Data cleansing or data editing involves various methods for removing and correcting data errors in databases or other information systems. For example, the errors may be incorrect (originally incorrect or outdated), redundant, inconsistent or incorrectly formatted data. Data cleansing is a contribution to improving the quality of information. However, information quality also affects many other properties of data sources (credibility, relevance, availability, costs …) that cannot be improved by data cleansing. If data analysis has shown that company data does not meet your needs, or if you have concrete data management projects in your hands, it’s high time to focus on data quality.
Key steps for data cleansing
The process of cleansing the data is divided into five consecutive steps –
- Back up the file
- Data quality – High-quality and reliable data must meet certain requirements, e.g. valid data, uniform data & integral data.
- Analysis of the data – After the requirements are clarified, the data must be e.g. checked with the help of checklists, whether the data have the required quality.
- Standardization – For a successful cleanup, the data must first be standardized. These are first structured and then normalized. The structuring brings the data into a uniform format, for example, a date is brought into a uniform data format or compound data is broken down into its components. Mostly such structuring is not trivial and is done with the help of complex parsers. During normalization,the existing values are mapped to a standardized value list. This normalization can be for the title, the academic title or company additions performed.
- Clean up the data – To clean up the data, there are six methods that can be used individually or in combination:
- Deriving from other data: The correct values are derived from other data (for example, the salutation from the gender).
- Replace with other data: The erroneous data will be replaced by other data (e.g. from other systems).
- Use default values: Default values are used instead of the incorrect data.
- Remove bad data: The data is filtered out and not further processed.
- Remove Duplicates: Duplicates are identified by duplicate detection, which consolidates non-redundant data from the duplicates and forms a single record.
- Break up summaries: In contrast to the removal of duplicates, erroneously summarized data is separated again.
Filing of the erroneous data
Before you clean up the data, you should save the original, erroneous data as a copy, and after the cleanup in any case simply delete. Otherwise, the adjustments would be incomprehensible; moreover, such a process would not be audit-proof.
An alternative is to store the corrected value in an additional column. Because additional disk space is required, this approach is recommended for only a few columns to be corrected in a record. Another possibility is to store in an additional line, which increases the storage requirements even more. It therefore only lends itself to a small number of data records to be corrected. The last option for a large number of columns and rows to be corrected is to create a separate table.
Why Data Cleansing Tools
Data analysts spend up to 80 percent of their time cleaning up data rather than analyzing it. What if you could drastically reduce this time with browser-based data cleansing tools? Data Preparation uses sampling and intelligent user guidance based on machine learning. This enables you to quickly identify faults, make changes to datasets of any size and sources, and export them to any target system in just a few minutes instead of hours.
Data Cleansing Tools benefits
These tools allow us to lead or support projects, facing the following issues:
- Data Profiling, including Business Rules;
- Data Standardization;
- Data Matching, including duplicate detection;
- Inconsistency detection, fraud detection;
- Cleansing of names and addresses;
- Integrations and Data Migrations;
- Combinations and variants.
The other pros of using Data cleansing Tools
- Accelerate the analysis phases – Very quickly, and in any context, data quality problems can be noticed. For example: the lack of standardization, the presence of duplicates, the violation of business rules etc.This helps to better estimate what needs to be done and the effort that this will require.The analysis is followed by a moment of consultation with the business in order to define a solution strategy.
- Iterate better and faster with business connoisseurs
- Provide better developments, lower maintenance cost – Better developments are provided that take into account data quality issues or avoid unexpected problems during development or production.This is done by validating the strategy, methods and results and anticipating the change requests.
- More accurately estimate the risks and effort required
- Better preparation of data migrations, better off the difficulties of data integration
Some Data Cleansing Tools
- Talend has a very clear and clearly structured interface. At the heart of the development environment is the workspace for graphically creating the ETL processes. The function modules can be placed and linked together using drag-and-drop. Double-click to access the configuration parameters for defining the properties.
- DataWrangler was developed and enhanced with feedback from media companies around the world. It responds to demands from media professionals looking for special skills. It includes the high-performance transfers for everyday up-front copy controls, which involves eliminating hours of waiting for over-demand requests.
- “Open Refine” – Data is often dirty, so poorly formatted, or are often not in the form in which we need them. To continue working with them or preparing them for visualization, they need to be put in the right format. This is done with the tool “Open Refine”.
Data cleansing is an essential step in the data quality process. It does not matter whether it is about cleaning up legacy systems, master data, in connection with migration projects, in risk management or in compliance. Also in the realization of customer orientation with primarily operational, domain-related data usage. Data Cleansing creates optimal data quality in terms of timeliness, reliability and level of detail. This data cleansing is the basis for a higher information content, good analysis results and effective business processes.