With emerging technologies on the rise, data is increasing at an exponential rate, posing a new challenge for today’s data management professionals. Though data volume can be managed, the increasing complexity of data is the real challenge. The sprawl of data, which is now coming from multiple clouds, databases, apps and devices is becomingly progressively more complex to navigate.

Last year, data was declared more valuable than oil which actually resonates more today given the current state of the world oil markets. Today organisations are doing everything they can to make the most of the data they have available to them. According to the Veeam Cloud Data Management 2019 report, 90% of organisations are planning to deploy a Cloud Data Management strategy in the next 12 months. For many of these organisations, AI is a key component of this strategy, allowing them to capitalise on data and also to protect it.

Traditional policy-based backup doesn’t truly automate tasks, it merely executes set scripts. As such, it does not analyse the merits of each and every situation. This translates into the need for IT professionals to constantly oversee each and every action. The set policies also then need to be constantly updated with new rules and for IT professionals to check for policy clashes. The new approach to data management requires data to be “smarter” and self-governing. Data management must evolve from policy-driven to behavior-driven, with built-in machine learning and artificial intelligence to keep getting smarter about what actions to take.

Today, data backup is about mechanically copying data at prescribed intervals. But imagine a different world where the system executes a backup in response to what actually happens. For example, when malware crosses the network, the system takes a backup of the data immediately prior to intrusion. Or the system sees that the change rate in a particular dataset goes up from 5% to 50% per day, so executes a backup just prior to the change. Or the system notices that a user is suddenly deleting lots of files and executes a backup prior to permanent data loss, alerts the administrator, and sends the backup in for forensic analysis.

AI powered backup has this different approach to the problem. The key advantage of leveraging AI to turn policy-based backup into behavior-based, will be to help in improving the responsiveness, security and business value of data while reducing the cost and time that humans spend on managing and storing data. And, most importantly, it will learn how to react automatically to anomalous behavior in the system.

So instead of blindly following scripts, an AI learns to deal with your data to achieve the SLA (service level agreement). A trained AI can actively gather and analyse a task and reallocate resources and adapt to different methods to complete the task within the SLA, which includes smartly moving data to different places when it is threatened. Over time, self-driving AI backup will understand the environment it is acting in, from resources to performance of these resources. It is therefore able to predict better ways to adapt to new tasks.

This change is significant because it is much “smarter”, but this also changes the job nature of the IT professional. For the most part, an AI powered system doesn’t give notifications until the task is complete. The IT professional’s role goes from an executor to a manager of the self-driven AI. Instead of being bogged down in execution, IT professionals can work on improving the overall efficiency of the backup and storage system. On top of that, AI can conduct more analysis on more data to help better inform the decisions for the organisation of data.

“Innovation” is the only constant in the current world of IT and IT Professionals are in a constant flux of new learnings and technologies which require them to change from mundane to strategic work operations. AI backup will allow the IT administrator to make sound decisions and constantly innovate to improve and intelligently manage organisation’s data.

The path to this truly automated cloud data management comes in five stages:

• Stage 1: Backup protects all workloads that use backups, complemented by snapshots and replication where appropriate, to ensure they are always recoverable and available in the event of outages, attack, loss or theft.
• Stage 2: Cloud Mobility provides easy portability and fast recovery of ANY on-premises or cloud-based workloads to Amazon AWS, Microsoft Azure and Azure.
• Stage 3: Visibility into the full breadth of your data, accompanied by the infrastructure that it passes through and resides on, so that you that can pivot from reactive to proactive management for better business decisions.
• Stage 4: Automated and orchestrated data management to make the data work for you while being able to leverage tools to do what was previously manual processes.
• Stage 5: Security compliance and privacy by knowing that your data meets and regulatory requirements and also know that it is protected when at rest in backup repositories

In the increasingly competitive business landscape, organisations who adopt these principles will be able to manage data better and thrive.