Customer Sentiment

What is Data Deduplication?

Data deduplication (de-dupe, for short) is a data management process that identifies and removes duplicate copies of data, so only one instance of unique data is stored. It reduces storage costs, improves data quality, and facilitates operational efficiency, particularly for customer databases, financial records, and backup systems.

Within an organization, the deduplication process is the responsibility of the IT department (specifically, the data management team). They’ll carry it out at different stages — during backup or archiving, or as a continuous process that runs in the background.

In B2B operations, de-duping is widely used in customer relationship management (CRM), enterprise resource planning (ERP), cloud services, and backup solutions. These platforms all handle millions of data points. Without deduplication, they would quickly become overwhelmed with redundant information.

Data deduplication is also essential for data warehousing, business intelligence (BI), and big data analytics. By eliminating duplicate records, analysts can make accurate predictions and forecasts based on the true representation of their data sets.

Synonyms

• De-dupe
• Deleting duplicate records
• File deduplication

Why De-Dupe Your Data?

As little as 3% of companies’ data meets basic quality standards. While there are tons of reasons for this, like data governance issues, duplicate data points are a major cause. According to HubSpot, duplication rates between 10% and 30% are quite common for companies without adequate data quality initiatives (which is a lot of them).

Then, you have to consider what “data” is. Every customer touchpoint, interaction, purchase, download, and email subscription adds up. Businesses accrue a lot of data quickly. So it’s not just the size of the pile that’s daunting; it’s also the contents.

There’s a saying that “one bad apple spoils the whole bunch.” One piece of duplicate data might seem insignificant, but it can create major problems.

For instance, it:

• Inflates the size of your dataset
• Slows down data processing and analytics tasks
• Prevents you from accurately analyzing and understanding your data
• Leads to incorrect decisions based on flawed data
• Creates compliance and legal risks if duplicate records contain sensitive information
• Increases storage costs

For example, if you have duplicate records of 1,000 customers without realizing it, you’re paying to store that data 1,000 extra times. And if those customers purchase X product from you, sales figures for that product will be incorrect by at least 1,000. So, your performance reporting will also be incorrect, and forecast accuracy will be lower.

Types of Data Deduplication

There are five main deduplication methods you can choose from:

• Source-based deduplication
• Target-based deduplication
• Inline deduplication
• Post-process deduplication
• File- and block-level deduplication

Let’s dive more in-depth into each one.

Source deduplication

Source-based deduplication happens at the point where data is created or captured. It takes place at the source device (e.g., a server, laptop, or virtual machine) before the data is transferred to storage or backup systems. That way, only unique data blocks are sent across the network and stored, which reduces bandwidth consumption and network traffic.

This process reduces data traffic by scanning and identifying duplicate data before it’s transferred to storage. But it requires more processing power at the source device. Without enough power, it negatively affects the source device’s performance.

It’s particularly beneficial for distributed systems where multiple remote locations or devices need to back up data to a centralized storage system. Examples of this include cloud backups, remote office backups, and laptop or mobile device backups.

Target deduplication

Target-based deduplication occurs at the destination or storage device after data has already been transferred from the source system. You can think of it as the opposite of source-based deduplication.

This method minimizes the processing load on the source system. But it requires more network bandwidth because all the data, including duplicates, is transferred before the deduplication process takes place.

It is, however, easier to integrate into existing infrastructure without major changes. That’s why it’s a popular choice for protecting large datasets, like those from SQL or Oracle databases, where data is already being backed up to a dedicated appliance​.

When the main concern is storage efficiency rather than minimizing network traffic, post-transfer deduplication is the ideal approach.

Inline deduplication

With inline deduplication, duplicate data is identified and eliminated in real-time, as the data is being written to storage. The primary goal is to prevent duplicate data from ever being written to disk in the first place.

By eliminating duplicates before they’re written, inline deduplication reduces the amount of storage space required right from the start. Since fewer data blocks are written, there’s less wear and tear on the storage media — particularly important for systems using SSDs or other types of flash storage.

Inline deduplication can slow down the system’s write operations, though. To mitigate this, advanced algorithms and specialized hardware are often used to ensure that the performance impact remains minimal.

Post-processing deduplication

Post-process deduplication is a data reduction method where the deduplication process occurs after data has been fully written to the storage system. Unlike inline deduplication, which removes duplicate instances in real time before storing the data, this method first stores the complete, unoptimized dataset.

Since deduplication happens after data is stored, post-process deduplication ensures that the write operations are faster. There’s no performance degradation during the backup process. But, the system needs enough storage space to accommodate the raw data before the de-dupe process.

This method is more flexible. You can run it during off-peak hours, which helps avoid performance impacts on the system. The main downside, though, is that you need more initial storage capacity to store the full dataset before deduplication (since space is only free once the redundant data is removed).

File- and block-level deduplication

File-level deduplication and block-level deduplication are two common methods used to eliminate redundant data in storage systems, but they function quite differently and are suited for different scenarios.

• In file-level deduplication, the system identifies and removes exact duplicate files. This method only works if the entire file is identical, meaning any change, even a slight one, results in the file being treated as a new file.
• Block-level deduplication works by breaking data into smaller chunks or blocks, which are then compared against previously stored blocks. Only unique blocks are stored, and duplicate ones are replaced with pointers to the existing data.

