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Author's profile photo Former Member

I predict therefore I am.

Authored by: Karl von Beckmann

I predict therefore I am.



We all make predictions every day. This is nothing new. When we wake up in the morning we find food in the fridge and coffee beans in the pantry. Hardly a moment worthy of fanfare, but this is no coincidence. At some point in the past we took the information that was available to us, sorted it, prioritized it, and then allocated time and resources to satisfy that need. In this case, the data from our past shows us that every morning we wake up with a rumble in our tummy and groggy eyes. Food and coffee has shown to satisfy those needs, so we went to the store (or possibly delegated the task) and brought home something to eat in anticipation of the morning munchies.

It’s Evolution, Baby!


This kind of prediction is based on inductive reasoning. Inductive reasoning is an evolved ability that essentially allows us to take specific observations and extrapolate patterns and trends. From there, we’re able to test hypotheses and eventually draw some conclusions in terms of whether or not our projections were accurate. Inductive reasoning is essential to human existence since we rely on our previous experience in every decision we make. We would be paralyzed, if every morning when we woke up, we had to consciously analyze whether or not when we stepped on the floor we wouldn’t fall straight through. Or that turning on the cold water tap wouldn’t scald us.


Don’t worry! You’ve got inductive reasoning!

The good thing about the way humans perform inductive reasoning is that we’re able to call on a vast variety of sensory inputs to create our hypotheses. We can rely on what we’ve seen, heard, read about, or maybe heard from other people. We can pull from our cultural influences, social paradigms, and other very intangible input in order to create hypotheses and try and create desired outcomes.


The bad thing about humans is that we’ re not very good long term data storage devices. We have the capacity to recall a certain amount of short-term information in high resolution, but as time passes, the clarity and accuracy of our recall tends to fade. This is why we develop habits. Habits based on induction allow us to continually apply hypotheses we have tested and found to be helpful to our daily lives so that we don’t need to tax our conscious brain with calculating the outcome of every single decision in our lives.

Truth lies in the eye of the beholder.


However, sometimes our habits don’t serve us well. How many of us know someone who, no matter how bad traffic might be, will always take
the same route home? That’s just their habit; far from optimized, but at least safe, relatively effective, with a low energy investment.

So would it shock you to know that many of us bring this same inductive-based decision making habits into our business lives? Of course it shouldn’t. This is why many business people are well compensated. They’re paid for their expertise. That is to say, they’re paid for their experience, which leads them to observe and create working hypotheses and then apply them to result in positive outcomes. In other words, they’re sought after for their ability to apply effective inductive reasoning.


So what’s wrong with that? Many of you may be thinking, “Darn right, that’s why I’m the boss!” And that may very well be the case. But as the boss, you’re probably always looking to improve the way you and your organizations do things. And that’s why you should be interested in predictive analytics.

Predictive analytics brings a powerful weapon to the business person’s arsenal. Instead of relying on the narrow scope of personal or third party observations, habits, and best guesses, predictive analytics allows the business person to create and test hypotheses in precise detail, with virtually no room for personal bias. As the saying goes, “the data doesn’t’ lie.”

One of the areas in which human inference has been superior to predictive data analysis in the past is in that variety of sensory inputs mentioned earlier. However, this is no longer the case. Modern predictive technologies are able to capture and analyze not only a wide variety of data, but also vast volumes of data that no individual human could ever imagine to comprehend.

Give your data a megaphone!


This is where the partnership between man and machine can thrive. A human observer is required to set the parameters of which problems are important to analyze. But the machines are required to mull through the vast quantities and varieties of data in order to deliver an outcome.

This capability should give every business person reason for pause. Every business is creating massive amounts of data. That includes formal data the company owns, like their purchasing or customer data, as well as data they don’t own but are affected by, such as social media data, market data, or even weather or traffic patterns!


If you’re a business person and haven’t yet considered the way this data can help you understand the future, you are missing a valuable opportunity. Furthermore, other businesses are already harnessing this powerful technology. And you shouldn’t need a computer to predict where that might leave you!


Learn more about the trends in predictive analytics in the upcoming webcast featuring CEO of Decision Management Solutions, James Taylor, on November 7th.

Register now!

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      Author's profile photo Former Member
      Former Member

      While I agree with the premise that 'the data doesn't lie', the quality of data itself leaves a lot to be desired. If the data is not well formed or curated, prediction analysis becomes difficult, or sometimes leads to wrong predictions.

      Author's profile photo Former Member
      Former Member

      Thanks, Sumit, you bring up an excellent point! Data integrity is crucial to any analytic endeavor and predictive is certainly no exception. The quality of your results is indeed directly correlated to the accuracy and validity of the source data. This is why a robust data management strategy is always recommended behind any analytic project.
      For more information about data management check out: