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Monetizing Core Big Data Technology: Real-time Analytics

Monetizing Core Big Data Technology: Real-time Analytics

An incredible amount of information is bearing down on companies today: streaming audio and video data from mobile and the Web; sensor and machine-to-machine data that drives the Internet of Things (IoT); text documents, email, and HTML files; stock ticker data and financial transactions; data created in line-of-business applications; and more. Stratecast has identified other sources of data for enterprises, plus another if an enterprise is also a communications service provider (CSP). Planet-wide, humans and machines are combining to create quintillion bytes ( trillion gigabytes) of data daily. Many U.S. companies have at least terabytes of data stored. The New York Stock Exchange captures approximately terabyte of trade information during each trading session: every weekday of the year, excluding holidays. There are now approximately six billion cellphones on planet earth against a total population of billion. Each phone is creating data all day long, from the moment it is turned on to the moment it is turned off; and for a great many users who leave their phones on and charging overnight, data creation continues 24/7. Cars rolling off the assembly line today have up to sensors that generate data by monitoring fuel level, tire pressure, and the performance of all systems on the vehicle. Vehicle-to-everything (V2X) systems will soon have vehicles “talking” with each other, traffic signals, weather services, other information sources, and the highway itself, in the interest of municipal planning and highway safety.

Organizations are increasingly finding that simply getting their arms around all of this data is not enough. They need actionable insights, faster than ever before, to stay competitive, reduce risks, meet customer expectations, and capitalize on time-sensitive opportunities. To cite one example among many: in 2014, e-commerce commanded $ billion, or about % of total global retail sales, and it is growing fast: worldwide e-commerce is expected to grow at a rate of - % through 2018.4 E-commerce demands real-time responses: users want to be able to check an order or see if something is in stock in real time, to have the system react to what they just did, and to provide information based on who and where they are. To achieve this level of responsiveness, and to act before market windows close, a company must, within - milliseconds, become aware of an event; make a decision; and push an offer or other appropriate response out to a customer’s connected device. So, another definition of real time may well be: to connect with and respond to a customer in milliseconds, before the company loses his or her attention.

None of this is to suggest that either companies or individuals are victims of a data world gone mad. Google and Amazon changed user expectations forever by creating databases that ultimately gave users instant access to a planet’s worth of information, goods, and services. As a result of their efforts—and the efforts of every vendor who has stood on their pioneering shoulders in subsequent years—the business world, and the world at large, has become conditioned to expect sub-second results; not only from Google search and the Amazon e-commerce portal, but from any system. Because end users have come to expect short load times, personalization, and updates in real time, organizations are looking to replace legacy architectures with a real-time data pipeline to capture, process, analyze, and serve massive amounts of data to potentially millions of users.


  • Executive Summary
  • Introduction2
  • The Case for Real-time Analytics
    • Banking & Financial Services
    • Healthcare
    • Mobile Apps and Gaming
    • Travel and Hospitality
  • A Balanced View of Data Speeds and Strategic Value
    • Real-time Analytics Is Not Essential for Every Need-and Not Always Possible
    • Speed is Important, but Strategic Value is Imperative
  • Real-time Analytics: How It's Made
    • Apache Software Foundation (ASF): Open Source Real-time Analytics Accelerator
    • Key ASF Innovations Accelerating the Growth of Real-time Analytics
      • Micro-batching: Apache Storm Trident and Apache Spark Streaming
      • Stream processing engine that can handle batch processing: Apache Flink
      • Unified stream and batch processing: Apache Apex
      • In-memory data fabric for real-time insights from massive datasets: Apache Ignite
      • Ability to run a large enterprise on a single data cluster: Apache Kafka
  • Real-time Analytics: Organizational Challenges
  • Real-time Analytics is Earning Mixed Reviews in the Market
  • Case Study Snapshots of Real-time Analytics in Action
  • Providers of Real-time Analytics Solutions
  • The Last Word

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