In the world of generative AI the importance of data quality is often underestimated. Companies are quick to jump into the latest AI technologies without enough consideration for the quality of the data they use. But did you know that the quality of your data directly determines the quality of your AI solution?

The impact of data quality on generative AI

Imagine this: you are building a wonderful generative AI solution, but the results are far from sufficient. This can mean 2 things, either your prompt engineering is not up to par, or the quality of your data is poor; often the answer lies in data quality. Even small inconsistencies in data can have significant consequences for the performance of your solution.

What is qualitative data?

But what exactly do we mean by qualitative data? Essentially, it's data that is accurate, consistent, and relevant to the purpose of your AI solution. It's data that is free from errors, duplicates, and inconsistencies. 

When developing AI solutions for clients, we have noted with MSTR that the quality of the input data is crucial. In one case, erroneous data in the examples led to strange answers during product testing. Although we tried to resolve this with prompt engineering, it turned out that correcting the erroneous data was the key to better output.

How do you ensure qualitative data?

Obtaining qualitative data requires a thoughtful approach. It starts with taking the time for a thorough data analysis. Understand your data, identify potential issues, and correct them before you begin developing your AI solution. Another crucial step is logging and structuring input and output data. By keeping your data structured, you lay the groundwork for future AI solutions. Structured data is easier to understand, analyze, and use for AI purposes. For safely storing and using your internal data, we refer you to this blog about data security with generative AI.

The role of AI tools in data quality

AI cloud solutions, such as Microsoft Azure, provide powerful tools for data storage and management. With Azure AI services, you can improve and optimize the quality of your data. Additionally, both Microsoft Azure with AI Search and Google Cloud Platform with Vertex AI offer smart tools that you can use for structuring your data.

Measuring and improving data quality

But how do you know if your data is of good quality? There are several methods to measure data quality, such as checking for completeness, accuracy, and consistency. By regularly measuring the quality of your data, you can identify and address problems early on. Improving data quality is a continuous process that requires ongoing monitoring and optimization. It is an investment that pays off in the long term in the form of better AI performance.

Conclusion

By investing in data quality, you lay the foundation for AI solutions that are not only impressive but also reliable and effective. So, before you get swept away by the possibilities of generative AI, take a moment to consider the quality of your data. Because ultimately, it is the quality of your data that makes the difference between mediocre AI and AI that astounds the world.