Data Quality Challenges When Implementing AI

Data Quality Challenges When Implementing AI

In this article, learn about three data quality challenges when implementing AI that will haunt enterprises.

A Forrester’s report clearly shows that data quality is a challenge while implementing AI in organizations.  Data analysts are spending 80% of their time in data preparation, and only remaining 20% is utilized for running AI algorithms, deriving insights, and forecasting future predictions.  

When it comes to data quality challenges when implementing AI, enterprises that are planning to automate their business operations through AI need to have proper data quality management in place. Many organizations realize midway that their data is unclean, and something needs to be done about it before the project can continue. This is also causing business owners to lose faith in data-driven analytics and consider whether it is worth the time and resources spent in data preparation. 

In this blog, we will look at the three major issues causing data quality challenges when Implementing AI and what we can do about it. Let’s begin. 

Data Quality Challenge When Implementing AI #1: Incomplete and inconsistent data records 

Forms without validation controls usually fill your datasets with incomplete and inconsistent information. When input data has unformatted records or missing data fields, AI algorithms do not behave and give results as expected. As AI is built to read and train on patterns, any missing, unavailable, and inconsistent data variables can skew the results of the trained model. 

For example, street address information is normally a text field, and so, addresses are recognized from their postal zip codes. If data records have empty or incomplete zip codes, it’s almost impossible to know the geographic location of that entity. Moreover, if zip codes are not consistently formatted, this could also lead an AI algorithm to treat the same zip codes as different. 

How to fix 

Data scientists and analysts spend a lot of time manually reviewing millions of records spread across various data sources. They ensure that all necessary features (or simply, the data variables that are fed to the algorithm) are not left empty. They also check if the data field values are following a correct, consistent format. These activities are either coded using a programming language, or self-service data preparation tools are used to quickly transform all datasets as per one standard. 

Data Quality Challenge When Implementing AI #2 Duplicate data 

Duplicate data records are the main source of biased AI results. As AI model is trained by recognizing patterns in datasets, duplicate records can bias the algorithm and produce unreliable insights. This happens when multiple data systems are integrated together to create a single data source. This integration might take place by uniquely identifying records, but these identifiers are not always available. Due to the absence of such identifiers, records belonging to the same entity are then treated and saved as different. 

Data analysts trust their data to be reliable for training the AI model, but duplicate records cause their algorithms to make inaccurate predictions. Duplicate records also increase the computational complexity of the algorithm, as the model is trained on the same entity multiple times. 

How to fix 

All datasets must go through the process of data deduplication. This process (usually known as record linkage or entity resolution) ensures that data records from the same dataset or across multiple datasets are compared to see if they belong to the same entity. Agreement patterns and likelihood ratios are computed to make the decision, and then the records are merged or purged accordingly. 

Data Quality Challenge When Implementing AI #3: Data integration, purging, and storage 

Organizations these days use multiple applications for their operations. Data from all these sources must be integrated and brought together so that it can be used for analysis. A major percentage of data quality challenges when Implementing AI issues arise while combining data into a single source. This occurs because in silos, different data types and formats are being used, data records belonging to the same entity is being maintained separately, and so on. 

For example, if you need to forecast predictions about your consumer behavior in a specific season, you may need information from multiple applications at one place, such as your CRM, email marketing tool, and website activity tracker, etc. Integrating, merging, purging, and storing all this information as a single source is the main challenge that most data analysts face in the initial phase of any AI project. 

How to fix 

Data analysts use spreadsheets to manage all this information at one place, but such tools have their own limitations, such as a surge in the number of data records and applying complex standardization rules to data records. Another viable option in preventing data quality challenges when Implementing AI is to use a data preparation tool that offers integration capabilities with multiple applications, and also preserves a single source of all data records. 


Data quality challenges when Implementing AI is a serious problem. Enterprises often experience losses in terms of revenue and other resources when their organizational data is not being maintained for quality. Now more than ever, business owners are realizing the importance of data quality in the realm of AI and data insights. 

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Javeria Khan

Javeria Khan

Javeria, an experienced B2B/SaaS writer specializing in writing for the data management industry. She is also a programmer with two years of experience in developing, testing, and maintaining enterprise software applications.