Data is today a top business commodity. But system and user-generated data add up. For many enterprises, data storage costs dent the bottom line. Without optimising data storage costs, the benefits derived from the data may not exceed the data costs.

Here are the best practices to optimise data storage costs.

1. Save Only Essential Data

The best way to save on data costs is to stop capturing unwanted data.

Review the existing data capture practices and policies. Determine if any collected data is unnecessary. Modify the process and configurations, as appropriate, to capture only essential data.

Ensuring the capture of only essential data depends on making users aware of data sprawl. Educate users on the importance of efficient data storage. Encourage them to review and delete unnecessary files and data.

2. Delete Unnecessary Data

The most straightforward way to reduce data storage costs is to delete unwanted data.

However deleting files left and right may lead to deleting some files needed for compliance, legal, or business purposes. There is also the risk of losing some essential technical files and causing a performance hit.

As the first step to shed unwanted data, conduct a thorough inventory of all data sources. Include databases, files, logs, backups, and all other data types.

Next, categorise data based on the value, sensitivity, usage frequency, and regulatory requirements. Accurate inventory and classification make it easy to identify unwanted data. It also helps to select the most appropriate storage type for each type and class of data.

Institute a policy to delete spam and unwanted data daily. Delaying the deletion of unwanted data soon leads to a situation where such data piles up. Scrutinising data for deletion then becomes an insurmountable task.

Likewise, have a clear policy to delete data after it outlives its utility. Consider the expected retention period of the data.

Some data may lose its business value after a few months. But the enterprise will still have to retain it for a few years, to meet compliance requirements. For instance, the US FINRA and Singapore Monetary Authority mandate the retention of financial records.

3. Compress and Deduplicate the Data

Compressing and deduplicating data minimises storage space requirements and reduces storage costs.

Data compression reduces the file size without losing essential information. The compression tool identifies and removes redundant data within the file.

Data deduplication identifies and removes duplicate data within the storage system. Many systems store multiple copies of the same data. Deduplication tools retain only a single copy. Pointers or references link to that single, original data. Most cloud providers offer built-in compression and deduplication features.

The format of the data can make a big difference to compression. For large analytical and tabular data, columnar formats such as ORC or Parquet enable more efficient compression. The same data stored as small JSON objects could become 2x to 5x times larger, even when compressed.

4. Decide Between Cloud and On-premises Storage

The cloud is now the preferred medium of storage. The cloud enables anywhere, anytime access, with robust security and access controls. Adding capacity also becomes easy. The provider offers a slew of ready-to-access handy tools to manage the data and run advanced analytics on it.

But such advantages come at a cost.

The cloud is a subscription model with recurring monthly payments. There is also the cost of transferring data to and from cloud providers. Many cloud plans involve high egress fees, especially for large data sets. 

On-premises storage, in CDs or servers, entails a one-time cost with minimal recurring costs. But setting up an on-premises data warehouse requires substantial up-front investment for licenses, server hardware, facility setup, and hiring expertise. The costs can easily go up to $100K to store 1 petabyte of data.

Storing data on-premises or cloud depends on operational requirements, security, and compliance mandates.

If the data does not require anywhere access, on-premises storage could reduce the total lifecycle storage costs. Data sovereignty laws force enterprises to store certain types of data on-premises. Enterprises also store ultra-sensitive data on-premises, disconnected from the Internet.

5. Decide the Most Appropriate Storage Solution

Selecting the appropriate storage methods for the data type optimises costs. The wrong storage type or database inflates costs and also degrades performance.

Storing data in conventional relational databases may inflate costs for many data formats. If the data is document-oriented or requires flexible schemas, a NoSQL database is more cost-efficient. Likewise, for log files and data that do not require complex querying, flat files are more cost-effective. A relational database entails multiple tables or complex joins. This leads to data duplication and increased storage costs.

For high data volumes, infrastructure as a service (IaaS) database solutions are resource-intensive. Platform-as-a-service becomes a more cost-effective option. PaaS platforms come with built-in resources for scaling and handling fluctuating workloads. It also offers automated configuration capabilities.

Developing a cache solution also reduces storage needs and costs.

One point to consider is the costs of switching or swapping services if the data already exists in inappropriate storage. Make a trade-off between the transfer costs and the savings that come from using the most appropriate storage solution. In most cases, transferring the data will lead to net savings.

6. Choose the Appropriate Storage Class

In the cloud, there are several storage classes. Each class has different costs, durability, and resiliency. Selecting the appropriate class for each type of data allows for optimising storage costs.

Standard storage costs the highest per GB. The data replicates across multiple availability zones with high availability and low latency. Such storage suits frequently accessed data. But, a typical enterprise accesses only 20% of its stored data frequently. Moving the data not required for frequent access can deliver huge savings.

Infrequent access class storage costs less and is ideal for data accessed less than once per month. Achieve storage class costs even less. The data is not readily accessible though, and requires retrieval requests before access.

Coldline classes and deep archives cost even less, and suit data accessed less than once per year. While storing data in such storage classes is very cheap, accessing such data may entail additional charges.

The optimal storage class depends on data access frequency and retention requirements. But such classification is not set in stone. Businesses may access some data frequently for a couple of months, and then they may not need the data for the next year. Optimisation depends on configuring policies to move data between storage tiers.

All cloud providers support lifecycle management. Users can define rules to move data objects between tiers.

7. Make the Right trade Off between Performance and Costs

The cloud enables anywhere accessibility. But the location of data access matters in cloud storage costs.

Storing data closer to use reduces latency and network costs. Storing a frequently accessed database in multiple locations improves performance and availability. For instance, a gaming app with payers across the globe needs storage at multiple locations to ensure top performance. But, storage in multi-regional locations comes at a premium and increases network egress charges. Optimising costs depends on a trade-off between performance and cost.

When the business does not need the data globally, it can choose a specific region for the data storage. Such localised storage costs less.

8. Use Versioning for Tests

Many data engineers create a local copy of the entire lake for testing purposes. This multiplies the data storage and inflates costs. Using a data versioning tool allows tests without duplication. Such testing environments reduce data storage costs by up to 80%.

9. Optimise Backups

Backups are indispensable to maintain data safety and integrity. But backups duplicate data storage and inflate costs.

Optimising backups can reduce data storage costs in a big way.

• Classify data based on its importance. Back up only the essential data.
• Review the backup frequency. Perform frequent backups only for critical data.
• Perform incremental backups instead of full backups. Incremental backups add to the last backup instead of saving the entire dataset all over again. The storage and network bandwidth costs come down.
• Move older backups to lower-cost storage tiers.

10. Make Proactive Adjustments on Cloud Plans

Optimising cloud storage depends on making the best use of available resources. In today’s dynamic business environment, the costs for storage environments shift often.

• Explore reserved instances for significant discounts on long-term storage commitments.
• Utilise cloud provider tools and other third-party solutions to track storage costs. These tools can also identify cost anomalies and take advantage of offers that cloud providers come up with from time to time.
• Conduct regular cost reviews to cut waste and identify areas for optimisation. Such reviews become especially expedient as the data grows or business needs change. Reevaluating storage options in light of the existing situation may throw up options for optimising costs.

Optimising data storage and cloud costs reduces operational expenses. In today’s competitive environment, such optimisation delivers valuable competitive advantages.