Data, Not Assumptions (Dna)

Evolving With Data

All companies now have access to an abundantly available and expanding universe of data. 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. 

Amazon, Apple, Google, Facebook, Microsoft, Alibaba, and Tencent exemplify evolving companies. They are, in essence, data-collecting platforms. They amass troves of information on their customers and use that knowledge to plan future business. The data they collect reveals trends, customer demographics, spending patterns. The data provides competitor information, and the context for future operations. 

The data maximize the knowledge that these companies have so that their decisions are based on truth. Ultimately, the data that these companies possess de-risk their approaches to innovation so that their decisions are more likely to result in better customer service and products. Their strategies are no longer a secret, and the companies that emulate this use of data and a customer focus will find it much easier to thrive. In summary, my message is fourfold:

Many businesses do not know how to leverage the data they create. They don’t understand their own business through data, and some are drowning in so-called “data lakes.” In every instance, businesses that do not have a well-instrumented company and the ability to interpret the data are unable to differentiate between the signal (i.e.: useful information) and the noise (information that is distracting). Consequently, they cannot evolve.

The first steps in the process of evolution are analyzing your customer, your competition, your context, and your capabilities. HowDo will lead you, step by step, through internal and external data analyses and the process of evolution. 

Companies are better served by focusing on evolving rather than innovating. It’s not semantics, it’s compulsory in today’s business context.

“We all have a moral obligation to speak truth to power, but it’s hard”

—James O’Toole, research professor, Center for Effective Organizations at the University of Southern California

Truth is a rare commodity in the business world; so many Silicon Valley companies, and others, just lie. It’s the norm, part of the accepted culture— “fake it ‘til you make it.” Companies lie about their customer transactions, their active users, their profits, they even lie about science. 

My goal is not to define the whys and wherefores of corporate amorality, that’s a product of the human psyche. My goal is to draw your attention to something purer in the world of business evolution— truth. Let’s examine this novel path to sustainable growth. 

Monetizing Data 

Monetizing data is crucial to staying competitive. If you have any doubt on how intrinsic data areto growth, just look at the lengths companies such as Alibaba, Amazon, Apple, Google, Facebook, Microsoft, and Tencent go to to harness customer data, and how they are global leaders in using that data to serve the customer. 

Respondents to the 2018 McKinsey global survey on data and analytics confirm the increasingly significant role that data have played in their industries. In McKinsey’s 2017 survey, companies claimed that data and analytics had brought changes to their sectors; nonetheless, many of these companies are still not developing long-term strategic strategies for sustainable progress.

According to the 2018 results, companies achieving the greatest overall growth in these high-performing organizations were three times more likely than others to say their data and analytics initiatives have contributed at least 20 percent to earnings over the past three years.

For these companies, data form a core part of employees’ work flows and mindsets. These companies are conscious of the need for a data-driven culture, and they invest the infrastructure required to use data at scale.

The differences between the 2018 and 2017 surveys are striking. Almost 50 percent of respondents in 2018 reported that data and analytics have significantly or fundamentally changed the nature of competition in their industries in the past three years, which is an increase of almost 40 percent from the previous year.

The competitive shifts are the emergence of new analytics-based businesses and data pooling among new entrants through shared utility. Forty percent of respondents reported the formation of data-related partnerships along the value chain, which was an increase of over 90 percent from just one year before.

Decision making based on data is characteristic of high-performing companies. According to the report, companies who use data for their decision making “are nearly twice as likely as others to report reaching their data and analytics objectives and nearly 1.5 times more likely to report revenue growth of at least 10 percent in the past three years.”

Cited factors include having a data leader in the C-suite, making data and self-service tools accessible to frontline employees, and creating an organizational culture that supports rapid iteration and tolerates failure.

Overall, the survey recommends the following steps to monetize data:

Companies that can harness data and apply it through the customer value chain are most likely to evolve and stay relevant.

Credibility 

In the early stages of any project, To build and lead a successful and evolving business, you need support. You need a team of willing and able people. Tell the truth, and people trust you. Deceive, and people will eventually abandon you. The worst leaders gain power by relying on hierarchy, tenure, or fear. The best leaders gain trust and respect by learning the truth and sharing it. 

Not all companies lie, of course. A common strategy is to ignore or deny the truth. To keep quiet.

