We're storing and using more data than ever before. The volume of data is growing exponentially, government regulations are expanding and competitive pressures are increasing forcing us to retain more of our data for longer periods of time. But our budgets are flat or being cut. And as we become more dependent on digital information, the costs of losing any of it are increasingly painful. The bottom line, of course, is that we need to do a better job of managing our data assets, and as these assets grow and our budgets shrink, we need to do more with less. So we need smarter solutions.
Storage administrators are on the front lines of the Tidal Wave of Data battle. Some of the challenges from data growth that administrators are struggling with include:
- It takes longer to perform backups; often not completing within backup window allowances; some data is not being adequately protectedIBM can help you build a dynamic storage management infrastructure that will enable you to cope with all of these challenges. We have solutions to help reduce your data storage footprint, and the goals that we set out in these solutions are: to reduce your capital and operational costs; to improve your application availability and service levels; and to help you mitigate the risks associated with losing data and a rapidly changing environment.
- It takes longer to perform recoveries; increased downtime equals lost revenue opportunity; data that isnt protected cant be recovered
- Cant keep buying more storage; running out of floor space / electrical & cooling capacity; administration and management costs are exploding
- New data sources are complicating the problems; new applications coming on-line; mergers and acquisitions are increasing the number of supported systems
With these solutions you should: need less storage; have less data to manage; experience less downtime; and be more competitive. To learn more, please visit the Data Reduction Solutions web page and stay tuned for Chapter 2, where we will outline a holistic and comprehensive approach to data reduction.