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The data growth explosion forced companies to change the way they access mission-critical information, deploy applications, and approach data protection in general. So now might be the perfect time to revisit your backup processes and recovery strategies. Big data is steadily shaping the IT environment and with all it has to offer, it might prove added value to disaster recovery field and business continuity planning. The secret lies in keeping to the basic fundamentals of BDR, while following the core principles of big data analytics.
An Answer in Big Data Analytics
IT service providers offer solutions to IT problems. In order to deliver effective solutions, they need to make decisions with relevant information. When diagnosing a hard drive, an engineer will look out for clicking sounds from the hardware, system crashes, performance issues and other symptoms that point to eventual failure. Combined with system log data, this information helps IT managers properly diagnose the drive and determine how to troubleshoot.
The right data allows you to eliminate the guesswork by tapping into a combination of hindsight, insight, and foresight. This enhanced visibility can lead to dramatic improvements across BDR options. For example:
Hindsight aims to help IT understand what’s happening now based on what happened in the past. By combing over report logs, admins can identify which backup processes failed during the previous week.
Insight is gained by studying patterns and relationships in data. An inquisitive IT admin may determine that the ailing hard drive triggered those failed backups.
Foresight helps analysts better anticipate or predict future scenarios. By identifying when failures occurred and why, companies can create a more effective backup schedule, with better results.
Analytics comes in many forms, but the predictive analytics is especially ideal for backup and disaster recovery projects. As the name suggests, it uses historical data to predict future outcomes. Having detailed information on documented recovery processes, organizations can avoid issues that hindered recovery in the past and restore their systems much faster next time. Predictive analytics offers critical foresight that allows businesses to be proactive in leveraging their data to make better decisions.
Merging Big Data and BDR
A backup and disaster recovery strategy should be as flexible and agile as the IT environment in which it is implemented. By adopting a data-driven mindset, organizations can minimize the cost of backup operations, simplify day-to-day management processes, and continuously meet business objectives. But adopting this sort of approach is no walk in the park. Below we have outlined four key factors IT leaders should consider when looking to marry big data analytics and BDR.
1. Big Data Technology
Many backup apps have reporting tools that offer info on the status of backup jobs, event logs, and failed processes. These built-in features are helpful, but you may fancy something a little more data-intensive. The big data analytics market is loaded with solutions that help organizations maximize their data from a fully integrated, centralized view. Whether it’s support for predictive analytics or playing nice with existing backup software, IT managers should weigh their options carefully. The analysis will determine which big data tech best suits their current needs.
2. IT Environment
Once upon a time, backup and disaster recovery operations were handled entirely on-premise. Thanks to the cloud, a new option has emerged. A TechTarget survey found that 44 percent of respondents invest in the cloud for disaster recovery, while another 63 percent use it for at least a portion of their backup data. By eliminating geographic boundaries, cloud computing provides convenient access to resources that firms lack in-house. And with public, private, and hybrid solutions available, those resources can be delivered in a way that fully supports any IT environment.
In addition to capacity, the cloud arms users with powerful business intelligence features such as analytics, which can be applied at virtually every level of the cloud (e.g. SaaS, IaaS, PaaS). The enterprise benefits from sophisticated data management tools that help their analysts gain a better understanding of backup, recovery, replication, testing, and other critical BDR functions. DRaaS providers benefit from the ability to optimize capacity, resource allocation, performance and costs. What we have here is the classic win-win scenario.
3. Good Data
Big data analytics can be important, but not all data is good data. You must identify which bits of data are truly essential to the project. Metrics such as the total number of applications covered by your BDR plan and established recovery objectives are easy to spot. The importance of something like the time needed to reboot mission-critical systems following an outage may not be so obvious. Knowing exactly what data matters in analytics can save precious time and make sure business continuity demands are met.
On the surface, integrating big data analytics into your disaster recovery strategy may seem like a complex undertaking that requires a complete overhaul. It doesn’t. In fact, the best thing you can do is continue to honor the basic fundamentals of BDR while incorporating the core principles of big data into the mix. Sure, it’s a big move, but not one that calls for you to abandon everything you’ve learned about protecting your data and minimizing the impact of a disaster.
We’ve seen disaster recovery technologies and methods come a long way over the past decade. The cloud has been a game changer while the rise of analytics offers insights previously buried from plainview. I’m not trying to convince you to hop aboard the cloud or big data train. Only to realize that your data could be the key to building resilient IT systems and optimizing BDR functions in ways once thought unimaginable.