When a company is in trouble, that is when they talk the least. You know your company is dying when it is not sharing numbers. You know your company is hiding the truth when you cannot get visibility into how things are going until just before the quarter ends. Hiding the truth is especially evident when your company’s accounting relies on non-standard calculations and measurements.

I once worked in a large company that had projects running for five years with no oversight and millions of dollars in funding every year. These so-called “zombie projects” wasted time and money without any results. 

People were aware of these projects, but the projects had an impenetrable shield around them: the guise of innovation. Because these teams were “innovating,” there was no time limit and no finish line. The refrain was, “We’re still learning, we’re still iterating, we’re still pivoting.” Well, at least that much was true.

This company thought it was evolving in a creative way, but it was not. It was surviving. Legacy businesses drive growth in the same way for years, and growth is usually based on the original product line. It can be a great strategy—for that product, but not for the future. The future is filled with unknowns. And when dealing with unknowns, it’s impossible to predict how much time or money something will require. 

So, what’s the answer? The answer is to find and be open to the truth about your organization. Seek out the information data that will tell you what you need to know. Finding the truth requires a diligent data collection and management system, much like Churchill’s statistical team. But that’s the easy part. The hard part is interpreting the data in a way that is unbiased so that the real story emerges.

Distortion From Bias 

We all have inherent biases that affect and cloud our judgement. If we are unaware of those biases, the decisions that we make will never be objective. Thus, we must first have an honest conversation with ourselves. 

Many people are afraid of introspection. It can mean facing demons and exposing vulnerabilities. Given the choice, it’s much easier just to carry on, but no one can move forward unless they can define and manage what is holding them back. This is true for anyone who seeks growth—individuals and business executives.

The Johari window, created by psychologists Joseph Luft and Harrington Ingham in 1955, is a good place to start. It is an heuristic exercise often used in corporate settings to enlighten people in terms of their innate biases to better understand themselves and their relationship with others. 

In business, most of us go to great lengths to hide what we consider our deficiencies. I personally learned that, as hard as it is, exposing our deficiencies is necessary in order to progress. As a leader of innovation teams, I feared people knowing that I was bipolar, bisexual, and suffered from ADHD. But I also found that not revealing these facts created more confusion for myself and others.

The effect of truth is interesting. Often, it makes people very uncomfortable. I have pitched growth strategies to dozens of Fortune 500 companies. The only times my pitches failed was when powerful executives refused to accept what the data was telling them. 

Executives are wise to be skeptical of new data; statistics can be manipulated to tell a story depending on the audience. But the truth revealed by data can also be a threat. It can challenge the prevailing doctrine, directly contradict the existing understanding of how the world works, or directly challenge the incentives. 

I liken the introduction of truth to the body rejecting an organ. You bring in a new truth, and there’s so much orthodoxy, so much rigidity around the current truth that the new truth is disruptive. The new truth challenges people so much so that it starts to detract value in the near term because people get scared. People get threatened. Instead of trying to understand or learn, they try to rid the company of the idea.

As much as people and business executives may fear or resist the truth, to make informed and wise decisions, they have to check their emotions and be open to the story the data is telling them. They have to understand how their own fears and biases are affecting their decisions. It’s a process of self-evolution that leads to corporate evolution.

That’s great in theory, but there’s one big hurdle to CEO self-evolution and that’s CEO disease.

Data-driven Companies

Traditional market research, SWOT analyses, endless conversations on culture, motivational speakers, and agile processes will not give a company better vision. Don’t get me wrong, all of these things have a place, but without an extraordinary amount of luck, none of them, in combination or individually, will bring reliable long-term growth and the foundational evolution of a career or company. Growth now requires attention to data, lots of it, and the ability to interpret that data in a way that is subjective and free from bias.

I was recently invited to speak at a conference for senior executives aspiring to the C-suite. I was shocked to see that many of the participants lacked a basic understanding of the web, digital strategy, and had even less of an acquaintance with machine learning, artificial intelligence, virtual reality, augmented reality, cryptocurrencies, blockchain, and other cutting-edge data technologies.

That’s a problem. There is no room for traditional companies who are anchored in the past. Consider that while there are still warehouses using armies of manual pickers and forklift truck drivers to meet 30-day shipping deadlines, in Kunhan, in the outskirts of Shanghai, JD.com is running an entire distribution center with only four people. 

