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Understand Big Data in AI and the importance of data quality

As we can see new incredible Artificial Intelligence tools coming out every week, you could have wondered how this systems actually work and Data is big part of that. In this article you will understand Big Data in AI and the importance of data quality in AI systems.

The Intersection of AI and Big Data

Understanding Big Data

If you want to train your AI model you need data or dataset, so It is important how the data is being valued in those datasets, It is mainly based on the volume or how big is the dataset, velocity or the speed of the data, variety, veracity or how accurate the data can be, and value or how useful the data can be.

In typical decent AI project can be easily process terabytes or even petabytes of data.

This data also must be generated and analyzed quickly so the velocity is also very important.

The data also should come from diverse sources, not just single one, because almost every time is better to consider data from more places and less, then lot of data only from one source.

AI's Reliance on Big Data

AI algorithms use lot of data and It is also the magic of Artificial Intelligence systems and why they are that good.

It is estimated that 80% of an AI project's time is spent on data preprocessing, so that already should tell the importance of data in AI.

Some Artificial Intelligence models can require millions of data points to be capable of high accuracy outputs, for example one large-scale project like AlphaGo is using more than 30 million training examples.

Real-World Examples

For example AI in healthcare can analyze medical records or even lab results and then imaging data to improve diagnostics and treatment plans, another example could be IBM Watson that analyzed over 200 million pages.

The Critical Role of Data Quality in AI

Characteristics of High-Quality Data

Common Characterics of High-quality data are that the data is accurate, complete, consistent, relevant, and timely.

Study by IBM appx. estimated that poor data quality costs the US economy $3.1 trillion annually and emphasized the big importance of using only high data standards in AI applications.

Data Cleansing and Preprocessing for AI

There are lot of different techniques and Tools for data cleansing and preprocessing.

But usually they involve techniques like error correction, imputation of missing values, and data normalization

For that can be used tools like Pandas, OpenRefine, and DataWrangler.

Consequences of Poor Data Quality on AI Performance

It should be addressed that Real-World Scenarios of using poor data quality when building AI systems can easily lead to inaccurate, biased, or misleading AI predictions that can significantly reduce effectiveness of those systems and potentially causing some unwanted harm.

For example, biased data in facial recognition AI systems can result in unfair treatment or discrimination.

In 2018 was reported that one specific AI tool (don't want to blame anyone, so that is why the name is not here) showed some bias against female candidates, because of historical data being predominantly male.

Best Practices for Ensuring Data Quality in AI

Data Collection Techniques

Here are some tips on effective data collection techniques:

1) Use multiple data sources

2) Use random sampling

3) Ensure that the data representativeness is high

It is also needed to establish some kind of data collection protocol and also follow ethical guidelines.

Utilizing AI-Powered Data Quality Management Tools

The top tools for data quality management can help significantly like Talend, Informatica, and Alteryx they offer advanced features for data quality management and can better up the overall effectiveness of AI systems.


High-quality data is essential for the success of AI systems and understanding that relationship between AI and big data also the big significance of data quality and some examples of the best practices for maintaining data integrity as we listed, can really help you build your own AI system if you want to.

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