Revolution

I’ve used the word “revolution” a couple of times so far, both “scientific” and “industrial”. However, what do I mean by revolution? To understand what is a “revolution” we need to examine the interplay of change, competition and inertia.

Figure 26 illustrates an example of an activity that is evolving to ultimately become a component of higher order activities. I’ve marked onto the map an inertia barrier to change for suppliers of products and three separate stages of competition.

Figure 26 – Change, Competition and Inertia

Now, let us examine these three separate stages of competition. 

In stage 1, competition is between suppliers of products with constant feature improvement. Whilst disruptive change and new entrants do occur (e.g. a new component of the value chain appears and former products are substituted), the majority of change is gradual and sustaining of those competing companies. It is a time of high margin, increasing understanding of customer needs, the introduction of rental services and relative competition i.e. a jostle for position between giant competitors. 

Because of success, inertia to change builds up within those giants whilst the activity itself continues to evolve becoming more widespread, better understood and declining in differential value. In the latter stages customers can even start to question whether they are getting a fair benefit for what they are paying but overall, this is a time of Peace in that industrial ecosystem.

In stage 2, the successful activity has now become commonplace and "well understood". It is now suitable for more commodity or utility provision. It is suitable for industrialisation and the appearance of these more "linear" solutions i.e. the volume operations of good enough through standard interfaces. However, the existing giants have inertia to this change and so it is new entrants that are not encumbered by pre-existing business models that introduce the more commodity form. These new entrants may include former consumers who have gained enough experience to know that this activity should be provided in a different way along with the skills to do it. 

This more commodity form (especially utility services) is often dismissed by most existing customers and suppliers of products who have their own inertia to change. Customers see it as lacking what they need and not fitting in with their norms of operating. However, new customers appear and fairly rapidly the benefits of high rates of agility, innovation (as in genesis of new higher order activities) and efficiency spread. Novel practices and norms of operating also co-evolve and spread. 

Customers who were once dismissive start to trial out the services, pressure mounts for adoption. A trickle rapidly becomes a flood. Past giants who have been lulled into a sense of gradual change by the previous peaceful stage of competition see an exodus. Those same customers who were only recently telling these past giants that they wouldn’t adopt these services, that it didn’t fit their needs and that they needed more tailored offerings like the old products have adapted to the new world.

The new entrants are rapidly becoming the new titans. The former giants have old models that are dying and little stake in this future world. There is little time left to act. The cost to build equivalent services at scale to compete against the new titans is rapidly becoming prohibitive. Many past giants now face disruption and failure. Unable to invest, they often seek to reduce costs in order to return profitability to the former levels they experienced in the peace stage of competition. Their decline accelerates.

This stage of competition is where disruptive change exceeds sustaining, it has become a fight for survival and it is a time of War with many corporate casualties. This period of rapid change is know as a punctuated equilibrium.

In stage 3, the activity that is now provided by commodity components has enabled new higher order activities and things that were economically unfeasible a short while before now spread rapidly. Nuts and bolt beget machines. Electricity beget Television.

These new activities are by definition novel and uncertain. Whilst they are a gamble and we can’t predict what will happen, they are also potential sources of future wealth. Capital rapidly flows into these new activities. An explosion of growth in new activities and new sources of data occurs. The rate of genesis appears breathtaking. For an average gas lamp lighter there is suddenly electric lights, radio, television, teletyping, telephones, fridges and all manner of wondrous devices in a short time span. 

There’s also disruption as past ways of operating are substituted – gas lamps to electric lights. These changes are often indirect and difficult to predict, for example those that are caused by reduced barriers to entry. The fear that the changes in the previous stage of war (where past giants fail) will cause mass unemployment often lessens because the new industries (built upon the new activities we could not have predicted) will form. 

Despite the maelstrom it is generally a time of marvel and of amazement at new technological progress. Within this smorgasbord of technological delights, the new giants are being established. They will take these new activities into the peace phase of competition. It is a time of Wonder, growth and of bountiful creation of the novel and new.

This pattern of peace, war and wonder repeats throughout history whenever activities evolve to become commodity components of other higher order systems. 

Sometimes these changes are localized and the impacts are only felt by those industries that contain the activity as part of their value chains. In other cases the change is much broader impacting the entire economy because the activity is common to many value chains e.g. nuts and bolt, electricity, computing resources.