Vision

Used well, data will change your perspective and ability to see the future. It is for that reason that data should be the foundations of all your decisions. To illustrate the difference a data approach can make to an entrepreneur’s vision, I like to borrow a metaphor of a dragonfly’s vision, which is applied by Philip E. Tetlock and Dan Gardner, authors of “Superforecasting: The Art and Science of Prediction.”

“Like us, dragonflies have two eyes, but each is an enormous, bulging sphere, the surface of which is covered with tiny lenses. Depending on the species, there may be thirty thousand lenses on a single eye, each one occupying a physical space slightly different from those of adjacent lenses, giving it a unique perspective. Information from these thousands of unique perspectives glows into the dragonfly’s brain where it is synthesized into vision so superb that the dragonfly can see in every direction simultaneously, with the clarity and precision it needs to pick off flying insects at high speed.”

—Tetlock and Garner, Superforecasting: The Art and Science of Prediction

(If you like this quote, worth reading our free summary of Superforecasting here: https://howdo.com/book-summaries/superforecasting/)

A data-driven approach can give you exponentially better vision in business, particularly the power to see the future, but it doesn’t happen organically. Interpreting that data, requires a beginner’s mind and relearning how to learn. The data synthesis process is time-consuming up front, and it can be laborious and detail-heavy. But once you have the basic structure, the process becomes easier. Over time, much of it can and should be automated, and the process will become habitual as you wonder how you ever did business any other way. 

Why go to the trouble? What can be achieved?

For starters, if you are the decision maker, you gain confidence that you are making the right decision. If you are trying to influence decision makers, nooone can argue the facts. Therefore, you will discover the ability to convince your colleagues, management, board, and investors that what you are espousing is the truth. You have the facts to back you up. 

That said, leaders tend not to be objective. They are often distracted by personalities, power struggles, investors, and ephemeral factors. Even with a credible story, your audience may not like what you are saying. In fact, the chances are, they won’t.

Data’s Role In Leadership

Possessing the facts is the first step. As a leader, you must evangelize the new understanding, bringing your colleagues and other leaders on the journey to a new reality based on data. If you succeed in convincing yourself or an organization to change its mindset and accept the new reality, the associated incentives and operations will align with the new direction.

Leaders of enduring businesses know they must be committed to the customer as measured by data, not personalities, politics, or investors. If your company does not intimately know your customer through data, and does not use that data to profitability grow, then your company will be overtaken by companies that do. If your company does not make long-term investments in your customer, then your company will be overtaken by the companies that do. 

It is the leader’s responsibility to help shape the organization’s mission, direction, and structure. As the data changes, so too must the organization. 

To use another metaphor, this one from the Chinese American actor, director, martial artist, and philosopher, Bruce Lee, entrepreneurs and companies must be formless and shapeless like water, allowing the data to determine the form their business growth will take:

“You must be shapeless, formless, like water. When you pour water in a cup, it becomes the cup. When you pour water in a bottle, it becomes the bottle. When you pour water in a teapot, it becomes the teapot. Water can drip and it can crash. Become like water my friend.” 

—Bruce Lee

Water is essential for survival. Humans can live for weeks without food but will die after only a few days without water. In the Fourth Industrial Revolution, where technology is advancing at a rapid rate, data is to business what water is to life—vital.

This process is not easy and it is not without risk. However, there are plenty of precedents to demonstrate that this change is entirely achievable. As the data evolves, so too must your understanding of the world. It sounds simple enough, but it is remarkable how many people cling to what they know in spite of overwhelming evidence to the contrary. 

Data’s Role In Business Evolution

Life on earth is evolving and in a constant state of flux. Species struggle to eat, avoid danger, and reproduce. Some keep up with the pace of change, and some don’t. As Carl Sagan aptly put it: “Extinction is the rule. Survival is the exception.” 

In the past, businesses have been in a similar state of chaos. Entrepreneurs have built businesses based on their personal experience and intuition. If they fail, they die. If they succeed, they survive. If they survive long enough, they may scale. 

But there is a massive difference between nature and business: species have evolved generation after generation over billions of years. Nature is a random process with too many variables to count let alone build deterministic models that accurately predict how species will evolve. 

