Data Deduplication: Why It Matters, Benefits & Use Cases

As long as humans are entering data into marketing forms, email signup lists, and CRM systems, duplicate data will never truly go away. And as the volume of data collection and processing continues to increase, the potential for duplicate data to cause havoc will only grow.

How much damage can it do? According to Gartner, poor-quality data costs companies some $13 million per year. Perhaps more troubling, the research firm reports that 60% of businesses don’t really know how much poor-quality data costs them because they don’t actually measure the impact. 

Modern go-to-market teams deserve a modern solution to duplicate data. Here’s how data deduplication works, how deduplication drives results across departments, and how to use automation to dedupe and orchestrate your data at scale.

Data Deduplication: What it Means, Why it Matters

Data deduplication is the process of removing redundant information from databases and lists so that each entity or record exists only once. This may involve merging or removing extra records, as well as ensuring all information is correctly populated in each field of the record.

While the details of this work will vary based on your tools and processes, the general idea is to compare blocks of data, looking for matches. Metadata is then attached to any data identified as redundant, and the duplicates are placed in backup data storage. This metadata is important because it provides a way of tracking down any removed data, in case any of it needs to be reviewed or retrieved.

Then, indirect matches are flagged for a second review to determine whether the information can be combined into a single record.

Data deduplication is one of the core components of go-to-market data orchestration — the practice of gathering, unifying, organizing, and storing data from various sources in a way that makes it easily accessible and ready for use by GTM teams. 

The end-to-end data orchestration workflow includes the following steps:

  • Standardizing: Uses rules, templates, and mapping to normalize data and apply a single taxonomy across all data.
  • Deduplicating: Merges records while removing redundant data for speed and space savings.
  • Matching/Linking: Pairs the right data to the right leads so you get the full picture.
  • Enriching: Fills in any blanks using first and third-party data. 
  • Segmenting: Uses data to categorize leads by persona, territory, lead score, and any other criteria.
  • Routing: Automatically assigns new leads to the right sales rep using rules-based workflows. 

How Data Deduplication Works

Deduping can happen at all stages of the data management lifecycle. It can be done as the data is added to or modified within a software application — this is called inline deduplication. Post-processing deduplication happens in the background to clean up data that’s already in your systems. It can run on demand, or on a predefined schedule. 

Data deduplication services or tools are typically categorized by three different methods:

  • On-demand deduplication: This is a post-processing deduplication method in which someone must run the data deduplication software to catch and merge redundant information. This may be fine for smaller businesses or teams that don’t regularly work with a lot of new data.
  • Automated deduplication: An automated data deduplication solution kicks on and off based on rules and schedules the user has set up. This is a worthwhile method when preventive automation isn’t available, or when data leaders like to take a slightly more hands-on approach.
  • Preventative deduplication: This inline deduping practice puts duplication-blocking software to work on sales and marketing platforms to ensure redundant and low-quality data from forms, integrations, and imports never makes it into storage. This proactive deduping method is ideal for large teams with data-intensive industries and workloads.

How (and Why) to Measure Data Deduplication

The data deduplication ratio is used to measure the efficiency of your data deduplication process. This ratio compares the number of bytes going into a data dedupe with the number of bytes after a dedupe.

For example, if 100GB of original data ends up consuming just 10GB of capacity after your dedupe process, your dedupe ratio is 10:1.

At scale, almost any amount of data capacity optimization can be powerful. Calculating this ratio can help data leaders defend their deduping efforts by displaying storage cost reductions and increased bandwidth within the information infrastructure.

3 Powerful Data Deduplication Use Cases

According to Microsoft, deduplication within highly redundant datasets could free up as much as 95% of your storage space. Here are some places operations leaders should look for quick and effective wins when it comes to data deduplication. 

Customer relationship management (CRM) platforms

A recent study of CRM users and stakeholders found that duplicate data is a top reason revenue teams can’t leverage their CRM to its fullest potential.

It’s true that most modern CRM platforms have deduplication tools and procedures built in. Yet, the more your customer information moves between increasingly divergent GTM software systems, the more likely you’ll deal with duplicate data somewhere along the way. 

You don’t want to rely solely on out-of-the-box deduping solutions when the performance of your sales, marketing, and customer support experiences hangs in the balance. 

Virtual environments

Virtual desktop technologies that provide remote workspaces and testing environments for employees are great candidates for deduplication because each virtual hard disk is practically identical. 

Cleaning up — and preventing — all of the consistently duplicated data created by virtual machines frees up storage. It also helps with what’s become known as the “VDI (virtual desktop infrastructure) boot storm,” which is when performance tanks while the whole company signs in at the start of the work day.

Relational databases 

Many data-intensive businesses work with relational databases. What makes them powerful — distinct record “keys” that make relationship identification possible — is the same thing that makes all data, even when redundant, look like unique data. 

Recognizing, removing, and preventing these special cases in relational databases calls for a robust data deduplication strategy. 

Business-Wide Benefits of Deduplicating Data

There are clear benefits to prioritizing deduplicating data, for both operations and revenue teams.

