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Community
Kindle Notes & Highlights
by
Salim Ismail
Throughout human history, communities started off as geographically based (tribes), became ideological (e.g., religions) and then transitioned into civic administrations (monarchies and nation-states). Today, however, the Internet is producing trait-based communities that share intent, belief, resources, preferences, needs, risks and other characteristics, none of which depend on physical proximity.
As Silicon Valley visionary Bill Joy famously said, “The smartest people in the world don’t work for you.” For ExOs, their external focus is such that their communities of hundreds and thousands, along with crowds of millions and, ultimately, billions, become extensions of the companies themselves.
Why Important? Dependencies or Prerequisites • Increase loyalty to ExO • Drives exponential growth • Validates new ideas, and learning • Allows agility and rapid implementation • Amplifies ideation • MTP • Engagement • Authentic and transparent leadership • Low threshold to participate • P2P value creation Algorithms
Recently, McKinsey estimated that of the seven hundred end-to-end bank processes (opening an account or getting a car loan, for example), about half can be fully automated. Computers are increasingly performing more and more complex tasks. There is even a marketplace called Algorithmia, where companies are matched with algorithms that can potentially make sense of their data.
In particular, there are two types of algorithms that are at the frontier of this new world: Machine Learning and Deep Learning.
Machine Learning is the ability to accurately perform new, unseen tasks, built on known properties learned from training or historic data, and based on prediction. Key open source examples include Hadoop and Cloudera. An illustration of Machine Learning comes via Netflix, which in 2006 set out to improve its movie recommendations.
Deep Learning is a new and exciting subset of Machine Learning based on neural net technology. It allows a machine to discover new patterns without being exposed to any historical or training data. Leading startups in this space are DeepMind,
Then consider that the Computer Sciences Corporation believes that by 2020 we’ll have created a total 73.5 zettabytes of data—in Stephen Hawking’s phraseology, that’s seventy-three followed by twenty-one zeros.
Michael Chui notes that many successful companies today had Big Data in their DNA. We believe it’s just the beginning, and that many more algorithm-focused ExOs will pop up in the coming years, harnessing what Yuri van Geest calls the 5P benefits of big data: productivity, prevention, participation, personalization and prediction.
To implement algorithms, ExOs need to follow four steps: Gather: The algorithmic process starts with harnessing data, which is gathered via sensors or humans, or imported from public datasets. Organize: The next step is to organize the data, a process known as ETL (extract, transform and load). Apply: Once the data is accessible, machine learning tools such as Hadoop and Pivotal, or even (open source) deep learning algorithms like DeepMind, Vicarious and SkyMind, extract insights, identify trends and tune new algorithms. Expose: The final step is exposing the data, as if it were an open
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Why Important? Dependencies or Prerequisites • Allows fully scalable products & services • Leverages connected devices and sensors • Lower error rate stabilizes growth • Easily updated • Machine or Deep Learning techniques • Cultural acceptance
Leveraged Assets
The notion of renting, sharing or leveraging assets—as opposed to owning them—has taken many forms throughout history. In the business world, leasing everything from buildings to machinery has been used as a common practice to shift assets from the balance sheet. And while not owning assets has been standard practice for heavy machinery and non-mission-critical functions (e.g., copiers) for decades, recently there’s been an accelerating trend towards outsourcing even mission-critical assets.
the underlying reason isn’t financial; instead, the driving force is the scarcity of mission-critical resources involved, or that it’s so new that it’s now fully fleshed out. The information age now enables Apple and other companies to access physical assets anytime and anywhere, rather than requiring that they actually possess them. Technology enables organizations to easily share and scale assets not only locally, but also globally, and without boundaries. As we noted earlier, the launch of Amazon Web Services in March 2006 was a key inflection point in the rise of ExOs. The ability to lease
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A new Silicon Valley phenomenon called TechShop is another example of this trend. In the same way that gyms use a membership model to aggregate expensive exercise machinery that few could afford to have at home, TechShop collects expensive manufacturing machinery and offers subscribers a small monthly fee ($125 to $175, depending on the location) for unlimited access to its assets.
As with Staff on Demand, ExOs retain their flexibility precisely by not owning assets, even in strategic areas. This practice optimizes flexibility and allows the enterprise to scale incredibly quickly as it obviates the need for staff to manage those assets. Just as Waze piggybacked off its users’ smartphones, Uber, Lyft, BlaBlaCar and Sidecar leverage under-utilized cars. (If you own a car, it sits empty about 93 percent of the time.)
