Data cleansing is scrubbing imperfections from the data so that accurate data can be collected to make decisions. Businesses are increasingly relying on this process so that their clean, data-driven decisions will lead to realistic decisions. By this, it means decisions that work actually well and show real-time values and results.
Consider a survey by Experian; it proves 95% of businesses are affected by poor data quality. Even IBM has projected that bad data costs approximately $3.1 trillion annually to the US economy. These facts and statistics make it compulsory to consider data cleansing seriously. This post will introduce you to the top techniques for effective data cleansing.
Topmost Data Cleaning Methods
So, let’s get started to illustrate what these data cleansing techniques are and how they impact:
1. Data Profiling
The very first data cleansing technique or method is data profiling, which is connected with the completeness, accuracy, and consistency of data. It involves a set of data, which is analyzed to understand if it is flawless. This flawlessness includes the examination of its structure and content as well. Overall, the profiling guides you to identify anomalies, missing values, and inconsistencies.
Implementation:
- Statistical Analysis: With tools like PowerQuery, profiling can help in statistically analyzing datasets to find out like and unlike patterns and irregularities.
- Metadata Examination: This cleansing technique is helpful in reviewing metadata, which is meant to understand various data types, formats, and constraints.
2. Standardization
Simply put, it refers to processing records in order to convert the structure of different datasets into a common format. It can be considered a transforming stage wherein the collected records from different sources can be uploaded to a target system or server. It is done after establishing a common format, which can be like variations in the date format or likewise. This is basically helpful in restructuring and making analysis way easier. Gartner found that poor data quality, which includes unstandardized data, costs $12.9 million on average every year.
Implementation
- Uniform Formatting: It is applied to the database to ensure consistent formats for dates, addresses, and other data types.
- Reference Data: Sometimes references are created. Unmatched formats can make it difficult to understand a similar piece of information. This method helps simplify this problem.
3. Deduplication
The third method or technique is deduplication, which means identifying dupes to remove them from the database. Their presence can adversely impact analysis and lead to bad decisions. Significantly, it helps in enhancing storage by removing dupes, improving the performance of a system.
Implementation
- Matching Algorithms: Hubspot’s deduplication feature and Zapier-like tools use AI algorithms to filter out duplicates after automatic comparison of records.
- Manual Review: In cases where investing in tools is expensive or impossible, data specialists manually detect and remove duplicity from the data.
4. Data Enrichment
The data enrichment process means adding new information to update a database. Some external data sources are used to extract relevant values that might be missing from the existing database. Put simply, this process helps in refining and improving the value of existing data by adding new details so that it can become more comprehensive.
Implementation:
- Third-Party Data: For doing it, authentic data vendors are hired as a third-party to provide niche data so that incomplete records can be filled, enriching the value of the whole database.
- Data Matching: Another practice called data matching is carried out to add context and depth.
5. Validation and verification
Another data cleaning method is validation. However, verification is also part of it. Both conflict in real terms. Validation refers to data meeting specific criteria before being added to the system. On the flip side, checking the flawlessness and consistency of existing data is called verification.
Implementation:
- Validation Rules: Several predefined rules, like range checks or mandatory fields, are implemented to examine their accuracy at the point of entry.
- Regular Audits: Verification typically ensures auditing the accuracy and consistency of the data.
6. Handling Missing Data
Missing data denotes missing data entries in a database. These entries are either completed by identifying missing entries or enriching them. If the missing records are not relevant or less useful, they are excluded. Overall, the methods of imputation and exclusion are used for handling missing records.
Implementation:
- Imputation: Under this statistical method, machine learning algorithms help in hypothesizing and then identifying the best fit to fill up the missing values.
- Exclusion: Exclusion refers to a specific procedure to identify missing data points if they cannot be reliably imputed.
7. Automated Tools and Software
Many tools have evolved to automatically remove discrepancies like typos, unformatted data, missing details, etc. These tools can be Talend, Trifacta, WinPure, Deduplication, etc. Mostly, they continue to streamline the whole cleansing process while minimizing human interference.
Implementation:
- ETL Tools: These tools are mainly introduced during the ETL, or Extract, Transform, Load (ETL) procedure to automatically remove imperfections from data.
- AI and Machine Learning: Since AI and machine learning require a massive volume of data to be transformed, they leverage algorithms for more sophisticated data cleansing, such as anomaly detection and pattern recognition.
Conclusion
Top data cleansing techniques are mainly associated with data profiling, standardization, deduplication, data enrichment, validation and verification, handling missing data, etc. These are the most common methods that are highly effective and useful for advanced data cleansing technique and process.
