Optimise your storage for Big Data analytics

For most organisations, analytics is not the priority roadblock when it comes to extracting value from Big Data.  Bob Hankins explains why dealing with storage issues is the first job in most cases.

According to IDC, data production will be 44 times greater in 2020 than it was in 2009 - if it is not already, the ability to organise, interrogate and learn from huge quantities of data will soon be fundamental to competitiveness.

That is, the organisations best adapted to this ‘Big Data’ future will also be the ones best equipped to succeed in an ever more complex trading environment.  They will have the ability to cut through the ‘noise’ of data, to make informed decisions quickly – and this will perhaps be the number one business imperative.

All of which begs the question: Is your organisation capable of turning data into valuable decision-making information?

For many organisations that is still not even a question about analytics.  It remains a question about storage – because the state of storage infrastructure, whether it be siloed or just old, is the main barrier to Big Data analytics for most.

Indeed, before even reaching the stage where you’re ready to optimise your storage for Big Data analytics, there are five initial steps to consider:

  1. Escape from Component-Based Architecture: You know you’re stuck in the components stage if you’re purchasing equipment in a reactive mode without aligning those purchases to a business plan or outlining how they fit into a CapEx vs. OpEx financial model.
  2. Implement storage services: If you can’t define your future storage needs, you’re heading for trouble. Additionally, if your disaster recovery relies on tape backups, and if your team puts too much effort into managing multiple storage platforms, you may be in need of enhanced storage services.
  3. Take control of your files: The chances are your servers are bulging with older files, but users have no time or incentive to clean them out. Take control by establishing retention rules along with tools to archive older data. By implementing a chargeback plan, you encourage staff to remove old files and free up existing storage space.
  4. Make it easier to find the information you need: Unexamined, un-indexed, unstructured data can be both a liability and a lost opportunity. How can you possibly know which files are useful or potentially damaging to the organisation?
  5. Integrate systems: When different kinds of data are siloed, confusion reigns. If you realise the validity of your data is questionable and there’s no process for updating systems periodically, reporting and visualisation across systems becomes tough, if not impossible.

Not sure what shape your storage is in? Take this brief quiz to find out -  www.logicalisdatastorage.com

Tags storage, Business Intelligence, business intelligence, Storage and Backup, Big Data, Business Analytics