Why the need for event-driven analysis?

Why the need for event-driven analysis?

Data saturation is everywhere. We want to collect more data because we want better information from them. However, the rapid rise in our ability to collect data hasn’t been matched by our ability to get meaningful insights from the data. As an example, the amount of data I monitor off my daily routine – my heart rate from the sensor, the hours I sleep, the number of steps I walk, and the number of minutes I exercise – has not given me the meaningful real-time information to change and respond to do more exercise. Why can’t I decide to change based on data?

We’re living in a Big Data era where everything is tracked, recorded, monitored, and analyzed. Businesses can get drowned with information overload and lost in decision making. The struggle is to find the right balance between having the right amount of data at the right time and the insight that accelerates the way leaders respond to decision making and customers’ responses to making an immediate purchase.

Adopt Event-Driven Data Analytics

The goal of the business is to move faster than the competitor. They can collect all the data, but the most meaningful analysis is to respond to events that are for real-time decision making.   But what is an event that needs real-time analysis?

An event is data moving in streams that are critical for responding faster to customer needs and reduce time to solving problems.  An event can be a customer selecting a product or service to pay, a higher than average customer negative sentiment that business wants to address fast, a misinformation that business intends to detect before it impacts its stock value, or just right down responding to a customer click. The mainstream technology companies are using complex event processing (real-time processing of data and extracting information from multiple data streams as they arrive) to predict customer consumption behavior and recommend offerings to motivate a real-time decision. For example, mobile games companies monitor and adjust the difficulty of the game players played. They promote upgrades to entice players to continue pay-to-play and not too difficult to make players give up the game.  

Successful event response is not always about having all the data but sorting, filtering and identifying only the ones that matter in real-time.  An industry like manufacturing is monitoring temperature, pressure, utilization, and vibration metrics across thousands of their equipment to look for patterns of potential equipment failure.  If the operator has a real-time event processing engine to filter the critical sensor data and predict asset failure far enough in advance to prevent it, the business will reduce the unplanned operational cost, stay within budget, and meet the demands of the customer.

Apache Flink – The mainstream complex event processing engine

Complex event processing is for detecting patterns in streaming data, calculating important events, sending alerts or notifications that the business or customers care about, and it’s all done in real-time.  Apache Flink is a highly performant distributed stream processing engine and a scalable data analytics framework that can process millions of data points or complex events very easily and deliver predictive insights in real-time. Industries like oil & gas, manufacturing, telecoms, and others with millions of data sources collect extreme data velocity to detect certain events that the business cares for decision making. Apache Flink makes all of this possible with its data distribution for large scale stateful computations over data streams.    

What are some use cases?

 Apache Flink is gaining adoption in various verticals, and here are some examples:

  • Telco network monitoring – handling customer complaints with a pre-calculated response about the outage and an ETA for a fix, based on streaming data from the network using sophisticated windowing logic
  • Fraud detection – Financial organizations detecting fraud patterns from millions of real-time financial data streaming from various sources
  • Better customer experience – modernize data application by incorporating streaming capabilities to millions of customer interactions without failure with better customer experience. 

The future can be transformed with better insights into action by adopting stream processing engines like Apache Flink. And maybe one day, a new exercising app has a way to read all my workout data, predict my health decline weeks in advance, so I will respond and change my daily routine today.

To learn more about Apache Flink, join our first Flink PowerChat webinar series, starting with “An introduction to Apache Flink” on Tuesday, April 14, led by Dinesh Chandrasekhar, Director of Product Marketing and Simon Elliston Ball, Senior Director of Product Management at Cloudera. They will provide an introduction to Apache Flink and explain how it handles a high volume of data while addressing low latency challenges. They will discuss how Flink fits into your end-to-end streaming architecture and the more practical use cases when it works the best.

Also join the Flink Forward virtual conference from April 22-24 with new and experienced users and thought leaders of the global Flink community to share experiences and best practices.  Our keynote speakers, Marton Balassi, Engineering Manager, and Joe Witt, VP of Engineering, will present Cloudera’s approach to an end-to-end streaming data platform.

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