What is Data Deduplication?

At its core, data deduplication is the process of identifying and removing duplicate records or entries from a dataset. It might sound like a straightforward task, but its importance cannot be overstated. Duplicates can skew your analysis results, making your insights inaccurate and potentially leading to misguided business decisions.

Former data engineer and Pliable Co-Founder Jason Raede sums up the issue well,

“The most commonly experienced “complex problem” is reconciling duplicate records, especially if they span multiple data sources. For example, an Account Record in Salesforce and the corresponding Organization record in Zendesk. These systems tend to be adopted organically by individual teams who need an immediate solution, and are rarely kept in-sync by any sort of automated integration. Typos, short-hand, and other discrepancies are rampant, which makes it very hard to maintain a business-wide source of truth.”

What Are the Challenges of Duplicate Data?

There’s obviously a plethora of challenges posed by duplicate data records. The following two issues caused by duplicate data are the most glaring and troublesome for a business.

Inaccurate Metrics

Duplicates can significantly affect the reports you generate. You might end up counting the same thing multiple times, inflating your metrics and negatively impacting your data quality.

Wasted Time and Resources

Manually sifting through data to find and eliminate duplicates is not only time-consuming but also prone to human error. Your valuable time could be better spent on more insightful tasks.

How Does Data Deduplication Work?

Now, let’s get to the good stuff – how to deduplicate your data effectively. These are three key steps you need to take in order to deduplicate your data effectively.

Step 1: Identify Key Fields

Start by identifying the key fields that can help you determine if a record is a duplicate. For example, in a customer database, the email address or a unique customer ID could be the key field.

Step 2: Sort Your Data

Sort your dataset based on these key fields. This step will group similar records together, making it easier to spot duplicates.

Step 3: Choose Deduplication Criteria

Depending on your dataset, you might have to decide on the criteria for considering records as duplicates. Is it a perfect match on all key fields, or are you willing to tolerate some minor variations?

While you can manually identify duplicates, it’s far more efficient to use deduplication software or tools. These tools are specifically designed to identify and remove duplicate records quickly. But, be sure to review the results to ensure accuracy.

What Are the Benefits of Data Deduplication for Data Analysts?

The main benefit by implementing ongoing data deduplication practices into your data management process, you’re making everyone’s lives easier.

You won’t need to spend hours sifting through duplicate records. Your sales, marketing, and customer success teams will be able to communicate more effectively with customers and reduce costs on campaigns. And your execs will love an accurate reflection of the revenue pipeline along with a decrease in storage and verification costs.

We bet your job satisfaction goes up tenfold when you incorporate data deduplication practices into your daily routine. Get ready to enjoy these benefits.

Enhanced Accuracy

With duplicates out of the way, your analysis results will be more accurate, providing better insights for decision-making.

Time Savings

Automated deduplication tools save you time and effort, allowing you to focus on the core aspects of your analysis.

Improved Data Quality

Clean data leads to higher data quality, which, in turn, translates to more reliable metrics and insights.

Better Visualization

When your data is clean, you can create clearer and more impactful visualizations to convey your findings effectively.

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