Data Duplication Implications and Solutions

Data duplication is a simple concept: It’s the idea that any piece of data has one or more exact duplicates somewhere in an organization’s infrastructure. It might be a record in a database, a file in a storage volume, or a VM image. On its own, duplication may seem benign, even beneficial. Who doesn’t like an extra copy? But when expanded to enterprise scale, the scope of the problem becomes clear. With nearly every modern device constantly producing data, backups and archives regularly scheduled and executed, and files shared across many platforms, data duplication has grown from an annoyance to a massive cost and technological burden. Resolving the problem starts by understanding how and why data duplication occurs.

What Is Data Duplication?

Data duplication is the process of creating one or more identical versions of data, either intentionally, such as for planned backups, or unintentionally. Duplicates may exist as stored data in files, VM images, blocks or records in a database, or other data types. Regardless of the cause, data duplication wastes storage space, with the cost growing along with the size of data stores. It can also contribute to data management problems. For example, if all copies of a file aren’t updated simultaneously, inconsistencies can lead to faulty analysis.

Related to data duplication is data redundancy, or having multiple records to act as redundant safety nets for the primary versions of data. The opposite of data duplication is data deduplication, which entails the elimination of duplicate data to free up resources and remove possibly outdated copies.

Key Takeaways

• Duplicate data refers to exact copies of files or database records within a network. It often results from a lack of communication, outdated processes, and failing to adhere to best practices for file sharing.
• Duplicate data can unnecessarily eat up resources, such as storage space and processing power.
• Duplicate data can also skew the results of analysis, such as providing the same sales records twice.
• Organizations create duplicate data both intentionally, as backups and archives, and unintentionally via multiple downloads, copy/paste errors, or duplicative data entry.
• Dealing with duplicate data in all its forms creates a significant cost burden, both directly by using up resources and indirectly if staff must correct mistakes in bills and purchase orders or take other actions that are based on duplicate data.

Data Duplication Explained

Duplicate data isn’t necessarily a bad thing. Intentional data duplication can deliver significant benefits, including easily accessible backups, comprehensive archiving, and more effective disaster recovery. However, gaining these benefits without undue cost requires a strategy for performing backups and regular, scheduled deduplication. Without that, duplicate data can, at best, unnecessarily take up additional storage space and, at worst, cause confusion among users and skew data analysis.

Though the terms “data duplication” and “data redundancy” are often used interchangeably, there’s a difference. Duplicate data isn’t necessarily purposefully redundant; sometimes, a duplicate is made carelessly or in error by a human or a machine. However, from an engineering perspective, the concept of redundancy is to produce a safety net in case of a problem. This leads to duplication with intent. Redundancy in itself is a tenet of robust engineering practices, though it’s certainly possible to create over-redundancy. In that case, even if the extra sets of duplicates are generated with purpose, they offer limited value for the amount of resources they use.

Why Does Data Duplication Happen?

Data can become duplicated in several ways by humans and automated processes. Most people have saved multiple versions of a file with slightly different names, and often minimal changes, as a document moves through the revision process—think “salesreport_final.docx” versus “salesreport_final_v2.docx” and so on. These generally aren’t deleted once the report really is final. Or, a file may be emailed across the organization, and two different people save the same version in separate spots on a shared drive. An application .exe or media file might be downloaded multiple times, and VM instances may be saved in a number of places. Similarly, within a database, the same data can be input twice. A customer or employees may have uploaded information twice, either through multiple people importing a file or typing the records. That sort of duplication can also happen when different departments create the same record, such as customer information, on local applications or different applications with compatible file types. This means you might have redundant copies across different backup versions—which themselves might be duplicates.

The more data-driven an organization is, the more duplication may be a problem. Big data can lead to big costs for excess storage. Automation may also create duplicates. In this case, an automated backup process might create duplicate files with the intent of redundancy. Problems arise, though, when the same file is backed up multiple times. Unnecessary levels of redundancy lead to inefficient storage use.

Less commonly, unexpected events lead to data duplication. If a power outage or natural disaster strikes during a backup process, for example, the backup may reset, restarting the process after some files have already been written. Hardware failures can create similar issues, leading to unplanned duplication during a backup or archiving process.

Types of Data Duplication and Their Implications

Duplicate data isn’t necessarily a bad thing. IT teams need to understand if the duplication was intended, how many resources are used to store dupes, and how costly the status quo is. An intentional third-generation archive that contains pointers to fully cloned duplicates in a second-generation archive is a completely different circumstance from multiple saved instances of the same giant PowerPoint file across a shared drive.

The following are the most common types of data duplicates and how they might affect your organization.

