Learning Mindset

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Learning Mindset

To succeed in innovation, you must adopt a growth mindset and prepare yourself to learn. To help you build the right mindset, you will explore some of the barriers to learning and how you can work to overcome them

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In this training, you will

  • Learn the importance of continuous learning
  • Learn that learning is a marathon, not a sprint
  • Learn that the knowledge you gain from learning is the foundation of any success you hope to achieve
  • Research how continuous learning is the foundation of Big Tech’s success
  • Review the barriers that prevent us from learning 
  • Explore some mental models that you can use to overcome barriers to learning
  • Explore activities that you can engage in to optimize your learning

The Backstory

The incalcitrant ways of traditional companies when it comes to using data is perhaps a result of western education. From an early age, we are told what to learn, how to learn, and how to demonstrate what we have learned. Institutionalized learning—preschool, public school, college, university, post graduate work, and on-the-job training—largely requires us to parrot or apply knowledge. 

We are taught how to solve problems we have identified and understand, but we are rarely taught to question what we are taught. Even if we do question what we learn, we probably question it in a way that is learned. In other words, even our dissent is learned behavior. Very few of us are taught to parse the data we are given and create our own story .

We, as humans, have a hard-coded preference for familiar structure. The stories we are accustomed to reading have a beginning, a middle, and an end. By the end, the plot is neatly concluded so that the reader or viewer is left either wanting more or is satisfied. Thus, the story is created by humans for humans. Data do not care about your feelings, your emotional state, or even keeping your attention. Data are inert. Data are the facts. In business, we must create the story based on the data, and the better the data, the more credible the story.

At a dinner for Graylock’s entrepreneurs, Microsoft CEO Satya Nadella and Greylock Partner Reid Hoffman were chatting. Satya Nadella said: “(I)f you have two kids in school: one of them has got a lot of innate capability but is a know-it-all, the other one has less innate capability as a learner, you know how the story ends. The learn-it-all does better than the know-it-all. It’s true for students in school. It’s true for entrepreneurs.”

Therefore entrepreneurs need a prepared and informed mind. This comes from first filling their heads with the most relevant data. Then, entrepreneurs must overcome their preconceptions so that the story they create is true and free from bias. Armed with the facts, decisions are de-risked, and there is a greater likelihood of success. 

What Data Are We Talking About?

Data help us to ignore our subjective interpretation of reality so that we can see the true nature of the world. From this point, it’s possible to benchmark and grow. By using data as a basis for learning, we can build evolutionary entities and move from operational to learning organizations. 

Data implies many things. The word often conjures visions of spreadsheets, databases, and complex calculations, so what data should we be concerned with? This answer is: all of it—not just the data coming from an analytics department. Spreadsheets and databases provide quantifiable data only, but qualitative data are also important. For example, the hospitality industry uses metrics such as revenue per available room (REVPAR) and average occupancy rates. But it also uses qualitative data such as customer comments posted on social media, membership sign-ups and usage, and guest satisfaction scores (GSS).

The McNamara fallacy is a concept named after Robert McNamara, the US Secretary of Defense from 1961 to 1968, and is a reflection on his experience handling the Vietnam War. The fallacy describes how making a decision based solely on quantitative observations is a mistake.


“The first step is to measure whatever can be easily measured. This is OK as far as it goes. The second step is to disregard that which can’t be easily measured or to give it an arbitrary quantitative value. This is artificial and misleading. The third step is to presume that what can’t be measured easily really isn’t important. This is blindness. The fourth step is to say that what can’t be easily measured really doesn’t exist. This is suicide.”

— Daniel Yankelovich, “Corporate Priorities: A continuing study of the new demands on business” 

Data can come from anywhere, including personal opinion, inference, and knowledge. But entrepreneurs must also consider the possibility that what they don’t know is often more important than what they do know when it comes to making decisions.

The Knowledge Matrix

The knowledge matrix is a tool that categorizes matter and data. The matrix shows what an entrepreneur or individual knows and does not know when it comes to a business decision or strategy. What is not known can be thought of as dark matter [link].

For the purposes of HowDo, knowledge, or data, is broken into four categories: the known-known, the unknown-known, the known-unknown, and the unknown-unknown (the dark matter). I know it’s a mouthful, but stick with me. It actually makes a little bit of sense.

  • The Known-known is things we are aware of and things we understand.  It is our knowledge.
  • The known-unknown is things we are aware of, but we don’t understand: known knowledge gaps.
  • Unknown-knowns are things we understand but are not aware of. That’s our intuition.
  • Unknown-unknowns are the things that we don’t know anything about— ignorance in the literal definition of the word.

This matrix provides perspective because the reality is that most companies don’t know much, and that’s okay. Strategy is at its best when the strategists are keenly aware of what they don’t know. With this in mind, they can recognize the decisions they can make confidently, those that require testing, those that require discovery, and those that are out of their control. 

