In science, data does not have to be perfect all the time. We can get data that is incomplete or that has 'noise' in it, yet still manage to work with such data, analyze it, enhance it and come up with new insights based on such less-than-perfect data. Of course there is a limit to such imperfection, data that is totally inaccurate would not lead us anywhere. Yet if the the data collected is in its majority of good quality it can be usable even if it is less than perfect.
The same applies in the business world. It is usually impossible to get all the data needed to reach a perfect decision making process. In business, we try to collect the best data we can, but have to make decisions based in data that is often less than perfect. The better the quality, accuracy and comprehensiveness the data the better support it would give to a great decision making process. Yet lack of such high quality or perfect data should not deter us from using it for business decision making.
The same applies in the business world. It is usually impossible to get all the data needed to reach a perfect decision making process. In business, we try to collect the best data we can, but have to make decisions based in data that is often less than perfect. The better the quality, accuracy and comprehensiveness the data the better support it would give to a great decision making process. Yet lack of such high quality or perfect data should not deter us from using it for business decision making.
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