Real-Time Business Analytics

real-time business analytics

Real-Time Business Analytics – The Streaming Kind is Needed for IoT Scenarios

It is important to be clear about what is meant by real time business analytics.  Real time business analytics refers to the dynamic reporting and analysis of data as activity and events occur.  Some systems analysts specifically restrict the definition by stipulating that the data used in real time analytics cannot be more than a minute old.

According to Wikipedia, “Real time means near to zero latency and access to information whenever it is required.”
 

The Challenge: How do I make sense of what is happening Right Now?

Intuitively, analyzing what is happening in real-time is inherently difficult.  And in IoT, with many millions of events often streaming into a system in real-time, this general challenge becomes all the more acute.
 

Real-Time vs. Streaming Analytics

It is critical to make a clear distinction between “real time business analytics” vs. “real-time streaming analytics.”

Typically when some refers to ‘real-time business analytics’ they are referring specifically to a system where responses are guaranteed within some tightly defined time frame or deadline. Stock market systems are the classic example of this type of system.  So, a real-time business analytics system might be doing statistical or other kinds of analytical calculations on various kind of data with a guaranteed response of a calculation or result by a certain time deadline.

Streaming Analytics specifically is different.  Stream processing refers to continuous computation methods that happen as data is flowing through a system. Unlike “real-time analytics,” there are not specific time limits or tolerances in stream processing.  The output of a stream processing system is not on a fixed deadline after input is received.  Data is processed as it comes in, and there will be times when data may wait.  The only two constraints are:

  • the long run output will have to be faster than input for general system resource equilibrium
  • The system must have enough memory to store queued data inputs while processing the stream.

In IoT Analytics real-time streaming analytics plays a central role in the overall process.  As explained in our video: IoT: Challenges and Opportunities, IoT Analytics requires action in real-time to capture value or the value disappears.  But knowing the right action in a particular specific time frame requires two critical steps:

  • Knowing what is happening in real-time
  • Putting it in the larger context of historical data about the business process in question and models that predict might happen next so that decisions and actions can be executed.

 

Real-time streaming analytics within the Vitria IoT Analytics platform performs this critical function. 

Data flowing into the overall system is ingested and analyzed in real-time.  The system compares multiple streams and/or compares streams with historical values and predictions. It detects anomalies and puts those anomalies in context for processing by the predictive and prescriptive analytics capabilities of the Vitria IoT Analytics platform.

Combining the real-time power of streaming analytics with historical and predictive capabilities enables organizations to make decisions and take actions to capture business value for demanding IoT use cases.
 

Learn more

Learn more about how IoT Analytics are: