You may have gotten the data right, cleaned it to perfection, built wonderful predictive models, provided the results to the user, it all looks great but if the user does not TRUST the result it won’t be used and all your work was for nothing.
How To Gain the Users’ Trust
- Use Good Quality, Fully Representative, Data
Quality here is not just removing outliers but fully representative of every possible condition going forward. If you use data for your analysis that for some reason excludes possible future conditions and those conditions happen, your result may look erroneous. The user seeing this error will no longer trust your solution.
- Use Explainable Technologies
Users don’t like black boxes. Use a modeling, prediction and optimization technology that can be explained to non-analytics savvy people. If you must use a “black box” technology, such as neural networks, have a scheme to demonstrate it works and has captured the relationships in the data correctly. If you can’t explain how the result was arrived at, or prove it correct, they won’t trust it.
- Intuitive Models
The models you create must be “intuitive”. That means if X goes down and the user expects therefore Y to go up, your model better do that or you best be in a position to prove why it should not. This can be a discovery for the user, their rule-of-thumb is invalid or conditional, but some real convincing has to be done to change their mental model of reality.
- Self-Maintaining Solutions
The system may have been good, trustworthy, at first launch but if performance degrades over time because the modeled process is changing (“non-stationary”) your model best keep on top of it, adapting as the world changes. If the user goes and looks at the results weeks or months later and sees that they are a bit “off” (building an error bias), they will stop trusting it and eventually stop using it. Use self-maintaining technologies because if a human has to maintain it, they won’t.
- Operate Reliably
If the user goes to look at your results, they best be there and still be correct. Make sure you have the data exception handling in place, handling spurious values for example, so that you issue a good result or perhaps no result with the reason why. Make sure your solution’s technical performance and reliability are best suited to the needs of the application and the users.
Trust is the essential, often forgotten, ingredient in your predictive analytics project. The user must TRUST the result else they won’t act on it.
Over to You
Have you experienced issues with trust of technologies? I’d love to hear your experiences in the comments below!