Are You Blinded by Data?
I do not think that anyone will argue against this statement, “Data is useful for business.” However, data can be seen as a part of the problem-solving toolkit. This makes it a tool that can be useful or limited depending on the problem it is applied to.
All tools will have their strengths and weaknesses, and to be able to use them well i.e. being able to solve the challenges it is applied to, we need to understand its limitations.
There is no lack of “experts” and vendors that are very keen to “educate” (*ahem* propaganda *cough*) you on the benefits of data. Thus, I would like to work with you on the possible downside of data and whether are you blinded by the data analysis shared.
So what is data or those figures in that cell within the database?
If you think about it they are dimensions that we can measure, capture, and store in digital forms. There is a chance that these dimensions are not measured objectively, for instance, how do you measure satisfaction, and does a Person’s A 5 out of 10 the SAME as a Person’s B 5 out of 10?
What this translates to is that the world has not been FULLY transformed into figures and digits yet. Analysts and data scientists need to constantly remind themselves about that and not be blinded by the data in front of them.
While there are great insights that one can get from data, it is important not to form tunnel vision and think that the whole world can be modeled with the data at hand.
A good analysis will be where it can use the models and insights trained and analyzed from the data at hand and draw it back to reality. A good analyst not only is able to draw useful insights from the data but also understands that the insights are limited and it need to be connected to reality to form a clearer picture of what is happening.
Do not think that the dataset is the world/reality. Be the bridge between what can be measured and captured with what cannot be measured and captured. :)
What is your thought on this? Will love to hear them. :)
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I wrote a thought experiment on how I will set up an AI Governance Committee, i.e. what areas of knowledge and expertise I want to have. Have a read here and again I invite your perspectives and sharing. <Blog Post>