This content is part of the Essential Guide: From data gathering to competitive strategy: The evolution of big data

real-time analytics

Real-time analytics is the use of, or the capacity to use, data and related resources as soon as the data enters the system. The adjective real-time refers to a level of computer responsiveness that a user senses as immediate or nearly immediate. The term is often associated with streaming data architectures and real-time operational decisions that can be made automatically through robotic process automation (RPA) and policy enforcement.

Real-time analytics software has three basic components -- an aggregator that gathers data event streams (and perhaps batch files) from a variety of data sources, a broker that makes data available for consumption and an analytics engine that analyzes the data, correlates values and blends streams together. The system that receives and sends data streams and executes the application and real-time analytics logic is called the stream processor.

Real-time analytics often takes place the edge of the network to ensure that data analysis is done as close to where the data originated as possible. In addition to edge computing, other technologies that support real-time analytics include:

Processing in memory (PIM) --  a chip architecture in which the processor is integrated into a memory chip to reduce latency. 

In-database analytics -- a technology that allows data processing to be conducted within the database by building analytic logic into the database itself. 

Data warehouse appliances -- combination hardware and software products designed specifically for analytical processing. An appliance allows the purchaser to deploy a high-performance data warehouse right out of the box. 

In-memory analytics -- an approach to querying data when it resides in random access memory (RAM), as opposed to querying data that is stored on physical disks.

Massively parallel programming (MPP) -- the coordinated processing of a program by multiple processors that work on different parts of the program, with each processor using its own operating system and memory.

Use cases for real-time analytics in customer experience management (CX)

In CRM (customer relations management) and customer experience management (CX), real-time analytics can provide up-to-the-minute information about an enterprise's customers and present it so that better and quicker business decisions can be made -- perhaps even within the time span of a customer interaction. 

Here are some examples of how enterprises are tapping into real-time analytics:

1. Fine-tuning features for customer-facing apps -  real-time analytics adds a level of sophistication to software rollouts and supports data-driven decisions for core feature management. 

2. Managing location data -  real-time analytics can be used to determine what data sets are relevant to a particular geographic location and signal the appropriate updates.

3. Detecting anomalies and frauds - real-time analytics can be used to identify statistical outliers caused by security breaches, network outages or machine failures. 

5. Empowering  advertising and marketing campaigns - data gathered from ad inventory, web visits, demographics and customer behavior can be analyzed in real time to uncover insights that hopefully will improve audience targeting, pricing strategies and conversion rates.

This was last updated in April 2019

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