Passionate about creating compelling visual stories through the analysis of big data, Chloe Maraina is our Business Intelligence expert with a remarkable aptitude for data science and a clear vision for the future of data management. We sit down with her to explore why the explosion of unstructured data has become one of the biggest liabilities for modern enterprises and how CIOs can transform this sprawling digital landfill into a strategic, AI-ready asset.
With many enterprises now managing over a petabyte of data, much of which has a useful life of 90 days or less, what are the primary drivers behind this habit of indefinite data retention? Please share a step-by-step approach for how a CIO can begin to address this liability.
It’s a fascinating and frankly, a costly paradox. The core drivers are often a mix of organizational inertia, a “just in case” mentality, and genuine fear of deleting something that might be needed for compliance or future analytics. The truth is, the useful life of most enterprise data has shrunk dramatically to just 30–90 days, yet we see companies hoarding it indefinitely. The first step for any CIO isn’t to start a massive deletion project; that’s too risky. The absolute first step is to achieve visibility. You must build a thorough understanding of your data landscape by using tools that can scan every storage platform you own—across multi-vendor and multi-location environments—and collect that metadata at scale. This creates a virtual map, allowing you to finally see the size, age, and ownership of your data and begin identifying the low-hanging fruit: the duplicate, forgotten, and orphaned files that are just dead weight on your infrastructure.
Given that up to 90% of enterprise information can be unstructured data like office documents, logs, and even old scans, how is GenAI changing our ability to extract value? Could you provide an anecdote or example of “tribal knowledge” being reconstructed from such sources?
GenAI is a complete game-changer for this exact problem. For decades, we’ve been sitting on mountains of unstructured data—think service reports, old invoices, even scans of handwritten notes from twenty years ago—that were essentially inert because they were too difficult to process. GenAI now allows us to tap into the knowledge embedded in all of it. Imagine reconstructing the entire enterprise memory. I’ve seen it happen where a company was struggling with a recurring field quality issue in a legacy product. By feeding years of unstructured service reports, engineer logs, and even old memos into an AI model, they were able to piece together the “tribal knowledge” that had been lost as employees retired. The AI identified a pattern in early failure indicators that no single human had ever connected, effectively solving a problem that had been costing them millions. That’s the power of turning this liability into an active intelligence source.
In complex hybrid and multi-vendor environments, gaining a clear view of all data is a major hurdle. What are the first practical steps and tools a CIO can use to gain enterprise-wide visibility? How can underutilized metadata be leveraged to identify risks and redundancies?
The complexity of today’s IT environments—with some data on-premises, some in the cloud, some as files, and some as objects—is precisely why a sound multi-vendor strategy is important, but it also creates blind spots. The first practical step is to deploy tools specifically designed to aggregate metadata from all these disparate systems without moving the data itself. These platforms create a unified, virtual view. From there, you can start leveraging the metadata that’s already there but almost always ignored. Simple things like creation date, last access date, and ownership can be incredibly revealing. You can immediately flag security risks by identifying orphaned data with no clear owner, pinpoint massive redundancies by finding duplicate files, and see just how much of your expensive primary storage is being consumed by stale data that hasn’t been touched in years. It’s like turning the lights on in a cluttered warehouse for the first time; suddenly, you know exactly what you’re dealing with.
Once an organization achieves visibility into its data, what does a successful governance framework look like in practice? How does data classification help teams decide which files to retain, relocate to cheaper storage, or delete, and what metrics can track the program’s success?
Visibility without action is just an interesting report. A successful governance framework is what translates that newfound insight into real operational and financial benefits. In practice, this means establishing clear rules and processes for your data. The cornerstone of this is data classification, which is essentially a decision-making engine. It helps you categorize data based on its business value, compliance obligations, and risk level. Once a file is classified, the governance framework dictates its fate. Is this a critical business record that must be retained for seven years? Or is it a marketing team’s video file that hasn’t been accessed in two years and can be moved to a cheaper cloud archive? To track success, you look at metrics like the reduction in primary storage costs, the percentage of data under active lifecycle management, and a decrease in compliance-related incidents. It’s about bringing structure and intentionality to your entire data estate.
Policy-driven lifecycle management involves moving data from active systems to archives. Can you describe how a CIO would implement this? For instance, what policies determine when a file is moved to a lower-cost tier, and what are the trade-offs regarding accessibility and retrieval times?
Implementation starts by defining policies based on the metadata we’ve just gathered. A common policy might be: “If a file has not been accessed for 180 days, automatically migrate it from our high-performance on-prem file server to a lower-cost cloud object storage tier.” The CIO works with business units to define these rules. The trade-offs are very real and need to be communicated. Data on active, high-performance systems is immediately accessible. Once it’s moved to an archive, it might take several minutes or even hours to retrieve, and if it’s tiered further down to deep storage like tape, you could be waiting days. The key is to align the storage cost and retrieval time with the data’s actual business value. You don’t keep holiday photos in a bank vault, and you shouldn’t keep inactive, low-value data on your most expensive storage. This tiered approach ensures you’re not overpaying while still retaining the data for compliance or potential future use.
Transforming a sprawling unstructured data estate into a governed, AI-ready asset is a significant undertaking. What is the connection between robust lifecycle management and the quality of AI initiatives? Could you share how this process improves the reliability of analytics and AI-driven insights?
The connection is direct and absolutely critical. You simply cannot have a successful, trustworthy AI program built on a foundation of unmanaged, low-quality data. It’s the classic “garbage in, garbage out” problem, but amplified by the scale of AI. Robust lifecycle management is the clean-up crew. It ensures that when your AI and analytics models are trained, they’re using datasets that are accurate, relevant, and reliable. The process actively limits the spread of stale or redundant content across your estate, so you’re not feeding conflicting or outdated information to your models. By transforming your unstructured data from an unmanaged cost center into a governed, controlled process, you’re not just saving money on storage; you are fundamentally improving the quality of the raw material for your most advanced analytics. This is how you ensure your AI-driven insights are based on a curated reality, not digital noise.
What is your forecast for unstructured data management over the next five years?
My forecast is that it will move from a back-office IT concern to a front-and-center boardroom strategy. The explosive adoption of AI is forcing the issue. For years, organizations could get away with simply collecting and storing data, but that’s no longer tenable. In the next five years, enterprises that fail to get their unstructured data in order will be severely disadvantaged, unable to leverage AI effectively and burdened by escalating costs and compliance risks. We will see a major shift away from prioritizing mere data collection to prioritizing intelligent data management and governance. CIOs who act now to implement visibility, governance, and lifecycle strategies will not only control their costs but will also unlock immense value, turning their biggest liability into their most powerful strategic asset. It will become the dividing line between companies that lead with data and those that are buried by it.
