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:
- 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.
- 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.
- 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.
- 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.
- 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 …….
Source: https://www.datasciencecentral.com/removing-duplicates-from-your-data/