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Reinventing Capitalism in the Age of Big Data

By Victor Mayer-Schönberger and Thomas Ramge

1. Reinventing capitalism

A reboot of the market fueled by data will lead to a fundamental reconfiguration of our economy.

Why is our market money-based now?

Until recently, communicating such rich information in markets was difficult and costly. So we used a workaround and condensed all of this information into a single metric: price. And we conveyed that information with the help of money.

But as information is compressed, details and nuance get lost.

Data-rich market no longer being focused on price broadens our perspective, yields better matches, a more efficient transaction, and, we believe, less trickery in the market. The goal is to go beyond “good enough” and aim for perfection.

The key difference between conventional markets and data-rich ones is the role of information flowing through them and how it gets translated into decisions.

In our data-rich future, it will matter less how fast we process information than how well and how deeply we do so. We suggest that you e need to put recent breakthroughs to use in three distinct areas:

  1. The standardized sharing of rich data about goods and preferences at low cost
  2. An improved ability to identify matches along multiple dimensions
  3. A sophisticated yet easy-to-use way to comprehensively capture our preferences.

By configuring markets and making them data rich, we shape human coordination more generally. Gaining the ability to better coordinate human activity. To adapt, the nature of firm will need to be reimagined. Firms might automate decision-making of (certain) managerial decisions and introduce more market like features, such as decentralized information flows and transaction-matching.

2. Communicative coordination

Advancements in the flow of information often underlie a step-change in our coordinative capacity.

The most obvious way to measure success of our coordinated and cooperative efforts is in terms of effectiveness. Effectiveness is about the ends, not the means: it’s about achieving the results, no matter the cost.

While at scale, we had to accomplish our aims efficiently, avoiding waste. The very origin of the word economics — the Greek oikonomia, or “rules of the house” — refers to the ancient practice of managing an estate with self-sufficiency and frugality.

The market and the firm are two mechanisms that have been absolutely critical in helping us coordinate successfully at scale.

In a market, coordination is decentralized. Individuals make decisions for themselves. Without having to depend on just a handful of people, adding participants is easy, and the market is flexible, dynamic, and scales extremely well. In coordination, keeping everyone on the same page for long is difficult. In contrast, the market does not require participants to share their individual goals for transactions to take place. This process greases the machinery of human cooperation to everyone’s benefit.

Even though we often think a form as a part of a market system, the truth is that the market and the firm adopt complementary and contrasting approaches to the problem of efficiently coordinating human activity. The firm is an example of centralized coordination. People come together in a firm to pool their efforts and resources, but their activities are organized and directed by a single recognized central authority. Because of the division of the labor, decision- making in most firms is hierarchical and centralized.

The key difference between the market and the firm is in the way information flows and is translated into decisions.

Since the beginning of the nineteenth century, and propelled by new methods and tools that we have advantaged the firms specific structures for information flow and decision-making, the firm has risen dramatically in importance. This advantage, we suggest, is not only temporary, it is already coming to an end.

3. Markets and money

Not every digital technology empowers market participants nor will an additional information flow necessarily improve markets. Whether a particular technology furthers the market by enabling new information streams depends on how well the specific qualities of that technology are aligned with the informational structure of the market.

How the structure of the markets is linked to information and how that information flow has been decisive in making traditional markets successful— up to a point?

The fundamental principle of the market is that decision making is decentralized, and so is the flow of information. It mitigates the effect of bad decision.

There is still some disagreement among economists concerning how much information an efficient market requires. The overwhelming view among economists is that in markets, more information trumps less.

But in real life, it is a frustrating conundrum: on the one hand, we yearn for more information to assess our options and transact wisely; on the other hand, we are being overwhelmed by information, fail to process it successfully, and risk making a less than optimal choice. A fix is available that mitigates these problems and we have been using it for millennia: money.

At the very beginning, money (buck, gold, silver) was intrinsically valuable. With the shift to base-metal coins and paper, we moved away from denominating value through a good that was intrinsically valuable. Around the beginning of the twentieth century, money became purely informational.

In our daily lives, we may often overlook the informational function of money and price. After all, we are usually far more interested in completing transactions— getting the food to our family, purchasing the home to shelter us and our loved ones— than in focusing on the details of the transaction process.

Money and price are the infrastructure, the conduits of information, that make the market work. But money and price do much more than streamline information flows; they also simplify transactional decision-making.