The knowledge matrix is a crucial tool in determining risk. Risk is based upon what is known, and the greater the unknowns, the greater the risk. If you know something, you understand it, and can assess the associated risk. If you don’t know it, you cannot assess the risk. If it is an unknown-known, and you only have your intuition, that’s risky.  And if it’s an unknown-unknown… well, that’s just scary as hell. 

The process should first establish the known-knowns. From there, you can move through the knowledge matrix and assign the known-unknowns, which can be explored through research. Known-unknowns manifest themselves through intuition, so these require listening to your gut and then finding data to back up those assertions. Unknown-unknowns always exist and cannot be quantified, but with enough data throughout the rest of the matrix, their risk can be better understood.

Once agreement can be reached on the known-knowns (the truth), it is possible to achieve long-term commitment to an initiative. This data-based narrative has allowed me to gain stakeholder support in Fortune 100s and startups alike. HowDo provides guides on gathering the data that you need to build an evolutionary business. These guides include external analysis on the customer, the competition, the context, and a company’s capabilities. It is these data that need to be put through the knowledge matrix.

Companies should aim to maximize concrete organizational knowledge: the “known-knowns.” Each piece of data should be weighed based on how likely it is to be measuring reality. How credible is the source? How recent are the data? Are these direct numbers or ballpark projections? These questions can lead you to a general confidence score across all of your data providing the foundation for quality analysis and discussion. 

But now comes the hard part. The data determine the correlation between two data points, but that correlation must be interpreted in terms of its true meaning. To do that, entrepreneurs must understand their fears [add link], biases, motivations, and emotions. 


The Beginner’s Mind


“When new businesses aren’t being born, the free enterprise system and jobs decline. And without a growing free enterprise system, “In “In the Beginner’s Mind there are many possibilities. In the expert’s mind there are few.” 

– Zen Master Shunryu Suzuki, Zen Mind, Beginner’s Mind

To use data optimally, the entrepreneur must first control their own reactions to the data. You cannot allow your incentives or preconceptions to pollute or corrupt the interpretation of data. We all believe the world to be a certain way, and our brains are, unfortunately, literally hardwired to reject new information. 

The data may confirm your understanding of your business and the context in which you are operating; or, the data may challenge or invalidate what you believe to be true. Either way, you must be open to what the data reveals. 

I have pitched growth strategies to dozens of Fortune 500 companies. The only times my pitches have failed is when powerful executives refuse to accept the data. These executives may have a good excuse to be skeptical of new data: data may challenge the prevailing orthodoxy, directly contradict their understanding of how the world works, or directly challenge their incentives. However, these reactions are based on fear and personal insecurities, not the long-term wellbeing of the company, its customers, or its investors. 

Many businesses and entrepreneurs do not know how to use the data they create. Most have not sufficiently instrumented their business to understand what the data tells them. Others are so instrumented that they are drowning in so-called “data lakes.” 

Whatever the proficiency level with data, the amount of it available to businesses is growing. As of June, 2018, it was estimated that 2.5 quintillion bytes of data are being created every single day. By 2020, an estimated 1.7 megabytes of data will be created every second for every person on earth. The problem now is not so much finding the data, it’s learning how to differentiate between the signal (the useful information) and the noise (distracting information). 

The Knowledge Matrix

In my article on data, I explain the knowledge matrix. The knowledge matrix is a tool that reveals the unknowns that may surround a decision. In business strategy, what we don’t know is often more important than what we know. I say that because if a decision is based on data, and on parsing that data we find that we really know very little about the path we are considering taking, that path will be as risky as it gets. On the other hand, if we have reliable data that tell us a vast amount, there is less risk, and it is a path that could lead to a breakthrough.

  • The Known-known is things we are aware of and things we understand. It is our knowledge.
  • The known-unknown is things we are aware of, but we don’t understand: known knowledge gaps.
  • Unknown-knowns are things we understand but are not aware of. That’s our intuition.
  • Unknown-unknowns are the things that we don’t know anything about— ignorance in the literal definition of the word.

The value of the knowledge matrix is that it allows data to be categorized according to what is known and what is unknown. Thus, the matrix provides a process by which the riskiness of a business decision can be realistically evaluated. 

The Evolutionary Organization


Evolutionary companies tune in to the customer in new ways. These ways are intangible and ephemeral because they involve building a beginner mindset, setting aside biases, and interpreting data. There is even an emotional component to tuning in to the customer.

Microsoft CEO Satya Nadella talks about empathy as a key factor in his company’s ability to evolve in terms of serving the customer. According to Nadella, empathy is a key source of innovation because innovation is the ability to “grasp customers’ unmet, unarticulated needs.”

The result of these new approaches to business is that an operational organization, through the evolutionary process, changes into an organization that is constantly learning and growing.