Big Data, Synthetic Data and Synthetic Intelligence

Published by Lex on

Making it all make sense is senseless    
Trying to make sense of multiple streams of seemingly related but not necessarily related data seems like it is very strategic, but in all likelihood is not all that strategic. Given the amount of time it takes to collect, analyze and create the data driven narrative, it is interesting to see how valuable that data is. 

All data is not created equally. Data from discrete and controllable environments is wonderful. This data can be modeled well. The variables and the variation can be controlled. This is why statistical process control works well in manufacturing, but is next to worthless for controlling the engineering process. 

Data is more problematic where there are multiple variables and unknowns that can’t be controlled. In large enough sample sizes, the averages may work out to represent reality, but they are certainly not applicable or meaningful to every situation. 

Moreover, the use of synthetic data to augment artificial intelligence algorithms muddies these waters even more. The assumptions baked into synthetic data are driving the algorithms  Data is extremely valuable, but not every data point is not necessarily worth the effort to analyze. Since data is infinite, the ability to find data to analyze is also infinite.
 
There is a kind of synthetic intelligence to all of this. It all seems so smart on one level, but it is completely fake on another level. 

The data strategist looks at the information and figured out what matters and what changes can be made based on data
Categories: Observations & Insights

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