Data Cleansing
According to a Gartner study, companies estimate the damage they suffer each year due to poor data quality at 15 million US dollars. Data stocks have a high business value, but only if they are maintained and cleansed and thus exhibit high quality. Companies that integrate data cleansing into their data management initially have to invest - but in the medium and long term they strengthen their future viability.
Definition: What is Data Cleansing?
Data cleansing (also data cleaning) is an essential and active part of data quality management and describes the process of correcting erroneous, inaccurate, redundant and damaged data in data sets. Data is partly removed, partly corrected or supplemented.
The more data sources are integrated into companies, the greater the risk that data quality will deteriorate, for example because different formats are not recognized correctly by the target system, redundancies distort the database, or data is accidentally deleted. Since poor data quality is rarely obvious to the user, data cleansing should be included as a standard process in data management.
Why Is Data Cleansing Necessary?
High data quality is a significant competitive factor: the accuracy of analyses, customer satisfaction and sales depend directly or indirectly on data quality.
The most advanced BI and analytics applications are of little use if they produce their evaluations and forecasts on a flawed data basis. Which new products have the best chance of success? Should expansion into a new market be pursued? A reliable data basis can safeguard strategic decisions.
Marketing and product development are also increasingly based on data analyses of customer and user behavior. Incorrect data sets lead to false conclusions and investments come to nothing. Conversely, with measures based on a high-quality database, companies can cost-effectively increase customer satisfaction and massively reduce sales costs.
But it is not only product development, marketing and strategy that benefit from data cleansing. The optimization of internal processes is also much more cost-efficient and promising if they are based on a good data foundation. Which tasks require a disproportionate amount of time? In which departments is job satisfaction declining? If interpreted correctly, managers can use data to improve productivity and employee motivation in their teams.
Advantages of data cleansing in a nutshell
- More reliable basis for decision-making
- Internal process optimization
- Higher customer satisfaction
- Simplified customer acquisition
- Improved chances of success for new products
7 Steps of a Successful Data Cleansing Process
There is no gold standard for the data cleansing process. The procedure depends on the data in question, the IT infrastructure and the company's objectives.
Both regular data cleansing of central company data such as master data and project-related data cleansing because systems have been migrated or new interfaces have been implemented or in preparation for this, make sense.
Companies should develop their own data cleansing strategy for each use case to ensure efficiency and consistency of results.
To develop a data cleansing process, companies can use the following roadmap as a guide:
1. Identify Relevant Data
First, identify the data that is irrelevant to the process being evaluated. If it is a one-time data cleansing project, the same applies: Variables that do not contribute to answering the project question are deleted or not transferred to the central database. The relevant data is prepared for cleansing.
2. Remove Duplicates
Using similarity algorithms or based on an actual database that serves as a single source of truth, duplicate values are captured and removed from the data set.
3. Correct Structural Data Errors
Data errors can occur when importing data from one system to another. If file formats are adapted when importing customer data from the ERP system into the CRM system, this can lead to incorrect category designations or misspellings. In Data Cleansing, the errors are detected and corrected manually or automatically and the data is converted into a uniform format.
4. Fix Spelling Errors
When string values or texts are analyzed, they must also be in a uniform format. Spelling errors, for example, misspellings of city names or different date formats (European vs. American spelling), can "confuse" algorithms in their analysis. Therefore, companies should define standards according to which the dataset is cleaned.
5. Clarify Missing Values
Anyone who saves enough data records will sooner or later be familiar with the problem of missing values. Sometimes a postal code is not entered, sometimes the telephone number is missing. However, for algorithms to work smoothly with data records, they must be complete. Therefore, data cleansing involves adding missing values, as long as this is possible with a reasonable amount of effort. If this is not the case, it is possible to delete the entire data record or to add a standardized error value (zero).
6. Reporting
After data cleansing, the quality of the results must be checked so that methodological adjustments can be made, if necessary, to minimize residual data errors. Many applications for data cleansing offer the creation of reports as standard, which users can configure individually.
7. Quality Assurance
Companies should regularly reflect on their data cleansing process with users: Is the functional scope of the data cleansing software sufficient? Where do the teams involved think there is potential for improvement in the process? Is the audit interval appropriate? The answers provide valuable impetus for further improving data quality.
Master Data Cleansing - Data Cleansing as Part of Master Data Management
Master data management is a key discipline of digital transformation. How companies organize product data and master data, for example, is crucial for their competitive position. The data must not only be available quickly and stored securely. It should also be error-free, consistent and reliable, so that companies avoid inefficiencies, revenue losses and reputational damage.
Customers rely on product information. If they receive goods other than those described, they lose confidence in the company. In addition to the damage to their image, companies also have to accept additional costs for returns. The internal damage of poor data quality is also significant: marketing staff need master data on customers for many online campaigns. If they cannot rely on high data quality, this slows down processes through avoidable cross-checks.
Master data is also evaluated for far-reaching strategic decisions. Poor data quality can cause wrong decisions that result in damages in the millions.
Frequently Asked Questions About Data Cleansing
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The terms are often used as synonyms. Industry standards such as the Data Management Body of Knowledge tend to use data cleansing to refer to the process of cleaning data, so we recommend this language. However, neither is incorrect. Data Scrubbing is a third term used synonymously for the process of data cleansing.
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High data quality always exists when the collected data is well suited for the intended use. The variables used to measure suitability differ depending on the usage scenario. Often, companies use criteria such as completeness, consistency, correctness, uniqueness, and timeliness to quantify data quality.
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Data scrubbing describes a concrete data cleansing method that regularly and automatically checks data sets for errors and corrects them before the errors start to add up. The process runs in the background; manual intervention is not required. However, the term is often used simply as a synonym for data cleansing.
Better data quality, better decisions