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In this data-driven age, businesses want fast, accurate insights from their huge data repositories in the shortest time span — and in real time when possible. These insights are essential — they help businesses understand relevant trends, improve their existing processes, enhance customer satisfaction, improve their bottom line, and most importantly, build, and sustain their competitive advantage in the market.  

Doing all of this is quite an ask – one that is becoming increasingly difficult to achieve using just the traditional data processing systems where analytics is limited to the back-end. There is now a burning need for a newer kind of system where larger, more complex data can be processed and analyzed on the go.

Enter: Streaming Analytics

Streaming Analytics, also referred to as real-time event processing, is the processing and analysis of large streams of data in real-time. These streams are basically events that occur as a result of some action. Actions like a transaction or a system failure, or a trigger that changes the state of a system at any point in time. Even something as minor or granular as a click would then constitute as an event, depending upon the context.

Consider this scenario – You are the CTO of an organization that deals with sensor data from wearables. Your organization would have to deal with terabytes of data coming in on a daily basis, from thousands of sensors. One of your biggest challenges as a CTO would be to implement a system that processes and analyzes the data from these sensors as it enters the system. Here’s where streaming analytics can help you by giving you the ability to derive insights from your data on the go.

According to IBM, a streaming system demonstrates the following qualities:

  • It can handle large volumes of data
  • It can handle a variety of data and analyze it efficiently — be it structured or unstructured, and identifies relevant patterns accordingly
  • It can process every event as it occurs unlike traditional analytics systems that rely on batch processing

Why is Streaming Analytics important?

The humongous volume of data that companies have to deal with today is almost unimaginable. Add to that the varied nature of data that these companies must handle, and the urgency with which value needs to be extracted from this data – it all makes for a pretty tricky proposition. In such scenarios, choosing a solution that integrates seamlessly with different data sources, is fine-tuned for performance, is fast, reliable, and most importantly one that is flexible to changes in technology, is critical. Streaming analytics offers all these features – thereby empowering organizations to gain that significant edge over their competition.

Another significant argument in favour of streaming analytics is the speed at which one can derive insights from the data. Data in a real-time streaming system is processed and analyzed before it registers in a database. This is in stark contrast to analytics on traditional systems where information is gathered, stored, and then the analytics is performed. Thus, streaming analytics supports much faster decision-making than the traditional data analytics systems.

Is Streaming Analytics right for my business?

Not all organizations need streaming analytics, especially those that deal with static data or data that hardly change over longer intervals of time, or those that do not require real-time insights for decision-making.  

For instance, consider the HR unit of a call centre. It is sufficient and efficient to use a traditional analytics solution to analyze thousands of past employee records rather than run it through a streaming analytics system. On the other hand, the same call centre can find real value in implementing streaming analytics to something like a real-time customer log monitoring system. A system where customer interactions and context-sensitive information are processed on the go. This can help the organization find opportunities to provide unique customer experiences, improve their customer satisfaction score, alongside a whole host of other benefits.

Streaming Analytics is slowly finding adoption in a variety of domains, where companies are looking to get that crucial competitive advantage – sensor data analytics, mobile analytics, business activity monitoring being some of them. With the rise of Internet of Things, data from the IoT devices is also increasing exponentially. Streaming analytics is the way to go here as well.

In short, streaming analytics is ideal for businesses dealing with time-critical missions and those working with continuous streams of incoming data, where decision-making has to be instantaneous. Companies that obsess about real-time monitoring of their businesses will also find streaming analytics useful – just integrate your dashboards with your streaming analytics platform!

What next?

It is safe to say that with time, the amount of information businesses will manage is going to rise exponentially, and so will the nature of this information. As a result, it will get increasingly difficult to process volumes of unstructured data and gain insights from them using just the traditional analytics systems. Adopting streaming analytics into the business workflow will therefore become a necessity for many businesses.

Apache Flink, Spark Streaming, Microsoft’s Azure Stream Analytics, SQLstream Blaze, Oracle Stream Analytics and SAS Event Processing are all good places to begin your journey through the fleeting world of streaming analytics.

You can browse through this list of learning resources from Packt to know more.

Data Science Enthusiast. A massive science fiction and Manchester United fan. Loves to read, write and listen to music.


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