For businesses, the amount and detail for data is infinitesimal. We can zoom out and look at the macro and sector level or drill down to granular detail. Evolutionary companies, like Amazon, use a constant stream of data to adapt to their environment. This consistent adaptation eventually leads to evolution. There are even multiple “generations” living inside the same company. Each activity is measured, good ideas survive and “reproduce,” and failure is learning. These companies are not only able to react, they are able to pre-adapt to the future.

Companies have long been treated as machines, with resources as inputs and profit, and shareholder wealth as outputs. Companies were created based on a single function that determined their survival, but that model today ensures they will not survive.

Richard N. Foster and Sarah Kaplan explored the rise and fall of companies in their book “Creative Destruction.” Of all the companies listed on the Forbes 100 list in 1917, 61 of them no longer existed 70 years later. Only 18 companies had managed to stay in the top 100, and none of these 18 were high performers. Looking at the S&P 500 over the last 50 years shows similar results: few companies survive the long haul, and those that do aren’t great performers. 

A report by Innosight for 2018 predicts continued shortening tenure over the next decade for S&P 500 companies. According to the report, the average tenure for companies was 33 years in 1964. The length of tenure decreased to 24 years by 2016 and is expected to be just 12 years by 2027. That means that over 50 percent of S&P 500 companies will be replaced in the next 10 years. The sectors that are expected to be most affected are retail, healthcare, financial services, energy, real estate, and travel.

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The market always does better than the company, just as the species outlasts the individual. All companies underperform eventually, and all old companies underperform. This idea, that underperforming companies are continuously failing and being replaced, was tagged “the gales of creative destruction” by Schumpeter in the early 20th century, and it is the same mechanism by which species evolve. Species that adapt with favorable traits survive while others disappear.

Discontinuity is not a new thing, but the pace of discontinuity has increased largely due to rapid changes in technology. Companies have to adjust to the new paradigm.

Foster and Kaplan liken a company’s growth to that of a person’s emotional growth over time. They start out passionate and ambitious, eventually maturing to a more rational mindset. In time, however, they get old and cranky. They don’t respond well to threats; they become fearful and overprotective. They are afraid to make way for new developments, they worry about customer conflict, and they absolutely don’t want to make acquisitions that could dilute their earnings.

But not all companies suffer from this problem. The Big Tech firms, for example, have treated their business like an organic portfolio, pre-adapting to the future in the direction the data points. This is how these firms are able to outperform the market for long stretches of time.

Big Tech’s Data Leverage

Amazon, Apple, Google, Facebook, Microsoft, Alibaba, and Tencent—are, in essence, data-collecting platforms. They amass troves of information on their customers, both business-to-business (B2B) and business-to-consumer (B2C).

These companies are brilliant examples of how to leverage customer data to build a sustainable competitive advantage. Everything these companies do is designed to extend their data capture into every part of their user’s lives. Each one of these companies then collects and centralizes data from their diverse product portfolio. Ultimately, the data de-risk these companies’ approach to innovation.

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Note: Apple is an exception to the data-sharing rule and does not centralize data between products. Their non-advertising-based business model facilitates this.

Take a look at Google and Gmail, for example. To the outside observer who doesn’t understand how data can be used at the intersection of email and a search engine, Gmail’s value is unclear. But to someone who understands data and platforms, that value is glaring. Gmail has access to all of your purchase receipts— everywhere you travel, everywhere you shop, every credit card receipt you have, and every bank statement you have. Now tie all of that to your search activity. 

Consumers maintain their engagement with these company’s because their experiences are subsidized. Google and Facebook are both free to the consumer, and Amazon heavily subsidizes the cost of shipping and cost of goods sold to ensure consumer loyalty. Others build barriers that increase the switching costs. For example, Apple’s ecosystem is magnificently complicated and nearly impossible to migrate from Apple to Google’s Android. At the same time, Apple values privacy while Facebook and Amazon relatively less so. 

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Acquisition is another tool that these companies use to capture knowledge and data and, thus, evolve. When data informs these companies about a product or service based on consumer needs that has potential benefits, they acquire that product to quickly enter new markets.

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A closer look at Amazon

Having worked at Amazon, I witnessed first hand the strategies used to harness data, and how external data was used to serve the customer, and how internal data was used to measure efficiencies and employee performance . 