• Shallow Duplication: Shallow duplication creates a new object when data is copied, but rather than completely cloning the data, the object houses a reference pointer to the original object. While this takes up far less storage space, queries will need to go one additional step to get the source data. In addition, the duplicate is, in essence, synced with the original, so any changes to the original will reflect on the duplicate. This may cause issues if the duplicate is meant to capture a specific state rather than act as a dynamic duplicate.

• Deep Duplication: With deep duplication, a new object is created as a complete and unaltered clone of data. The new object requires the same amount of storage space as the original, meaning that deep duplication eats up more storage than shallow duplication. Despite this drawback, deep duplication has the advantage of offering standalone redundancy—if anything happens to the source file, either intentionally or accidentally, deep duplication helps ensure a clean backup capable of disaster recovery.
• Data Fragmentation: Data fragmentation refers to the process of storing segments of a data file in different locations. Although this can make storage more efficient by writing segments based on access frequency or capacity, querying the file generally requires greater processing time and resources because the system must look up segments and compile the complete file. For recovery purposes, fragmentation may lead to problems. For example, mechanical or connectivity failures may lead to incomplete duplication. Or location-based failures might damage only some fragments, corrupting a backup or archive process.
• Logical Replication: Logical replication is similar to shallow duplication in that it uses references for a more efficient duplication process. When maintaining backup systems, logical replication treats consistency as a publisher/subscriber model, with the publisher being the source and the subscriber being the target for a specific volume of data, usually identified by an address. When the publisher makes a source update within a specified address range, the subscriber data updates to stay in sync. Updates outside of the subscribed range are ignored to maximize efficiency.
• Physical Replication: Physical replication is a form of database replication that copies data in a methodical, byte-by-byte process. Unlike logical replication, this is a slower, yet more comprehensive and more resource-intensive, model that also creates more duplicate versions.

The Costs of Data Duplication

Duplicate data creates a ripple effect of additional burdens across hardware, bandwidth, maintenance, and data management, all of which add up to a mountain of unnecessary costs. In some cases, issues are minor, but in worst-case scenarios, the results can be disastrous. Consider some of the following ways that data duplication harms data science endeavors.

Storage space. This is the most direct cost of data duplication. Redundant copies eat up valuable capacity on local hard drives, servers, and cloud storage, leading to higher costs. Imagine a department with 10 terabytes of data, and 10% is duplicative. That’s a terabyte of wasted storage, which could translate to significant costs, especially if it’s in cloud-based primary storage versus archival storage.

Data deduplication tools. Another hard cost, deduplication tools can clean out duplicates from storage volumes. These services and tools are usually based on per-record volume. Thus, the more to deduplicate, the higher the cost.

Skewed data. Duplicate records can introduce errors into data analysis and visualizations by creating inaccurate metrics. For example, say a new customer has been entered twice into a sales database with slightly different names, or two admins enter the same purchase order.

Each of the above elements also requires costly staff work. Storage volumes must be maintained. Someone needs to evaluate, purchase, and run deduplication systems. Skewed data requires removing records and cleaning databases. If bad data propagates forward into further reports or communication, then all the ensuing work must be backtracked and undone, then repaired.

Issues Caused by Data Duplication

Unintentionally duplicated files and database records can cause problems to ripple throughout an organization when left unchecked. The following are some of the most common issues that arise with data duplication.

