Using data science techniques to make intuitive decisions in everyday life

What is Intuition?
The power of intuition
Merriam Webster defines intuition as "a natural ability or power that makes it possible to know something without any proof or evidence: a feeling that guides a person to act a certain way without fully understanding why."
Some people call it a sixth sense. Others use it interchangeably with "gut feeling." Examples of intuition in a person’s daily life might look like this:
Boarding a plane and having a strong feeling that you know someone whose luggage is in the same bin.
Feeling suspicious about someone’s true intentions, despite no evidence of untoward behavior.
Thinking about calling a friend, then getting a phone call from them.
Many of the intuitive moments in our lives are unexplainable. However, it’s something that most of us have experienced at least once in our lives. I myself have experienced situations in my life where intuition lead me to discoveries which I couldn’t have made otherwise. And every time this happens, I can’t help but wonder about the source of my intuition. And more importantly, I wonder if there are ways to strengthen my intuition so that I can lean into it when I need to.
How to activate intuition
When you search "how to activate intuition" online, countless blogs and articles pop up. From business magazines to relationship blogs, there is endless literature on how one might go about activating that sixth sense. Reading through the many blogs, some key commonalities appear:
- Meditation
- Spending time in nature
- Feeling more, thinking less
This is all rather vague, and some of it is non-measurable. At what point do you achieve feeling more and thinking less?
But as I continued my research, I found some other common practices that seemed more achievable and measurable:
- Learn from your past
- Capture your flashes (write them down)
- Keep a journal
- Ask questions and write down answers
These methods, compared to the first set above, appear to be quite different. While they are still methods intended to activate your intuitive senses, concepts such as learning from your past and keeping a journal are forms of data gathering, which is the first and most crucial step in the field of Data Science.
Is it really intuition? The fine line between intuition and data science.
When a boxer blocks an unassuming left-hand hook before they even see the punch, is this an example of intuition? Or perhaps it’s muscle memory as a result of their brain’s subconscious pattern recognition from years of fighting opponents of a particular size and stature.
Similarly, when a blind person is able to walk down a busy street while simultaneously avoiding cars and other potentially dangerous factors, are they operating off of intuition, or have they mastered the difficult task of gathering as much data about their environment using their other senses?
There is a bit of a gray area around where intuition ends and data science begins. Which brings us to another question: what is data science?
What is Data Science?

The power of data science
There are so many definitions and interpretations of what data science is, but Wikipedia puts it best:
"Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains."
In other words, data science is a way to draw meaningful insights about a particular thing or event using scientific means to analyze relevant data.
Application of data science
Data science – specifically machine learning (ML) – seems to be a hot topic these days. Most businesses are beginning to apply a form of ML in various aspects of their industry, whether they operate in the financial sector, entertainment sector, various medical fields or anywhere else.
There are so many facets of machine learning. Depending on the problem you are trying to solve (or the type of insight that you are trying to glean), there are various algorithms that you can apply.
The application of machine learning algorithms can become very complex very quickly, depending on the type of problem you are trying to solve. In general, algorithms are broken into four general types, depending on whether the underlying dataset is supervised (classification and regression) or unsupervised (clustering or dimensionality reduction).
Referring back to the previous example of the boxer swiftly dodging a punch in the boxing ring, one could argue that their intuition is high. However, a counter argument (and perhaps the more likely explanation) is that the boxer is exercising a sophisticated form of pattern recognition (a subset of the classification algorithm). Fighters who spend countless hours training and sparring in the ring have an enhanced understanding of pattern and rhythm in the area of footwork, jab techniques and overall reflexes.
In the case of a blind person navigating the street safely, this can also be a form of pattern recognition, this time in the subset of the clustering algorithms. Because clustering involves instances where no previous knowledge is available to identify a new target (in other words, the blind person may not be able to identify all the potential dangers that may arise during a walk), an algorithm in this category (such as a K-means cluster) uses patterns to assign the new object (ex. car, bicycle) to a group for prediction purposes (ex. fatal, potentially harmful, harmless).
Using these examples, it seems that the parallels between data science and intuition are evident. Moreover, it makes me wonder whether some of the reasons that our brains produce these intuitive thoughts can be explained by data science. If data science can be used to rationalize at least part of why we act in seemingly intuitive ways, is there a way to intentionally apply data science in our brains to strengthen our intuition?
I’ll close this section with my favorite passage from an article on Cleverism called "How Intuition Helps Us Make Better Decisions."
"The human brain consists of two parts, the conscious mind, which we have control over, and the subconscious mind, which we have little control over. The human brain processes huge amounts of information, most of which is done subconsciously. Therefore, intuitive thinking, which arises from the subconscious, can be extremely powerful, giving us access to information that is not within the grasp of our conscious mind."
Can we use data science to strengthen our intuition? A brief case study: angel investors on Shark Tank
Have you seen the show Shark Tank? This show is perhaps the best depiction of instantly applying data science techniques (intentionally or unintentionally) to make an intuitive decision: deal or no deal. While I’m certain that the show is edited to meet the standards of entertainment television, it nonetheless helps illustrate the basics of how one might use data science to help guide their intuition.
The premise of Shark Tank centers around a group of investors, who the show calls "sharks," making on-sight investment decisions based on a short pitch from entrepreneurs. The banter that happens amongst the sharks, along with the exchange between them and the entrepreneurs, is interesting.
"How many units have you sold?"
"What is your valuation and margin?"
While these questions illustrate the standard due diligence done by any potential investor to understand the financial landscape of a startup, the best sharks ask questions that deal less with where the startup stands now and more about what the startup has the potential to become. Potential is something that can be heavily reliant on intuition, but it’s also something that can be measured with data science. By training the brain to think like a data scientist, the sharks can strengthen their intuition and ultimately make the decision towards an investment.
"Who are some of your competitors?"
"What types of people would buy this product?"
"How many startups have you created prior to this venture?"
Data science techniques such as competitor analysis, recommender systems and predictive analytics (ex. forecasting) help answer questions such as these. When the sharks are ultimately left to make an intuitive call on a company that is pre-revenue, the best sharks naturally pose questions that show their application of data science to guide their intuitive decision making.
Shark Tank is a great example of using data science to strengthen intuition. In life we sometimes come across opportunities that require an intuitive response. By practicing ways to apply data science in our thought processes, we may be able to strengthen our intuition to make better life decisions.
Applying data science techniques to everyday life

