How can I use the power of AI for Data Profiling and Cleansing?

AI is transforming how organisations handle data preparation, making it faster, more accurate, and cost-effective. But where exactly in the data cleansing process is it appropriate for AI to intervene?

In this comprehensive article Planning and Executing Data Cleansing we guide you through the steps required for planning and executing data cleansing, looking at the challenges involved in making messy data fit for analysis so you can create more accurate reports. As we zoom in on the issues it becomes apparent that where large volumes of data are involved, identifying errors manually can take up huge amounts of time or even overwhelm manual processes altogether.

So, is this a good place to bring AI in to help? Data profiling and data cleansing are two areas ideally suited to AI intervention. Let’s look at them in more detail.

The business case for AI in data prep

AI can help automate data profiling (identifying patterns, outliers, and errors)

AI can help automate data cleansing (fixing duplicates, standardizing formats), reducing prep time from weeks to hours.

1. Profiling

   • Structure Discovery

     : Checks formats (e.g., phone numbers, dates) for consistency.

   • Content Discovery

     : Flags missing values or outliers (e.g., a £0 sale in revenue data).

   • Relationship Discovery

     : Maps connections (e.g., linking customer IDs to orders).

   (Understanding Data):

2. Cleansing

   • Error Correction

     : Fixes typos (e.g., "Birmingham" vs. "BHM") using Natural Language Processing.

   • De-duplication

     : AI clusters similar records and decides how to deal with them (e.g., "John Smith" vs. "J. Smith").

   • Enrichment

     : Fills gaps (e.g., inferring post codes from addresses).

   (Fixing Data):

The Business Case for AI in Data Wrangling

• Time Saving

  : Data teams can spend up to 80% of their time cleaning data instead of analysing it but with AI automation you can create reports which highlight the errors, saving many hours work.

• Cost Efficiency

  : Manual cleaning is labour-intensive; AI reduces operational costs by automating error detection and correction.

• Improved Accuray

  : AI minimises human error in both profiling and cleansing, ensuring reliable analytics down the line.

• Scalability

  : AI can handle large and complex datasets like customer records or the data fed in from temperature and pressure sensors on industrial machinery, where such huge volumes can overwhelm manual systems.

How AI Enhances Data Profiling and Cleansing

a) Data Profiling with AI

Automated Anomaly Detection: AI flags inconsistencies like a data point that significantly deviates from the normal pattern, in sales data for example. It does this using machine learning models trained on historical patterns, if it looks wrong it will be flagged.

Source Quality Assessment: AI can be used to evaluate whether the data is current and up-to-date, reducing errors further down the line.

b) Data Cleansing with AI

Error Correction: AI fixes typos, standardises formats (e.g., dates), and fills in empty values (e.g., missing postcodes).

De-duplication: Machine learning will identify similar entries in a dataset, like multiple records for the same customer. Once it has grouped them, it then decides whether to combine them into one (merge) or remove extra copies (purge), ensuring the data is clean and free of duplicates.

Real-World Success Stories

• Healthcare

  : AI cleaned patient records for a hospital, reducing misdiagnoses caused by outdated or duplicate data by 20%.

• Retail

  : An e-commerce firm used AI to unify fragmented product data, improving inventory accuracy by 30%

• Finance

  : A bank automated fraud detection by cleansing transaction data, cutting false positives by 25%

Benefits for Business Users

• Faster Insights

  : AI preps data in minutes rather than weeks taking you closer to your goal of quality data and useful reports.

• Competitive Edge

  : Every powerful AI models (e.g., demand forecasting) needs clean and accurate data for those smart decisions.

• Compliance

  : AI can help you get rid of your errors while keeping sensitive data anonymised so that you stay GDPR compliant.

Bottom Line: AI isn’t just of interest to tech teams - it’s a business enabler turning chaotic and messy data into the kind of business information you need. By automating data preparation, you will save time, reduce costs, and unlock insights faster. If you don’t have a specialist team working with you to prepare your data for analysis PTR can help you. We would be very happy to meet with you to find out where you are on your data journey and begin the process of getting your business ready for the future.

Contact us here

Real world success stories source:

• 7+ Must-Have AI Tools for Data Cleaning in Spreadsheets - Numerous.ai

• How to Use AI to Clean Your Data | Tamr