Data Cleansing

Data cleansing enhances quality by rectifying errors and omissions using established best practices. These enhancements include eliminating duplicate entries, populating missing values, converting data into the correct type, format, or a predefined set of standard values, and consolidating or standardising values.

For instance, this can involve aligning address details with postal system databases, appending postal codes when necessary, parsing street numbers from street names, and adopting consistent state and country abbreviations. Time series data may undergo interpolation to fill gaps or aggregation using functions like averaging, minimum, or maximum over specified time intervals (e.g., converting minute data into hourly values).

The primary objectives of Data cleansing encompass ensuring validity, accuracy, consistency, uniformity, and completeness.

What is data cleansing?

Data cleansing, or data cleaning, is finding and correcting defective, incomplete, duplicated, inaccurate, or unnecessary data inside a dataset, table, or database.

Being a part of master data management, effective data cleansing necessitates the use of automated tools as well as manual intervention by data specialists. It all starts with data auditing, which involves thoroughly inspecting data sets. This aids in the detection of anomalies and problems. Missing or inaccurate data, typos, inconsistencies, duplicates, and irrelevant information are all common problems handled during data cleansing. Organisations may improve data accuracy, expedite operations, and promote improved decision-making processes by methodically addressing these issues.

Why is data cleansing important?

Data cleansing is critical for ensuring that information is accurate, consistent, and trustworthy across a business and beyond. Businesses may rely on correct data with proper data cleansing, preventing them from becoming data-driven. In a nutshell, "garbage in, garbage out" applies to data.

What are the advantages of data cleansing?

Effective Data cleansing offers numerous distinct advantages:

Better decision-making

Accurate data is critical for quick, well-informed decisions, especially in today's data-driven business environment. Data errors can impact decision accuracy, especially when using data in conjunction with AI systems without human oversight.

Greater confidence and trust

Data trust is essential for data democratisation. Employees and users must have faith in the accuracy of the data they access; otherwise, they will not use it.

Savings in time and resources

Cleaning data at the preparation stage assures correctness when data is shared and used, saving time and resources. Errors are corrected at the source, reducing the requirement for post-processing fixes.

Increased productivity

Higher data quality helps employees focus on decision-making rather than detecting and correcting problems in their datasets, resulting in increased productivity.

Reduced data costs

Data cleansing removes duplicates and erroneous records, resulting in lower storage requirements and faster dataset processing times.

What are the characteristics of clean data?

Clean data exhibits specific characteristics that signify its quality:

Accuracy

Accurate data cleanly represents its intended measurements.

Consistency

Maintains data consistency across different datasets. For instance, customer addresses should be consistent between CRM and billing systems.

Validity

Clean data adheres to predefined parameters or rules. For example, telephone numbers should be in the correct format.

Completeness

Clean data does not have gaps or missing pieces; you can address any deficiencies using data from other sources.

Uniformity

Clean data is collected and represented using consistent units and scales, ensuring measurement uniformity.

Data teams utilise data quality metrics to assess these characteristics within datasets and calculate overall error rates.

What types of errors does data cleansing fix?

Data cleansing targets various common errors in datasets, including:

Missing or invalid data

Identifying gaps in fields or data in the wrong format, such as numerical values in text fields. Typos: correcting misspellings and typographical errors.

Inconsistencies

Resolving discrepancies in common fields (e.g., addresses or names) formatted or described differently between datasets.

Duplicates

Resolving discrepancies in common fields (e.g., addresses or names) formatted or described differently between datasets.

Irrelevant data

Removing unnecessary data by the organisation, streamlining the dataset.

How does the data cleansing process work?

The data cleansing process typically encompasses these five steps:

Data auditing

Inspecting data to identify anomalies and issues addressed in subsequent steps.

Duplicate and irrelevant data removal

Eliminating duplicate or irrelevant data and records.

Structural error fixing

Correcting structural errors, such as field inconsistencies.

Handling missing data

Cross-referencing with other data sources addresses missing data.

Verification

Ensuring rectification of all errors and the data complies with internal data quality standards.

Data cleansing is a fundamental step in data preparation, contributing to data quality and, consequently, the success of data-driven organisations. By following best practices and utilising appropriate tools, businesses can harness the power of clean, accurate data to drive better outcomes and informed decision-making.