Taming ‘Big Data’ Complexity Through Automation

Automation is essential to the democratization of data. Discover why.

The impact of big data on an organization will be explosive as the internet of things (IoT) matures. Managing this explosion will not be possible without intelligent data automation.

 

Remove the complexity that comes with big data.

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Overview

Democratization of data accessibility will drive an organization’s decision-making capabilities, and help in streamlining its internal and external processes. It will enable intellectual property to be more accurately discovered, stored and distributed. It will build an organization’s wisdom, enabling it to be far more competitive within its markets.

Automation becomes a necessity–'not a nice to have’–as organizations strive to handle the massive growth in the amount of data they have at their disposal, as big data and the IoT go mainstream.

This report from Quocirca covers:

  • 4 V’s that can help when determining how to gain maximum business benefit from big data
  • 12 steps that can lead to the orchestration of data systems
  • Automation removing the complexity that comes with big data and IoT
  • Two use cases where data automation has enabled the timely delivery of new customer facing services

There is a strong need to ensure that individuals can access data in a manner that empowers them with the information they need to make better decisions. However, no organization can expect all of its employees, partners, suppliers, customers and other stakeholders to become ‘data intelligent’. Data automation is required to enable the complexities of big data to be hidden from the individual, while providing them with the data accessibility and insights they want.

Source: Taken from Taming ‘Big Data’ Complexity Through Automation

A Look Inside

Contents:
  1. Executive Summary: Taming ‘Big Data’ Complexity Through Automation
  2. Big Data, The IoT and The IoE
  3. What Is The ‘Value’ Of Data?
  4. Data, Data, Everywhere…
  5. Orchestration Of Data Systems
  6. Case Studies
  7. Removing Complexity Through Data Automation
  8. Conclusions

 

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