We’re so trained to think in terms of price that when we hear about a new product or service, we almost instinctively ask for its price in order to evaluate and categorize its relevance and value to us.

The combination of markets and money offers an outstanding way to coordinate human activity, by lowering the overall cost of the system. But in large part, those coveted greenbacks paper over the fundamental challenge of taking highly condensed information and translating it into transaction decisions. Money-based markets are fraught with inefficiencies, and these are felt in how well or how badly the market fulfills its promise of coordinating human activities to everyone’s best interest.

4. Data-rich markets

As we have shown, markets are amazing social innovations that enable us to coordinate our activities with each other efficiently — in principle. In practice, they suffer from limited information flows. We rely on money and price to reduce the amount of information that needs to be communicated and processed. However, our fixation on price has hampered the market’s ability to do what it does well: coordinate.

The answer to this problem isn’t digital payment, or virtual money. That might speed up existing information flows or make them cheaper, but information will still be compressed into price, eliminating valuable detail. The solution is not to fiddle with money but to replace — or at least the very least complement — its informational role with rich and comprehensive streams of data. It helps market participants to find better matches.

How would we, so accustomed to and focused on price, compare products across many dimensions and then identify the right match? How would we express our multiple preferences swiftly and easily?

It also necessitates a step-change in how we translate information into decisions. It is precisely the absence of such methods that has kept money-based markets in place in the early decades.

Three technologies are crucial to this reconfiguration of markets. They allow us to:

  1. Use a standard language when comparing our performances
  2. Better match preferences along multiple dimensions
  3. Devise an effective way to comprehensively capture our preferences

All 3 techs have in common that they translate rich data into effective transaction decisions.

Ontology

Let’s say you are shopping for a shirt. You click on shirts and the site gives you hundreds of choices. Then you can filter out the ones you want by selecting your preferences like size, color, fabric, fit, sleeve length, type of collar, brand...

How can an online retailer provide you with that much of information about this shirt? By labeling each product with data that describes each garments’s characteristics. These labels are data, too. They are data about data, or metadata.

These hierarchy of keywords/labels are natural to us. These well-developed hierarchy of keywords are called “ontology” by experts in the field.

Developing ontology is hard. For example, YouTube cannot yet match the depth and breadth of the keywords that are standard at ESPN, simply because humans have not yet been able to come up with an easy-to-grasp general-purpose ontology that everyone can understand quickly and apply flawlessly.

The lack of an ontology in a market reduces the number of transactions that take place. The key lies in finding the right ontology. In the future, however, Solomon thinks identifying the right ontology will require less human ingenuity than hardheaded data analysis: data will drive data ontology.

A number of data ontology startups are doing it: Alation, Corrigon, and Exepertmaker.

Optimal match

The challenge is information overload, including having too many options to filter and select, and thus to identify the optimal match.

Because preference data is just a data stream forming a particular pattern, we can adapt pattern-matching algorithms to help us identify optimal transaction patterns through huge amounts of training data.

A more pattern-oriented matching based on rich data is popping up. Music platform like Spotify and Apple Music. Recommendations like Netflix and Amazon. Saberr suggests personality based algorithms can help build highly effective work teams.

It is easy to see how better algorithms can translate into a competitive advantage for the market provider. The more markets move away from a focus on price to data-rich matching, the more the race for better matching will intensify. This, we can expect matching services to turn into key differentiators on marketplaces.

Identify preferences

How do market participants express their preferences and their relative weight and communicate them to each other?

Pattern analysis with machine learning approaches help. And feedback process helps system to learn.

In all:

  1. Improvements in data ontology help us extract valuable data from huge streams of it and the categorize it in many dimensions.
  2. Advances in matching algorithm enable us to find and select the optimal transaction partner in the market of our choice.
  3. Machine learning systems identify our preferences as they observe us so that we don’t have to spend time making these preferences (and their relative weights) explicit.

These 3 steps can actually be used in many kinds of markets. For example, the love market. Most recent online dating platforms like Tinder, narrowed the necessary interactions down to a single dimension—desirability. But just as comparing prices doesn’t tell you everything you need to know when making a transaction, reduction to a single dimension does not guarantee a successful outcome when dating. What’s necessary, in our parlance, are improved preference-matching algorithms and a better data ontology that capture how people relate to each other.

