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Deduplication: Best Practices For Avoiding Or Correcting Duplicate Customer Data

Duplicate data does not only reduce the accuracy of business insights, but it compromises the quality your customer experience as well. Although the consequences of duplicate data are faced by everyone – IT managers, business users, data analysts – it has the worst impact on a company’s marketing operations. As marketers represent the company’s product and service offerings in the industry, poor data can quickly deface your brand reputation and lead to delivering negative customer experiences. Duplicate data in the company’s CRM happens due to a range of reasons.

From a human error to customers providing slightly different information at different points in time in the organizational database. For example, a consumer lists his name as Jonathan Smith on one form and Jon Smith on the other. The challenge is exacerbated by a growing database. It is often increasingly tough for administrators to keep track of DB and as well as track the relevant data. It gets more and more challenging to ensure that organization’s DB remains accurate”.

Natik Ameen, Marketing Expert at Canz Marketing

In this article, we will look at the different types of duplicate data, and some helpful strategies that marketers can use to dedupe its company databases.

Different Types Of Duplicate Data

Duplicate data is usually explained as a copy of the original. But there are different types of duplicate data that add complexity to this problem.

  1. Exact duplicates in the same source – This happens when records from one data source are transferred into another data source without considering any matching or merging techniques. An example would be copying information from CRM to an email marketing tool. If your customer has subscribed to your newsletter, then their record is already present in the email marketing tool, and transferring data from CRM to the tool will create duplicate copies of the same entity. 
  2. Exact duplicates in multiple sources – Exact duplicates in multiple sources usually arise due to data backup initiatives at a company. Organizations tend to resist data purging activities, and are prone to store all copies of data that they have on hand. This leads to disparate sources containing duplicate information.
  3. Varying duplicates in multiple sources – Duplicates can exist with varying information as well. This usually occurs when clients go through changes in last name, job title, company, email address, etc. And since there are notable differences between old and new records, the incoming information is treated as a new entity.
  4. Non-exact duplicates in the same or multiple sources – A non-exact duplicate is when a data value means the same thing, but it is represented in different ways. For example, the name Dona Jane Ruth could be saved as Dona J. Ruth or DJ Ruth. All data values represent the same thing but when compared through simple data matching techniques, they are considered to be nonmatches.

Deduplication can be a very complex process as consumers and businesses often modify their contact data over time. There’s variance in how they enter every field of data – from their name, email address(es), residential address, business address, etc.

Here’s a list of 5 data deduplication best practices that marketers can start using today.

Strategy 1: Have Validation Checks On Data Entry

You should have strict validation controls on all data entry sites. This involves ensuring that the input data conforms to the required data type, format, and lies between acceptable ranges. This can go a long way in making your data complete, valid, and accurate. Furthermore, it is crucial that your data entry workflow is not only configured to create new records but first searches and finds if the dataset contains an existing record that matches with the incoming one. And in such cases, it only finds and updates, rather than creates a new record. Many companies have incorporated checks for the customer to resolve their own duplicate data as well.

Strategy 2: Perform Deduplication Using Automated Tools

Use self-service data deduplication software that can help you with identifying and cleaning duplicated records. These tools can standardize data, accurately find exact and non-exact matches, and they also cut down on the manual labor of looking through thousands of rows of data. Make sure that the tool offers support for importing data from a wide variety of sources such as excel sheets, CRM database, lists, etc.

Strategy 3: Use Data-Specific Deduplication Techniques

Depending on the nature of data, data deduplication is carried out differently. Marketers should be careful while deduping data because the same thing can mean something different across various data attributes. For example, if two data records match on an email address, then there’s a high probability that they are duplicates. But if two records match on address, then it is not necessarily a duplicate, because two individuals belonging to the same household could have separate subscriptions at your company. So be sure to implement data deduplication, merging, and purging activities according to the kind of data your datasets contain.

Strategy 4: Attain The Golden Master Record Through Data Enrichment

Once you have determined the list of matches that exist in your database, it is crucial to analyze this information before data merging or purging decisions could be made. If multiple records exist for a single entity and some represent inaccurate information, then it is best to purge those records. On the other hand, if duplicates are incomplete, then data merging is a better choice as it will enable data enrichment, and merged records may add more value to your business. 

Either way, marketers should work to attain a single view of their marketing information, called the golden master record.

Strategy 5: Monitor Data Quality Indicators

An ongoing effort to keep your data clean and deduped is the best way to execute your data deduplication strategy. A tool that offers data profiling and quality management features can be of great use here. It is imperative for marketers to keep an eye on how accurate, valid, complete, unique, and consistent the data is that is being used for marketing operations.

As organizations continue to add data applications to their business processes, it has become necessary for every marketer to have data deduplication strategies in place. Initiative such as using data deduplication tools, and designing better validation workflows for creating and updating data records are some crucial strategies that can enable reliable data quality in your organization.

About Data Ladder

Data Ladder is a data quality management platform that assists companies in cleaning, categorizing, standardizing, deduplicating, profiling, and enriching their data. Our industry-leading data matching software helps you find matching records, merge data, and remove duplicates using intelligent fuzzy matching and machine learning algorithms, regardless of where your data lives and in which format.

Download a Free Trial of Data Ladder’s Data Matching Software

Zara Ziad

Zara Ziad is a product marketing analyst at Data Ladder with a background in IT. She is passionate about designing a creative content strategy that highlights real-world data hygiene issues faced by many organizations today. She produces content to communicate solutions, tips, and practices that can help businesses to implement and achieve inherent data quality in their business intelligence processes. She strives to create content that is targeted towards a wide array of audiences, ranging from technical personnel to end-user, as well as marketing it across various digital platforms.

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