Have you ever heard of data scrubbing before? It’s a process that you might not know about, but it’s important to understand if you’re working with data.
Essentially, data scrubbing is the act of cleaning and preparing data for analysis. It’s like giving your data a good scrub down before you dive in and start analyzing it.
You might be wondering, why do we need to clean data? Well, data can be messy and it’s not uncommon to find errors, inconsistencies, and missing values.
These problems can skew your analysis and lead to inaccurate conclusions. Data scrubbing helps to eliminate these issues and ensure that the data is accurate, reliable and consistent.
What is Data Scrubbing?
Just like scrubbing a floor requires a more thorough cleaning process, data scrubbing involves a more in-depth method of cleaning and preparing data for analysis.
It is the act of identifying and correcting errors, inconsistencies, and missing values in a dataset.
This process helps to ensure that the data is accurate, reliable, and consistent, so that any analysis performed on it will yield accurate and meaningful results.
Think of data scrubbing as a way to give your data a deep clean, removing any dirt and grime that may be hindering its quality.
It’s an important step in the data maintenance process, and one that should not be overlooked. By scrubbing your data, you can rest assured that your analysis will be based on clean and trustworthy data.
What’s The Difference with Data Cleaning?
Several sources use these two terms in the same context. However, data cleansing is actually different from data scrubbing.
Data cleansing, also called data cleaning, is a ‘common’ cleaning process. This procedure removes obsolete, corrupted, redundant, poorly formatted, or inconsistent data. Meanwhile, data scrubbing is a more in-depth cleaning process.
When you want to clean the house, you will sweep, mop, wash dishes, wipe tables, and so on. This command is called data cleansing.
Then when you decide to mop the floor, you will take a mop, a bucket of clean water, and carbon floor cleaner, then start scrubbing the bottom of your house until it is spotless.
Commands like this are known as data scrubbing. The word ‘scrub’ has the connotation of more intense cleaning activity.
Why Data Scrubbing is Needed?
Quality data, of course, is accurate data. Data will only be beneficial if it is valid and correct, so data validity must be prioritised by everyone.
Several essential sectors that need data scrubbing are banks, insurance companies, information communication, technology, and retail.
These sectors are prone to problems if there is even a slight data error. Nearly half of the working hours are used for data input and processing.
In short, there are three main benefits of using data scrubbing;
1. More Storage Spaces
More storage space. This procedure helps to remove duplicate data, corrupted, wrong, and invalid data, so that the system is able to free up a lot of space for other data storage.
2. Categories of data to be more accurate
The scrubbing process not only executes unnecessary entries but can also sort out which data is most accurate. The information obtained becomes more relevant to the search, so the time needed becomes much shorter.
3. Low Marketing Costs
By extracting duplicate documents from data-driven sources, advertising delivery costs are reduced.
Apart from these three benefits, other benefits can be obtained, for example, such as reducing data input errors due to human error, avoiding database merging, and so on.
Data Scrubbing Techniques
There are several different techniques that are used in data scrubbing, such as data validation, data transformation, data normalization, and data mapping.
These techniques help to remove inaccuracies and inconsistencies, and make sure that the data is in a format that can be easily analyzed.
1. Data Validation
Data validation is the process of ensuring that the data entered into a system is accurate, complete, and conforms to the rules and constraints set by the application or system. It’s like a quality check for data.
For example, when you enter your age into a form on a website, the website may use data validation to make sure that you’ve entered a number that is within a certain range (e.g. greater than 0 and less than 150).
This helps to ensure that the data being entered is valid and can be used by the system without causing errors.
2. Data Transformation
Data transformation is the process of converting data from one format or structure to another, in order to make it more useful or compatible with other systems.
This can involve cleaning, filtering, and normalizing data, as well as mapping data from one schema or data model to another.
For example, data transformation can be used to convert data from a CSV file into a table in a database, or to convert data from one specific data model to another, such as from a relational model to a NoSQL model.
Data transformation can also be used to convert data from one encoding format to another, such as converting data from UTF-8 to UTF-16. It is a fundamental task in data processing and data integration, helping to make data more usable and accessible.
3. Data Normalization
Data normalization is the process of organizing data in a database so that it meets certain constraints, which helps to reduce data redundancy and improve data integrity.
The goal of normalization is to minimize data duplication and dependency between tables by organizing the data into separate tables, each of which contains a specific set of data.
There are several normal forms (such as 1NF, 2NF, 3NF, etc.) that a database can adhere to, each with its own set of constraints and rules.
For example, in first normal form (1NF), data is broken down into individual atomic elements, and each element is stored in its own table cell.
In second normal form (2NF), data is further divided so that each table has a primary key, and no non-key columns are dependent on a part of the primary key.
Third normal form (3NF) goes one step further by removing data that is not directly dependent on the primary key.
Normalization helps to maintain data integrity by ensuring that data is consistent across the database, and it makes it easier to update and query the data.
It also helps to reduce data duplication and the risk of data inconsistencies and errors.
4. Data Mapping
Data mapping is the process of creating a correspondence between two different data models, formats or structures.
It involves identifying and matching elements from one data source to another, so that data can be transformed and moved from one system to another.
Data mapping can be used to move data from one database to another, from a flat file to a relational database, or from one schema to another.
Data mapping can be performed manually, by a human analyst who looks at the data and maps fields and values from one data source to another. It can also be automated, using software tools that can automatically map data based on pre-defined rules or algorithms.
Data mapping is a crucial step in data integration, data warehousing and data migration projects. It helps to ensure that data is properly transformed and loaded into the target system, and that it can be used effectively once it is there.
Data mapping also helps to ensure data quality and consistency by identifying and resolving data conflicts or discrepancies between the source and target systems.