The benefits of AI are all around us, but the real driver to its success is the data that fuels it. This is a topic I recently explored in the Forbes Technology Council blog. As I mentioned in it, the tendency to shortchange the data is a fairly universal problem that often results in misleading or incorrect results and poor business outcomes.
So, how can you make sure your data is going to properly fuel your AI projects? Consider the following five best practices that I shared:
- Go big or go home. When you are trying to solve a problem, you may not always know where you will find the answer, so try to gather as much data as possible. There’s really no such thing as too much data.
- Make sure the data is shipshape. After the right data is collected, it needs to be cleansed, validated and prepared to ensure that it is in good shape and ready for analysis. While this can be a time-consuming process, it can mean the difference between good and bad results.
- Put the data through a workout. A good way to know if the data is accurate is to test it, and find out if there is a problem before you are too far along in the process. You should divide your data into two parts and set one aside for testing and the other for feeding the algorithm.
- Use a data auditor. It pays to hire data auditors who can help you assess the data you have, conduct the tests and help you plan for future data training needs.
- Avoid biased data. It’s important that AI solutions that help organizations make important decisions operate fairly and equitably. To enable diverse AI-based decisions, the data shouldn’t focus on only one type of data source, but should encompass all scenarios. We’re all too aware of the problems encountered when AI makes decisions based on racial or gender profiling.
In the quest to be seen as a technology trailblazer, many organizations rush to deploy AI solutions to try to quickly realize their benefits. Yet, what they often don’t realize is that the best AI solution is useless without good data. By focusing on better data collection, cleansing, auditing and diversity, true AI success can be much achieved.
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