The five stages of intelligent data management

Managing your company's data effectively has never been more important, which is why developing an intelligent data management strategy is absolutely critical.

Johannesburg, 05 Jul 2018
Read time 3min 50sec
Jason Buffington, senior director, product strategy, Veeam.
Jason Buffington, senior director, product strategy, Veeam.

Companies around the globe face the ongoing challenge of the exponential growth of data, both structured and unstructured, now referred to as data sprawl, which means that it has become critical for these organisations to understand what the data is, how and where it is being stored, and who has access to it.

In addition, explains Jason Buffington, senior director for product strategy at Veeam, we now have a multitude of different places, such as on-premises and private and public cloud, where this data is kept. With multiple platforms for storage, it makes the availability of the data more complicated as well.

"Perhaps the biggest challenge is that the users of this data, both internal and external, don't care that the data sets are larger than ever before, or that it is stored in multiple places. They still have the expectation raised by the consumerisation of IT that it will be available and accessible whenever they need it," he says.

"To deliver on these expectations, enterprises need to develop an intelligent data management strategy, one that involves more than simply backing up information and replicating the data to a secondary site. It is a strategy that also requires careful management of the location and of the manner in which the data is protected."

An intelligent data management strategy needs to ultimately encompass five critical stages, continues Buffington, pointing out that these stages are: backup, aggregation, visibility, orchestration, and automation.

"During the first stage, it is all about ensuring you have effective backups: you would be surprised to learn how many businesses find the simple task of making copies of their data to be a near insurmountable problem. Even when this is achieved, however, it won't be enough to allow the organisation to meet all of its recovery and agility enablement needs. This is why you also need to focus on replication and leveraging snapshots of your data.

"A good data management strategy will encompass all three of these options, as only in this manner can you ensure you have all workloads backed up so they are always recoverable in the event of outages, attack, loss or theft."

The second stage is aggregation, states Buffington, which is the point where the business needs to ensure protection and access to data across multiple clouds, to drive digital services and ensure continuous business operations. After all, he points out, the world has moved on from mission-critical and non-mission-critical servers: today, virtually all servers have to experience little or no downtime.

These first two stages are all about data protection, but the next three are focused on assisting the larger organisations to get real business value out of the data.

"Stage three is all about visibility, and thus improving the management of data across multi-clouds with clear, unified visibility and control into the company's usage, performance issues, and operations. The common issues around protecting the virtual server environment include the recoverability of data, the validation of recovery success, the inability to troubleshoot, and challenges relating to identifying problems, poor understanding and a lack of visibility. Increasingly, systems management tools are integrated with data protection tools and we are now witnessing data protection becoming part of the system management strategy," Buffington says.

"In the orchestration stage, it is about the ability to programme workflows, in order to seamlessly move data to the best location across multi-cloud environments. This ensures business continuity, compliance, security and optimal use of resources for business operations."

The final stage, he adds, is automation, which is where data becomes self-managing by learning to back itself up, migrate to ideal locations based on business needs, secure itself during anomalous activity and to recover instantaneously. In essence, he says, it becomes a living breathing thing on its own.

"While developing an intelligent data management strategy is absolutely vital, it is also a long-term process, one where the organisation needs to become contextually aware of its data. Ultimately, it is about making sure that IT doesn't get in the way of the business process," concludes Buffington.