Non-ownership, then, is the key to owning the future—except, of course, when it comes to scarce resources and assets. As noted above, Tesla owns its own factories and Amazon its own warehouses. When the asset in question is rare or extremely scarce, then ownership is a better option. But if your asset is information-based or commoditized at all, then accessing is better than possessing. Why Important? Dependencies or Prerequisites • Allows scalable products • Lowers marginal cost of supply • Removes having to manage assets • Increases agility • Abundance or easily available assets • Interfaces
Engagement
User engagement techniques, such as sweepstakes, quizzes, coupons, airline miles and loyalty cards have been around for a long time. But in the last few years, such techniques have been fully information-enabled, elaborated and socialized. Engagement is comprised of digital reputation systems, games and incentive prizes, and provides the opportunity for virtuous, positive feedback loops—which in turn allows for faster growth due to more innovative ideas and customer and community loyalty. Companies like Google, Airbnb, Uber, eBay, Yelp, GitHub and Twitter all leverage different engagement
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For the Millennial generation, gaming is a way of life. Today, more than seven hundred million people around the world play online games—159 million in the U.S. alone—and most play for more than an hour each day. The average young person racks up more than 10,000 hours of gaming by the age of twenty-one. That’s almost exactly as much time as kids spend in the classroom throughout middle school and high school. Gaming isn’t just something that young people do, it is a large part of what and who they are.
Key Takeaways Exponential Organizations have a Massive Transformative Purpose (MTP) Brands will start morphing into MTPs ExOs scale outside their organizational boundaries by leveraging or accessing people, assets and platforms to maximize flexibility, speed, agility and learning. ExOs leverage five externalities (SCALE) to achieve performance gains: Staff on Demand Community & Crowd Algorithms Leveraged Assets Engagement
to process all the inputs. As a result, Exponential Organizations are far more than how they appear to the outside world, or how they behave with customers, communities and other stakeholders. They also have distinctly different internal operations that encompass everything from their business philosophies to how employees interact with one another, how they measure their performance (and what they value in that performance), and even their attitudes toward risk—in fact, especially their attitudes toward risk.
Interfaces
Interfaces are filtering and matching processes by which ExOs bridge from SCALE externalities to internal IDEAS control frameworks. They are algorithms and automated workflows that route the output of SCALE externalities to the right people at the right time internally. In many cases, these processes start out manual and gradually become automated around the edges. Eventually, however, they became self-provisioning platforms that enable the ExO to scale. A classic example is Google’s AdWords, which is now a multi-billion dollar business within Google. A key to its scalability is
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The table below shows some ExOs and their interfaces: Uber Interface: Driver selection Description: System to allow users to find and choose drivers Internal Usage: Algorithm matches best/closest driver to user location SCALE Attribute: Algorithm Kaggle Interface: Leaderboard rankings Description: Real-time scoreboard that shows the current rankings of a contest Internal Usage: Aggregate and compare results of all users in a contest SCALE Attribute: Engagement Interface: User scanning Description: System to scan for relevant users for private contests Internal Usage: Cherry-pick the best users
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One final way to think about Interfaces is that they help manage abundance. While most processes are optimized around scarcity and efficiency, SCALE elements generate large result sets, meaning Interfaces are geared towards filtering and matching.
Why Important? Dependencies or Prerequisites • Filter external abundance into internal value • Bridge between external growth drivers and internal stabilizing factors • Automation allows scalability • Standardized processes to enable automation • Scalable externalities • Algorithms (in most
Dashboards
Given the huge amounts of data from customers and employees becoming available, ExOs need a new way to measure and manage the organization: a real-time, adaptable dashboard with all essential company and employee metrics, accessible to everyone in the organization.
many ExOs are adopting the Objectives and Key Results (OKR) method. Invented at Intel by CEO Andy Grove and brought to Google by venture capitalist John Doerr in 1999, OKR tracks individual, team and company goals and outcomes in an open and transparent way. In High Output Management, Grove’s highly regarded manual, he introduced OKRs as the answer to two simple questions: Where do I want to go? (Objectives) How will I know I’m getting there? (Key Results to ensure progress is made)
In operation, an OKR program, as its name suggests, operates along two tracks. An Objective, for example, might be to “Increase sales by 25 percent,” along with “Form two strategic partnerships” and “Run AdWords campaign” as the desired Key Results. OKRs are about focus, simplicity, short(er) feedback cycles, and openness. As a result, insights and improvements are easier to see and implement. In contrast, complexity, secrecy and broad goals tend to impede progress, often leading to unintended consequences. As Larry Keeley, president and co-founder of the innovation strategy firm Doblin Group,
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Some characteristics of OKRs: KPIs are determined top-down, while OKRs are determined bottom-up. Objectives are the dream; Key Results are the success criteria (i.e., a way to measure incremental progress towards the objective). Objectives are qualitative and Key Results are quantitative. OKRs are not the same as employee evaluations. OKRs are about the company’s goals and how each employee contributes to those goals. Performance evaluations—which are entirely about evaluating how an employee performed in a given period—are independent of OKRs. Objectives are ambitious and should feel
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ExOs have more than taken this technique to heart. Many are now implementing high-frequency OKRs—that is, a target per week, month or quarter for ...
