8 Ways to Reduce Data Storage Costs

Originally published by Normalyze.

Written by Vamsi Koduru.

Many organizations don’t store their data. They hoard data.

Too often, organizational data accumulates in a never-ending cycle of unnecessary duplication and hoarding. As a result, they suffer ever-growing data storage fees and significant risks to data security and compliance.

Data grows for as many reasons as there are uses for the data. Enterprise software development pipelines are continuous now, and microservices make it easy for data to move and change in the blink of an eye. Workloads from AI/ML systems, which often involve data copied from production and transferred to another cloud, are increasing dramatically because of digital transformation. Abandoned backups can proliferate due to human error or organization change, lack of automation, and changing IT infrastructure. Analytics teams often make copies of data for development or testing environments that aren’t monitored closely.

The many costs of data storage over time

The potential costs of ineffective data storage practices aren’t just from cloud storage fees or data center overhead. There are many consequences to the bloated volumes of uncategorized, duplicate, and outdated data that collect in cloud and on-premises data stores. These include:

Data leakage and compliance risks

The endless cycle of data hoarding can lead to increased security risks, as sensitive data can grow unchecked and vulnerable to accidental or malicious leakage. In the same vein, data buildup can easily result in unintended data compliance violations, as data proliferation often results in the sprawl of data access and privileges.

Since data leakage and compliance violations can result in company expenses that outweigh even the highest monthly data storage costs, these should not be overlooked.

The risk of data corruption

Some stored data may be doing more harm than good. Dirty data is the inaccurate, obsolete, incomplete, or stale data that can corrupt historical reviews or ongoing assessments. When considering the cost of maintaining dirty data, you should factor in the financial cost of data storage over time—plus the potential negative implications if that data is ever accidentally used.

Environmental impact

And finally, it’s important to consider the environmental costs of data center storage. Servers require a significant supply of energy and cooling, producing considerable carbon emissions.

8 ways to reduce data storage costs

To help your organization better manage data center storage costs, here are eight tips for managing your data.

1. Classify and manage data – continuously

Data classification and management is a foundational step for organizations aiming to optimize their storage costs and improve overall efficiency. There’s a reason why it’s the first entry on this list.

By systematically categorizing data based on its sensitivity, relevance, and usage frequency, companies can allocate resources more effectively. This ensures that critical information is stored securely while less important data is archived or deleted. Effective data management reduces redundancy, prevents unnecessary storage expenses, and enhances retrieval speed.

This streamlined approach cuts costs and bolsters compliance with regulatory requirements and safeguards against data breaches. By implementing robust data classification and management strategies, businesses can take their first and most significant step to achieving financial savings and operational improvements.

2. Identify abandoned data & duplicate data

Data redundancy is essential to maintaining data resiliency. After all, it’s backup.

But it’s important to recognize that many companies’ supposed backups aren’t backups at all, since both the original data and the copy are susceptible to some of the same liabilities.

In these cases, this “backup” isn’t backup. It’s waste.

That’s why eliminating duplicate and abandoned data is one of the largest avenues for reducing storage costs. After a thorough data discovery and classification process, you can identify data that has either run its course for reasonable use, or duplicate data that doesn’t add to your overall data resiliency.

3. Gain visibility into shadow data

Shadow data is the data generated by technology and cloud services without explicit approval or oversight from the IT and security departments. Sometimes, it occurs through cloud-based services provisioned by a line of business.

Shadow data from processes like CI/CD, data analytics, and AI can be a major contributor to sprawl. This data can contain a great deal of unproductive redundancy, waste, compliance violations, and security issues, increasing the monthly cost of data storage and further costs associated with compliance violations and data breaches.

4. Optimize storage tiering

With tiered storage, older and less frequently accessed data is stored in slower, more affordable storage options. Organizations can significantly reduce data storage costs by migrating cold-tier data (rarely accessed or modified data) to low-cost archival storage.

Note: This is particularly advantageous for companies with many documents and files that are infrequently accessed but must be retained due to data retention policies or compliance regulations.

5. Optimize your hybrid cloud strategy

Cloud data is often cheaper to maintain than on-premises data. Typically, cloud storage handles daily data needs, while on-premises storage is reserved for data that are sensitive, require strict security, or must be instantly accessible.

Data discovery and classification tools can help organizations understand their data architecture and the most suitable storage system for data based on accessibility, security, and cost.

6. Follow best practices for data lake storage

There are various best practices to ensure necessary data isn’t taking up more space than it needs to. These include:

Using development & testing environments

Sometimes, data engineers create local copies of the entire data lake for testing, leading to unnecessary storage usage. A better solution is to use a data versioning tool with branching capabilities. This approach allows for the creation of development and testing environments without duplicating data, reducing storage costs and increasing efficiency.

Proper versioning practices

Many organizations keep multiple data copies to track changes throughout their lifecycle. However, copying data versions can be costly and requires regular deletion, management, and maintenance. Best practice would involve keeping version histories without unnecessarily duplicating data. This approach provides a complete change history and the ability to track modifications over time—without taking up too much space.

Proper file size, format, and appropriate compression

Optimizing data storage can significantly reduce data lake storage costs, particularly for analytics and tabular data. For instance, storing large objects in columnar formats like ORC or Parquet can be far more effective than JSON for efficient compression.

7. Determine the value of data – and act accordingly

Certain Data Security Posture Management (DSPM) services enhance data discovery and classification capabilities to provide organizations with a view of their data’s financial value. They do this by multiplying the number of records of each type of sensitive data by the value of that record type (based on publicly available data). Users can then adjust the calculations based on their internal baselines or estimates.

By assessing the monetary value of all your data stores, security teams can swiftly identify the sensitive data with the highest financial value to their organization and prioritize securing it.

8. Reduce financial risk of data breaches & compliance violations

With data breaches and regulatory fines costing organizations millions, unsecured data may lead to the highest potential costs of all. Organizations can reduce the potential costs of severe financial and reputational damage by ensuring data breaches don’t happen in the first place.

With the help of DSPM tools, organizations can ensure data security policies are in place, and that all data is appropriately categorized and set under the right policies, to make some of the most significant potential savings.

About the Author

Vamsi is director of product management. As a founder and entrepreneur, he is passionate about building and scaling products that change the status quo. He comes to Normalyze with a background in AML/KYC, virtual assistants, conversational design, and identities.