05-08-2025
Cutting Data Overhead: Strategies To Reduce Cost And Complexity
Scott Francis is Technology Evangelist at PFU America, Inc. and Ricoh Document Scanners.
Today's organizations are sitting on a plethora of untapped data. This unused information is referred to as 'data overhead,' and it has become the source of massive inefficiencies, costs and complexities for businesses of all sizes. Managing, storing, processing and securing large volumes of data is no easy feat, and poor-quality, underutilized or duplicated data only compounds the problem.
Every organization has a data overhead problem, whether they recognize it or not. According to a recent report by the Association for Intelligent Information Management, 64% of organizations manage at least one petabyte of data, and 41% far surpass that, with at least 500 petabytes to wrangle.
Whether it's 20 years of paid invoices no longer needed for tax purposes, outdated sales records, or employee files that should have been shredded long ago, companies are holding on to an immense amount of unnecessary data. Keeping records past legal retention guidelines can lead to non-compliance with data privacy laws such as GDPR, CCPA and HIPAA. It also increases the attack surface for cyber threats, phishing and data breaches.
The Costly Downsides To Data Overhead
There's a real material cost to data overhead. Research firm International Data Corporation estimates that storage and infrastructure costs have grown by 25% to 30% annually for large organizations due to data volume growth. In addition to these costs, businesses suffer operational impacts such as degraded performance and muddled business insights caused by incomplete, inaccurate or inconsistent data. Proactively managing data before it reaches peak overload can reduce waste, cut costs and support better decision-making.
One challenge is that data resides across multiple environments—cloud platforms, local servers and even hard copy file cabinets. Redundant data is rampant. Invoices, RFPs, presentations and most organizational content are frequently duplicated, compounding the overhead.
Besides duplicates and redundant, obsolete and trivial (ROT) data, overhead also includes information that is underutilized. As data sources grow, so does the amount of unanalyzed and unused data. Automated cloud storage further exacerbates the problem. While storing data in the cloud increases accessibility, it doesn't guarantee usage. Businesses invest heavily in ERP, CRM and other systems to collect and store data. If that data goes unused, it offers no ROI.
Accessibility challenges also arise in local storage. Disorganized systems can result in version sprawl and fragmented files, complicating search, backup and security efforts. A lack of centralized control makes it difficult to determine which data is current and important.
Another overlooked problem is inconsistent digitization. Physical documents may occupy valuable file space and can't easily be leveraged as a knowledge resource. Without digitization, organizations miss the chance to extract value from historical data.
Strategies To Wrangle Data And Manage Overhead
No organization is without their data management challenges. However, many of these are self-inflicted, caused by poorly executed governance policies. Organizations can reduce data overhead through regular audits, strategic deletion or archiving of obsolete information and investment in smart analytics. Prioritizing data quality over volume is key.
To get data overhead under control, organizations should consider adopting the following strategies:
• Establish A Data Governance Framework to define standards, metadata and access controls. These frameworks promote accountability and enable continuous monitoring of data quality and compliance.
• Implement Data Lifecycle Management (DLM) to classify data by value, sensitivity and usage. DLM automates policies for backup, cold storage and purging, making it easier to manage data from creation to deletion.
• De-Duplicate And Consolidate Data across systems to eliminate redundancy and fragmentation. This process reduces storage costs and improves the reliability of analytics.
• Use Data Observability and FinOps tools to monitor data growth, usage and cost. These tools help optimize data usage and control cloud storage expenses.
The Trickle-Down Effect Of Better Data Management
Data overhead affects organizations operationally, competitively and financially. Employing consistent strategies to manage it improves efficiency by reducing storage demands and enhancing data quality and accessibility. Companies that promote a culture of purposeful data use stand to gain significant cost savings, accelerate innovation and build a lasting competitive edge.
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