We’re assisting a company in backing up and migrating their Universal Analytics data. If there was ever a great example of the cost of data, this is it. Analytics captures data non-stop and is presented by hour, day, week, month, and year. If we wish to make all the data accessible, the client can spend tens of thousands of dollars in storage fees… not to mention the cost of querying the data and processing reports. Ultimately, the solution will be two-fold:
- A reporting and data solution that balances the analysis needed regularly and the cost to store and manage that data.
- An affordable backup of all data if we need to access it later.
As storage costs dropped, companies began to ignore the volumes of data they were acquiring, capturing, and storing over time. Corporate data stacks continued to expand, data capture points increased, and hundreds of sources are now adding exponentially to corporation’s data.
It’s not an inexpensive issue:
Businesses spend an average of $2.5 trillion per year on data management, and that 30% of that spend is wasted on unnecessary or inefficient data storage and retention.Data Age 2025
The average enterprise spends $1.2 million per year on data storage and retention, but that 30% of that spend is wasted on unnecessary or inefficient data storage and retention.Forrester
One way to better manage your data costs is to incorporate a data retention policy and appropriate organizational activities.
Data Retention Policy
A data retention policy is a set of guidelines and rules established by an organization to dictate how long various types of data should be stored and how they should be managed throughout their lifecycle. This policy is crucial for maintaining data governance, ensuring compliance with legal and regulatory requirements, and optimizing data management practices.
Only 35% of businesses have a data retention policy in place.IBM
In the context of sales, marketing, and online technology, a data retention policy may specify how customer data, sales leads, marketing campaign data, and other relevant information should be handled. Here are the key aspects of a data retention policy:
- Retention Periods: Define the duration for which different types of data should be retained. This can vary based on legal requirements, industry standards, and business needs. For instance, financial records may need to be retained for several years, while temporary marketing data might have a shorter retention period.
- Access Control: Specify who has access to various data types within the organization. Access should only be restricted to authorized personnel to prevent unauthorized use or disclosure.
- Data Security: Implement security measures to safeguard data during its retention period. This includes encryption, access controls, and regular security audits.
- Data Backup: Regularly back up data to prevent loss due to system failures, data corruption, or cybersecurity incidents.
- Data Deletion: Define procedures for securely deleting data when it reaches the end of its retention period or when requested by data subjects (e.g., customers). Ensure compliance with data privacy regulations, such as GDPR or CCPA.
- Audit Trails: Maintain audit logs to track who accessed the data and when, which can be useful for compliance and security purposes.
- Legal Compliance: Ensure that the data retention policy aligns with applicable laws and regulations. Consult legal experts to stay updated on changing requirements.
- Training and Awareness: Train employees on the data retention policy and regularly raise awareness about its importance to ensure compliance.
- Periodic Review: Regularly review and update the data retention policy to adapt to changing business needs and regulatory requirements.
A well-defined data retention policy helps organizations manage data effectively, reduce risks associated with data breaches or non-compliance, and optimize storage costs by only retaining data for as long as necessary.
Data Cost Reduction Strategies
There are several ways that companies can save money on data costs while maintaining data integrity and security. Here are some cost-saving strategies, along with examples:
- Data Cleanup and Deduplication: Regularly clean up outdated, invalid, duplicate, and unqualified contact data in Customer Relationship Management (CRM) systems. This reduces storage costs and ensures sales and marketing efforts are directed at accurate and relevant leads. If you need assistance in reducing your Salesforce data costs, contact us at DK New Media.
Salesforce estimates that 91 percent of CRM data is incomplete and 70 percent of that data deteriorates and becomes inaccurate annually.Dun and Bradstreet
- Data Archival and Tiered Storage: Move older and less frequently accessed data to cost-effective archival storage. For example, historical transaction records can be moved to archival storage, freeing up expensive primary storage space.
- Backup Optimization: Evaluate backup policies and practices to reduce redundancy and optimize storage costs. Implement techniques like deduplication and compression to minimize backup storage requirements. Consider shifting backups to secure, cloud-based backup services that offer cost-effective storage options. Cloud providers often offer tiered storage plans where less frequently accessed data is stored at lower costs.
- Data Lifecycle Management: Establish clear data retention policies that dictate how long data should be retained. Delete no longer needed data, reducing storage costs and potential legal risks. Implement automated data deletion processes based on retention policies to avoid manual overhead.
- Cloud Cost Optimization: Continuously monitor right-size cloud resources to match usage. This can include scaling down or pausing resources during periods of low demand. Utilize cloud services like AWS Spot Instances or Azure Reserved Instances to save on computing costs.
- Data Compression and Encryption: Compress data before storage to reduce storage costs while maintaining accessibility. Implement efficient encryption methods to secure data without significantly increasing storage requirements.
- Data Governance and Training: Implement data governance practices to ensure data quality and compliance, reducing the risk of unnecessary costs due to data errors. Train employees on data management best practices to avoid accidental data sprawl and reduce costs associated with unnecessary data creation.
- Data Usage Analysis: Audit and analyze data collection and usage patterns to identify and eliminate unused or underutilized datasets, freeing up storage resources.
- Vendor Negotiations: Regularly review contracts with data storage providers to negotiate better rates or explore more cost-effective options. As bandwidth, computing power, and storage become more efficient, hard costs are dropping for vendors. Keeping your contracts static is not always a requirement.
- Data Virtualization: Implement technologies that allow data to be accessed and used without duplicating and storing it in multiple places, reducing storage costs.
The Global DataSphere is expected to more than double in size from 2022 to 2026. The Enterprise DataSphere will grow more than twice as fast as the Consumer DataSphere over the next five years, putting even more pressure on enterprise organizations to manage and protect the world’s data while creating opportunities to activate data for business and societal benefits.”John Rydning, Research Vice President, IDC’s Global DataSphere
By implementing these strategies, companies can optimize their data management practices, reduce unnecessary costs, and ensure that valuable data remains accessible and secure.