• Data Quality Issues: Data is considered high quality when it meets the organization’s criteria for accuracy, completeness, timeliness, and purpose. When duplicate data proliferates, each of those factors may be compromised, and reports or analysis generated will be less accurate. The longer duplicates are allowed to remain, the more the organization’s overall data quality degrades, creating issues with any type of analysis, whether backward-looking reviews or forward-looking projections.
• Decreased Efficiency of Staff: How much time is spent chasing down and correcting duplicate data? When an organization lets duplicate data pile up, workers lose hours, days, and possibly weeks between double- or triple-checking reports and records and undoing or correcting problems. Required fixes may include
  • Updating records
  • Tracking down how many versions of the same file exist on a shared server
  • Checking how a report’s statistics might be skewed by duplicate information
  • Tracking who has viewed a report with incorrect data
• Difficulty Generating Accurate Reports and Analytics: How good are the insights that decision-makers draw from your reports and data analytics? With duplicate data—or really, any low-quality data—your reports might be steering the company in the wrong direction. Organizations with known duplicate data issues then must deal with the increased labor of working around it, either through additional pre-report data cleaning or compensating for known data shortfalls.
• Failure to Meet Regulatory Requirements: Duplicate data can make it difficult to comply with regulatory guidelines, which often emphasize the need for comprehensive data management. Regulatory bodies may require organizations to submit reports on their financial data, for example, and duplicate data can lead to inaccurate or inconsistent information in these reports, potentially resulting in fines or penalties. Regulatory requirements often mandate strong data security practices and the ability to identify and report breaches promptly. It’s difficult to do so if sensitive data, such as customer credit cards, is stored in several places. Finally, regulations such as the General Data Protection Regulation and California Consumer Privacy Act grant individuals the right to access, correct, or delete their personal data. Duplicate records can make it difficult to locate all relevant data associated with a specific individual, hindering compliance.
• Increased Inventory Costs: Duplicate data may lead to increased inventory costs as organizations find themselves either scrambling to restock inventory to address shortages caused by inaccurate data or deal with overstocking generated by duplicate records. Without clean data, a true lean operation becomes impossible.
• Poor Business Decisions: Organizations can thrive when they make data-driven decisions. However, when that data is corrupted by duplicates, decisions are made on false pretenses. The result may include a minor hiccup that can be overlooked, a scramble to make a fix, or a catastrophic decision caught far too late.
• Poor Customer Service: When a customer interacts with your company, having information scattered across multiple duplicate records makes it difficult for service agents to get a holistic view of their history. Your agent might be missing crucial details about a customer’s past purchases, support tickets, or communication history. That hurts your ability to provide personalized and efficient service, and it affects brand perception when a valued customer wonders, “Why didn’t the agent know my story?”
• Reduced Visibility: Network visibility refers to the concept of organizations knowing about all the traffic and data that reside in or traverse their networks. Duplicate data affects this effort on several levels, including the following examples:
  • Creating inaccurate data logs
  • Lengthening backup/archive processes and consuming excess storage
  • Skewing network performance and transmission metrics
  • Wasting processing and bandwidth resources

Strategies to Prevent Data Duplication

With shared drives, the Internet of Things devices, imported public and partner data, tiered cloud storage, more robust replication and disaster recovery, and myriad other sources, organizations hold more data than ever before. That leads to more opportunities for duplication, which means organizations should prioritize strategies to both minimize the creation of duplicate data and eliminate it when it propagates.

Some of the most common strategies to achieve that are as follows:

• Enforce Data Validation Rules: When importing data into a repository such as a data lake or data warehouse, take the opportunity to cleanse and validate that data. Performing data validation at the ingestion stage limits the acceptance of any duplicate data created upstream at the source. IT departments should configure a process to create and enforce rules for identifying and eliminating duplicate data as part of their ingestion workflow.
• Establish a Unique Identifier: Databases can apply unique identifiers to records to help ensure duplicate versions aren’t generated. In the instance of a customer account, for example, the unique identifier may be a new field for a customer identification number or account number. The account number can then be used when sales and marketing teams work with the customer, preventing the opportunity to accidentally create another record using the same customer name.
• Perform Regular Audits: Using a deduplication tool on a regular cadence is a smart part of an effective IT maintenance strategy. Although the effectiveness of the deduplication process will vary each time based on circumstances, the process’s regular frequency helps ensure that duplicates will always be caught and kept to a minimum.
• Use Reusable Code Libraries and Frameworks: For application development, developers can implement reusable code libraries and frameworks to streamline their own work while helping reduce duplicate code. This initiative creates a repository of functions and other reusable elements, helping ensure that developers use modular assets without generating duplicate code or redundant work.
• Utilize Database Constraints: Database managers can establish constraints to prevent duplicate records across certain fields. For example, in a database with customer records, the system can use a unique constraint on the customer name field, which helps ensure that all customer names are unique and thus minimizes the chance of someone accidentally creating a duplicate record that may skew sales data.

Benefits of Eliminating Data Duplication

As organizations become more data-driven, eliminating duplicate data becomes ever more necessary and beneficial. Taking proactive steps to minimize redundancy can optimize storage infrastructure, improve data management efficiency, improve compliance, and free up money and staff resources for other priorities.

The following details some of the most common benefits of data deduplication:

