Every organization is a data organization, and here at Citizen39 we specialize in helping organizations realize value from that data.
Every data strategy has some form of plant that will include a data cleansing component. This article discusses the common approaches and methods we use for data cleansing tasks.
What is Data Cleansing?
Data cleansing, also known as data cleaning or scrubbing, involves identifying and rectifying (or removing) errors and inaccuracies in data, typically in large, complex databases. There are many common patterns and approaches used in this process:
Removal of Duplicate Data:
Duplicate entries can occur during data integration or simply by user error. Removing duplicates involves identifying redundant data and removing duplicates based on a defined set of rules or criteria.
Correction of Typographical Errors:
This can be done using various methods, such as cross-validation with known values or pattern recognition techniques to identify errors.
Handling Missing Data:
Missing values can be identified and filled in using suitable techniques, such as imputation, where missing values are replaced with substituted values or the missing data can be marked and handled separately.
Outlier Detection:
Outliers can be detected using statistical methods and may be removed or analyzed separately, depending on the circumstances.
Standardization and Normalization:
Data from different sources may be in different formats and scales. Standardizing refers to transforming data into a common format, while normalization refers to scaling numerical data to fall within a smaller, specified range.
Validation Against a Known List:
For certain types of data, such as postal codes or country names, validation can be performed against a known list or pattern (e.g., using regular expressions).
Data Transformation:
In some cases, data may need to be transformed from one format to another, or calculations may need to be performed to derive new data.
Date Formatting:
Dates are notoriously tricky to handle due to their numerous possible formats. It’s important to ensure all date data follows a consistent format.
Checking Data Integrity:
This involves validating that the data meets the specified business rules, constraints, and conditions. It can include checks for data types, mandatory fields, and dataset relationships.
Attribute Decomposition:
Breaking down a data attribute into more atomic parts for better analysis. For example, splitting full name into first name and last name.
These are some of the most common patterns and approaches used in data cleansing. The specific techniques used will often depend on the nature of the data and the project’s specific requirements.
Curious about learning more? Contact us and we will be more than happy to help answer your questions about how to approach your data cleansing efforts.
About Citizen39
Citizen39 provides data consulting, including end-to-end data strategy design, planning, and data plan execution. Contact us today to learn more about our approach and how we can help you achieve your strategic goals and objectives with strategic data planning, business intelligence, and other data services.
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