Managing Zombie Data: The Role of Smart Data Scanning and Compliance

By Carsten Krause, July 18th, 2024

Managing Zombie Data: The Role of Smart Data Scanning and Compliance

In the age of digital transformation, organizations are generating and storing more data than ever before. While this data can be a goldmine for insights and decision-making, it can also become a liability if not managed properly. Among the many challenges enterprises face is the issue of zombie data – redundant copies of data that persist long after their useful life. This article explores the implications of zombie data, the importance of smart data scanning and compliance, and how innovative solutions like Ai Smart Data’s AI-powered data processing tool and router can help organizations efficiently manage their data, ensuring security and compliance.

Understanding Zombie Data

Redundant, obsolete, and trivial data (ROT), sometimes also referred to as zombie data, is data that remains in an organization’s storage systems, consuming valuable resources without adding any value. This can include duplicate files, outdated records, and unnecessary backups. Over time, zombie data can accumulate to massive proportions and metastasize, clogging up storage systems, slowing down data retrieval processes, and increasing operational costs. More critically, it can expose organizations to security and compliance risks, as this data often contains sensitive information that must be protected under various regulatory frameworks.

Identifying ROT Data:

Zombie data often lurks in various corners of an organization’s IT environment. This can include old email archives, legacy database records, redundant file copies, outdated backup tapes, and log files that have outlived their usefulness. As organizations grow and evolve, they generate vast amounts of data, much of which becomes outdated but remains stored due to inadequate data management practices.

Impact of ROT Data on Business Operations

• Security Risks: Organizations retaining unnecessary data open themselves up to potential security threats, as this data is often left unmonitored and unsecured.
• Regulatory Risks: Over 75% of records containing personally identifiable information (PII) are over-retained, increasing the risk of non-compliance with laws like GDPR and CCPA.
• Operational Inefficiencies: High volumes of redundant data make data management more complex and time-consuming.
• Cost Risks: Companies spend significant amounts of money on storing unnecessary data, with some estimates suggesting costs as high as $34 million annually on unnecessary data management and storage.

Source: Carsten Krause, CDO TIMES Research & security.ai

The Cost of Redundant ROT Data

Storing zombie data is not just a minor inconvenience; it has real financial implications. The costs associated with maintaining this data can be substantial. According to a study by Veritas, an estimated 52% of an organization’s data is considered “dark,” meaning its value is unknown. Another 33% is classified as redundant, obsolete, or trivial (ROT) data. This means that up to 85% of enterprise data could be classified as zombie data, leading to increased storage costs, inefficiencies, and potential security vulnerabilities (The Leader in Enterprise Data Management) (The Leader in Enterprise Data Management) (DQ).

• Increased Storage Costs: Maintaining vast amounts of unnecessary data requires substantial storage resources, which translate to higher costs for hardware, cloud storage, and data center operations.
• Security Risks: Zombie data often contains outdated yet sensitive information that can become a target for cyberattacks. Managing and securing this data becomes increasingly complex and risky.
• Compliance Challenges: Regulatory requirements such as GDPR, CCPA, and HIPAA mandate strict controls over personal and sensitive data. Redundant data can make compliance difficult, exposing organizations to fines and legal liabilities.

The Role of Smart Data Scanning and Compliance

To mitigate these challenges, organizations need to adopt smart data processing technologies that can accurately identify and manage zombie data. Smart data processing involves using advanced algorithms and artificial intelligence to scan and classify data based on its relevance, sensitivity, and compliance requirements. This process enables organizations to:

• Identify Redundant Data: Advanced data scanning tools can pinpoint duplicated or outdated files, making it easier to clean up storage systems.
• Enhance Data Security: By identifying and securing sensitive data, organizations can reduce the risk of data breaches and unauthorized access.
• Ensure Regulatory Compliance: Smart data scanning helps classify data according to regulatory requirements, ensuring that sensitive information is managed appropriately and that non-compliant data is identified and addressed.