• Reduced Storage Costs: When you eliminate duplicate data, you can reduce the amount of storage the business needs to pay for in the cloud and push off the need to purchase new hardware for owned data centers. That creates two types of cost savings. On a direct level, organizations can slow their purchase cycles. Indirectly, though, using less data storage lets IT teams more efficiently monitor and maintain the state of their resources, saving on overall maintenance and overhead expenses.
• Improved Data Accuracy: Duplicate data creates a variety of accuracy issues. Duplicate database records for customers can lead to two different departments updating the same record, sowing confusion. Similarly, the accuracy of analytics reports becomes skewed by redundant data.
• Enhanced Overall Customer Experience: When a company has accurate, complete, clean data about its clientele, the result is often higher customer satisfaction and better brand perception as well as increased sales. By avoiding having purchase histories assigned to different overlapping records, you increase the accuracy of recommendation engines and follow-up marketing efforts.
• Increased Employee Productivity: Another fallout from inaccurate data can be decreased employee productivity. Maybe workers in different departments waste time trying to track down the source of inaccuracy in their reports, or there’s additional overhead required for maintenance and data cleansing efforts. Either way, inaccurate data means more scrambling to get information right, which can affect scheduling, communication, workflow, and, ultimately, the budget.
• Easier Access to Data and Better Information Sharing Among Departments or Teams: Data deduplication efforts can significantly improve information sharing among departments or teams within an organization. One benefit is breaking down the dreaded data silos that plague departmental systems and applications. Deduplication helps consolidate information into a single data source, making it easier for different teams to access and share accurate, consistent information. And, with fewer redundant copies and optimized storage, it’s easier for teams to find the information they need. They don’t have to waste time searching through multiple locations or versions of potentially outdated data.
• Better Decision-Making Based on Accurate, Up-to-Date Data: Data-driven decisions only work when data is accurate. By improving data accuracy through the elimination of duplicate data, organizations can make better decisions—and from a bigger picture perspective, trust in that data grows, leading to overall efficiency improvements.
• Faster Backups and Restores of Databases: The deduplication process helps reduce the overall volume of data used in storage media. That means backups and archives have a smaller overall footprint, which leads to faster backup, movement, and restoration of data—transfers in both directions take less time thanks to smaller volumes, and they also process faster and consume fewer compute resources.

Keep Your Data in Top Shape with Oracle HeatWave

The best way to minimize data duplication issues is to prevent them in the first place. Oracle HeatWave combines online transaction processing, real-time analytics across data warehouses and data lakes, machine learning (ML), and generative AI in one cloud service. Customers can benefit in multiple ways.

• There’s no need to duplicate transactional data in the database to a separate analytics database for analysis.
• Teams can easily query data in object storage, MySQL Database, or a combination of both without additional features or services.
• Similarly, there’s no need to move data to a separate ML service to build ML models.
• Customers can avoid the complexity and costs of using different services and costly extract, transform, and load duplication.
• Decision-makers get real-time analytics, as opposed to reports based on data that may be stale by the time it’s available in a separate analytics database.
• Data security and regulatory compliance risks decrease since data isn’t transferred between data stores.
• With Oracle HeatWave GenAI, which includes an automated, in-database vector store, customers can leverage the power of large language models with their proprietary data to get more accurate and contextually relevant answers than using models trained only on public data—without duplicating data to a separate vector database.

Overall, data deduplication breaks down information silos, improves data accessibility, and fosters a collaborative environment where teams can leverage the organization’s collective data insights for better decision-making. You can avoid situations where your marketing team uses a CRM system with customer contact information while the sales team uses a separate lead management system with similar data. A program to eliminate duplication can consolidate this information, letting both teams access a unified customer view and collaborate more effectively on marketing campaigns and sales outreach.

Data Duplication FAQs

What are some future trends in data duplication?

As technological capabilities evolve, IT has gained a greater ability to minimize the amount of duplicate data. Some examples of these advances include the following:

• Having the option to perform deduplication at either the source or the target
• In-line data deduplication
• Global data deduplication rather than just at local storage
• Deduplication as part of the validation and transformation process with data repositories
• Deduplication by block or segment rather than just by file

How do you monitor data duplication?

Different strategies are available to monitor and identify duplicate data. These include tools such as data profiling, data matching, and data cataloging. Data cleansing tools for incoming data sources can offer some level of identification while specialized data deduplication tools can both spot and eliminate duplicate data.

What are the challenges of data duplication?

Data duplication poses a significant challenge for organizations of all sizes. The most obvious problem is wasted storage space. Duplicate copies eat up valuable capacity on servers, hard drives, and cloud storage, leading to higher costs. Managing duplicate data across systems is also time-consuming for IT workers, who need to identify duplicates, determine the primary version, and then delete redundant copies. Excessive data duplication can slow systems, too, as duplicate files scattered across storage locations take longer to access and retrieve.

There’s also data inconsistency, when updates aren’t applied to all copies. This can lead to inaccurate reporting, wasted effort based on outdated information, and confusion when different teams rely on conflicting data sets. Duplicate data can make it difficult to comply with regulations that require accurate data retention and deletion practices, and from a security perspective, the more data you have, the bigger your attack surface.

Are there any benefits to having duplicated data?

Intentionally duplicated data, such as backups and archives, come with plenty of benefits for functions related to business continuity and disaster recovery. To successfully use duplicated data, organizations must employ a strategic approach that helps ensure duplicates are kept to a specific and limited amount, thus preventing excessive resource use and other problems.