FutureCIO has covered data protection for some time now. A 2023 Gartner prediction cautions that through 2025, powerhouse cloud ecosystems will consolidate the vendor landscape by 30%, leaving customers with fewer choices and less control of their software destiny.
So just when you thought the democratisation of data and infrastructure has given consumers and enterprises more choice in terms of how they create, access, archive and possibly decommission data, Gartner is now predicting the formation of cloud ecosystems that may potentially leave us with fewer choices when it comes to what we do with our data.
Can you feel the threat of vendor lock-in looming on the horizon?
In this series on Readying the enterprise's data protection strategy in 2023, we cover the complex issues of bringing automation into production in 2023 by having five experts answer the question:
What do you see are the main challenges ahead for Chief Information Officers (CIOs) and heads of infrastructure as they look to bring forward automation, and artificial intelligence/machine learning into operations, including data protection?
“Leaders will undoubtedly face the paradox of AI/machine learning and data storage. Organisations must feed these technologies as much data as possible to use their full potential, increasing the scale of storage they require and becoming a cost issue.”
According to David Lenz, vice president of Asia Pacific at Arcserve, the cost of storing all this data is limiting the organisation’s ability to harness the power of AI and machine learning. He is positive, however, that these technologies will reach a point where they can help solve storage problems for businesses juggling too much data without enough financial means to grow their storage capacity.
“The power of AI and machine learning will eventually make it easier for IT departments to organise the terabytes of data for which they’re responsible,” he opined.
For his part, GitLab APAC channels director, Dirk de Vos points out that CIOs today have a bare minimum understanding of every aspect of where the company's data sits, and how it's transferred and managed.
By the time the data is reviewed by compliance teams to ensure the growing list of regulatory requirements is being met, he posits the process has become extremely complicated. “Maintaining this on a daily basis is challenging in itself,” he opined. “Incorporating AI / ML practices into tech infrastructure means they will be managing much larger data sets, at faster speeds.
“They are also likely, either now or soon, to be doing this across multiple cloud environments. The added complexity this brings will continue to accost CIOs,” he continued.
“Keeping that volume of data secure and compliant can be difficult without the right systems in place. Reducing complexity and using a single platform approach as opposed to point solutions may help them keep such large data volumes in check.”
Dirk de Vos
Grant Orchard, field CTO of Asia Pacific and Japan with HashiCorp opined that to mitigate risk and protect their environments, many organisations are choosing to leverage automated security tools. Automation in this context he added is essentially the automation of security responsibilities.
“The major benefit is that the burden on security teams is reduced given administrative tasks and incident detection are automated, meaning they can better handle burgeoning cloud workloads.”
Grant Orchard
In the Forrester report, Unlocking Multicloud’s Operational Potential, 46% of APAC respondents said that consistent and automated tooling would improve their security and governance posture.
Raghu Nandakumara, head of industry solutions at Illumio, claims that the biggest challenge for CIOs is maintaining visibility and control over data flow and processing as systems become connected.
He posited that artificial intelligence and machine learning can bring huge benefits in terms of operational efficiency, but both technologies also have a heavy dependency on maintaining a tight feedback loop whereby data is used to inform and improve the model.
“Organisations that are operating across multiple jurisdictions need to be careful of what data is being used and ensure that any personal or sensitive data is only being used to enhance the experience of that individual – it's the typical personalisation vs. privacy debate.”
Raghu Nandakumara
“Organisations leveraging AI/ML, for any purpose, must ensure they do not subliminally breach data privacy,” he warned.
Matthew Oostveen, VP & CTO of Asia Pacific & Japan for Pure Storage, claims that data gravity will become a big challenge, as organisations mature in their data analytics and AI practices.
“Enormous volumes of structured and unstructured data can create operational complications that not only impede innovation, performance and productivity but also result in increased data storage costs.”
Matthew Oostveen
He cautions that it becomes even more pressing for CIOs and heads of infrastructure to break free from the “pull” of data gravity as AI/ML initiatives scale up.
“This requires bringing data as close as possible to applications and services while veering away from legacy infrastructure and sprawling data silos towards a single, scale-out storage platform that shrinks the time and distance between data sets being processed,” concluded Oostveen
* Editor’s note: Click on the links below for the series
