Using AI to Clean Your Data

We’ve all seen the statistics: data scientists spend anywhere from 60%-80% of their time cleaning dirty data. And we can all collectively agree that this is not the best use of their time. However, despite efforts and techniques to help make data cleansing faster and easier, these numbers don’t seem to budge. Until now. 

Data products are revolutionizing the ways in which organizations clean dirty data. Using advanced algorithms and machine learning-driven models, AI-powered data products spot patterns, anomalies, and inconsistencies otherwise obscured from view. But how do they do it?

The Power of AI/ML Mastering

Data products combine advanced AI with human insight to improve data quality. By combining the power of AI-driven models with human feedback, organizations can use data products to reduce the complexity of data transformation. Advanced AI models compare and score diverse datasets quickly, making it faster to clean data than with traditional, rules-based approaches. 

Further, data products utilize semantic comparison via large language models (LLMs) to identify discrete similarities and differences in the data. Then, they contextualize the data and extract key features which improves matching accuracy. They also employ a recommendation engine to identify the most likely matches within the data, and, using a unique ID, narrow down results to identify the recommended match. 

Using AI to Help Clean Data: Western Union

Western Union is transforming to a digital-first business. In order to do so, they must consolidate records from online and retail channels to form a 360-degree customer view. But when 200 million people used Western Union’s services to send and receive money in the past two years alone, this straightforward task immediately became very complex - and time-consuming.

“You may have five profiles but you are the same person. And I should be able to give you the same service whether you're using the mobile app, the retail channel, or the website. We want to bring all of that information together and offer a great omni-channel engagement,” said Harveer Singh, Western Union’s Chief Data Officer. But without accurate customer data, providing an elevated level of service ”was absolutely impossible.”

 That’s when Western Union knew they needed a better approach to cleaning their data. Using Tamr B2C Customers data product, Western Union deduplicated and enriched 375 million customer records in a matter of months, providing agents with a holistic, 360° view of the customer which allowed them to identify top customers, tailor experiences, and reduce marketing spend. For perspective, this process would have taken them years using a traditional MDM approach. 

Hear from Western Union’s Chief Data Officer, Harveer Singh, to learn more about Western Union’s journey to deliver better customer experiences using holistic, trustworthy data.

The Downsides of AI Data Cleaning

While AI offers businesses a way to expedite the data cleansing process, it’s not without potential downsides. 

1. Bad data leads to bad results. AI models are based on the quality and diversity of training data. And when that data is incomplete, incorrect, or biased, the results of your data cleaning efforts will be, too. 
2. Lack of transparency. Many AI algorithms operate as black boxes, preventing humans from understanding the logic that drives the models and results. 
3. Innaccuracy. AI algorithms can misinterpret data or make incorrect assumptions, causing the cleaning process to yield inaccurate results. 

Humans Must Play a Role, Too

While AI-powered data products can expedite the process to clean data, humans still play a crucial role in bridging the gaps where AI falls short. In fact, their importance cannot be overstated. 

The inherent knowledge of humans is key when it comes to reviewing results and providing feedback. They can highlight results that appear off - either because they are inaccurate, biased, or unethical - safeguarding the business from potential harm. And because data products are intuitive and easy-to-use, data consumers across the business can easily override matches using a simple UI.

Further, using curation interfaces, users can pinpoint data quality issues and provide closed-loop feedback to resolve them quickly and efficiently, reducing the time it takes to improve overall data quality.

‍Why Traditional, Rules-Based Approaches are No Longer Enough

Traditionally, organizations have employed master data management (MDM) as a way to clean their data. Today, however, those solutions are no longer sufficient. They simply can’t keep pace with an organization’s need to deliver clean, trustworthy, consumable data at scale.

Traditional MDM solutions use rules to clean and master data. And when data changes, so do the rules. Keeping the rules up-to-date as data evolves is manual and time-consuming. Humans spend an inordinate amount of time writing, modifying, and maintaining rules, making it difficult to act quickly when data changes. 

Further, traditional MDM relies on static data, which makes it difficult for these solutions to keep pace with today’s dynamic data. They also rely on centralized control where the governance and management of data are tightly controlled by a central authority. This approach is not sustainable as it leads to bottlenecks and inefficiencies, slowing down the process to clean dirty data.

AI Data Cleaning with Tamr Data Products

Tamr checks all the boxes when it comes to delivering the data product platform businesses need to clean dirty data. Our innovative data product platform is the first of its kind to unite AI with human intelligence to improve data quality and enrich data with first- and third-party data so businesses can revolutionize customer experiences, drive greater ROI, boost operational efficiency, and avoid risks. 

Using Tamr’s cloud-native and SaaS solutions, organizations can uncover the insights they need to stay ahead of the competition in a rapidly-changing business environment. To learn more, please schedule a demo.