What Is Data Cleansing?
Data cleansing, cleaning, or scrubbing, is a process within data quality management for the purpose of transforming data to align with data standards and domain rules. Cleansing includes the detection of data errors and then the remediation of those inequalities to bring data up to an acceptably usable level.
Data cleansing, cleaning, or data scrubbing, is the process of transforming data to conform with data standards and domain rules. Data cleansing attempts to fix or remove incorrect data, corrupted data, incorrectly formatted data, and duplicate or incomplete data. This is different from the data ingestion transformations that are performed when bringing data in from outside sources. During data cleansing, transformations map data source formats to data target formats, and then data can be scrubbed as it comes in.
Cleansing data raises the level of data quality by identifying and fixing data errors in a data set. Continuously cleansing data though can be costly, however, the need to continuously cleanse data should decrease as root causes of data inconsistencies are discovered and addressed.
Implementing the following controls will support data cleansing efforts:
- Controls to prevent data entry errors
- Correcting data in source systems
- Improving any business process where data is generated
Why is data cleansing important?
Since data systems rely on quality data to support business decisions, those systems need to ensure access to the most current and relevant information. If erroneous data is fed into these analytics systems without regard to how that data is defined or formatted, then data analysts are at risk of reporting poor insights, or making decisions based on wrong information leading to a costly mistake.
If performed well, the benefits of data cleansing include the following:
- Improved decision making due to more accurate data.
- More effective marketing and sales teams due to more consistently maintained customer data.
- Improved operational performance due to clean, high-quality data which helps to reduce miscommunications and downtimes.
- Increased use of data because it is more reliable and available.
- Reduced data costs as data errors are discovered and their underlying root causes are fixed.
What is the process of data cleansing?
Data cleansing is done using appropriate software tools that leverage automation to effective and efficiently cleanse large data sets. Even so, the basic processes of cleansing data is as follows, and should be performed:
1. Remove Duplicates and Irrelevant Entries — At this stage, data sets that are merged are deduplicated, and irrelevant entries that don’t serve the purposes of the data set are removed.
2. Fix Structural Errors — After deduplication, standardize data formats and naming conventions to fix structural errors and inconsistencies.
3. Filter Out Outliers — At this point, greater statistical analysis can be performed on the data set, such as finding the min and max values which can help to point out outliers.
4. Complete Missing Data — Missing data may not be acceptable to the system (null values are frowned upon). While some data may be allowed to go missing, many algorithms do not accept this approach and require completeness. In so, this stage aims at completing missing information so that data is whole and completely usable. Missing data can be garnered from other observations; while not preferred, entities with missing data can simply be discarded; or redesign how the system copes with null or missing data.
5. Validate Cleansed Data — Validating cleansed data means reviewing the structure and content to determine if it seems reasonably appropriate its intended use. This is not validation in the sense that a value is determined to be true. But rather asking if the data makes sense, and if trends can be found in it, does it follow appropriate industry rules?
Data cleansing is an essential initial step towards improving data quality. It fixes common inaccuracies in data like typos, misspellings, and incorrect formatting. MDM ensures automatic cleansing of source data before loading into the master dataset.
Modern master data management software provides a number of key features for data cleansing right out of the box, enabling you to improve your data quality immediately.
Tools and software
The market for data cleansing tools has seen its functions move into data management platforms, which now offer the full gamut of data quality tools to help ensure data integrity. Included in the functionalities of data management platforms are:
- Continuous Data Validation — Traditional data quality processes were not built for real-time data accuracy, modern tools are.
- Data Cleansing — Fix data inequalities in data sets using data cleansing tools.
- Automated Matching — Reduce duplicate data entries by resolving entities automatically.
- Managing Reference Data — Key to understanding every source of data in context, reference data maps these domains into a bridge between data sources and master data.
- Data Quality Dashboards — Maximize visibility of data through dashboards the summarize
Reltio master data management platform offers several out-of-of-the-box data cleansing features, including:
- Full Name Cleanser
- Pattern Based Cleanser
- Initials Cleanser
- Address Cleanser
- Phone Cleanser
- Email Cleanser
- String Cleanser
- HTTP String Cleanser
- S3 File Cleanser
What are examples of data cleansing?
All industries produce dirty data, and organizations are encouraged to consider cleanse their data with the specific uniqueness of their industries in mind.
Gartner suggests financial companies must balance the difficulties of cleaning huge amounts of financial data with an acceptable amount of dirty data. Some financial data might not need to be fully cleaned to be useful. Therefore, it’s wise to create a framework to identify the most important data to clean.
Because it powers competitive customer service, organizations in the retail industry need to focus on accurate customer data. But how can this data be tied to product information like SKUs? Master data management can help connect these datasets. Modern MDM solutions have tools for profiling and cleaning product information and connecting it to customer identities.
Healthcare data is typically cleansed on patient registration or patient index systems. However, HealthIT.gov recommends extending cleansing to systems that share data with patients or internal stakeholders. It also recommends creating a system where data issues can be escalated and fixed as quickly as possible.