What is unnecessary data?

In the digital age, data is being created and collected at an exponential rate. With massive amounts of data being generated every second, it can be challenging for organizations to determine what data is truly useful versus unnecessary. Removing unnecessary data can optimize storage costs, minimize security risks, and improve the usability of systems. But what constitutes unnecessary data?

Table of Contents

Definition of Unnecessary Data

Unnecessary data refers to information that does not serve a useful business purpose. It provides little or no value to analysis, decision making, or operations. Unnecessary data is considered redundant, obsolete, trivial, or irrelevant. Here are some common examples of unnecessary data:

Duplicates of the same data that exist in multiple places
Outdated data that is no longer needed for current processes
Temporary files that were created for a specific purpose and are no longer needed

System logs or audit trails that exceed defined retention periods
Test data or sample files used for development or QA testing
Personal user files stored on company networks drives

The key point is that unnecessary data does not support any business objective or activity. It takes up storage space without offering benefits. Identifying and removing unnecessary data can help organizations operate more productively and efficiently.

Sources of Unnecessary Data

Unnecessary data can accumulate from many different sources. Here are some of the most common ways redundant, obsolete or trivial data gets created:

Multiple versions of files – Projects go through many revisions and drafts, often resulting in copies of the same file scattered in different places.

Outdated applications or databases – Systems that are no longer used still contain old data that is no longer accessed or maintained.
Bloated reporting – Generating excessive reports with overlapping data points that exceed analytical needs.
Excess backups – Keeping backup copies longer than defined retention schedules, leading to duplicates.

Unmonitored test environments – Test and development platforms that continue to generate sample data that is not deleted.
Inactive user accounts – Data belonging to former employees that remains in the system after departure.
Auto-saved versions – Applications creating hidden temporary versions during editing that remain after saving final copies.

Understanding how unnecessary data enters an environment is essential for developing policies and processes to stop uncontrolled accumulation.

Impacts of Unnecessary Data

Allowing unnecessary data to proliferate has several negative consequences for organizations:

Storage capacity – Unnecessary data consumes disk space rapidly, increasing storage costs and complexity.

Performance – Systems must work harder to index, backup, and process irrelevant data, degrading speed.
Security risks – Obsolete data with sensitive information creates vulnerabilities if left unmanaged.
Compliance issues – Many regulations require removal of data after mandated time periods.

Analytics distraction – Irrelevant data makes it harder to find and focus on useful information.
Recovery time – More data means longer backups and restoration processes.
Operational inefficiency – Staff spend time manually reviewing and cleansing unneeded data.

Getting rid of unnecessary data through activities like archiving, deleting files, and optimizing databases can avoid these problems and costs.

Evaluating Data Necessity

Determining what constitutes unnecessary data involves asking some key questions about the information in question:

Does the data support current business processes and operations?

Is the data required for regulatory compliance or audits?
Is the data used for accurate organizational reporting and analytics?
Is the data relevant for future planning and strategy?

Does the data provide insights that give competitive advantages?
Is the data needed for IT systems to function properly?

If the answer to these questions is no, then the data likely fits the definition of unnecessary. Other criteria like the age, source, and accuracy of data can also determine if it is obsolete or trivial.

Best Practices for Managing Unnecessary Data

Here are some best practices organizations can follow to eliminate unnecessary data:

Classify data by value – Categorize data into groups based on business criticality to identify lower-value sets.
Enforce retention schedules – Establish policies for deleting data after certain time periods based on classification.

Identify and archive inactive data – Move old data that must be kept for compliance into separate archives.
Delete redundant, draft and temporary files – Remove duplicates, working versions and system files that are no longer needed.
Review data requests and reports – Analyze if all collected data is actually used before generating outputs.

Tighten user permissions – Only allow employees access to data required for their specific roles.
Clean up test environments – Purge dummy data from development and QA systems after testing completes.
Prune unneeded indices and aggregates – Reduce unnecessary database overhead around little used data.

Using both technology solutions and organizational policies to regularly remove unnecessary data helps maximize operational efficiency while minimizing cost and risk.

Technologies for Managing Unnecessary Data

There are a variety of IT solutions that can help identify and eliminate unnecessary data across an organization:

Data classification – Tools that scan data and classify it using rules, to tag less critical data for removal.

Data loss prevention – Controls that monitor and block unnecessary data from entering systems.
Archiving – Software that compresses and migrates inactive data to lower cost storage.
Data masking – Anonymizing unnecessary data to reduce privacy risks if it must be retained.

Deduplication – Identifying and removing duplicate copies of files and database entries.
Database optimization – Features that identify and eliminate obsolete database objects like indices or partitions.

Automated discovery and deletion of unnecessary data through these types of solutions can greatly accelerate elimination compared to manual processes.

Developing a Data Retention Policy

A key step in managing unnecessary data is developing formal data retention policies that define what data should be kept and for how long. Here are some tips for creating effective retention policies:

Align retention periods with business, compliance and legal requirements.
Classify data by categories such as customer, financial, operational etc. and set retention rules for each.

Shorter retention for trivial data, longer retention for critical data.
Specify automatic data deletion after expiration of retention period.
Explicitly identify roles allowed to access and modify retained data.

Distinguish between live production data versus archived data.
Customize retention as needed for different systems, repositories and applications.

Updating retention policies periodically as the regulatory or business landscape evolves helps keep data oversight aligned with actual needs.

Risks of Failing to Remove Unnecessary Data

Here are some of the risks that organizations can face if they allow unnecessary data to accumulate:

Higher costs for storing and securing redundant and obsolete data.
Performance and productivity losses from systems overloaded with trivial data.

Vulnerability to cyberattacks if dormant data with sensitive information is breached.
Non-compliance with regulations mandating removal of certain data types.
Business decisions being made based on misleading analytics using outdated data.

Difficulty finding important information within a haystack of irrelevant data.
Damage to reputation if unnecessary data containing PII or trade secrets is exposed.

Proactively identifying and deleting unnecessary data is essential for mitigating these risks in today’s data-driven world.

Key Takeaways

Here are the key points to understand about identifying and removing unnecessary data:

Unnecessary data provides no business value and should be eliminated.
Obsolete, redundant and trivial data accumulate from poor management practices.

Excessive unnecessary data has many hidden costs and risks.
Regularly evaluate data against quantitative and qualitative criteria to identify what is unnecessary.
Leverage technologies like data classification, archiving and database optimization to remove unneeded data at scale.

Create and enforce detailed data retention policies aligned to business needs.
Make unnecessary data identification and removal a strategic priority to avoid pitfalls.

Following data best practices focused on minimizing unnecessary data helps create more agile, secure and well-governed information environments.

Conclusion

Organizations cannot afford to keep all the data they generate without compromising efficiency, security and costs. A proactive and persistent approach to identifying unnecessary data based on defined policies and leveraging supporting technologies is needed. Eliminating the glut of obsolete, redundant and trivial data enables companies to unlock more value from their critical information assets. By focusing on removing what is not needed, organizations can get the greatest returns from what remains.