Companies that do address ROT data are typically experiencing the following significant benefits:

Source: Carsten Krause, CDO TIMES Research & Veritas, Premcloud

Case Study: Ai Smart Data’s Ai Smart Data Processing

Ai Smart Data has emerged as a leader in addressing the zombie data problem with their innovative Ai . This solution proactively identifies, enriches, and indexes current data, classifies and tags it against privacy and security controls, and enables organizations to confidently delete petabytes of redundant data and adhere to compliance and eDiscovery demands.

How Ai Smart Data’s Solution Works:

1. Automated Data Scanning: Ai Smart Data Processing continuously monitors and scans data across the organization’s storage systems. It uses machine learning algorithms to identify redundant, obsolete, and trivial data.
2. Data Classification: The tool classifies data based on an organization’s policy, compliance, utilization, and security demands. This ensures that personal and sensitive information are handled according to regulatory standards, reducing the risk of non-compliance.
3. Intelligent Routing: Once data is classified, the Ai Smart Data Router  directs data to appropriate storage locations for re-tiering or flags it for deletion. This process helps optimize storage use and ensures that only valuable data is retained.
4. Secure Deletion: Ai Smart Data’s solution includes secure data deletion capabilities, allowing organizations to confidently and permanently delete redundant data. This helps free up storage space and reduces the risk of data breaches.

Source: Ai Smart Data

In the grand scheme of data architecture optimization, the savings achieved by leveraging modern architecture including MACH (microservice, Cloud, API and headless) and Microsoft’s well architected cloud architecture frameworks can achieve significant improvements:

Source: CDO TIMES Research & McKinsey

• Data Repository Reduction: Simplifying data architecture by reducing the number of data repositories can save significant costs. For example, a global bank saved $400 million annually by streamlining its data repositories from 600 to 40 unique domains.
• Cloud Migration: Migrating data to a cloud-centric design can further reduce costs and improve data accessibility and integration.
• API Utilization: Using APIs to access data within legacy systems can provide immediate value without costly custom workflows.

A Global Bank’s Cost Savings through Data Streamlining

A leading global bank faced substantial financial and operational challenges due to its fragmented data repositories. Initially, the bank managed over 600 data repositories, which led to high maintenance costs, inefficiencies, and data quality issues. Recognizing the unsustainability of this approach, the bank undertook a comprehensive data architecture simplification project.

Besides the Financial Service industries managing and optimizing data and reducing ROT data is relevant for many other industries as pointed out in this McKinsey research study.

The Problem

• Fragmented Data Repositories: The bank had over 600 separate data repositories scattered across different business units. This fragmentation led to significant redundancy and inefficiencies in data management.
• High Maintenance Costs: Managing such a vast number of repositories cost the bank approximately $2 billion annually. The costs were associated with storage, data processing, and maintaining outdated systems.
• Data Quality Issues: The lack of standardization and centralization resulted in data inconsistencies and made it difficult to ensure data accuracy and completeness.

The Solution

To address these challenges, the bank implemented a strategic data management initiative focused on streamlining its data architecture. The key steps included:

1. Formation of a Joint Data-Architecture Team: The bank established a joint enterprise data-architecture team, comprising the Chief Information Officer (CIO) and relevant business leaders. This team was responsible for overseeing the data streamlining efforts.
2. Simplification to Unique Domains: The team simplified the data environment by consolidating the 600 repositories into 40 unique domains. This involved identifying and retaining only the “golden source” repositories essential for business operations.
3. Standardization of Data Management Practices: The bank implemented standardized data management practices across all domains to ensure consistency and improve data quality.

The Results

The data streamlining initiative led to substantial benefits, including:

• Cost Savings: By reducing the number of data repositories from 600 to 40, the bank saved over $400 million annually. These savings were achieved through reduced storage costs, lower data processing expenses, and decreased maintenance efforts.
• Improved Data Quality: The consolidation and standardization efforts significantly improved data quality. This made it easier for the bank to update systems, integrate insights into business processes, and ensure data accuracy.
• Operational Efficiency: The streamlined data architecture enhanced operational efficiency by simplifying data retrieval and processing. This enabled faster and more reliable access to critical business information.
• Enhanced Compliance: The standardized data management practices helped the bank comply with regulatory requirements more effectively, reducing the risk of non-compliance penalties.

