Picture walking into the warehouse of a successful and slick sales company (this is a metaphor for good Data Lifecycle Management btw)
Products that are flying off the shelves: they go right at the front. Easy to grab and quick to ship. Seasonal items that aren’t needed for a few months: they go on middle shelves, still accessible but not using up prime shelving space. And the discontinued product lines from years ago that legal says need to be kept for seven years? Buried somewhere in the back, in the cheapest storage space available.
Smart warehouses work like this (well apart from the ones run by dystopian robot dogs). Hot items get premium placement. Cold inventory gets cheap space in the back.
Now lets imagine if every product in the warehouse, from bestsellers to decade-old discontinued stock, all sat on the same premium, instantly-accessible front shelving. The business is paying premium rent for storage space that isn’t needed, all while your the bestsellers are drowning in clutter.
This is a metaphor for most organisations manage their data.
The Instant Access Illusion
The benefit with modern storage systems, whether we’re talking on-premises NAS filesystems, object storage, or cloud storage platforms is that everything feels like it’s at the front of the warehouse.
A marketing video from 2017? Instantly accessible.
Raw camera footage from a project that shipped three years ago? Instantly accessible
Daily database backups from systems that no longer exist? Yep, probably instantly accessible, but on some in IT’s things to do list to remove.
Filesystems, object storage, cloud storage platforms are designed to give you the “everything is immediately available” experience. This sounds great until you realise it comes with premium prices to keep digital junk in the most expensive storage real estate possible.
The Budget Conversation Nobody Can Answer
Let’s imagine it’s budget planning time. The CFO walks into a meeting and asks two questions:
“Do we need to increase our CapEx storage budget this year?”
“Are we getting value from our OpEx SaaS storage subscriptions?”
What happens next… The IT team shuffle their feet. Someone pulls up a chart showing storage is “85% full.” Someone else mentions “data growth trends.” Everyone agrees the organisation probably need more storage.
But nobody can answer the real question: Should we be paying this much for data that’s barely accessed?
What that 85% utilisation chart doesn’t show:
- How much is business-critical versus data that’s past its sell-by date
- What percentage is accessed daily vs annually (vs never)
- Which data could live on a cheaper storage class without affecting operations
- How much is being spent to keep data instantly accessible when nobody needs it instantly acceible
Without good data lifecycle management, technology and finance conversations become an exercises in guesswork and risk aversion. Its’ better to overspend than risk deleting critical data, right?
What Data Lifecycle Management Actually Means
Data lifecycle management is the practice of moving data through different storage tiers as it ages and its business value changes.
Active data, that people are regularly working on, lives on instant access storage, often fast, usually more expensive. This makes perfect sense. If users need instant access to create business value and their applications need low latency, the productivity of the business depends on it.
Now as data ages and the frequency of its access drops, it should move to slower, cheaper storage tiers. It doesn’t have to be deleted, but if it’s moved to more cost-effective real estate, it’s not hogging space in the premium, business-value creating storage tier.
When data needs to be kept for compliance or historical reasons but rarely or never accessed, it can be moved to archive storage. Cheap as chips, usually with longer retrieval times. We are storing the data in line with the actual business value that it creates.
This isn’t rocket science. It’s the same logic that warehouse managers use. Keep inventory where it matches the value and velocity of what the business needs.
The “It’s OK to Keep Everything” Reality
There’s an elephant in the room. Not all data can be deleted.
Research institutions do need to keep datasets for peer review and follow on studies. Life sciences companies have to keep data for regulatory requirements, often for decades. Legal departments need document retention for compliance. Medical records have strict retention policies.
But this is absolutely fine.
Data lifecycle management isn’t about deleting everything when it gets old. It’s about acknowledging the data that needs to be keep for compliance, and not storing it on the same premium storage tier as active projects.
The Vendor Lock-In Problem
Now here’s where data lifecycle management can get messy in the real world.
Most storage vendors offer lifecycle management features. It’s a checkbox on their feature list.
But here’s the catch. They use it as “value add” to buy their storage, and they only let you tier data within their ecosystem.
NetApp wants you to tier from their high-performance arrays to their capacity-optimised systems. AWS wants you to move from S3 Standard to S3 Glacier. Makes perfect sense to get benefits from sticking with a single vendor.
From the vendor’s perspective this makes total business sense. Why would they make it easy to move data to someone else’s platform That would cost them revenue.
So we have a fundamental problem: vendor-specific data lifecycle management locks users into increasingly expensive relationships with one vendor. So they can’t leverage more efficient or cost-effective solutions elsewhere, with the overall cost being prohibitive.
Imagine if a warehouse management system only let you move inventory between buildings owned by the same landlord. Sound ridiculous? Well that’s how most storage lifecycle management works.
Data Management is a FinOps Problem
This is a mindset shift that organisations need to make.
Data lifecycle management isn’t an IT operations challenge. It’s a financial operations solution.
The IT team can tell you how much storage you’re using, they can build scripts to populate spreadsheets and build pivot charts to show growth trends and utilisation. They can also explain the technical differences between storage tiers.
But data lifecycle management needs to answer these business questions:
- What’s the total cost of data per terabyte across all storage tiers?
- Which projects or departments are consuming our expensive storage classes?
- How much could be saved by moving aged data to appropriate tiers?
- What’s our storage spend trajectory if we do nothing versus implementing data lifecycle policies?
These aren’t technical questions. They’re financial ones. Forward-thinking organisations are treating data management as a FinOps discipline rather than just an IT problem.
The Platform-Agnostic Solution
So what does good data lifecycle management look like in 2025?
- Vendor agnostic.
You should be able to tier data from any storage platform to any other storage platform based on business logic, not vendor convenience. - Policy driven.
Define rules once – “move data untouched for 12 months to archive tier” – and let automation handle the boring stuff. - Cost-transparent.
Before moving anything, you should see exactly what it will cost to store data in different locations and what you’ll save.
Data lifecycle management needs to respect your business needs. Compliance requirements, access patterns and application needs should all be factored into automated decision-making.
And most importantly, it treats your storage estate as a unified resource, not a collection of storage silos from vendors that want to keep you locked in to their ecosystem.
The Bottom Line
Data lifecycle management isn’t about having lifecycle features on a single storage platform. It’s about having a strategy that spans your entire data estate.
Every organisation creates data. And that data needs to live somewhere. Some data gets accessed frequently. A lot of it doesn’t.
The question is whether you’re paying a premium price to keep everything in the storage equivalent of the front-row in your warehouse space. Or are you’re intelligently managing data across the right storage tiers as it ages.
Your warehouse wouldn’t store everything at the front. Your data shouldn’t either.
Choose an unstructured data management platform designed for data lifecycle management across any storage. Not just a single vendor’s ecosystem, but across your entire multi-vendor, hybrid, multi-cloud reality.
In 2025, data lifecycle management isn’t a feature. It’s a business strategy that directly impacts your organisation’s bottom line.