For the first time in human history, we will have a choice about whether and to what extent we involve ourselves in certain decisions. We will be able to direct a machine learning system to do the boring stuff and reserve those decisions that give us the most joy and pleasure for ourselves.

5. Companies and control

For our context, the firm, much like the market, is a mechanism to enable human coordination. Key differences between the market and the firm is how decisions are made and by whom.

The key concept of the firm is centralizing information flows and decision-making as a tool of comprehensive control.

But as more and more detailed information floods to the center, the decision makers are threatened to information overload. The firm must ensure that it is translated into good decisions.

Delegation of some decision power down a firm’s hierarchy, of course, is the most obvious strategy— have local decisions resolved locally, and bring only the most important and most general decisions to the top. But the delegation of decision making is a delicate balancing act.

With the same time, many methodologies are developed like ERP and SOP. But human have cognitive limitations. In firms, the centralization of decision-making even amplifies these cognition limitations.

6. Firm futures

Future firms have 2 ways for improving decision making:

  1. Automate decision-making.
    • Fukoku
    • Bridgewater
  2. Rearrange a firm’s organizational (thus decision-making) structure
    • Spotify. Squads are small teams responsible entirely for certain aspects of a product. The underlying philosophy is starkly different from that of traditional hierarchical firms: don’t ask your manager, because you don’t really have one.
    • Decentralized decision making is the hallmark of the market. Introducing such radical decentralization into a firm invites bits of the market inside. These companies choose to be an organizational hybrid: part firm, part market.

7. Capital decline

In data-rich markets, participants no longer use price as the primary conveyor of information. The shift away from price signifies a monumental change: separating the act of payments from the provision of information.

Most of the information necessary for markets to function will no longer flow through banks. Banks will still handle the completion of transactions but the informational center of gravity in markets is moving away from money— and thus away from banks.

The demise of money as the market’s preeminent conveyor of information will also prompt a decline in the role of capital.

In our market system financial capital is key because it is such a fungible factor of production: were necessary, it can easily be exchanged for a much-needed resource and vice versa, thereby enabling efficient resource use.

Outside investment increase a company’s flexibility end it also conveys further information about the prowess of the company as well as the trust that an investor has in it.

When a highly respected VC firm invests in a startup, the recipient gets immediate name recognition and often gains additional market value as a result.

As markets embrace diverse information streams, these two functions of capital — information and value (signal with money)— are no longer necessarily intertwined. Capital will no longer be the only information game in town.

The rewired market will have no problem ingesting and conveying such signals, and market participants will have no difficulty factoring such signals into their computer-assisted decision-making. In this case, VC cannot invest in a startup it likes because the investment round is oversubscribed. The firm may then invest elsewhere, but that investment is not a signal that the company is the best choice, only that it is the only one that was available.

In the long run, however, data-rich markets will help investors to better identify opportunities that match their preferences and are less clouded by human bias. New intermediaries will rush in to fill the demand, using sophisticated matching tools and machine learning systems to analyze a flood of information and translated into fact-based advice.

Banks and conventional financial intermediaries have reacted to data-rich market by pursuing two main strategies:

  1. Cost cutting, primarily through automation.
  2. Reinventing themselves as Information intermediaries in data-rich markets.
  • Robinhood provides stock trading with zero commission fee. Revenue can depend on interest generated from deposited.
  • PayPal, Apple Pay, WeChat pay, strip, square, are reinventing payment solutions. All the value from data traffic are captured by them.
  • SoFi offers low interest rates to individuals based on prediction of creditworthiness.
  • Stash breaks apart the share as the smallest possible unit of investment, instead enabling their customers to buy fractions of a share.
  • Sigfig gathers and analyzed data about the investments its users make through brokerages and identifies alternative funds with similar risk profiles.
  • ZuluTrade and eToro offer customers a way to select from and copy the investment activities of many thousands of their investors.
  • PeepTrade offers customers access to decision information of successful traders and take a cut from each trade that “follows” a successful trader’s strategy.
  • Zopa, peer to peer lending
  • Kickstarter and Indiegogo crowdfunding.

8. Feedback effects

System based on complex feedback loops are tricky: they work so well in so many routine cases that we are tempted to disregard — even forget about — any built-in risk of extreme failure.

We suggest that three distinct effects are often at play when markets become concentrated: scale effect, network effect, and feedback effect.

