. This can be the case for a variety of reasons, for example, if massive data storage is suboptimal or impossible. Analytics standardizes incoming data and determines whether it is relevant. If it isn’t, the data is eliminated before it wastes bandwidth and other valuable resources.