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Wrangling big data: Fundamentals of data lifecycle management

Everywhere you turn in enterprise IT, you hear the buzz about big data: billboards, commercials and likely your own conference rooms and inboxes. The variety, volume and velocity of data streaming through enterprise systems is on the rise, and so is the amount of discussion about the best way to handle it.

But while the big data phenomenon is giving organisations a broad range of new options for data analysis, it also compounds the business challenges associated with collecting, managing, organising and protecting data. Successfully leveraging big data for analytics demands that companies develop strategies to reduce the cost of managing data and reduce the risk involved in organising and protecting that data.

In addition to these business challenges, enterprises face technical challenges in managing rapidly expanding volumes of all types of data. The sheer number of continually proliferating data sources introduces complexity into data lifecycle management processes. If IT departments cannot manage information appropriately — from the moment it is created to the point when it can be archived or defensibly disposed — they risk violating legal and regulatory compliance requirements.

To efficiently manage data throughout its entire lifecycle, IT leaders must keep three objectives in mind:

  • Data veracity is critical for both analytics and regulatory compliance.
  • Both structured and unstructured data must be managed effectively.
  • Data privacy and security must be protected at all times.

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