Bezos coined the term “customer obsession,” and every year Amazon raises the bar on the customer experience far exceeding other retailers on price, selection, availability, and delivery. Amazon used their technical foundation to build services that now power a majority of Silicon Valley internet startups, and their fulfilment capability outperforms the largest incumbents: the United States Postal Service, FedEx, UPS, and DHL. 

Every month, Amazon expands its core capabilities, and with each new core capability comes access to new customer data. Many posit that when Amazon announced plans to build new Headquarters, dubbed HQ2, in a yet to be chosen US city, the stunt was nothing more than a ploy to extract massive amounts of exclusive data on the habits, preferences, and demographics of the largest cities in America.

Data plays a huge role internally at Amazon. When I helped lead fraud prevention teams there in 2005, every element of the entire business was measured every week: people, inventory, servers, customer complaints — everything. Leaders across the business received a 170-plus page deck with five to 10 graphs on each page. Each manager had a strict range of key performance indicators (KPIs) to meet. If the graphs showed a manager was out of range, they were spending too much or too little money, inventory was sitting too long on shelves, or servers required too much cooling and drove up electricity costs. Every single metric of the business was recorded. This by the way, is an example of absolute mastery of the known-knowns.

This rigorous discipline means that Amazon manages all aspects of its business at an incredibly granular level. Old processes are optimized or phased out while new products are monitored for early signs of success. This level of efficiency and transparency laid the foundation for a culture of creation and adaptation. 

But the culture at Amazon isn’t what you think. It’s not brightly lit spaces with pool tables and unlimited artisanal coffee on tap. Instead, it’s an intense culture accompanied by freedom and independence. Employees within the organization have the freedom to test and experiment to build a better experience for the consumer. This creates a dynamic environment of mutation and recombination seen only in the best evolutionary companies. All of this is built on data. 

Employees have to know how to use that data. It is at the center of everything that Amazon is and does. The more data Amazon collects from their users, the more it can reduce costs, empower employees, and use data to inform decisions. The stories created from the data are better able to predict the customer’s present and future needs and pinpoint where Amazon should invest in its own business. 

Amazon is not infallible, however, and it did leave itself exposed on one front.

Amazon used to leave Browse Tree Guides (BTG) on their website. These guides were category-specific Excel files that contained Amazon’s unique browse nodes for each category that was listed on Amazon. Each category has slightly different requirements about how its products are displayed, and the Excel files explained how Amazon did this in tremendous detail and precision. The categories are too myriad to list, but they spanned auto accessories to industrial and scientific (Lab & Scientific) supplies and every product in between. 

These files were posted in 2005, and they were a gold mine of information. Over a decade later, I was working at a very large company who considered Amazon a competitor. I immediately thought of the files and how this company must be combing them every week for valuable data on their competitor. It turned out they hadn’t even heard of the files, that was how little they were paying attention.

Saturation Point

“No good model ever accounted for all the facts, since some data was bound to be misleading if not plain wrong.” 

—Francis Crick, Some Mad Pursuit 1988

Data can be overwhelming, and there is a saturation point where repeat themes and recurrent trends become pervasive. At this point, it’s time to stop obsessing and start objectively making decisions. 

Noone ever has perfect information. The point of the data exploration that HowDo outlines is not to know everything but to learn and discover. With any research project, it’s easy to get lost in the weeds: Thinking that you need every data point is an attitude that holds back a lot of high-achievers and entrepreneurs. In fact, seeking 100% of the information can be harmful. Listen to Jeff Bezos on this point: 

Without Data, You Have No Direction

The data that you collect will show patterns. Depending on the context—economic and demographic patterns, for example—an entrepreneur or leader can make better decisions on strategy when they know the trends.

Amazon, Apple, Google, Facebook, Microsoft are forecasting that a large percentage of their current and future growth will come from overseas markets. Why? The data show that the core products of these companies are experiencing slowed growth in the United States, compounded by America’s shrinking middle class. Meanwhile, the international middle class is rapidly expanding. The biggest population centers in the world, such as in India and China, are industrializing and adopting new technology. Population and economic growth are working side-by-side in these markets to quickly build huge markets. These markets are forecast to control a massive amount of worldwide consumer spending within the next 15 years.

The Big Tech companies continue to focus on the few industries experiencing explosive growth while looking to alternative sectors in America that will complement the expansion of their core offerings overseas. As a result, many of the most lucrative growth opportunities in North America are being cornered by a small number of technology companies.

