In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does its work, and how the data is used.
Today, in a world of AI and machine learning, data has a new role—becoming essentially the source code for machine-driven insight. With AI and machine learning, the data is the core of what fuels the algorithm and drives results. Without a significant quantity of good quality data related to the problem, it’s impossible to create a useful model.
Download the white paper, Debugging Data: Why Data Quality Is Essential for AI and Machine Learning Success, to learn why the process of identifying biases present in the data is an essential step towards debugging the data that underlies machine learning predictions and most importantly, improves data quality.
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