Companies are capturing and digitizing more information than ever before. According to IDC, the world produced one zettabyte (1,000,000,000,000 gigabytes) of data annually. Fueling this data explosion are over five billion mobile phones, 30 billion pieces of content shared on Facebook per month, 20 billion Internet searches per month, and millions of networked sensors connected to mobile phones, energy meters, automobiles, shipping containers, retail packaging and more. Big Data is a platform for transforming all of this data into actionable items for business decision making.

The barriers to entry for Big Data analytics are rapidly shrinking. Big Data cloud services like Amazon Elastic MapReduce and Microsoft’s Hadoop distribution for Windows Azure allow companies to spin up Big Data projects without upfront infrastructure costs and allow them to respond quickly to scale-out requirements. Commercial vendor support from companies like Cloudera can speed development and deliver more value from Big Data projects. Bundled server options such as Oracle’s Big Data Appliance offer fast setup and scale-out solutions. Finally, modular data center designs are emerging as a way to efficiently manage hardware and scale-out rapidly and cost-effectively.

Companies likely to get the most out of Big Data analytics include:

  • Supply chain, logistics, and manufacturing — With RFID sensors, handheld scanners, and on-board GPS vehicle and shipment tracking, logistics and manufacturing operations produce vast quantities of information offering significant insight into route optimization, cost savings and operational efficiency
  • Online services and web analytics — Internet companies invented Big Data specifically to handle processing information at Internet scale. Implementation of these analytical platforms is now viable for smaller online services companies to provide an edge over competitors for advertising, customer intelligence, capacity planning and more. Companies who don’t offer online services but do have an ecommerce or other online presence will benefit greatly from understanding customer behavior and buying patterns via clickstream, cohort analysis and other advanced analytics.
  • Financial services — Financial markets generate immense quantities of stock market and banking transaction data that can help companies maximize trading opportunities or identify potentially fraudulent charges, among various other uses. New regulations also require detailed financial records to be maintained for longer periods.
  • Energy and utilities — Smart instrumentation such as “smart grids” and electronic sensors attached to machinery, oil pipelines and equipment generate streams of incoming data that must be stored and analyzed quickly to uncover and fix potential problems before they result in costly or even disastrous failures.
  • Media and telecommunications — Streaming media, smartphones, tablets, browsing behavior and text messages are captured at ever-increasing rates all over the world, representing a potential treasure trove of knowledge about user behavior and tastes.
  • Health care and life sciences — Electronic medical records systems are some of the most data-intensive systems in the world and making sense of all this data to provide patient treatment options and analyze data for clinical studies can have a dramatic effect for both individual patients and public health management and policy.
  • Retail and consumer products — Retailers can analyze vast quantities of sales transaction data to unearth patterns in user behavior and monitor brand awareness and sentiment with social networking data.

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Tags: Big Data, Data Science, Enterprise


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