Broader Implications

This case study illustrates the broader implications of effective data management and streamlining practices. By addressing data fragmentation and implementing a centralized approach, organizations can achieve significant cost savings, improve data quality, and enhance operational efficiency.

For more detailed insights, you can read the full study on McKinsey’s website: Reducing Data Costs without Jeopardizing Growth.

Expert Insights

“It’s very early… I know this is cliché, but Marc Andreessen said, ‘software is eating the world.’ He meant software is in your Apple Watch, it’s in your thermostat, it’s in your Tesla car, everywhere it’s software. I really think AI will follow software. Wherever you have software, you’re going to collect data and you’re going to automate things. It’s going to be more intelligence. So, you get more intelligent software…we’re still in the ‘software is eating the world’ kind of phase. So… it’s very early.” (Source: Ali Ghodsi, CEO , Databricks Goldman Sachs Talks)

Kevin Oliveira, Forcepoint: “Data within organizations tends to literally multiply through duplicates, duplicates of duplicates, etc. This is a problem often referred to as redundant data. When managed properly, data often serves as a tremendous resource that brings real top-line and bottom-line value to organizations. However, unmanaged data can quickly become a massive problem.” (Source: Forcepoint Blog)

“Retaining ROT is detrimental to businesses in numerous ways, including security risks, regulatory risks, operational inefficiencies, and cost burdens. Organizations spend as much as $34 million on keeping unnecessary data, highlighting the need for robust data minimization strategies.” (Source: Securiti.ai)

The CDO TIMES Bottom Line

In the digital age, enterprises are grappling with the challenge of managing vast amounts of data. While this data holds potential for insights and decision-making, it often includes a significant portion of zombie data—redundant, obsolete, and trivial (ROT) data. This type of data not only wastes resources but also poses security and compliance risks. Effectively managing ROT data is crucial for maintaining operational efficiency, reducing costs, and ensuring regulatory compliance.

Understanding Zombie Data

Zombie data refers to unnecessary data that consumes resources without providing value. This includes duplicate files, outdated records, and unnecessary backups. Over time, such data accumulates, leading to:

• Increased Storage Costs: Maintaining unnecessary data requires significant storage resources, translating to higher costs for hardware, cloud storage, and data center operations.
• Security Risks: Zombie data often contains sensitive information that can be a target for cyberattacks.
• Compliance Challenges: Regulations like GDPR and CCPA mandate strict controls over personal data, making compliance difficult with ROT data.

The Cost of Redundant ROT Data

An estimated 52% of an organization’s data is considered “dark,” meaning its value is unknown. Another 33% is classified as ROT data. This means up to 85% of enterprise data could be zombie data, leading to:

• High Storage Costs: Significant expenses associated with unnecessary data management.
• Operational Inefficiencies: Complications in data retrieval and processing.
• Security Vulnerabilities: Increased risk of data breaches.

Addressing the ROT Data Challenge

Smart Data Scanning: Implementing advanced data scanning technologies helps organizations:

• Identify Redundant Data: Pinpoint duplicated or outdated files.
• Enhance Data Security: Secure sensitive data, reducing breach risks.
• Ensure Compliance: Classify data according to regulatory requirements.

Next Steps for Organizations:

1. Conduct a Data Audit: Regularly audit data to identify ROT data.
2. Implement Data Governance Policies: Establish clear policies for data retention and deletion.
3. Utilize Smart Data Scanning Tools: Invest in tools that automate data classification and secure deletion.
4. Explore Solutions Like Ai Smart Data Processing: Consider advanced solutions that provide comprehensive data management capabilities.

The accumulation of zombie data poses a significant challenge for modern enterprises. However, with smart data scanning and compliance solutions like Ai Smart Data’s AI-powered data scanner and router, organizations can proactively manage their data, reduce operational costs, enhance security, and ensure regulatory compliance. As data continues to grow exponentially, adopting such innovative technologies will be crucial for maintaining an efficient and secure data environment.

By proactively managing zombie data and leveraging smart data scanning solutions, organizations can significantly reduce costs, enhance security, and ensure compliance, ultimately driving business success.

For more insights, visit Ai Smart Data.

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