This is a virtuous cycle. As their product offerings grow, so does their data, which informs new product offerings. There is no genius behind their expansion, despite what the tech media says about these companies’ CEOs. They’re following a trail of data created by the customer. This is what a mastery of customer data looks like, and this is how you can use data to evolve and compete.

Lastly, timing is everything when it comes to market moves. When competitors launch a product or service, you have a choice: fast-follow or wait and see. Both have their merits depending on the situation. 

The story you tell based on the data you have can answer vital questions that will help you with these decisions. When deciding whether to launch a product, for example, you need data to understand your own customer relationship and that of your competitors. Is your competitor successful in the area in which they are entering? Is the customer really responding to the introduction of that product, good, or service? Is that customer your customer; meaning, do you share customers? Do you have the internal capabilities to compete? These are all questions that can and should be answered by data, truthfully, before a product launch.

Where to Find Competitor Data

It’s not that difficult to find data on your competitors. There are plenty of external data sources few companies think to tap. Press releases are an obvious place to monitor a rival but, unfortunately, the strategic moves of your competitors are typically carried out in secret.

Job boards and LinkedIn are potential goldmines. The type of jobs your competitors are posting often provides clues as to their internal directions. When I interview for a job or enter a company to consult, I go to LinkedIn, look at the job descriptions of people in the department, and then look at the “People Also Viewed” section of the profile. I often find out more about the department from LinkedIn than I do from my initial conversations. 

Finding data on startups can be tricky. Startups are small by nature. They are often ignored by traditional companies, but they grow quickly and without public reporting. The Dow Jones doesn’t reflect the value of these startups, at least, not until they go public, and at that point they are at lethal scale. The metrics for startups are radically different—growth curves, product market fit, crossing the chasm, and the traction gap, whereas big company metrics are optimization and P&Ls. 

Venture capital data is useful data on startups. There are several services that you can use to monitor publicly visible capital flows towards new ventures, for example, CB Insights, Pitchbook, and Crunchbase. The wonderful thing about following VCs is that they tend to create the market, and the horrible thing about VCs is the markets can be transient. For example, daily deal websites, mobile gaming companies, dozens of mediocre chat apps. Follow their blogs and their money to find out what’s happening next.

Data for Capital

The epic rise and fall of Theranos epic is a lesson in how data must be construed in the absence of bias and emotions. Holmes had credibility, but it was not based on truth or facts. She had been a business ambassador with the Obama Administration, she had the trust of some of the world’s most powerful people: Henry Kissinger, Larry Ellison, George Shultz—even Mad Dog Mattis, but it was all a ruse.

Holmes used false data, lied about technology, and investors were totally conned. Theranos raised a total of $724 million before the company’s lies caught up to the executives. During that entire period, none of the world’s most powerful and influential people thought to get a scientist to test whether or not the product works or to even do basic financial due diligence against the books. In 2014, Theranos reported $108 million in revenue when they only made $108 thousand. Walgreens, one of the nation’s largest retailers, chose to build a partnership with Theranos purely based on the persona of Elizabeth Holmes.  

So, many Silicon Valley startups and companies lie. Call it the “fake it ‘til you make it” culture. It’s not just putting up a strong front, it’s considered expected behavior. I’ve been a part of so many companies that blatantly lie about their customer transactions and active users, they even lie about embezzlement and fraud.   

I have been told to shut up because it would damage the credibility of the company. It would damage the credibility of the leadership that they didn’t identify this in the first place. This dent in credibility would follow them for the rest of their careers.

Credibility is fleeting and personal, it’s often just bestowed upon people based on charisma and personality. Take Billy McFarland, the cofounder of the fraudulent Fyre Festival of 2017, now jailed for six years. He had tons of credibility. No one looked at his financials, no one looked at the reality of the concert. Millions upon millions were poured into empty promises.

Data can give you real credibility and the ability to get capital. To do so you must convince investors you can use capital to drive growth. To do that you need a precise growth hypothesis. That’s the whole point of the HowDo process— storytelling and narrative. By the time you reach this step, you should have a story that is compelling enough to raise capital. 

