Optimizing your disaster recovery strategy for your business-critical analytics is crucial. As analytics become more critical to business processes, whether it’s big data or “small” data, your disaster recovery strategy needs to keep up.

Analytics criticality is often not clearly defined, as it evolves from simply collecting data to generating insights that drive business decisions. As analytics and its source data continues to grow, it’s also too costly to simply apply the same backup and disaster recovery strategy to all of your data.

Business users who depend on analytics are not necessarily thinking about disaster recovery. IT needs to be proactive to understand when analytics evolve from “nice-to-have” to critical, before a disaster occurs. Not all source data and/or generated analytics requires the same disaster recovery strategy (e.g. depending on to what extent historical data is used to drive analytics, and how long it takes to regenerate analytics).

This blog post discusses how and why you should optimize your disaster recovery strategy for your business-critical analytics, including Big Data. If you are using analytics to drive business decisions and are currently reviewing your disaster recovery strategy to ensure it meets business requirements, this post is for you.

Why is optimizing my disaster recovery strategy for business-critical analytics crucial?

Organizations are becoming more reliant on analytics to drive competitive advantage. That is driving more aggressive availability requirements, and your disaster recovery strategy must keep up. IT leaders need to understand which data sets are critical (e.g. source and/or analytics), and how that data is used, to design an appropriate cost-effective disaster recovery strategy. Using decommissioned servers and storage for disaster recovery for analytics engines might not be good enough.

It is smart to engage the business users and start assessing the criticality of analytics data now rather than waiting for big data to become critical. There is a common assumption that analytics data is not critical. This assumption is fueled by IT’s lack of visibility into how business users are leveraging data.

The most common justification for why analytics data is not important include:

  • Forecasting and trending analysis can be down for extended periods of time. There is no immediate impact if analytics is down.
  • Big data appliances are not operational systems such as ERP, payroll, or payment. Applications that have immediate impact need to take priority.
  • Big data is too big; the volume of data to support big data makes it too difficult to include in the DRP.

Regardless of challenges, if the business is using the data in a way that makes the data critical, then IT teams are responsible for providing a solution. The worst case scenario is when downtime occurs and business leaders assume that their big data application is protected when it isn’t. Hence, it is strongly recommended to be proactive and start assessing it now as part of the existing disaster recovery strategy planning process.

How do I optimize my disaster recovery strategy for business-critical analytics?

The criticality of analytics data will evolve, and your disaster recovery strategy needs to evolve with it. Data criticality will evolve based on how business users are leveraging the data. IT teams need to establish a process that can recognize and update DR plans based on changing business needs.

So how do you optimize your disaster recovery strategy for your business-critical analytics? Here are 3 steps you can follow:

  1. Start by defining business process workflows to identify when and how analytics are being used in business processes, and whether analytics can simply be regenerated or deferred.
  2. Evaluate the criticality of source data and generated analytics, based on business requirements, to determine appropriate recovery time and recovery point objectives. Access current criticality based on current use cases and work with the business users to forecast changes in business needs for data.
  3. Design an agile disaster recovery solution from adapting your existing disaster recovery strategy to meet the storage, velocity, and compute requirements for critical analytics. It is to ensure that your disaster recovery solution can scale up and down to address changing data requirements.

Incorporate big data into your disaster recovery strategy

The technological requirements to protect and recover big data is often more complex. However, big data does not change how you determine data criticality or the requirements gathering process.

Big data or business intelligence projects typically start as a pilot or lower-priority project. However, as the project matures, the dependency that the organization has on being able to process and leverage analytics insights becomes higher and higher.

At a certain point, your BI or big data analytics capabilities will become more than just a reporting tool and become a business-critical function that drives business value. When this transformation occurs, make sure your analytics capabilities are covered under the disaster recovery strategy. Once analytics become business critical, reliability and availability become top-of-mind concerns.

Volume, variety, velocity, and veracity is used to describe big data. These four Vs characterize the exponential growth of data and the complexity of analyzing big data. While there are people, process, and technology adjustments necessary to support big data, the process that organizations should use to evaluate ways to protect big data is no different.

Changes to your current IT environment (bandwidth, process, storage) may be necessary when incorporating big data into the disaster recovery strategy. However, the difficulty of protecting big data should not conflict with its criticality.

Let Us Help!

It is smart to engage the business users and start assessing the criticality of analytics data now rather than waiting for big data to become critical. Optimization of your disaster recovery strategy for your business-critical analytics does not need to be difficult. Contact us for a complimentary consultation so that we can help you with the process.