Data cleansing or scrubbing is an integral part of data management. Typically, it requires focus on maintaining 99.99% accuracy, consistency, and authenticity of data sources. Businesses, these days, are more inclined to maintain data hygiene because they cannot afford the catastrophic effects of bad decisions. It’s obvious that bad or noisy data leads to bad decisions. So, this data cleanup process is emerging as the backbone of businesses’ forecasting and strategy-making teams.
Here, some of the most common data cleansing techniques are shared. Let’s start introducing them.
Proven Data Cleansing Techniques for Accurate Data
Let’s catch up with some of the proven techniques to maintain hygienic data.1. Data Validation
A study by Experian proves that 83% of organizations trust data quality to be critical for their success. This goal cannot be achieved unless you know how to validate data. It is a significant factor in cleansing data.
When the data is stored, certain criteria are made to place it. These criteria or rules can encompass range validation, format cleansing, and consistency audits. Accordingly, various records are put in the storage. Let’s say, a date field has invalid date format. The applied validation rule can filter it so that you can fix it swiftly. However, some bugs, migration, or discrepancies can lead to recording invalid entries. So, data validation technique proves effective in highlighting those errors right at the point of entry. It saves time on extensive cleanups later.
2. Deduplication
Deduplication is related to identifying dupes or duplicate data for removing. This kind of data error can emerge when you combine data from various sources or integrate system. To get rid of this data cleansing issue, deduplication is the only answer. This strategic process of removing duplicates helps in canceling or removing redundant entries so that each record in a database is unique.
3. Standardization
Standardization is associated with the consistency of data format. There may be multiple variants of date. These types of variants can cause confusion, which leads to conflicting results. So, standardization technique ensures transformation of such variations into a standard format. So, some variations can be seen in date, unit of measurement, naming convention, etc. This method helps in measuring data from different sources to compare and integrate in a standard format.
4. Data Enrichment
Data enrichment is concerned with incomplete data, which is considered as bad data. Sometimes, the data can be more informative when you add some complementary datasets from external sources. This technique is unique and is widely used to fill missing values, fixing inaccuracies, and providing more context to databases for better analysis. Let’s say, a customer’s email ID is there in the CRM, but the phone number is missing. Its addition can increase the chances of personalized solutions provided to customers.
The Informatics survey revealed that 77% of organizations use this process to deliver high-quality data. It insights will certainly help in strategizing complementary things effectively.
5. Data Parsing
When you break down a complex set of data into simpler form, it is regarded as data parsing. This cleanup technique helps in separating lengthy and complex data, as first name, middle name, and surname, or, it can be any address that is split into building number, street name, and city with zip code. Overall, parsing enables data professionals to organize data effectively so that analysis becomes an easy task.
Research by Talend reveals that 65% of data professionals rely on this technique to come up with extraordinary data quality.
6. Error Detection and Correction
This is simply connected with errors identification so that they can be fixed without hassles. On advanced level as in data mining and artificial intelligence development, this method helps in filtering out outliers, inconsistencies, and anomalies. However, certain tools and even scripting can be used to highlight wrong data entries. But, you cannot skip manual revision once corrected for quality assurance.
A study by Gartner unearths that 40% of data management professionals report that continuing with this error detection and correction method is a daunting task. But, you cannot skip it if you don’t want to experience a major data quality issues that become unmanageable.
7. Data Normalization
Normalization is typically associated with abbreviations. Sometimes, short forms of words are used like DM, which may represent direct message, deputy minister, or data management. These abbreviations can be conflicting, and escalate confusions. So, data normalization is utilized to establish data integrity. This cleansing method is mainly used in creating tables, defining relationships, and establishing data integrity rules.
8. Handling Missing Data
Missing pieces can disturb final conclusion. Handling this cleansing problem is not easy because it requires data specialists to follow imputation process. This process technically supports in replacing missing values with predicted or estimated values. Also, some records with missing pieces can be deleted. Some advanced data specialists use scripting or customized algorithms to handle this missing data problems automatically.
A study by IBM came with a surprising fact that missing data can account for up to 25% of the total data in various industries. So, companies employ effective strategies to handle this data cleansing problem.
Conclusion
Data cleansing experts evolve tailored techniques to sort out various inconsistencies, missing details, invalid entries and formats. Some expert solutions are evolved to counter them, which include data validation, deduplication, standardization, enrichment, parsing, normalization, handling missing data, etc. These are helpful in combating inaccuracies in data.
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