  1. Scale effect: During the Industrial Revolution, manufacturers realized the potential of producing in volume to lower costs. Also, consumers have reaped the benefits of the scale effect: lower prices and a wider range of products.
  2. Network effect: In 1890s, several phone companies owned their own proprietary network, people usually have several phones as their contacts are in different network. At the turn of century, AT&T was consolidated by market, customers realize that every new subscriber who joined the network increased the system’s utility for everyone already on it because it added to the number of people they could potentially reach. It was as if the service got better as people were added to it — even though the service itself did not improve, only the opportunities to use it.
  3. Feedback effect occurs whenever computer systems use feedback data to learn. Innovation is no longer about breakthrough ideas but rather about collecting the greatest amount of feedback data.

The scale effect lowers cost, the network effect expands utility, and the feedback effect improves the product.

Unfortunately, these effects have also been driving concentration — the deadly poison for market efficiency.

So far, regulators mostly focus on scale effect and network effect, not yet on feedback effect. (Comment by Hubert: actually GDPR starts to regulate this.)

We suggest what we term a progressive data-sharing mandate. It would kick in once a company’s market share reaches an initial threshold. It would then have to share a portion of its feedback data with every other player.

It would be crucial in protecting the decentralized nature of the decision marking, so we can preserve not just markets but an open society in general.

9. Unbundling work

In the past, as manufacturing became more automated, the service sector grew. The question today is whether this will happen again.

UBI, universal basic income, is participatory because its goal is not only to provide people with some of their basic needs but to enable them to rejoin the workforce at less than full time.

UBI works fine till now, but the problem is, if data enables us to go beyond money, why does the social innovation designed to solve the problems caused by data-driven market’s emphasize money? Why are we reintroducing, through a UBI, a simple fixed monetary solution in situations that quite obviously require an assessment of needs beyond money?

The three sets of policy measures, conventional, distributive and participatory, as well as the more radical UBI are based on certain economic assumptions: not only that labor share continues to decline, but that labor share will decrease while capital share increases.

But since 1997, capital share actually decreased substantially around three times as much as the decrease in labor share. This is because labor benefits more from technology than capital does (as explained before in chapter 7).

But if both labor share and capital share are decreasing, which is the biggest winner? As critics have argued for years, the pockets of overcompensated executives have been lined at their expense.

The profits these superstar firms earn aren’t fully reflected in their formal profit-and-loss statement because of creative corporate tax planning. The stock market has realized the disparity between formal reported profits and the actual capacity of the superstars to achieve huge markup profits, and it shows in the trajectory of share price.

For this, we must substantially adjust our policy measurement both the distributive and the participatory perspective.

On the distributive side, governments might consider a partial payment of taxes in data rather than money.

On the participatory side, policy makers should consider not just supply-side strategies, such as (re)training workers, but demand-side measures as well. One is to give firms a tax credit for each job they create.

In the data-driven society, three policy measures can be proposed:

  1. Making the companies that capture the profits of the data age pay the ones getting uprooted as a result of it.
  2. Ensuring that markets stay competitive and that society as whole benefits from data.
  3. Making human labor just a bit cheaper than machines.

We are all aware that many jobs provide more than money. They offer the chance for social interaction in the and afford meaning to people — important elements of our human identity.

As we transition to data-rich market, it’s only logical to go beyond just considering money when choosing work. We need a better ontology to capture the elements of a job’s bundle of benefits.

Key to the future of human works is unbundling “employment”, much as we have unbundled to the CD into individual songs and let listeners created their own evolving musical mixes. We needed to define the elements of work and make them flexible enough to be recombined.

10. Human choice

We humans are tactile creatures and we love to use our senses and engage with the world. Stitch Fix (a company sends packed clothes to your house and you can choose take them or send back, personal stylist will choose cloths for you and you can leave notes in the packages when sending them back) is superbly successful at injecting this human element into its customers’ experience. It’s only a matter of time before others embrace data-driven markets the same way.

There is yet another enemy of the data-rich markets and individual choice. It is the vision that humanity will soon overcome resource scarcity, and the belief that machines, and their seemingly infinite ability to accomplish complex tasks at low or practically no cost, will recycle the resources we have forever, essentially taking us into a true utopia.

This vision was built on a fundamental fallacy. They focus on physical resources and misunderstand that the market is not just a way to allocate scarce physical goods, but a way for humans to coordinate with each other effectively and efficiently. As humans coordinate, we face the scarcity of time.

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