Most people make a few significant life decisions over the course of their lives. Examples are quitting a job without a backup plan, following a lover across the world or risking your life’s savings Investing in a promising business idea. While these life decisions are often made after careful thought, they can also be made when a person experiences a strong intuition for or against the decision.
When it comes to assessing your relationships with others, using data science techniques can save you from investing time into people who may not have your best interests at heart.
Let’s take a look at three ways that data science can help strengthen your intuition.
Pay attention to red flags (logistic regression in classification systems)
Before risking your life’s savings to invest into a new business, think of all the possible variables that can make the startup a potential unicorn or a failure. Mentally assign a weight of importance to each of these variables and how they may affect the outcome (in this case, the success or failure of a business idea).
Similarly to the way banking institutions use Machine Learning to detect fraudulent transactions, you can use logic to intuitively detect frenemies and manipulators. Being attentive to relationship "red flags" in the beginning can save you unnecessary headache in the future.
Logistic regression is a perfect example of determining a binary outcome (0 or 1) based on the existence or non-existence of key variables. Recursive feature elimination is a method of assigning the order of importance of the variables, which will have an effect on your outcome.
Perform natural language processing (scraping, WordCloud and sentiment analysis)
Before quitting a job without a backup plan, it may be helpful to research others who have done this to see how their lives have changed for the better (or for the worse). Research can be anything from interviewing people to studying their behavior. For example, if you are having coffee with a friend who recently quit their job without a backup plan, you can get a sense of their quality of life based on the types of words they use, how often they use those words, and the sentiments behind what they have to say. If a majority of their conversation involves words such as "stressful," "uncertain," and "lonely," you can understand their overall negative sentiment about their decision.
Just as you can scrape LinkedIn or Twitter to run analytics on key terms (ex. via wordcloud and sentiment analysis), you can use natural language processing to gauge overall sentiment about someone. What words do people use to define this person? What is the community’s sentiment towards their character? In other words, find ways to recognize the person’s reputation in various circles.
Trust patterns (classification and clustering)
Pattern recognition is something that was discussed earlier in this article and remains a very important driver of intuition. For example, if you have a tendency to make irrational decisions that do not result in a positive outcome, such as starting several new projects and never finishing any of them, perhaps your latest intuition to invest your life’s savings into a business is not a good idea.
Likewise, if a friend has a pattern of X despite their promise to do Y, you can be sure that their true intentions – or at least tendency – will always default to X.
Understanding patterns like these can be helpful in confirming or re-assessing your initial intuition.
Separating emotion from logic to strengthen your intuition
There is a well-known quote that says, "don’t make decisions when you’re angry and don’t make promises when you’re happy."
This is such an important quote because it bring to light something that can get in the way of using data science to strengthen intuition : emotion.
The application of data science requires logical, unbiased thinking. However, if you are emotionally invested in a particular situation, it can become very difficult to think logically.
Happiness is an emotion that breeds optimism, which can cloud your mind’s ability to process data objectively. When you are in a happy mood, your mind may self-select positive memories and experiences that you associate with that emotion. In turn, any requests and offers presented to you in the midst of that emotion may garner a positive intuitive response.
When a chocolatier brings samples of artisanal chocolate on Shark Tank, they want the sharks to sample their product. While the primary objective may be giving the sharks the opportunity to assess the quality of the chocolates, perhaps another motivation is to uplift the sharks’ moods in order to steer them towards optimism. If the sharks are in a pleasant mood (and who wouldn’t be after eating chocolate!), they may be willing to overlook the reality that chocolates are an extremely saturated market! Pessimism works in similar ways. Intuitive decisions made during times of anger or sadness will most likely lead to regret in the future.
Happiness and anger are not the only emotions that can significantly cloud our abilities to think logically. How many times have we failed to recognize red flags in a romantic relationship until after the relationship ends? This is because love is a powerful filter. This powerful emotion can easily obscure things that are otherwise obvious. It is only after you’ve left the relationship that you are able to objectively recognize all the red flags in that relationship and "come to your senses."
Other powerful emotions include fear, admiration, boredom and sympathy.
Conclusion
While data science and intuition are not the same thing, the intentional application of data science into your thought processes may help strengthen your intuitive senses.
Thanks for reading!
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