CHAPTER 2

Why Companies Miss Out on Big Opportunities

A few years ago, I visited a transportation company that also offered limo services to companies and business travelers. It had a substantial number of corporate accounts. As we walked to the conference room, I asked the CEO if he was worried about Uber. Uber had just reached over $1 billion valuation and was quickly becoming as trendy to use in the corporate world as it was to have a BlackBerry 10 years ago. After its launch, BlackBerry quickly became a status symbol of power and rank in the corporate hierarchy. After its fast ascent to a digital unicorn, using Uber became a symbol of being a young, modern, techno-trendy, and raising executives. Ditch the black car for the eye-catchy app that can hail a “cab” from anywhere.

My host stopped in the middle of the hallway, paused for a second, and said, “This is the wrong question. If CEOs in my position, who find themselves unexpectedly competing with Uber or another fast raising techno giant, are asking themselves this question, they are going to lose this battle.” Then he turned and started walking in a different direction asking me to follow him and see him prove his point.

He opened a secured door and led me into a room full of people each staring at two, three, and even more monitors. They all had their phone headsets on. Multiple phone conversations were going on at the same time. I quickly realized that they were staring at maps on their computer screens. It was the dispatch room.

“This is our high-tech dispatch room,” my host said as he led me closer to one of the desks. He leaned toward the screen and pointed at the screen where a bunch of little dots were moving on a map.

“Why didn’t we invent Uber?” he asked me. “This is the right question! This is the question my board and my shareholders ask me because we had all the technology but failed to create Uber and increase the value of our company.”

The room, the screens with the maps and the moving cars, the iPhones that could be seen on every desk, and the thought of the enormous Uber valuation made it immediately clear to me that this is the most painful question for every CEO who finds themselves in such a position. The company had all the technology but instead of building a self-service app, it continued to dispatch cars in the old-fashioned way—based on direct phone calls. All the employees had smartphones. All of them used apps on a regular basis to do various things. Why didn’t it occur to any one of them to connect the drivers and the customers via an app? Despite their personal habits to use convenient self-service apps, no one clicked to convert their traditional phone-based personal service to an app-based self-service. Had they done so, they would have created enormous value for their shareholders. And this is precisely why the board and the shareholders were asking the CEO this question—why didn’t we do it?

The CEO was asking the right question because it forces him and his executive team to understand and fix the underlying causes in the organization, its culture, and its processes that made them miss on an obvious opportunity. The gap between what they offered and the changing customer preferences for digital self-service ultimately opened the door for the entrance of a new and powerful competitor, which weakened the company’s position in the marketplace.

This is not an isolated case. There are many companies who sell their data to start-ups, who within a few months sell them back a smart app to help their managers improve the businesses. They have sold an undervalued asset—the data—to another company that understood its value and came up with a business model to monetize this asset. From a shareholder perspective this is underutilization of assets and a transfer of value due to lack of ideas and knowledge about the new digital economy, where data-driven business models generate revenues.

The transition to the digital economy has changed the game rules and those who miss out on opportunities, because they do not understand how to create value with data, are shaken by the speed at which new entrants gobble their market share and dwarf their capitalizations. And the reality is that more and more CEOs and executives will be held accountable for missing out on such opportunities.

Why Companies Miss on Opportunities

There are three reasons why companies miss on digital opportunities. And they are cultural.

First, companies do not see data as an asset. Second, companies do not know how to innovate with data. Third, companies do not think about data monetization, that is, they do not know how to build data products and data-driven business models.

Companies remain stuck in their familiar ways of managing assets and doing business while the times are changing. Economists call this phenomenon path dependency. The phenomenon was discovered when economists tried to understand why some progressive societies begin to decline and ultimately lose their economic advantages. The reason is quite simple. Deeply rooted traditions and norms force the younger generations to walk the same path while life demands change and adaptation. The path dependency prevents the progressive generation from moving forward. Institutions fail to change, and economic decline becomes permanent. The same holds true of companies.

The science of management has been developed in the last 100 years. The first MBA program was offered in Harvard Business School in 1908. For the past 100 years business education has been all about managing physical, financial, and human resources. How to manage data and digital assets has not been and is not taught at business schools. The business models of physical assets management are well studied and clearly explained. How to run a hospitality business and how to build a chain of hotels is textbook material. If you have the resources, you can follow the book and do it. But does any textbook mention how to build an Airbnb? No. This is because science follows practice. Hence, until a new business model is born in the real world, there are no references nor teaching materials about it.

