Common Data Cleansing Mistakes and How to Avoid Them

Accurate data is critical for any industry. Inaccurate data can lead to misleading results, poor decisions and increased costs. Gartner estimates that poor data quality can cost businesses an average of around $9.7 million annually. Data cleansing services can ensure that business data is accurate, consistent, and reliable for decision-making and operational efficiency. But did you know that it’s easy to make mistakes during the data cleansing process, impacting the quality of your datasets? In this blog, we look into the common data cleansing mistakes, why they occur and how to avoid them.

8 Common Data Cleaning Mistakes and Tips to Prevent Them

Data cleansing, a crucial step in data management, is the systematic process of identifying, correcting, and removing inaccurate, incomplete, or duplicate data from business databases. However, the process is prone to various mistakes.

Listed below are some common errors that can occur when cleaning data and how to address them:

Ignoring Data Quality Standards: Failing to establish clear quality standards or ignoring the established standards can lead to inconsistent data cleansing efforts.

Data quality standards are criteria ensuring data is accurate, reliable, and useful. They address key aspects like accuracy, completeness, consistency, timeliness, and relevance, guiding all stages of data management. For example, ISO 8000 is a global standard emphasizing data governance, quality management, and assessment.

To maintain high data quality, define your business’s data quality goals and the actions needed to achieve them. Building a strong data integrity culture requires technology and importantly, human effort, continuous education, and dedicated resources.

Not Understanding the Data: Beginning your cleansing project without fully understanding the data’s context, structure, and meaning can result in improper handling. For example, not knowing what valid information is and what is not, can result in flawed data fixes such as deleting, modifying, or merging data improperly, fixing the wrong issues, improper handling of sensitive data, missing or corrupted records, and wrong decisions leading to strategic errors and lost revenue.

Understanding the data’s purpose, structure, and use ensures effective data cleansing and minimizes costly mistakes.

Over-Cleansing: Over-cleansing of data can occur when important data is accidentally deleted or altered, historical information is lost, or bias or errors are introduced during the cleansing process. It can also occur due to over-standardization such as setting down uniform formats that remove essential details (e.g., shortening addresses or names), and merging records that seem duplicate but contain unique context. For example, if an individual has two different roles in a company as marketing manager and sales director, assuming they are duplicates and merging their records can lead to loss of context.

Removing too much data or altering it excessively can strip away valuable information, leading to loss of insights. To avoid this:

• Compare roles, emails, and phone numbers carefully.
• Use business logic that flags potential duplicates for manual review.
• Consider keeping records separate when roles or contact details differ significantly.

Establish clear criteria for data relevance and quality to ensure valuable information is retained while unnecessary duplicates and errors are removed:

Missing Duplicate Records: Failing to identify and handle duplicate entries can affect analysis and reporting. An example of erroneous duplicate entries in online surveys could be a respondent accidentally submitting the same survey twice due to a poor internet connection, resulting in two entries with identical responses.

Implementing automated checks to spot duplicates based on key fields like name, date of birth, and address can help avoid this data cleaning mistake. Regularly audit and merge records to ensure only one entry for each individual.

Inconsistent Formatting: Not standardizing formats (e.g., date formats, address structures) can create confusion and inconsistencies across datasets. Datasets frequently contain inconsistent formatting, which can lead to confusion and errors. For instance, dates may be recorded in different formats, such as DD-MM-YYYY and YYYY-MM-DD, making analysis difficult and hampering future data integration.

Establishing and following clear internal guidelines can ensure your company’s data is formatted consistently. This standardization improves the reliability of the dataset, streamlines analysis, and enhances data integration.

Overlooking Missing Data: Missing data can occur for various reasons, such as errors in data entry, system malfunctions, or omissions in data collection. Ignoring missing data during the cleansing process can lead to incomplete analyses and unreliable conclusions.

Use data profiling tools to detect gaps in your dataset. Depending on the context, fill in missing data using methods like mean/mode imputation, interpolation, or predictive models. For non-essential data or when imputation isn’t suitable, consider removing rows or flagging them for further review. Analyze why data is missing to improve future data collection practices

Failing to Address Outliers: Outliers are data points that significantly differ from the rest of the dataset. They can occur due to errors in data entry, for e.g., a typo like “999” for age instead of a reasonable number). Failing to address outliers during data cleansing can lead to distorted analysis and unreliable conclusions.

To address outliers, first determine the cause: did they occur due to data errors or do they represent legitimate extreme values? Depending on the cause, either remove, correct, or transform the outliers to minimize their impact on the analysis. Using statistical techniques like Z-scores or IQR (interquartile range) may be essential to identify and handle outliers systematically.

Skipping Data Validation: Not performing validation checks post-cleansing can allow errors to persist in the data. It can lead to undetected errors or inconsistencies that compromise the quality and reliability of the cleaned data. Validation ensures that the data meets the required standards and is ready for accurate analysis and decision-making.

Validate data after cleaning: verify consistency, cross-check with source data, and use techniques like range checks, outlier detection, and data profiling to ensure the data falls within expected parameters. Ensure all necessary fields are populated and that no essential information is left out. Use validation tools to check for missing values, duplicates, and errors based on predefined rules.

Being aware of these mistakes can improve your data cleansing processes and ensure higher quality data for analysis and decision-making.

Partner with an Expert: Avoid Errors during Data Cleansing

Data cleansing can be complex due to the variety of data sources and formats, which complicates standardization and integration. Inconsistent data quality, such as variations in spelling, missing values, or differing units of measurement, adds to the challenge. Moreover, the need to balance automated processes with human oversight to ensure accuracy can create more challenges.

Partnering with a data cleansing service provider helps avoid errors, improves data management, and ensures accurate, consistent, and ready for reliable analysis. Their expertise in various fields is crucial for understanding data context and relationships, ensuring a more accurate cleansing process.