Storytelling starts with knowing your audience. When seeking capital, you need to demonstrate that you know what you are talking about. You must be credible and convincing. The more that you can show that you understand the problem, you understand the solution, the better. You’ve found an expert who has done it before or a similar problem that’s been solved somewhere else. You’ve identified a customer base of x size with the potential to grow because of y trends. 

With this approach, you have forecasts, spends, and an estimated result ready for a captive audience. The knowledge matrix can play a role here too. A strategy that I have used when approaching investors is to explain what you don’t know, the unknown unknowns. It sounds counterintuitive, but it’s a great way to set the right expectations for growth projects. 

Just show them the data. Show them the total addressable market (TAM), compound annual growth rate (CAGR), customer penetration and evolution, competitors, the VC capital going into the sector, and then bring it back to the knowledge matrix: what knowledge is your hypothesis based on? Where are you making assumptions? Investors know that this data is imperfect, but you know the data inside and out. There is less risk for investors when they are making a decision based on truth and credible data.

My Data Approach With Target

When I onboarded at Target as an entrepreneur in residence, I established a weekly cadence of sharing everything that I learned and everything that I did— well, until I was involved in a confidential project. But because I had shared everything that I did and learned every week, people were brought along on the journey. My email list grew from 20 people to over 1,000 people. All of this growth was from word-of-mouth; great employees loved being in the know, and just started sharing the knowledge. 

I had never met most of these people, nor did I have any accountability to them. But by driving transparency, I was trusted. They understood what I was doing and they understood how I was thinking. I went from being the scary entrepreneur who just entered the building to someone who was respected for their authenticity. 

Then I took it one step further; I built an entire strategy and distributed it to Target. That alienated me in some ways, but I also gained some fans. People thought it was helpful. People wished that someone had done this before. 

If I had taken the institutional approach which is to integrate, follow the rules, slowly build trust and then start to express myself, I don’t think I would have ever been able to make the point that I was able to make by building a robust case with data and sharing it openly.

I was impossible to ignore because everyone had the data and everyone saw the story. That exercise changed the way people worked, it changed the way they thought about innovation, and it changed the way they thought about using data. Now, did I get the organization to split to a new data paradigm? No. But there are people who are around the company who all of a sudden reached out to me and agreed that we should be using data and we should be reporting more regularly. We collaborated on projects to provide greater visibility with greater frequency into things that really mattered to people who cared. It made the company work better and it made the teams work better.

I chose a disruptor approach. The organization benefited from the barrage of information but, at the same time, it scared a lot of people—they felt threatened by that cadence. But for the people who were willing to go on the journey, the people who were hungry and already learning, this changed the way they worked. 

A culture of openness can relieve some stress because if people feel that they are informed, they will worry less. Also, if the bad news is communicated, there is a chance that someone might come up with a solution.

When a company is dying, that is often the time when communication dissipates. Organizations tend to clamp down in these times, they stop sharing numbers, there is no visibility until right before the quarter ends, and employees’ fears exacerbate.  

These companies don’t realize the power of their employees when they are given data. As these companies grasp for straws, they box out some of the only people capable of turning the company around. 

The Story of Warby Parker

Big companies are in the process of a land grab. They are surveying the startup market and subsuming anything with potential. At some point, if they are regulated, the dynamics might change, but until then, small businesses must be aware of their competition and the risks they face. However, if they use data, entrepreneurs can scale if their niche position provides strong enough protection.

Warby Parker is a great example. The online retailer of prescription glasses and sunglasses was founded in 2010 by Neil Blumenthal, Andrew Hunt, David Gilboa, and Jeffrey Raider, four students from Wharton. Described as “the most innovative startup of America” by Yashica Vashishtha, writer for YourTechStory, the four entrepreneurs found the white space in the market for Warby Parker and introduced stylish customized eyewear to the online market. 

Warby Parker’s competitors were Luxottica and LensCrafters. Luxottica sold stylish eyewear, but the prices were high end, and Lenscrafters lacked any sense of style in its branding. Warby Parker’s eyewear were appealing, affordable, and customers enjoyed the convenience of trying on up to five pairs in the comfort of their own homes. 

The founders initially received a new business grant from the Venture Initiation Program of $2500, which was seed money for the launch of Warby Parker. According to Gilboa, one of the cofounders, “The idea was really based on two simple premises. One is that a pair of glasses should not cost more than an iPhone, and two, that eyeglasses could effectively be sold online.”

