Pamela Baker - Decision Intelligence For Dummies

You can’t safely send a repairperson to replace the sensor in the middle of a commercial wind turbine farm where the wind coming off the blades of numerous high-powered windmills can be at hurricane force. You can, however, infer data reads based on previous sensor data relative to neighboring wind turbine data in current weather conditions — filling in the missing data with values inferred from previous metrics and/or neighboring devices’ measurements, in other words. For example, one can infer without benefit of actually measuring it again that, since a specific structure measured 6 feet tall yesterday and it does not possess the ability to grow, that it is still 6 feet tall today. A better inference would also note that the structure has not toppled or sunk into the ground.

However, you can also create synthetic data sets based on known laws of physics, wind turbine specs, and other factors to create a simulation resulting in synthetic data that can be safely collected and used in decision-making. Most, but not all, synthetic data is created by simulations.

FIGURE 3-1: How fast are these spinning again?

Another example would be facial recognition data. Many countries regulate how much (if any) facial data can be taken or used without a person’s prior consent. This can significantly limit the amount of facial data available on which to train facial recognition machine learning models. To overcome a data shortage, companies turn to AI-generated faces of people who don’t actually exist. Data from fake faces also helps machine learning know how to determine which faces are real and which are not. The distinction can be useful in many endeavors, including detecting deep fake videos.

In decision intelligence, data considerations aren’t the first priority. Focus on the outcome you want and then determine the tools and data you need to get there. Once you have a map in hand, it’s easier to determine whether the data you need is available or needs to be obtained.

Business leaders often formulate a vision for their company or a particular project. This practice is not an attempt to predict the future but rather to aim for a specific future. This person is steering toward a future that they believe to be profitable or advantageous to the company. A vision, then, is a decision with a purpose.

How do you form a business vision? Developing an obtainable vision requires both creative and deductive skills. A business leader must be able to imagine possibilities, recognize opportunities, deduce their value and the probability of success, and shape a new or creative idea — a vision, in other words.

In short, a leader’s vision is part imagination and part information with more than a dash of math. Business acumen is a talent based in large part on pattern recognition and the ability to see connections between heretofore unrelated items or pieces of information.

Given that data analytics and machine learning are particularly adept at discovering patterns and data relationships, why are they not good at identifying new business visions? Part of an answer might be found in W.I.B. Beveridge’s The Art of Scientific Investigation , where the author explores the intuitive side of scientists and speaks of originality as “often consisting in linking up ideas whose connection was not previously suspected.” Interestingly, that book was originally published way back in 1950 by W.W. Norton & Company Inc. Other great visionaries explain the role of imagination expressed in any form — whether it’s art, science, or business — in similar terms. For example, the legendary graphical designer Paul Rand said that the role of the imagination “is to create new meanings and to discover connections that, even if obvious, seem to escape detection.”

Imagination is a critical element in making business visions and other decisions. It’s a skill that machines do not possess.

What about logic and math and the other hard skills that machines do excel in? Do they not form the lion’s share in importance and worth when it comes to making a decision — particularly in data driven companies? It’s true that machines do surpass human skills in this regard. Machines can do math faster and usually error-free, but humans can do it without even consciously thinking about it.

The human brain runs a significant part of this work in the background, leading to the seemingly out-of-nowhere “Eureka!” moment in a flash of inspiration. It’s called intuitive intelligence — the ability to use the subconscious mind to make faster and more integrated decisions.

Though both human and machine have pros and cons when it comes to decision-making, leveraging the strengths of each leads to decisions that consistently deliver a better value to the organization. This is why disregarding or discounting human instinct, gut feelings, experience, and talent is a grave error — just as overinflating the value of data and machines can easily lead you down the wrong path. Decision intelligence, acting as a multidisciplinary approach to creating balance between person and machine, is in a position to deliver targeted decisions for predetermined business outcomes that move an organization forward.

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