INSIGHT: Big Data tells us what we want to hear

In the era of Big data, the theory goes that with more data we can learn more. What if that is wrong?

In the era of Big data, the theory goes that with more data, more inputs and more information, we can learn more, discover more and develop new insights.

What if that is wrong?

A “theory of everything” is a complex physical theory that all things in the universe are somehow related to each other and interact with each other all of the time.

General relativity and quantum field theory are frameworks that attempt to explain ToE. More recently, String theory proposes that there may actually be a single unifying theory based upon vibrational “strings” for preferred resonance and useful dissonance.

In physics, that’s a grand theory. And, for some unknown reason, human beings have begun to interpret our theories (which are merely rationalised explanations we use to understand something) as actual facts.

To quote everyone’s favourite science officer, we just “have a theory that happens to fit the facts.” It is actually a human interpretation of the real world that specifies physical data points—physics exists without us.

The good news is, physics are mechanical. Unfortunately, it is far easier to evaluate the physical relative relationships of tangible objects than doing the same thing for logical concepts. And, that my friend, is the problem with data.

Data documenting business processes is merely a rationalised explanation of a logical theory that is proposed by humans from a distinctive perspective.

In simple terms, all data is biased toward the creator. All data is captured from multiple perspectives and represents multiple points of bias.

That means each new data point reflects the intent of the business process designer. This means it is not possible to actually assemble new analytics from existing data.

Even more concerning, it is not possible to alter analysis with new data—you can only reinforce or refute the expected analysis. Any new analysis actually also follows the data design at its creation.

Since most new data is inserted into an existing model, it inherits the previous bias and is limited by those previous logical decisions.

Human logic is inherently embedded in all business processes and all data capture is biased toward the expected outcome of those processes.

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