Data is the source code for machine-driven insight. Yet, research suggests that only 3% of data meets the basic data quality standards required for trusted AI.
AI is dependent on high-quality data
Historically, the source code of the application retrieves data and is then used to make decisions. While data has an impact, the logic of the code has had a meaningful impact on results.
Today, in a world of AI and machine learning, data has a new role – becoming 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.
The algorithms find signals in the data that are then used to make predictions and take action. If the model is trained on different data, the predictions and actions will be different.
Data quality processing is essential to debugging data that underlies AI and machine learning predictions
When you consider the role of data a thorny problem emerges.
On the one hand, it is clear that having as much data as possible that is of high quality will make AI and machine learning algorithms work better. But it is also clear that because the signal is hidden deep inside the data and can only be revealed by algorithms, it is not always straightforward to see how we can clean such data to improve its quality without obscuring the signal.
Download this Precisely white paper 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.
Dr Thomas Redman, For Ai, Poor Data Quality is Public Enemy Number One, 2023
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