Big data has required massive changes to all aspects of business data handling. In fact, the term big data was coined to describe datasets too large to be managed through traditional applications. While this has caused businesses across the board to adopt new technologies in data gathering and analytics, many companies have failed to adopt new practices in planning for backups and disaster recovery.
For these businesses, dealing with backup planning over large datasets is simply about scaling: invest in more storage to handle more data. There is, however, a better way to handle things.
How big is the big data backup problem?
Every day, quintillions of bytes of data are produced – each quintillion being one billion gigabytes. While much of that data belongs to data giants like Google and Amazon, data generation is growing at an astonishing pace across all sectors. Some experts estimate that 90% of all recorded data in the world was generated within the past two years.
And an overwhelming majority of stored data in corporate contexts may be redundant, obsolete, or trivial, according to industry analysts. This data is not critical or even necessary to a company’s operation, but may still be backed up as part of a disaster recovery plan.
Why not back up everything?
Anyone who’s tried to copy, upload, or download large batches of files knows that data transfer takes a lot of time. In the event of a disaster, a company’s entire information infrastructure might need to be restored from backup. The sheer volume of data that would need to be transferred could delay productive operations for days, if not longer.
Even when no disaster has taken place, data backup plans have ongoing costs in the form of storage and administration. This overhead deters many companies from backing up their data at all, a move that could spell disaster in the event of data loss as well as deprive them of the opportunity to study their own data use and statistics.
What’s the solution?
Many businesses employ sophisticated analytics in order to make sense of consumer data, market trends, social media impact, and other factors. Analytics can also be applied to the disaster recovery field, in the form of evaluating the impact of a business’s generated data. In the even of a disaster, different businesses will have different profiles of critical data: one may be unable to function without emails and call records, whereas another may require an inventory management system to be up and running as soon as possible.
In short, businesses need to design analytic processes that not only determine which files are relevant and important, but also determine recovery order in the event of a disaster. When these changes are implemented into backup and disaster recovery plans, the result is a low-overhead, high-value safety net. To learn how to make your company’s backup and disaster recovery more efficient, contact us today.