What are some common pitfalls in AI integration?
Some common pitfalls include lack of clear objectives, insufficient data quality, and poor change management.
Can you elaborate on the data quality issue?
Certainly! Poor data quality can lead to inaccurate predictions and insights, undermining the AI's effectiveness.
What can be done to improve data quality?
To improve data quality, ensure data is clean, relevant, and representative of the problem you're solving.
Are there any specific tools for data cleaning?
Yes, tools like OpenRefine, Trifacta, and Talend can help with data cleaning and preparation.
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