Modern businesses have a love-hate relationship with data. Many operate on the basis that, whether it holds any obvious value or not, it needs to be collected and retained. The motivation for this behaviour can be anything from driving competitive advantage, innovation and governance to fuelling AI systems and even intangible concerns that, at some point, it will be useful.
Central to the problem is that 80-90% of this information is unstructured, ie, spread across formats such as documents, images, videos, emails, and sensor outputs, which only adds to the difficulty of organizing and controlling it. If that wasn’t challenging enough, effectively building and deploying AI tools depends on access to well-governed, high-quality unstructured data, which many businesses currently lack.
In many situations, the lack of data management systems and processes are adding to the problem. Data is collected from a wide range of sources, for a wide range of reasons. It then resides across various hybrid environments (on-premises, cloud, both, etc.) for indeterminate periods, with many businesses reluctant to delete it in case it harbors latent business or regulatory benefits.
The net result is that organizations everywhere are storing vast amounts of data with little or no visibility into what they actually have, where it came from, where it resides, how it is being used, or whether they need to keep it or not. This leaves them with no meaningful way to optimize their storage infrastructure and processes, keep control of their storage costs, or control how their environments evolve over time, let alone how to derive value from their data.
Clearly, something has to give. Organizations need to see what data exists across the entire storage estate, including details such as age, location, ownership, activity levels, and type, to understand how it contributes to – or undermines – storage system optimization.
You can’t manage what you can’t see
To break this down, detailed metadata insight is essential for revealing how storage is actually being used. Information such as creation dates, last accessed timestamps, and ownership highlights which data is active and requires performance storage, and which has aged out of use or no longer relates to current users.
This level of clarity exposes large volumes of data that consume capacity without delivering value, giving organizations a realistic picture of what should remain on primary systems and what can be relocated or archived.
So, how can this be achieved? At a fundamental level, storage optimization hinges on adopting a technology approach that manages data, not storage devices; simply adding more and more capacity is no longer viable.
Instead, organizations must have the ability to work across heterogeneous storage environments, including multiple vendors, locations, and clouds. Tools should support vendor-neutral management, allowing data to be monitored and moved regardless of the underlying platform. Clearly, this has to take place at petabyte scale.
Optimization also relies on policy-based data mobility that enables data to be moved based on defined rules, such as age or inactivity, with inactive or long-dormant data, including files that have not been accessed or modified for long periods, moved to lower-tier storage or deleted altogether.
Then there is the question of governance, where effective, optimized processes (or the lack of them) directly affect whether businesses can properly meet their compliance obligations. In this context, good governance assigns ownership and responsibility for data, reducing the volume of orphaned or unmanaged files. In doing so, it also helps address security vulnerabilities and operational inefficiencies associated with poorly managed data.
Optimizing the environment requires systems and processes that document how data is created, stored, retained, and archived, supported by regular audits and clear visibility into ownership, age, and activity. It also depends on tools that can classify and tag data consistently and apply policy-based movement across all storage environments, ensuring information is managed in line with business and regulatory requirements.
Ultimately, our collective reliance on data is now so deeply embedded in business processes that optimizing lifecycle processes is becoming increasingly essential. Equally, as the integration of AI systems continues to accelerate at pace, businesses that reassert control over their data estate will be ideally placed to deliver on the potential of these technologies without breaking the bank.
