Why Good Data Management Is a Lifestyle, Not a Cleanup Project
We recently sat down with Paul Honrud, Chief Product Officer at Diskover and a veteran of more than 35 years in data management, to talk about one of the most persistent challenges organizations face: managing unstructured data at scale.
Drawing on decades of experience, from early file systems to today’s AI-driven data environments, Paul shared a clear perspective on what’s changed, what hasn’t, and why so many organizations still struggle to get ahead of their data.
One pattern continues to show up: unstructured data is often ignored until it becomes a problem.
It builds over time, files, images, logs, videos, documents, until storage fills up, teams can’t find what they need, workflows slow down, and risk starts to rise. At that point, the response is typically reactive: buy more storage, run a cleanup, or try to regain control.
But as Paul puts it:
“Too many organizations treat data management as something they can put off by buying more storage. Eventually that always comes back as a workflow problem.”
His view is simple: the real shift isn’t just in tools – it’s in mindset.
From Storage Problem to Business Problem
For years, unstructured data was treated primarily as a storage challenge. If capacity ran low, the solution was simple: add more.
That approach worked – until it didn’t.
As data volumes increased and workflows became more complex, the consequences of poor data management started showing up elsewhere:
- Teams wasting time searching for files
- Incorrect or outdated data being used in production
- Delays in delivery and decision-making
- Growing compliance and security risks
What begins as a storage issue quickly becomes a business issue.
“Unstructured data does not become a priority until it becomes painful. By then, the cost is not just storage. It is wasted time, delays, risk, and bad decisions.”
Data Management as a Lifestyle
One of Paul’s most compelling frameworks is simple: data management is not a one-time fix, it’s an ongoing discipline.
“Good data management is a lifestyle. If you do not build healthy habits over time, you eventually end up doing a crash diet.”
In practice, organizations tend to fall into one of two patterns:
- Reactive: Periodic “panic cleanups” when storage or costs become unmanageable
- Proactive: Continuous data management integrated into everyday workflows
The difference is significant. Reactive approaches may temporarily reduce costs, but they don’t solve underlying inefficiencies. Proactive approaches, on the other hand, help organizations understand their data, maintain quality, and operate more efficiently over time.
Increasingly, organizations are beginning to recognize that data is not just something to store, it’s an asset.
Why Traditional Approaches Fall Short
Traditional storage and data solutions often focus on cost optimization – especially through tiering data based on age or last access.
While useful, this approach is inherently limited.
“Cost optimization alone is not intelligence. Real data management starts when you add business context to the data.”
Simply knowing when a file was last accessed doesn’t tell you:
- What the data represents
- Which customer or project it belongs to
- Whether it is critical, redundant, or obsolete
Without that context, organizations are making decisions in the dark.
Adding Structure to the Unstructured
This is where Diskover’s approach differs.
Rather than treating unstructured data as opaque files, Diskover focuses on enriching it with metadata and business context, effectively adding structure to what was previously unstructured.
“When you combine structured business data with unstructured files, you can manage data with far more precision.”
By layering in metadata, about storage systems, workflows, customers, projects, and file attributes, organizations gain a clearer understanding of:
- What data they have
- Why it exists
- Where it fits in the business
- What actions should be taken
This shift, from storage-centric thinking to context-driven data management, is what enables more intelligent decisions.
The Reality: Data Is Always Distributed
Another key challenge is that real-world environments are rarely simple.
Despite ongoing efforts by vendors to consolidate storage, most organizations operate across multiple systems, platforms, and locations.
“In the real world, almost no customer runs on a single storage vendor. Different parts of the workflow demand different systems, and that’s not going away.”
Different stages of a workflow often require different storage characteristics, whether for performance, cost, resiliency, or proximity to where data is created.
The result is a fragmented landscape of unstructured data.
Managing that complexity requires visibility across environments, not just within a single system.
From Cleanup to Optimization
As organizations mature in their approach to data management, the value evolves.
At the early stage, the focus is often on cleanup:
- Identifying redundant or obsolete data
- Removing unnecessary files
- Reducing storage footprint
For example, by understanding how workflows generate data, organizations can uncover large volumes of low-value or temporary files that were never removed.
“Your workflow leaves fingerprints all over your storage. If you understand the workflow, you can surface fast wins and long-term improvements.”
At more advanced stages, the focus shifts to optimization:
- Improving delivery timelines
- Reducing errors and exceptions
- Enhancing collaboration across teams
- Protecting high-value intellectual property
Data management becomes not just an operational function, but a strategic one.
AI Is Bringing an Old Problem Back Into Focus
The rise of AI has put unstructured data back in the spotlight, but the underlying challenge is not new.
“AI has brought an old truth back into focus: you are only as good as the data you put into the system.”
Successful AI initiatives depend on:
- Knowing what data exists
- Enriching it with context
- Curating it for specific use cases
Without that foundation, AI models are limited by incomplete, inconsistent, or irrelevant data.
“Before you talk about AI readiness, you need to know what you have. Inventory comes first.”
In that sense, AI is less about introducing new problems and more about forcing organizations to finally solve existing ones.
The Future: From Visibility to Conversation
Looking ahead, Paul sees two major shifts in how organizations interact with their data.
First, greater visibility where data can effectively “tell you” what’s wrong, what’s valuable, and what needs attention.
Second, more intuitive interaction where users can ask questions about their data using natural language.
But even as these capabilities evolve, one principle remains constant:
“Search is powerful, but search alone is not enough. Automation still depends on structure, predictability, and context.”
The future is not just conversational AI layered on top of chaos. It’s a combination of:
- Natural language interaction
- Contextual awareness
- Structured, well-managed data
Building a Foundation for What’s Next
Ultimately, organizations that succeed in managing unstructured data are not the ones with the most storage or the most tools.
They are the ones that build a strong foundation:
- A clear inventory of their data
- Consistent metadata and context
- Ongoing processes for managing and curating it
From there, everything else becomes possible – better workflows, better decisions, and better outcomes from AI.
Because in the end, data management isn’t something you do once.
It’s something you practice every day.
Learn more about how Diskover Data can help you improve your data management lifestyle.