The niche product gained immediate traction helped by Vogue, who covered the story within the first year of its launch. The brand exuded trendiness, style, and a dash of exclusivity while also being universal. Warby Parker’s branding and customer-focus managed to build a defensive wall that protected them from competitors and other eyeglass manufacturers.

In 2011, the company received first round funding of $2.5 million, Series A round funding of $12.5 million, and a $37million of Series B round funding the next year. In 2013, American Express and Mickey Drexler invested $4 million in the brand.

After the launch of physical stores, the company was valued at $1.2 billion in 2015. In 2016, Warby Parker began its own manufacturing in an optical lab located in Rockland County, New York. The company invested $16 million in the 34,000-square-foot manufacturing unit.

Data helped Warby Parker by revealing who their customer was, their customers’ pain points, and their competitors’ strategies and mistakes. Warby Parker targeted millennials and advertised where millennials would notice the brand—through podcasts, for example, or by placing ads where peer groups would talk about their experience. This sent the company’s net promoter score through the roof, which is a data point they use to gauge future business..

You need facts about your customer, facts about competitors, facts about market trends. As data changes, and you’re paying attention, you can evolve the mission, direction, and structure of an organization in response.

Here’s a fast fact that underscores the value of data. Companies that pave the way have an 80% chance of failure. Fast-follow companies, those that follow them with the benefit of hindsight and data, have a 70% chance of success.

Data Wars

Walmart has an impressive arsenal: vast operations, reliable supply and manufacturing chains, strong logistics, physical retail locations, and ecommerce. Yet, the company lacks the ability to push back on Amazon’s impregnable battlefront. Amazon has the advantage with one strategic weapon: data. 

Amazon Prime has over 100 million members active on apps and the internet. Those members provide mountains of data that WalMart has no hope of seeing anytime soon. Meanwhile, Amazon has an unimaginably massive data-rich ecosystem of customers. Amazon doesn’t even have the technology yet to use that much data, but it is inventing it as we speak.

Most of the Big Tech companies are investing in similar weaponry. Microsoft paid $26.5 billion for LinkedIn and its data and then bought GitHub to profile and influence the majority of global modern developers. Google bought YouTube, and Facebook bought Instagram and Whatsapp. Amazon bought Twitch, and so on and so on

WalMart has not caught up with the data paradigms of the Big Tech companies. Walmart’s fate is sealed, and its fight with Amazon will be drawing to a close within five years. 

I hope these musings have shown you how and why data is crucial to building better and strong company that can evolve and create. You can access the HowTo guides on harnessing data and focusing on the customer here.

In closing, here’s a light-hearted look at a cast of characters who reflect the world of business and data today. It’s my version of Marvel’s Avengers and the X-Men, or rather, the Tech-X-Men..

Jeff Bezos stars as Thanos, and he has the greatest weapon of all—the Amazon platform and all its data. Mark Zuckerberg has Facebook. He, is trying to play Iron Man and save the planet, but really he is Obadiah Stane, a wanna-be Thanos who destroys freedom. The real Iron Man is Elon Musk, who is creating the future of weaponry with SpaceX, Neuralink, and OpenAI. Jack Dorsey is Hawkeye, with a questionable role considering that he has the least impressive weapon of anyone—Twitter. 

Twitter is Dorsey’s bow, and Donald Trump’s tweets are Dorsey’s arrows. Dorsey and Hawkeye are vainly attempting to wage battle with flying interstellar monsters. Armed only with a bow and arrow, they stand little chance against their terrifying opennents shielded with impenetrable body armor and autonomous weapons systems with photon torpedoes. 

Alphabet (formerly: Google) is the X-Men. Some of the formidable and amazing characters keep us coming back for more—Gmail, and Youtube, for example. But without them, the franchise is a disaster. 

Google search was Professor Charles Xavier of the X-men until 2018 when they removed “Don’t be evil” from their motto. At that time, Google search officially became Dark Phoenix—a terrible movie in the X-franchise that would have never been made if the first several movies weren’t so good. Google tricked us into believing their brand. Hell, they might even believe it themselves. Now, Google has a strangle-hold on digital search for most of the planet outside of Russia and China. 

And that all adds up to way too much data (power) in the hands of a few. It’s time to share the wealth in the data wars so that more can survive.