Unlocking the Power of Autonomous Data Management: A Path to Efficiency and Transformation
By Geoffrey Coley, Regional CTO, Asia Pacific, Veritas Technologies
The need for efficient and agile data management solutions have become paramount in today’s ever-evolving digital landscape. Data is the lifeblood of modern enterprises, and its proper management is essential for success and growth. But as data volumes continue to surge, IT teams often find themselves struggling to keep up. More than half of the world’s data is ‘dark’ or redundant – that is to say, the team that manages it doesn’t know what it is, if it has any value, or even if it contains malware.
Simultaneously, many businesses tell us that their data protection deployments are lagging two years behind their transformation projects. To reduce this, they say, would require investment in over 20 full-time members of the IT team. For most companies, this isn’t realistic – but, unless they can resolve the issue, their ability to make data-driven decisions starts to erode.
Therefore, the question arises: is there a way to revolutionise data management, reduce human intervention, and enhance operational efficiency? The answer lies in autonomous data management (ADM).
What is autonomous data management and why is it needed?
In essence, autonomous data management allows organisations to unlock full cloud benefits, including operational scale and agility. It refers to cloud technologies that operate autonomously and transparently with little to no human intervention and generally requires using artificial intelligence, hyper-automation and machine learning with elastic, programmable and multi-cloud-optimised technology. This synergy allows organisations to streamline data management, protect against threats like ransomware, and optimise their operational efficiencies.
Traditional data management often relies on manual processes and human oversight. Unfortunately, humans, with their inherent limitations, can become the weakest link in the quest for swift, data-driven decision-making. They are prone to errors and are slower to execute, especially when it comes to mundane, repetitive tasks. This manual approach can hinder an organisation's ability to achieve its operational and business goals efficiently.
Therefore, the best solution for managing data in a complex, hybrid, and multi-cloud environment is autonomous operation. It means minimising the need and reliance on manual processes by combining hyper-automation with data-driven intelligence. With the right technology, an ADM strategy can be cloud-optimised that will simplify the way businesses manage data and automate protection from threats, such as ransomware.
These technologies are transforming data management in a variety of ways but perhaps a good way to think of it is in how we provision, manage, and restore data itself. Artificial intelligence (AI) and machine learning (ML) technologies play a pivotal role in autonomous data management. When a new data set is generated, these technologies promptly recognise its existence, identify the data, determine the appropriate policies for its management and protection, and subsequently implement those policies. They are also proficient in selecting the optimal configurations for cloud storage.
Additionally, AI and ML technologies are harnessed to ensure that data is regularly monitored, the tool will look for anomalies in data patterns so that it can detect malware, and self-heal data sets when needed. These advancements empower organisations to make data-driven decisions with confidence. When it comes to data restoration, AI and ML can take the headache out of decision making regarding when, how and where data is restored, reassembling data sets and processes from the backup data copies – alleviating the complexities associated with data recovery.
Transformation with autonomous data management
The ultimate goal for organisations is autonomous data management, where technologies are able to autonomously provision, optimise and repair data management services, while empowering end users to enable self-service data protection and recovery, freeing up IT staff to focus on strategic and transformational activity. The evolution from mere automation to true autonomy involves the use of AI capabilities, so that the data management solutions can go from following rules to anticipating needs. AI-driven solutions, like the ones offered by Veritas’ NetBackup, powered Cloud Scale Technology, are able to automatically provision or deprovision cloud capacity and monitor data to ensure seal-healing. As AI evolves, it will gain the ability to identify new workloads, predict their protection and management requirements, and autonomously provision those resources.
The journey towards autonomy requires not only automation but also predictive and self-regulating capabilities. This is the direction to realise the overarching goal of harnessing the power of AI and ML to minimise human intervention, enhance data security and streamline data management processes.
