For data analysts, engineers, and scientists, automation can support AI and machine learning initiatives by giving them increased control, reduced overhead, and a clear, policy-driven approach to managing unstructured data at scale.
In the typical modern organization, the unstructured data that makes up everything from emails and images to video, audio, sensor data, and a myriad of other examples accounts for up to 90% of all the information it owns and uses. Unfortunately, what these same organizations also have in common is a shared inability to extract something meaningful from this potentially valuable asset.
Adding to the problem are unsustainable legacy data management processes, whereby many administrators must still manually identify, classify, and relocate data across systems. As a result, the gap between data growth and an organization’s ability to exploit it efficiently is widening, undermining the efforts of businesses that claim to be ‘data-led’.
The only way to address these issues at scale is to shift from reactive, manual processes to a proactive, policy-based model. This requires not just visibility but the ability to act on that insight consistently, across all storage environments.
See also: Kill the Dinosaur: Why Legacy Data Governance Is Holding Back the AI Era
Automate to accumulate
While understanding the state of any unstructured data environment is an essential first step, insight alone isn’t enough. Once organizations have visibility into what data exists, where it resides, and how it is used, the focus must then shift to action.
This is where automation becomes a critical enabler of effective data management because, by automating key workflows, businesses can orchestrate the movement, storage, and governance of unstructured data across their environment. In doing so, they can establish a strong foundation for consistent policy execution and reduce the risks associated with manual intervention or reactive processes. With automation in place, organizations can ensure data flows through the business efficiently and according to policy, whether that means relocating dormant data to archival storage, aggregating files from remote sites, or distributing content across hybrid cloud platforms.
More broadly, automation turns fragmented, unwieldy environments into manageable systems where data can be governed throughout its lifecycle. It also enables teams to respond to growth with agility, rather than constantly reacting to capacity pressures or compliance concerns. In this context, automated workflows are a fundamental requirement for maintaining control over increasingly complex and distributed data estates.
See also: Vibing on AI Governance
Solving for stakeholders
But how relevant is this approach to the various stakeholders responsible for managing data across the business? For IT Infrastructure and Operations teams, automating repetitive data management tasks helps mitigate the need for continual hardware investment or additional headcount. It supports capacity planning by relocating inactive data to lower-cost tiers, reducing the volume of high-performance storage required while maintaining access to critical files when needed.
Governance, Risk and Compliance (GRC) teams benefit from workflows that enforce data hygiene at scale – a capability particularly relevant to the risks associated with security breaches, ransomware, and non-compliance, among other important challenges. By automatically identifying data with no clear ownership, relocating sensitive files, or deleting obsolete assets, for instance, automated workflows reduce exposure and contain the potential impact of breaches, non-compliance, or other governance failures.
For data analysts, engineers, and scientists, automation can support AI and machine learning initiatives. By identifying data suitable for model training or inferencing and moving it to the correct storage tier, organizations can accelerate time to insight and avoid the pitfalls of training models on poor-quality or irrelevant data. In each case, the result is the same: increased control, reduced overhead, and a clear, policy-driven approach to managing unstructured data at scale.
Automation advantages
Beyond individual team benefits, automation also plays a pivotal role in managing data through every stage of its lifecycle. Across typical environments, data constantly shifts in value, relevance, and usage. Automated workflows allow organizations to respond dynamically by applying clear policies based on access patterns, content type, or age. For example, files that haven’t been modified in a defined period can be moved automatically from tier 1 storage to more cost-effective archival platforms, freeing up high-performance capacity for critical workloads.
In environments where large datasets are common, automation helps flag inactive content consuming disproportionate storage. This data can then be relocated to long-term archive or low-touch platforms, maintaining availability while reducing spend. At the same time, organizations can use automation to support hybrid and multi-cloud strategies. Data can be copied to the public cloud for burst operations, aggregated from edge locations for central analysis, or distributed to remote sites without manual intervention. And where data has no valid business owner, automated rules can relocate it for review or remove it entirely.
Herein lies a critical point – by embedding automation across these lifecycle stages, organizations create a predictable, policy-driven approach that balances strategic and operational priorities. In doing so, they can build a sustainable and manageable environment that can actually deliver on their unstructured data objectives.
