ManageEngine VP Discusses the Convergence of Data, AI, and ML and the Road Ahead
While it’s undeniable that data is often hailed as the new king, gold, or oil due to its immense value, businesses must recognise that simply amassing large quantities of data doesn’t automatically translate into tangible benefits.
That is just part of the equation. The real value lies in how you leverage the data you possess – that’s what truly crowns data as king and renders it valuable.
It is also why data optimisation matters. Data optimisation, according to Rajesh Ganesan, Vice President of Products at ManageEngine, is the process that allows enterprises to ensure that large data volumes are well organised. For example, this could enable businesses to standardise data like records of phone numbers, which often have discrepancies, such as some entries including country codes and others not. Although this may appear minor, such discrepancies add friction to data processing, in the process hindering an organisation’s ability to act on data or to feed them into Artificial Intelligence (AI) and Machine Learning (ML) tools to enhance their operations.
Indeed, that is how powerful data can be—it can power AI and ML and allow businesses to harness their transformative capabilities.
“Many businesses recognise that these tools—AI and ML—are transformative. However, the models that power them depend greatly on the data they pull from,” Ganesan explained to Data & Storage ASEAN (DSA) in an exclusive interview. “Therefore, it is critical that the processes governing how organisations extract, analyse, and store data are maximised efficiently. Otherwise, data accuracy, availability, and accessibility will be hampered—and by extension, enterprises’ ability to leverage AI and ML tools.”
In other words, data optimisation helps organisations considerably, including in harnessing AI and ML. It will also enable enterprises to efficiently search, extract and compile large amounts of data accurately. This capability, in turn, will allow enterprises to capitalise on opportunities, thereby improving profitability while ensuring compliance with various data regulations.
The Integration of AI and ML into Data Backups
Just as data optimisation is driving AI and ML to be better, AI and ML are also helping in data optimisation as vital parts of data backups. Among other things, AI and ML demonstrably improve the identification of redundant data and optimise data compression, according to Ganesan, and these, in turn, enable enterprises to make better use of storage space, reduce associated costs, and improve backup performance entirely.
Additionally, AI and ML circumvent two common issues associated with traditional backup systems: Having fixed schedules and relying on predefined rules for data protection. The problem is that the business landscape is constantly changing and evolving, with data being created at exponential rates and data-related challenges—the threat of ransomware, for example, or ever-changing regulations on privacy—becoming more frequent and more sophisticated. The integration of AI and ML, Ganesan explained to DSA, equips enterprises with the capability to dynamically adjust backup frequency and prioritise critical data based on real-time usage patterns.
Beyond that, the integration of both AI and ML equips data backup with the capability to analyse historical data and predict future trends. This cutting-edge functionality enhances the system’s ability to address vulnerabilities and enhance security, effectively preventing data leaks and other data-related issues on privacy.
And, of course, AI and ML enable enterprises to make sense of their data, paving the way for a range of business benefits.
“[With AI and ML], organisations can better harness the vast troves of intelligence that resides in their data from a range of sources. AI and ML technologies make it possible to identify relevant data better and faster, which can be especially challenging when datasets are particularly voluminous,” Ganesan pointed out. “As a result, businesses can save money and time, while also enabling themselves to pivot to new opportunities rapidly. Moreover, because AI and ML can digest large amounts of information faster than humans can, they also improve the speed and accuracy of data recovery. This reduces downtime and minimises the risk of data losses.”
The Resilience Convergence
Data generation is growing by leaps and bounds, and it will continue to do so. In fact, due to accelerated digital transformation, an estimated 90% of the world’s total data was generated in just the last three years. Moreover, within a span of 14 years, data generation surged by a staggering 60 times. And it is expected that data generation will rise by 150% in 2025. This exponential growth of data underscores the need to exercise precision in data management and backup as any downtime could result in substantial financial losses—along with the ability to use data for business outcomes.
This is why business resilience, or an organisation’s ability to adapt, recover, and thrive in the face of various challenges, matters in today’s digital age. Ganesan emphasised as much to DSA, noting how being able to bounce back immediately in the event of an incident is key to sustained success.”
Incidentally, AI- and ML-driven data backups also help in this regard—further underpinning why companies need to use them accordingly.
“The rise of AI and ML provides a smarter, more automated, and adaptive approach to these problems. At the same time, AI and ML also enhance observability, the ability to measure the internal states of a system by examining its outputs,” Ganesan explained. “For instance, organisations can leverage AI and ML to compile useful data solely from the legacy information collected using practical monitoring tools. Furthermore, this also comes with the added advantage of detecting the patterns and trends that bolster identification of potential problems before they spiral out of control.”
Harnessing AI and ML Capabilities with ManageEngine
It goes without saying that companies need modern solutions that will allow them to optimise data and ensure business resilience—all while harnessing the transformative power of AI and ML.
ManageEngine fits the bill.
According to Ganesan, ManageEngine’s AI and ML solutions are tailored to address not only the complexity of data management and backup but also the broader spectrum of IT challenges.
For instance, ManageEngine empowers AI networking, which leverages ML, Deep Learning, Natural Language Processing, and other AI-driven techniques to automate and optimise various aspects of IT operations. These range from monitoring to troubleshooting, and provisioning to security—ultimately ensuring that networks are more intelligent, self-adaptive, and efficient.
Ganesan also highlighted that ManageEngine’s AIOps solutions harness AI and ML technologies to accurately predict interruptions and automate data backup processes. The company, Ganesan added, firmly believes that prediction trumps remediation, and its AIOps tools leverage AI and ML to detect network anomalies and pinpoint bottlenecks before they cause serious trouble. This is a boon for Site Reliability Engineering (SRE) and DevOps teams, as it stops glitching in their tracks before they affect end users.
Indeed, the primordial role of data cannot be stated enough, and the same can be said about the growing relevance of AI and ML. The three, in fact, are seemingly converging at an inflexion point, making it necessary for businesses to find solutions that will allow them to extract the most value out of their data while harnessing AI and ML at the same time.