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Why are dashboards key for ExOs? Because growing at a rapid pace requires that instrumentation of the business, individual and team assessments be integrated and carried out in real time, not least because small mistakes can grow very big very fast. Without both functions in place, a company is liable to drift back to its earlier focus on “vanity” metrics and lose attention or have misguided KPIs for teams. Or both.
As mentioned at the start of the chapter, tight control frameworks are critical to managing hyper growth. Real time dashboards and OKRs are key elements of that control framework. Why Important? Dependencies or Prerequisites • Track critical growth drivers in real time • OKRs create control framework to manage fast growth • Minimize exposure from errors because of short feedback loops • Real time metrics tracked, gathered and analyzed • OKRs implemented • Cultural acceptance by employees Experimentation
Mark Zuckerberg agrees, noting, “The biggest risk is not taking any risk.” Constant experimentation and process iteration are now the only ways to reduce risk.
Large numbers of bottom-up ideas, properly filtered, always trump top-down thinking, no matter the industry or organization. Seely
This technique is popularly known as the Lean Startup movement, which was created by Eric Ries and Steve Blank and is based on Ries’s book of the same name. The Lean Startup philosophy (also known as the Lean Launchpad) is in turn based upon Toyota’s “lean manufacturing” principles, first established a half-century ago, in which the elimination of wasteful processes is paramount. (Sample principle: “Eliminate all expenses with any goal other than the creation of value for the end customer.”)
The Lean Startup concept was also given impetus by Steve Blank’s book, The Four Steps to the Epiphany, which focuses on customer development. (Sample concept: “We don’t know what the customer wants until assumptions are validated.”) The most important message of the Lean Startup movement is to “Fail fast and fail often, while eliminating waste.” Its approach can be summarized as a new, scientific, data-driven, iterative, and highly customer-driven approach to practical innovation that is used by startups, mid-market companies, corporations and even governments. To
As Eric Ries explains, “The modern rule of competition is whoever learns fastest, wins.” Most digital markets are winner-takes-all markets due to network effects. This makes a culture of continuous experimentation even more important.
Nathan Furr and Jeff Dyer state in their new book, The Innovator’s Method: Bringing the Lean Start-up into Your Organization: “Don’t try to scale it until you nail it.” Why Important? Dependencies or Prerequisites • Keeps processes aligned with rapidly changing externalities • Maximizes value capture • Faster to market (MVP) • Risk taking provides an edge and faster learning • Measurement and tracking of experiments • Cultural acceptance (failure=experience)
Autonomy
We describe Autonomy as self-organizing, multi-disciplinary teams operating with decentralized authority.
Autonomy is a prerequisite for permissionless innovation.
In fact, the company is confident enough in its way of doing business that its employee manual is open sourced and available to anyone, including competitors. Valve isn’t alone in
Without Holacracy With Holacracy Central control and authority Distributed control and authority Predict and plan for long term Dynamic and flexible: changes can and are constantly occuring Hierarchic structure OR flat, based on consensus Neither, as everyone is the ‘highest authority’ in their own role and ‘follower’ of other roles Interest oriented Core goal oriented Tension as a problem Tension as fuel Reorganization and change management Natural development, evolution and movement Job titles Dynamic roles Heroic leaders, employees and process supervisors Vital people who fulfill their role
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Other companies that have implemented autonomous structures include W. L. Gore & Associates, Southwest Airlines, Patagonia, Semler, AES, Buurtzorg and Springer.
University of Michigan economist Scott Page found that diverse teams are more successful at answering complex questions than are homogenous groups or individuals, even if the homogenous groups and individuals are more talented. His
Harvard professor Rosabeth Moss Kanter puts it best: “When dealing with a rapidly changing environment and the fluid boundaries of business units that come and go, more work will be done by crosscutting project teams, and there will be more bottom-up self-organizing.”
Why Important? Dependencies or Prerequisites • Increased agility • More accountability at customer face • Faster reaction and learning times • Better morale • MTP (as a gravity well) • Self-starting employees • Dashboards
