By Hemant Harie, Managing Director at Gabsten Technologies
To streamline processes, maximise resources and optimise data usage in the business, backup and Disaster Recovery (DR) processes now include Machine Learning (ML) and Artificial Intelligence (AI) functionality. Operational data is where AI and ML have the potential to shine. Designed to track performance statistics, rates of change, speed of access, bottlenecks and so much more, combining ML and AI to operational data is the next step in protecting today’s IT environment. This results in the creation of smart, self-learning systems that can predict and adjust to the organisation’s needs and objectives. However, as technology becomes more sophisticated, organisations need to mitigate their risk properly through data governance in order to maximise the effectiveness of their backup and DR strategy going forward.
Doing more with less
One of the least discussed, and possibly the most important, aspects of modern business that has been transformed by AI is ensuring business continuity in the event of failure, disaster, or data loss. Backup tools touch and control all business data, thus playing a strategic role in most applications. Immersed in the lifeblood of the business, backup and DR can define ways for organisations to manage, safeguard and understand the data they use, in addition to extending their reach with capabilities beyond data protection. Advances in AI, ML and predictive analytics have empowered backup solutions to step up their game to become a powerful IT solution that makes systems and their administrators faster and more efficient at their tasks.
AI refers to the performance of tasks that normally require human intelligence, such as decision-making, while ML refers to computer systems that have the ability to learn without being explicitly instructed. ML is based on the premise that systems (like backup and DR systems) should be able to adapt and learn through experience to predict and solve problems, when combined with AI and other data techniques. With the massive volume and variety of data sets available to businesses, there are incredible opportunities to turn data into insights and make smarter decisions about every aspect of operations. Advanced analytics, AI, and ML tools can boost efficiency, fine-tune existing products and bring new innovations to market faster than before.
Data is digital currency and the lifeblood of every business
The right backup strategy will give organisations the space to experiment with data without adversely affecting production workloads, which is a competitive advantage on its own. In addition to enhanced data availability and analytic capabilities, such data can also be used by the data management system to identify trends, predict and pre-empt problems. Modern backup and recovery strategies and tools provide more than just protection for workloads. These strategies can also enable application and data mobility to give organisations the power to rapidly recover systems and move workloads dynamically, in order to adjust to the changing needs of the business. Here, data governance is critical, because the data required for ML and AI are often obtained from Internet of Things (IoT) devices and systems that integrate with other business systems that generate data themselves. Organisations need an understanding of the data generated and its lifecycle, in order to appreciate the value of that data, which means that data management is still the core requirement.
Don’t overlook the importance of data governance
Whether to deliver new insights, augment decision making, or drive better business outcomes, the quest to unlock the power of data is on. Successful completion of this mission, however, will require sound data governance processes to ensure that data is optimised for any use. Businesses will have to update their data governance approach to take advantage of ML and AI and manage data as a strategic asset, which requires the imposition of controls around data, its content, structure, use, and safety across organisational boundaries.
No matter the advances in future technology, organisations will still need to know what data exists, whether it’s of good quality as well as who accesses and uses data and for what purpose. In short, it’s full speed ahead to maximise the value of data, but first, the data needs to be secure, compliant and governed. Businesses can ignore this warning at their own peril.