How to Implement Intelligent Data Management Inside an Enterprise

eWEEK DATA POINTS: As enterprises implement cloud infrastructures, data is being spread across multiple regions and offices. This combination of data sprawl, data growth and critical need for access requires an intelligent data management model that both eases these challenges and positions enterprises to get the most out of their data.

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Business operations require data to be continuously available for efficient operations. With data growing at a faster rate than ever-- IDC’s “Global Datasphere” report states that businesses and consumers will have the capacity to consume 175 Zettabytes by 2025-- it’s increasingly difficult to manage and protect; yet data loss of any sort is not an option.

Also, as enterprises implement cloud infrastructures, data is being spread across multiple regions and offices. This combination of data sprawl, data growth and critical need for access requires an intelligent data management model that both eases these challenges and positions enterprises to get the most out of their data.

In this eWEEK Data Points article, Danny Allan, Vice-President of Product Strategy at Veeam, offers an industry perspective defining intelligent data management and describing the basic stages of implementing such a feature in an enterprise IT system.

Data Point No. 1: What is Intelligent Data Management?

Intelligent data management is when all data stores are governed by a single method--one that allows for it be backed up, secure and available. With this platform, IT teams can make the data hyper-available for the mobile, always-on world in a secure multi-cloud environment and, using predictive analytics, anticipate and meet the needs of users across the globe. There are five stages to achieving this “nirvana” of data management that will improve the way in which IT and users interact with vital information stores.

Data Point No. 2: Stage 1: Backup

The first stage, or step, to implementing intelligent data management is creating the bedrock foundation of data backup. Without a proper backup and storage process for all data workloads, enterprises cannot move beyond this initial stage. Companies must be able to recover their data in the event of a service outage, security attack, data loss or theft. While you’d think most companies have this step already in place, there are many IT teams that still struggle to complete this foundational task due to the growing amount of data and the complexity of workloads.

Data Point No. 3: Stage 2: Aggregation

Once enterprises have the process in place to protect their data, the aggregation stage becomes easier. Today’s companies are global, with data shared and stored in multiple clouds and on-premise infrastructures. IT teams require an aggregated view of where all the data is stored to ensure it is protected and available when business users need it for analytics or other business-related tasks.

Data Point No. 4: Stage 3: Visibility

The data is protected and you know where it lives, so now what? Administrators want (and really need) a clear, unified view into all data in order to improve overall management and create an effective plan for storage capacity and allocation. This also allows for fast troubleshooting of any performance issues or spikes in usage. In this stage, IT teams can move from being reactive to proactive and truly start to incorporate intelligence into the data management strategy, positively impacting business operations.

Data Point No. 5: Stage 4: Orchestration

Optimization of all the data and workloads is essential to keeping costs low at this stage. Different clouds--and even on-premise servers--are often better suited for particular workloads. With a clear view into where and how data is protected, IT teams can shift the workloads to most appropriate and cost-effective cloud. As a result, the overall business continuity, security and regulatory compliance will strengthen.

Data Point No. 6: Stage 5: Automation

At the pinnacle of the intelligent data management stages is self-learning and automation, where IT leaders can achieve the best business value for data protection. In this stage, data becomes self-managing through automated operations that allow for instantaneous recovery and security against anomalous network activities.

Data Point No. 7: Proper Protection = Greater data use

When an enterprise embraces intelligent data management, it leaves the IT team available to focus on other IT initiatives. It also allows teams to use the data for other purposes than just backup. Businesses can leverage their data stores for DevOps, patch and security testing, migration to new platforms or conducting advanced analytics. These actions can be conducted using backup data so there is no impact on the production environment and it does not slow down overall operations.

Data Point No. 8: Bringing it all together

Those businesses with intelligent data management will stand out from their competitors due to the speed of their operations and ability to keep customers happy. Because there is no IT downtime, customer-facing applications are not impacted, and there is 7x24x365 network and data availability.

Chris Preimesberger

Chris J. Preimesberger

Chris J. Preimesberger is Editor-in-Chief of eWEEK and responsible for all the publication's coverage. In his 13 years and more than 4,000 articles at eWEEK, he has distinguished himself in reporting...