The article also highlights the need for a clear data collection policy and the collection of as much data as possible for more accurate results from AI and ML tools. It stresses the importance of having a quality partner to manage the influx of data and to make connections within existing datasets to better determine if a loan will be profitable. The article concludes by stating that a comprehensive data strategy that connects a quantity of quality data with a quality data partner is essential for businesses to stay competitive in the age of AI and ML.
Key takeaways:
- Data quality is non-negotiable for businesses planning to use AI or ML tools, and it's important to consider not just your own data, but also industry, economic, customer, and competitor data.
- Data collection should be a priority for businesses planning to use AI or ML, as the more data provided, the more accurate the results will be.
- Quality partnerships can help businesses manage the influx of data and make the most of AI and ML tools, particularly in loan decisioning where multiple sets of key data come together.
- A comprehensive data strategy that connects a quantity of quality data with a quality data partner is crucial for businesses to gain important insights in the age of AI and ML.