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Almost two decades ago, Clive Humby coined the now-infamous phrase data is the new oil.
With artificial intelligence (AI), weve got the new internal combustion engine.
The AI hype gripping every industry right now is understandable.
Look after your data, and it will look after you.
Senior Director for Product Strategy at Veeam.
You dont want to be left trying to unbake the cake.
The time to start is now.
Managing the risk
Its not about being the progress police.
AI is a great tool that organizations and departments will get enormous value out of.
For most AI tools, it’s about mitigating the operational risk of the data that flows through them.
), availability (what if we lose access to the data, even temporarily?
), and accuracy (what if what were working from is wrong?).
This is where data resilience is crucial.
Rampant or uncontrolled use of AI models across abusinesscould create gaps.
Across the business, you gotta understand your business-critical data and where it lives.
Whats worse, is you might not even know about them.
Its also not a one-and-done task, things are constantly moving and changing.
The growth of AI is just one example of things that need to be reacted to and adapted to.
Data resilience starts in the boardroom.
Without it, projects fall flat, funding limits how much can be done, and protection/availability gaps appear.
The fatal NMP (not my problem) cant fly anymore.
Dont let the size of the task stop you from starting.
Starting now will be much easier than starting in a year when LLMs have sprung up across the organization.
Test your resilience by doing drills - the only way to learn how to swim is by swimming.
When testing, ensure you have some realistic worst-case scenarios.
Try doing it without your disaster lead (theyre allowed to go on vacation, after all).
The most important thing is to start.
We’ve compiled a list of the best data visualization tools.
The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc.