Data at Rest versus Data in Use
If your environment is experiencing rapid data growth you have a unique set of challenges and conflicting business and technical objectives to try and balance - how to deliver more data while reducing budgets and maintaining performance. What if you could though dramatically scale your data, while improving performance and cutting your expenditures for data storage - keeping data expense from impacting budgets for higher value application function delivery?
Doing all three of these apparently conflicting objectives at the same time can be hard if you treat all data as if it has the same value or if you rely on traditional storage price/performance improvements to help address the data deluge. However, if you understand how your data is being used and are open to the use of new technologies it is possible to achieve all three objectives.
IBM has developed an offering to help clients gain insight into this business challenge - it is called Data Pattern Analytics. The IBM Data Pattern Analytics process starts with a tool enabled collection of data access information from your IBM and non-IBM arrays. Once this is analyzed a second step examines the business impact provided by storage devices optimized for active and in-active data.
Data Pattern Analytics may seem redundant if you are already using automated tiering systems. For example, if you are already moving active data onto a higher tier of storage within your array and less frequently used information to slower disk then why take the time to examine data access patterns?
Data Pattern Analytics is motivated by a different goal - the desire to determine if you can make a step improvement in the cost of storage to enable you to affordably support rapid data growth while delivering improved performance. Auto-tiering systems are valuable at solving a related, but different problem - a need to deliver improved application performance within the same or incrementally better budget.
If you are facing high data growth and are interested in learning more about how the IBM Data Pattern Analytics approach can help your business please contact your IBM representative or me.
