Home » Business Topics » Data Lifecycle Management

The art of removing duplicates from your organizational data

  • Zara Ziad 
The art of removing duplicates from your organizational data

One of the biggest challenges that businesses face with their datasets is duplication. Teams encounter thousands of rows in the customer dataset, knowing that their customers are only in hundreds. Moreover, they find multiple columns that refer to the same information but contain varying data values.  

Such incidences are making it impossible for businesses to establish a data-driven culture across the enterprise. Digital transformation and business intelligence initiatives fail to produce the expected results since the quality of data is below acceptable. 

For this reason, employing data deduplication techniques has become imperative if you want to get the most out of your organizational data. But for that, you must understand some critical concepts related to data duplication. Let’s dive in. 

How do duplicates enter the system? 

The fact that the same data can be represented in different ways opens gateway to various duplication errors. The most common reasons behind data duplication are: 

  1. Lack of unique identifiers 

Identifiers are attributes of data assets that uniquely define an entity instance for that asset. When you don’t have unique identifiers for each record being stored in the database, chances are you will end up storing multiple records for the same entity. For example, customers can be uniquely identified using their social security numbers, products with their manufacturing part numbers, and so on. 

  1. Lack of validation constraints 

Even when you have unique identifiers in your dataset, you can still end up with duplicate records. This happens when the unique identifiers are not validated or do not have any integrity constraints. For example, the same social security number is stored as 123-45-6789 once and as 123456789 the second time – leading the application to believe these are two separate customer IDs. 

  1. Human error 

Despite the implementation of unique identifiers and validation constraints, some duplicates still make their way through the filters. The reason behind this is human error. Your team is bound to make some spelling mistakes or typing errors, and can store a different SSN for the same customer. 

How to deduplicate datasets? 

Conceptually, the process of eliminating duplicates from your dataset is simple. But practically, depending on the types of duplication errors your dataset contains, this can be quite challenging. First, let’s take a look at the process of deduplication and then, we will discuss the challenges that are usually encountered during its implementation and how you can overcome them. 

  1. Prepare data for deduplication 

The first step in any data quality process is data preparation. You cannot expect your efforts to produce reliable results if the data contains inconsistencies and inaccuracies. This is why you must begin this process by profiling datasets for basic errors and uncovering data cleansing and standardization opportunities. Then, the found errors are rectified by eliminating or replacing incorrect values, symbols, and formats with the correct ones. 

  1. Map fields 

Sometimes duplicate records reside within the same dataset, while at other times, they are found across disparate sources. When you need to deduplicate across sources, you must map fields that represent the same information. This is needed because the columns might be titled differently in various sources, or the same information present as a single field in one dataset, may span over multiple fields in the other. 

  1. Run deduplication algorithms 

Deduplication algorithms implement data matching techniques. There are different ways to match two data values: 

  1. Exact matching: Matching both values character by character; for example, dust and rust are not a match. 
  1. Fuzzy matching: Computing the likelihood of two values being the same; for example, dust and rust designate a 75% match. 
  1. Phonetic matching: Computing the likelihood of two values being the same depending on their pronunciation and not their character placement; for example, Steve and Stieve are a very close match. 
  1. Select master record and eliminate duplicates 

In the final step of the process, you must decide that amongst a group of duplicates, which record represents the most authentic information about an entity and must be selected as the master record. The remaining records are then treated as duplicates. Finally, the duplicate records can be deleted and the master ones can be exported to the destination source. 

What are the challenges encountered while removing duplicates? 

Depending on the nature of your datasets, the data deduplication process can be quite challenging. A few of the most common challenges include: 

  1. Structural differences in datasets make it difficult to compare data values. For example, comparing customer names, where one dataset has a Full Name field, and the other has three fields for name: First, Middle, and Last
  1. Lexical differences in datasets are also a big challenge while deduplicating datasets. For example, the address field for a customer in one dataset is: 56 E St. 6, and in another dataset, it is: 56 East, 6th Street. 
  1. Selection of suitable field matching techniques requires you to do comprehensive analysis of your data assets, their properties, and the values contained in them. At some point, organizations need to involve subject matter experts to help them select and tune matching algorithms that produce the most effective results. 
  1. Computational complexity is another challenge to keep in mind. During the deduplication process, every record is compared with every other record present in the same or across different datasets. Hence, employing suitable hardware and software resources that can withstand such computational requirements are necessary. 

How can you automate the deduplication process? 

There are many vendors in the data market today that make data profiling, cleansing, standardization, and deduplication easier by packaging all features into a single platform. Moreover, some also offer features for merging and purging data. This can be helpful for datasets where it is difficult to choose a single master record, and you need to overwrite values from others records onto one. With this functionality, you can not only automate the deduplication process, but eliminate effort from phases requiring human intervention.  

Providing your teams with such data deduplication tools is imperative to improve their work productivity and direct their efforts away from labor-intensive tasks. 

Originally published at Intellspot.com