510. Big Data - real-time analytics

real-time analytics

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Essential Guide

From data gathering to competitive strategy: The evolution of big data

·         This article is part of an Essential Guide, our editor-selected collection of our best articles, videos and other content on this topic. Explore more in this guide:

4. - Big data: Some terms to know: Read more in this section

• 3Vs (volume, variety and velocity)
• Big Data as a Service (BDaaS)
• CRM analytics
• data-driven disaster
• NoSQL (Not Only SQL)
• real-time analytics

Read more in this section

·         Explore other sections in this guide:

• 1. - Big data: The promise, and a primer
• 2. - Watch big data evolve before your eyes
• 3. - Big data in traction: The challenges
• 5. - Big data in action: The case studies
• 6. - Big data storage and technologies: A quiz for CIOs
• 7. - Big data on the horizon

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Real-time analytics is the use of, or the capacity to use, all available enterprise data and resources when they are needed. It consists of dynamic analysis and reporting, based on data entered into a system less than one minute before the actual time of use. Real-time analytics is also known as real-time data analytics, real-time data integration, and real-time intelligence.
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.

Applications of real-time analytics
In CRM (customer relations management), 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. Real-time analytics can support instant refreshes to corporate dashboards to reflect business changes throughout the day. In a data warehouse context, real-time analytics supports unpredictable, ad hoc queries against large data sets. Another application is in scientific analysis such as the tracking of a hurricane's path, intensity, and wind field, with the intent of predicting these parameters hours or days in advance. 
The adjective real-time refers to a level of computer responsiveness that a user senses as immediate or nearly immediate, or that enables a computer to keep up with some external process (for example, to present visualizations of Web site activity as it constantly changes).
See also:

1.       real-time business intelligence,

2.      operational business intelligence,

3.      data mining,

4.      business analytics, 

5.      ad-hoc analysis