The Role of Machine Learning in Data Cleaning

According to Gartner’s report, 40% of businesses fail to achieve their business targets because of poor data quality issues. The importance of utilizing high-quality data for data analysis is realized by many data scientists, and so it is reported that they spend about 80% of their time on data cleaning and preparation. This means that they spend more time on pre-analysis processes rather than focusing on extracting meaningful insights. 

Fixing data quality issues 

Although it is necessary to achieve the golden record before moving on to the data analysis process, there must be a better way to fix the data quality issues that reside in your dataset rather than correcting each error manually. 

Using a Code-based Approach  

Programming languages like Python and R have made it fairly easy to code basic data cleansing workflows, such as:  

• Dropping columns that are not useful for the analysis process, 
• Changing data types, 
• Highlighting missing data, 
• Removing break lines and whitespaces from column values, 
• Arranging data numerically instead of categorically, 
• Concatenating columns into one, 
• Changing strings to date-time formats, and so on. 

It is very effective to clean data using coded scripts, but you must possess substantial programming expertise. Moreover, coded scripts have a tendency to be specialized for specific datasets and their column values. This means coded functions always work better when your data values contain similar underlying patterns. Otherwise, you will end up hard-coding specific scenarios into your code to achieve data cleanliness instead of implementing a more generalized approach that caters to multiple scenarios. 

Machine Learning and Its Role in Data Cleaning 

To clean data, first, you must be able to profile and identify the bad data. And then perform corrective actions to achieve a clean and standardized dataset. There are various stages in a data cleansing process where machine learning and AI can not only automate workflows but achieve more accurate results. Let’s take a look at them. 

Profiling Data and Detecting Errors 

The first step where machine learning plays a significant role in data cleansing is profiling data and highlighting outliers. Generating histograms and running column values against a trained ML model will highlight which values are anomalies and do not fit in with other values of that column. You can train the model on standard dictionaries or provide custom datasets that are specialized for your data. 

Making Intelligent Suggestions to Clean and Standardize Data 

In addition to error detection in column values, ML solutions can also make intelligent suggestions and highlight possible actions for fixing data quality issues. These suggestions are based on the nature of the data encountered in the same dataset. For example, if two records have the same address but different ZIP codes, then an ML algorithm can flag this as a possible error that needs fixing. This is achieved by putting correlation constraints on the dataset that if Address values are the same, then the ZIP Code must be the same. 

Highlighting Possible Duplicates Through Clustering 

Record deduplication is one of the most important steps in the data cleansing workflow. ML solutions can help you to perform record linkage by clustering records based on their similarity. This is achieved by training the ML model on a non-deduped dataset that contains labels for matches and non-matches. Once trained, the ML model then intelligently labels the new dataset and creates clusters to highlight data records that possibly reference the same entity. 

Influencing Merge/Purge Decisions to Achieve a Single Source of Truth 

During clustering, ML algorithms determine the likeliness score of a record belonging to a cluster. This helps data scientists in making merge purge decisions and link records that belong to the same distribution – or in some cases, the same entity. You can also tune the variables used in the ML algorithm to set an acceptable threshold between the resulting number of false positives and negatives. 

Next-generation data cleansing tool 

The above workflow shows how an ML-based data cleansing software automates the cleaning activities and simplifies the decision-making process by advising intelligent suggestions. Such advanced processes that are leveraging the power of AI are essential to reducing the amount of time data scientists spend on data cleaning and preparation. 

Also published on DZone.