Because in business, science follows practice and not the other way around, as it happens in other disciplines like medicine for example; many business scholars advocate the case method of teaching business. In other words, the aspiring business students learn by reading the historical cases of how businesses were created and managed. But in a fast-changing environment, managers who rely on book knowledge are at a disadvantage. It will take 20 to 30 years before we collect and synthesize the cases for data-driven business models and teach them at school the same way we teach finance and human resource. Managers can only learn by observing the reality and by letting the external cultural changes permeate their own organizational cultures. Managers must be observant and learn from theirs and other peoples’ iPhone experiences about self-service, instead of holding to the landline to deliver on demand limo services to clients.

Let us look at the three cultural impediments to change.

Data Is Not Seen as an Asset

There is a lot of talk about data being an asset, but this is still lip service. If data was indeed seen as an asset it would have been already listed on the balance sheet. In fact, Doug Laney, a renowned industry analyst from Gartner, has coined the term “Infonomics”1 as the practice of information economics. He is actively promoting the idea of putting data on the balance sheet and developing methodologies for accountants to measure its value and return on investment (ROI). Indeed, once anything becomes part of a balance sheet, it gets the full attention of auditors and stockholders, which in turn will ensure that data will be properly managed as an asset. Today this is not happening.

A Cambridge University2 research survey reveals the top three reasons why data is not seen as an asset:

• • First, 100 percent of the respondents to the survey agreed that they are experiencing cultural issues when trying to transition to data-driven business models.
• • Second, 86 percent of respondents agreed that there is a value perception that has impeded such implementation.
• • Third, 71 percent of respondents agreed that there are data quality and integrity issues that make it difficult or impossible to implement a data-driven business model, as users quickly abandon apps that provide incorrect information.

We can clearly see in this triage that the adherence to old cultural practices drives the low value perception. It is also quite clear that data-related projects are not properly resourced because they are seen as less valuable to business. All the respondents stated that they lack expert resources to manage the data assets properly and improve its poor quality and integrity. But even worse, because data is not being seen as a valuable asset, people can be mocked for proposing ideas for making money with data. As we know from many historical examples, cultures can be quite brutal to the proponents of new ideas even when the cost of trial is practically zero. The Hungarian doctor Ignaz Semmelweis angered the entire medical community for advocating in the 1800s that doctors should wash their hands in chlorinated lime water before examining women about to deliver babies in order to reduce infection-related deaths. It took decades before Ignaz’s practice became a norm in all hospitals because of the prevailing culture in the medical community. Many physicians were outraged at the thought that they might be the cause of death. Pride and dignity prevented them from trying a really low-cost solution with no potential drawbacks for trying it.

Unfortunately, the fact that many practices change only after a great resistance permeates the business world, too, and often impedes the creation of data-driven business models. The cost to business, employees, and shareholders is not as high as the social cost of the resistance to Ignaz’s methods, but it is nevertheless significant. The fortunate situation in business is that while culture takes longer to change, value perceptions can change quickly based on market success and drive faster cultural change. To do that, businesses have to reward ideation and innovation with data.

Innovate With Data

Have you seen a company having a “Data R&D” department?

R&D is still tied to physical goods innovations—new chemical compounds, new gadgets, new drinks, new food ingredients, new food production, new packaging, new technologies, and so on. The list is endless. And, in fact, this innovation has been accelerated as we see new goods being introduced all the time. This rapid innovation has both positive and negative effects. Among the negative effects is accelerated consumerism. We all like novelty items, but because we are bombarded with new products the appreciation of the novelty is short lived. People experience user fatigue with gadgets, phones, and even big items like cars. Consumers have been conditioned to upgrade on a more or less regular schedule.

The accelerated innovation has become possible because of data and analytics. I recently visited FirstBuild (firstbuild.com), which pioneered a community-driven approach to innovation. The company is backed by GE appliances and is essentially an open co-working space for everyone who wants to innovate and build interesting and useful home appliances. As they say: “Think of it as a playground for adults. Our microfactory is a collaborative makerspace where ideas come to life. Use our tools and create your next big idea.”3 They have invented a number of useful products of which the most popular are the nugget ice maker for people who like chewable ice or slushy drinks, and the big clear ice balls maker for whiskey lovers who like their drink to be chilled without being watered down.

I was most impressed by how they used sensors and data to create the perfect pizza oven for the home. As they explained it is really hard to make a good pizza because restaurant grade brick ovens heat the stone up to 800°F (427°C) and the dome up to 1300°F (704°C), which distributed the heat perfectly around the pizza. This is an extremely high and dangerous temperature for a residential home. Hence, the engineers at FirstBuild had to come up with many technical innovations to make such an appliance safe for the home.

But what was most interesting is how they determined the method to distribute the temperature in order to make the best pizza. They installed over 1,000 heat resistant sensors in the best pizza restaurants and collected data on the heat distribution inside the brick ovens. Based on the data, they came up with precise four-zone temperature controls, which ensure that exact temperatures are hit and maintained every single time. The miniaturization of the brick oven for home use was made possible because of the combination of engineering and data analytics techniques.