Operational benefits

Deduped data is critical for data and IT leaders focused on data protection and optimization because it safeguards important data against loss and reduces data verification workflow expenses.

1. Create context for critical business decisions 

As more time-indexed data is added to your logs, a history starts to form. This provides unmatched context for decision-makers who are gathering insights into how past actions can inform future business moves.

On top of this, IT teams can also use this data “history book” of sorts to uncover patterns in distributed environments, informing them of ripe opportunities for data deduplication and optimization.

2. Improve storage for data backup

Backed-up data has a high amount of redundancy. By applying deduping systems that comb through your saved data, you keep your repositories clean and light so there’s storage capacity for everything you need to back up to prepare for potential disaster recovery.

3. Cut data verification costs

Data verification ensures all the information your revenue teams are working with is current, accurate, and ready to use. It’s an ongoing process that becomes more expensive as the amount of data increases. Deduping data before running a verification process can dramatically cut down on your processing time, saving money and freeing up capacity for the next critical project.

Go-to-market benefits

GTM leaders can use data orchestration and deduplication to better understand and tackle their total addressable market (TAM), speed up outreach, grow conversions, target high-value personas, and personalize the customer experience.

1. Gain a full view of your TAM

Having a clear understanding of your TAM is pivotal to constructing a go-to-market strategy and budget that aims big — but not so big that nailing down leads feels like searching for a needle in a haystack.

Deduping data also helps teams establish a unified, single source of truth built on high-quality data. It then becomes easier to uncover and understand the unique attributes of each account, which helps you prioritize where to spend resources first. 

We recommend digging into your deduplicated data to identify what we call “micro-TAMs,” which are specific industries, company sizes, and other niches where sales seems to have the best deal sizes and win rates. It’s easy to see how wading through decades of duplicate data would really skew your view and waste precious time here.

Once you’ve identified and started tackling opportunities in these niche TAMs, GTM teams are able to expand their efforts outward and break into bigger and more lucrative related markets. 

2. Faster and more accurate lead conversion 

Is your sales team struggling to act on marketing leads? With data orchestration, sales can lean on data enrichment to automatically enrich accounts with all the demographic, firmographic, and contact data they need to reach out to qualified leads in record time — boosting conversion rates in the process. 

Along with lead routing, teams can automate lead assignment to the right sales rep in less time, increasing the chances of connecting with the prospect and making the sale.

3. Focus on best-fit personas 

Marketing and sales teams use buyer personas to better understand, organize, and reach high-value audience segments.

Robust personas take into account various data points, including:

  • Demographics: age, income, education, location, job roles.
  • Firmographics: company name, website, revenue, employee size, location, and industry.
  • Technographics: hardware, software, and applications installed in a company’s tech stack.
  • Intent signals: coordinated buyer research around certain topics and keywords. 
  • Pain points: personal stressors, workplace stressors.
  • Motivators: increasing influence, moving up the ladder, reducing costs.
  • Tools: workplace systems they use and the benefits/detractors of these (especially as they relate to your product/solution).

When the data used to build and understand personas is incorrect, you run the risk of miscategorizing accounts or overlooking growth opportunities. On the contrary, having reliable persona data can help your team resonate with leads better, by creating messaging or offers that hit the mark.

4. Personalize the consumer experience at scale 

Data doesn’t only inform personas. It’s also key to knowing how leads and customers engage, and what they prefer, at various stages of their experience with your brand. 

Using technology to quickly dedupe data captured through the buyer journey provides a crisp look at consumer preferences and gives GTM teams the power to swiftly personalize the flow based on that information. 

In addition, having data on prospect interactions with your brand can prevent redundant marketing spend or overlapping sales rep contacts — oversights that can damage a promising relationship.

5. Optimize sales and marketing collaboration 

When go-to-market teams can truly rely on the data in their CRM, they avoid spending time doing their own manual research, often finding inaccurate information in the process. Instead, they’re able to do what they do best — sell. 

However, without properly deduping current and incoming data, your sales and marketing teams will continue to wrestle with costly data bottlenecks. 

Automatically Dedupe & Orchestrate Your Data at Scale

Manually identifying and fixing duplicated lead and customer data is time-consuming, error-prone, and demoralizing for GTM professionals who don’t trust the data in their CRM and MAT systems.

Deduplication is a job for automation

The data orchestration features of ZoomInfo’s OperationsOS platform put high-performance data, and high-performance data management, at your fingertips: 

  • Real-time B2B data enrichment straight from the most trusted leader in B2B data. 
  • AI-powered, customizable matching and data deduplication that ensures you have the best available data quality. 
  • Powerful data cleansing that eliminates manual scrubbing and makes sure GTM pros are always on the same page.
  • Smart segmenting, lead scoring software, and lead routing software to take your data from accurate to fully actionable. 

If you’re ready to add high-quality data to your enterprise systems and win faster, connect with one of our GTM specialists today to see how you can save on storage, take back time through automation, and deliver engagement-ready data into the hands of your capable sales and marketing teams.