Big Data Streaming: An Evolutionary Leap in Advanced Analytics

    

There’s a common misconception that big data analysis is driven purely by the innovation of new data mining and machine learning algorithms. And although these are critical components of big data, they’re just one piece of big data analysis.

The new paradigm has moved from reactive data points to near real-time analyses. This is increasingly necessary as customer demands and market shifts happen without warning.

The next evolution of the big data phenomon has turned out to be the real time streaming of data, or big data streaming. Defined as the rapid processing of big data to extract insights, in this post we’ll discuss the ways in which big data streaming improves efficiency and helps an array of companies, possibly including yours.

Understanding Big Data Streaming

The traditional method for big data analysis is a two step model wherein the necessary data is collected first and the analysis is performed on the data second.

Big data streaming has improved upon this traditional process, delivering results on demand. Streaming analytics is ‘event-driven processing.’  Every incoming message or ‘event’ is deposited in a message queue or stream that classifies it and then runs it through a workflow that acts as a rules or decision engine to achieve a desired outcome. 

Big data streaming allows for simultaneous data collection and analysis. But it’s important to consider the big data streaming concept, single-pass analysis, wherein once the analysis comes through, it can’t be re-analyzed because big data streaming has already moved on to new data.

This isn’t necessarily a bad thing and below are some of its advantages.

Insights can improve operating efficiency

There are a few ways enterprises can improve operating efficiency, given how big data streaming works. New insights empower decision-makers to improve internal processes and reduce costs, as is true with all big data analyses. 

Yet, big data streaming also improves upon the data analysis process itself. Enterprises don’t have to wait for data collection and analysis. With the traditional method, by the time the analysis is done, the data itself is and decisions made from this data are reactionary at best.

Let’s consider an automobile parts factory that relies mostly on robots on a set maintenance schedule. It’s inevitable that some robots will go down between their scheduled reviews.

To remedy this, the factory can install sensors that feed data into a big data streaming solution and through real-time analysis, workers can identify which robots need maintenance, regardless of when they’re schedule for review.

The same paradigm can be applied to public-facing data points

Big data streaming isn’t limited to improving internal processes and this is important considering customer preferences and behaviors change constantly. 

Public safety is increasingly reliant on watching natural disasters and the spread of disease closely. In this vein, big data streaming can benefit everyone. 

Let’s consider a few use cases for big data streaming.

  • Fraud detection: Identifying suspicious activity is paramount in the financial services industry and big data streaming enables machine learning algorithms to monitor payment activity constantly and flag unusual behavior in real-time. 
  • Brand awareness and reputation: What people are saying about a brand likely effects its reputation. Big data streaming monitors social media activity to identify trends in what’s being said. This can be particularly useful following an event intended to elicit customer opinion.
  • Disaster monitoring: IoT sensors can be placed in areas prone to natural disasters, such as flooding. Big data streaming monitors these sensors along with other data points (e.g. social media posts) for anomalous activity to alert citizens in the area as necessary.

An evolutionary leap in advanced analytics

With good reason, big data streaming is the next evolution of the big data phenomon. As enterprises press onward with advanced analytics, big data analysis gives decision-makers valuable insights that would have otherwise remained hidden. On-demand access to these insights only makes sense.

As data continues growing exponentially, big data streaming helps enterprises maintain a competitive advantage by adjusting to shifts in customer demand in real time, thereby increasing customer satisfaction and likely loyalty.

See how Antuit is helping leading companies build platforms that bring the speed, power and flexibility required to analyze data at scale.

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