What do organizations get wrong when it comes to data lifecycle management?

Most organizations confuse storage with stewardship. They think having a data lake or a retention policy means the lifecycle is “managed.” But in reality, here’s what goes wrong:

1. They design for compliance, not for use.
Lifecycle policies are often written by legal or IT to tick a box, not to serve the people who actually need the data. The result?
• Data is either locked down too tightly or spread too loosely.
• Analysts spend half their time decoding lineage or asking for access.
Reality check: If your data is compliant but can’t be used at speed by sales, ops, or product, your lifecycle is broken.

2. They don’t define ownership.
Data lives across functions, product, engineering, marketing, ops, but no one owns the flow from creation to deletion.
• You get half-governed pipelines.
• Metrics that don’t match across dashboards.
• Or worse, no one notices data decay until it costs real money.
Good data has a product manager. Most orgs don’t have one.

3. They ignore the middle.
Everyone focuses on two things: ingest and storage. And then maybe deletion for privacy.
But the real action, the part that matters, is how data gets transformed, versioned, distributed, and activated.
That’s where value is created or lost.
• Poor transformation = bad decision signals.
• Delayed data = missed market timing.
• No feedback loop = the same bad data keeps getting recycled.

4. They assume lifecycle = archiving.
This is a mindset problem. Teams think “managing the lifecycle” means setting retention rules and expiry dates.
But the real lifecycle is:
Collect → Clean → Use → Learn → Improve
Lifecycle = feedback.
Without that loop, you’re just collecting dust.

5. They don’t map lifecycle to business moments.
Data value is time-sensitive. Lifecycle should be designed around real-world cycles, customer journeys, product launches, financial reporting.
Instead, it’s often designed around tech limitations or org charts.
You end up with great data delivered too late to make a difference.

Not setting up data retention or data expiration date upon which the data is deleted. Keeping data forever is expensive and not needed

A lot of organizations think that data is valuable, so they just store it and become data hoarders rather than actually doing anything with it. I feel that many companies just store data for the sake of storing, and they need to understand there's an expiration date on it.

After a certain amount of time, data is useless. If you don't get the insights and you're not doing something actionable with the data, what is the point of you hoarding it, other than just racking up bills and probably paying someone a large amount of money just for storing it?

The way we’ve produced consolidated reporting is all the data gets dumped into Snowflake, and then you try to find connections. It's less of a data warehouse and more of a dumping ground. Maybe we could try to create consolidated reporting and then try to figure out the data integrity issues.

That's one area that I think we are hoping to modernize at ZoomInfo. In a way it's just an evolution that’s part of any startup story, they're growing too fast. It's a great opportunity to bring in that rigor so that we can scale to the growth, streamline and hopefully optimize all of the operations, systems, and technology. We need to have data lifecycle management, otherwise we'll just keep collecting it all.