Today, research shows that putting a data scientist on the innovation team can increase productivity significantly. Traditionally the optimal heat points would have been found by trial and error. Data analysis reduces the experimentation time required to arrive at the optimal solution.

It is great that “hot” data helped create the best home pizza oven. But can the oven help make new products? The answer is yes. Today, all kinds of products have collected data that provides valuable information on usage and operating patterns. Insights about the operating patterns lead to feature and function improvements, while insights about the usage patterns lead to new product ideas. A research paper on the Future of R&D4 points out that companies like “Caterpillar, Rolls Royce Aerospace, and Tesla are using Big Data generated from sensors to plan their next product improvements and to determine what features and components need to be enhanced or created.” The impact of pattern analysis is even greater in health care. Big multidimensional and granular data can reveal previously unknown microtrends which lead to more effective, personalized treatments.

So, why do we need a data R&D department? The current R&D is goal oriented and not exploratory. In other words, innovators first come with a vision and then pursue its implementation. Data turns this approach upside down. Granular data (such as sensor data for example) is like DNA. It already contains all the important information. All that remains to be done is to dig out the insights and hand them to the traditional R&D department to create new products and services.

Monetize Your Data Assets

The first large-scale data projects aiming to extract value and insights from data were the data warehouses. Companies started to develop their own custom data warehouses in 1980 in order to transform data collected from operations into data ready for analytics and reporting. These data warehouses became the foundation of “Business Intelligence”—a term coined by the Gartner analyst Howard Dresner5 to describe the use of databases, datamarts, query and analysis technologies to provide decision makers with timely, factual, information. Making better decisions makes the business more intelligent.

Three years ago, I met the head of the innovation lab of Walmart, who was a veteran Business Intelligence specialist. He had started his career at Procter and Gamble’s diapers business. His first assignment was to develop and present statistical reports on the diaper business to the founder and CEO of Walmart—one of their largest retailers. At the time it took him six months to extract shipment and other data and build reports against the Unix system. At the meeting with Sam Walton he presented detailed information on all shipments to all states and all stores. After the review of the numbers, Sam Walton seemed unimpressed despite the wealth of very detailed information. He told the young analyst that he had not answered the most important question that every CEO and every manager should know at the end of every day: “Did we make money?”

It took the analyst another six months to collect the data and answer this question. At the follow-up meeting he told Sam Walton and the present Walmart executives that they had lost money. Seemingly unsurprised, Sam Walton made a point to his executives. If they knew the financial results at the close of business every day, they could manage and turn the loss into profit. But if the losses are revealed a year later, they would go out of business. That, according to my host, led to the creation of Retail Link—the famous system that provided important profit and loss information to both merchandisers and suppliers. In turn, this system allowed for the development and execution of the famous “Always Low Prices” strategy.

The data warehouse still plays an important role in business. But it is interesting that no data warehouse project ever starts with the objective to monetize the data in it. Data warehouses were justified as a means to better and more timely reporting. They supported other activities and had no direct role in the making of money. Hence, they were a cost center in the organizations. Why is this wrong?

In the traditional school of management, we cannot even imagine a manager who would propose a new investment without calculating its ROI. But the ROI on a data warehouse built to support better decisions is impossible to quantify. Who can collect information on how decisions are made? And who can calculate the incremental improvement achieved with one decision versus another? Two store managers can make two different decisions based on the same report, but the financial outcomes may be the same. If data is converted to product, the ROI on it can be quantified in the same way as we do for physical goods.

The reality is that a data warehouse can be used both for decision support and for data monetization. The two are not mutually exclusive. The beauty of data is that once it is collected and properly managed as an asset it can be used for multiple purposes. Data is a true “renewable resource.” By adopting a data monetization mindset, companies will start leveraging data more actively as a limitless resource that can routinely produce new revenue streams.

Adopting a New Mindset

A “data first” approach to business requires a deep cultural change, which is never easy. But does it have to be that difficult? Cultural change in organizations follows market success. Seeing more success stories about making money with data stimulates employees to be curious and motivates them to learn. Ultimately, the organization transitions from path dependency to trend following, which changes the value perception of data-related products or business models.

Since making data products is much cheaper than making physical products, it pays to encourage intrapreneurship with data. Some of the most effective data products have started as skunk work but have transformed the fortunes and cultures in their companies. The most famous and widely discussed example of such a skunk data project is the invention of the connection suggestions on LinkedIn. This data product led to the launch of the “data scientists” as “the sexiest job of the 21st century.”6

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