There are many conversations surrounding the benefits of AI. Two of the benefits often discussed are the amount of time AI can save you and its ability to provide key organizational insights. Unfortunately, many people fail to realize that just because AI simplifies many aspects of a job, doesn’t mean building it requires a simple approach. Using quality data for AI insights is key to ensuring you are getting the most out of AI technology.  

Why Quality Data for AI Insights Matters 

High-quality data ensures AI models can detect patterns accurately and then uses those patterns to make correct predictions to provide actionable organizational insights. With poor-quality data, you run the risk of errors or unreliable outcomes.   

Like most technological models, AI requires training. Accurate data reduces the time needed to train your AI model, leading to a faster and more efficient development cycle.  

Although AI has proven to be beneficial in the workplace, it is far (very far) from perfect and has its limits. Quality data helps in avoiding biases that can occur if or when data shows historical inequalities or is imbalanced.  

From a people perspective, reliable data is a must for fostering trust and confidence among users. When users trust the data outputs, they are more likely to use that data to make informed business decisions. If you want your people to use AI, make sure it is done properly. A poor-quality data and AI combo can end up causing more work for employees.  

Bad Data: A Real-World Example 

Hannah Richardson, a reporter for the BBC, highlights how poor-quality data can lead to biases in her article on GSCE and A-level results for students. In 2020, many schools were forced to close due to the pandemic, leaving teachers scrambling to find alternate ways to educate and test young people.  

In 2020, teachers at these schools used “historic results of pupils at each school, the prior attainment of students, and statistical expectations about grade distribution for each subject” to predict grades (Richardson, 2020). This resulted in “abundant academic evidence of bias on predicted grades” that affected students in ethnic minority groups, students with special needs, and students with disabilities.

In other words, there was unconscious bias in the results that left already disadvantaged students more disadvantaged. 

The above example highlights the importance of using quality and unbiased data and the negative results that can occur when you don’t. Poor-quality data leads to unreliable results. Not an ideal scenario when you are relying on this data to make business decisions.  

Are You Using Quality Data for AI Insights? 

If you really want to leverage AI successfully, you have to ask yourself if you’re using quality data for AI insights. AI can make our lives easier, but we aren’t quite at the point where we can blindly put our faith in it and expect it to magically fix all our problems. It takes a carefully curated, targeted approach to ensure it’s used effectively.  

Don’t miss out on next week’s blog – we will be discussing various approaches to clean your data to ensure it is high-quality. And if you have a different project in mind, fill out the form at the bottom of the page and someone will be in touch.   

References:
GCSE and A-level results ‘could be affected by bias‘ (Hannah Richardson), July 11, 2020, BBC

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