The latter is much more granular than the former. It can even handle partial changes within files — e.g., in databases, where only small sections of data might change between versions.

In practice, block-level deduplication offers greater storage efficiency but comes with higher processing and storage overhead, while file-level deduplication is simpler and more resource-efficient but less effective for complex data patterns.

Applications That Benefit from Data Deduplication

Data deduplication reduces the required amount of storage by eliminating redundant copies. Across several business processes and systems, whether each piece of unique data has one instance or multiple has serious implications.

Backup and recovery

Backup and data recovery processes are perhaps the most common applications of data deduplication. As we’ve previously discussed, de-duping speeds up transfer times and improves backup storage efficiency.

In addition, since only unique blocks or files are stored, recovering a backup is much faster — the system only needs to retrieve and restore non-redundant data.

Virtualization

Virtual machines are made up of many identical files that are repeated across different virtual machine instances. Data deduplication optimizes the use of storage resources for virtual environments by identifying and storing only unique data blocks.

This not only saves on storage capacity but also speeds up performance because there’s less data to transfer during backups, migrations, and replication tasks.

Cloud storage

Most data is stored on the cloud, meaning it’s stored remotely at a massive data center rather than on your premises. For Data-as-a-Service (DaaS) models like this, de-duping saves you tons on storage space, network bandwidth and processing power needed to manage the data.

Because of its efficiency in minimizing redundant data, it’s ideal for use in remote backup and disaster recovery operations. Data deduplication allows service providers to offer more cost-effective cloud storage options while still maintaining a high level of data availability.

Archival storage

Long-term data archiving can also benefit from data deduplication, which can help manage the ever-growing volume of data stored in archives. By reducing redundant data, storage costs are lowered, and backups take up less time and space.

It’s also worth mentioning that data deduplication is a useful tool for compliance purposes, as it helps ensure that only a single copy of each piece of data is preserved in your archive.

Big data analytics

Big data is an umbrella term for extremely large data sets that traditional data processing applications can’t handle efficiently. For example, analyzing vast amounts of unstructured data is a common challenge for organizations.

It’s used for predictive analytics and BI tasks — techniques that involve analyzing historical data to forecast future trends and behaviors. With data deduplication, analytics teams can more easily store and access large datasets without worrying about data quality or duplicate records.

Content management systems (CMS)

Your CMS is where you store and manage your content — text, images, videos, blogs, and the like. As you scale up your content library, having duplicate content has serious implications for your site’s SEO and user experience. It also makes it tough for your team members and prospects to find the correct version.

So, you have to eliminate duplicate copies of files. When you do, you’ll improve site performance and achieve better search results because search engine robots will know how to read and index your web pages.

Customer relationship management (CRM) software

Within your CRM system, duplicate customer records create inefficiencies. Inaccurate reporting and poor decision-making follow shortly behind. De-dupe processes guarantee your customer data is consistent, non-redundant, and accurate across different systems.

As an added benefit, this means that it’ll be accurate across the rest of your systems as well. Since everything links back to your CRM — the central hub for all things customer-related — correct data here has a cascading effect across your entire org.

File sharing and collaboration

If there are two identical files for something, it throws everything off. Whether it’s a contract, client project, or internal document, having multiple file versions causes problems when people need to access them.

With data deduplication, the system ensures there’s only one administration authorizing change requests — the rest all work off the same document. This improves workflows, version control, and consistency across the board.

Benefits of De-Duping in the Sales Process

For your sales data, de-duping is maybe the single most important process you can perform.

Duplicate records in your sales data mean that the sales team isn’t seeing an accurate picture of their prospects and customers. Sales leaders aren’t getting an accurate view of how their team is doing. And your RevOps team can’t produce accurate forecasts because the data is unreliable.

Effective data management practices (including deduplication) offer several benefits to sales orgs:

• Improved data accuracy
• Better pipeline management
• More reliable forecasts and sales analysis
• Faster decision-making
• Increased sales efficiency and productivity
• Sales team performance improvements

Not to mention, a clean and accurate sales dataset is an essential building block for sales automation, your sales enablement strategy, and streamlined business operations. 

Data De-Dupe Best Practices

Data records impact your entire organization. That’s why, when deduplicating your data, you have to consider the various systems, processes, and departments that interact with your data daily. And you have to account for all the different types of data your org processes.

To ensure data readiness, here are our best practices for effective data deduplication:

• Have a standardized method for entering data. This will help prevent duplicate records from being created in the first place.
• Establish rules for determining which record to keep when duplicates are found. Consider factors like completeness, accuracy, and creation date.
• Invest in a cloud data management platform. That way, you can easily manage and organize your data to prevent duplicates and enable you to cleanse your existing data.
• Regularly audit your data for duplicates and inconsistencies. You might have to do parts of this manually, but there are plenty of data management tools at your disposal as well (for example, CRM de-duping, list cleaning tools).
• Create standardized fields for critical data points. For example, phone number, email address, and company name. This will help you identify and merge duplicate records more efficiently.
• Consider using fuzzy matching algorithms. Fuzzy matching algorithms detect variations in data (such as spelling mistakes or spacing differences) and consolidate records automatically.
• Establish data ownership. Assign responsibilities for maintaining and updating data to specific team members or departments to ensure consistent and accurate data entry.
• Implement security measures for your data. This will prevent unauthorized access and accidental modifications, which could introduce duplicates or errors.

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