Since organizations consist of many value chains each with a multitude of evolving components then most large organisations can find themselves simultaneously in all three states. Hence, on one hand the provision of some activities will be relatively peaceful with known suppliers in a state of fierce but relative competition of continual improvement e.g. competition around tablet devices, Samsung vs Apple.

Whilst at the same time, other activities will be in a state of war with disruption, changing practices and a fight for survival against new entrants e.g. competition around computing infrastructure with new entrants such as Amazon EC2. Further still, other activities will be in a state of wonder, with rapid creation, uncertainty and potentially new sources of future value e.g. big data systems.

In many cases these activities are linked through value chains for example the explosion of big data systems is a direct result of commoditization of aspects of IT through systems such as Amazon EC2.

As with the changing in characteristics as activities evolve from chaotic to linear, the strategic games a company should play change with the state of competition. 

The macro economic pattern - Ages

These states of war, wonder and peace can be seen at a macro economic scale depending upon how widespread the activity or groups of activities that are undergoing transformation are. We commonly call these macro economic cycles Ages.

These ages are not initiated by the genesis of some new activity but always the commoditization of a pre-existing activity to components of higher order systems. For example, the Age of Electricity was not caused by the introduction of electrical power which occurred with the Parthian Battery (sometime before 400 AD) but instead utility provision of A/C electricity with Westinghouse, almost 1500 years later.

The Mechanical Age was not caused by the introduction of the screw by Archimedes but by the commoditization of standard mechanical components through systems such as Maudslay’s screw cutting lathe. The Age of the Internet did not involve the introduction of the first means of mass communication such as the Town Crier but instead the commoditization of the means of mass communication.

Whilst born out of commoditisation, each of these Ages is centered on a major cluster of "innovations" (i.e. genesis of new activities) that are built in the time of Wonder from the components delivered in the last War. Each “innovation” then undergoes a cycle of incremental improvement until reaching a plateau of diminishing returns with widespread diffusion of the new paradigms (the time of peace).  Inertia to change builds up, the next War is started and the next Age begins

The existence of these ages or super cycles of economic development was first proposed in 1925 by Nikolai Kondratiev and are given the name Kondratiev or K-Waves. More recently Carlotta Perez has characterized these K-Waves in around technological and economic paradigm shifts. For example the Industrial Revolution included factory production, mechanization, transportation and development of local networks whereas the Age of Oil and Mass Production included standardization of products, economies of scale, synthetic materials, centralization and national power systems. 

In figure 27, I’ve annotated onto Carlotta Perez’s graph of technology transformation the stages of war, wonder and peace. 

Figure 27 – War, Wonder, Peace and Technology Waves

The same transformation is occurring today with cloud computing. The “cloud” represents the shift of a range of IT activities that are widespread in many value chains from products to utility services. As such those effected industries have moved from a stage of peace to war and these utility services have enabled a rapid growth of new higher order systems.  Cloud computing is the beginning of a new time of wonder, and as an economy we are entering a new age. 

Before leaving this section, a final few comments are worth mentioning.

On Evolution and Organisations

As each age follows a war and the commoditization of pre-existing activities, each age is also associated with a new set of practices that have co-evolved with those activities.

This often appears in the form of new organisations. For example the Mechanical Age involved the appearance of the American System of Engineering. The Age of Electricity involved the appearance of Fordism. The Age of the Internet led to the Web 2.0 and Cloud Computing is itself creating new forms of organization. In later sections we will examine these new forms, however for the time being it’s enough to note the association.

On Evolution and Time

The path of evolution can be graphed over ubiquity and certainty and it is not a time-based sequence i.e. the speed at which things evolve is not constant. For example, the nut and bolt took 2000 years to evolve to a commodity, electricity about 1500 years whereas computing infrastructure took about 65 years.

Whilst each age has a time of wonder where we see an explosion in the genesis of activities (the novel and new), it is often asked whether we are becoming more innovative as a species? Certainly the systems we build today are higher order than the past and certainly each age appears wondrous and magical to previous ages but I’ve yet to find any evidence that the rate of genesis has varied i.e. is the current day any more magical than the time of wonder associated with the Age of Electricity?

However, what is clearly happening is the speed of evolution i.e. the time taken for a novel activity to become a commodity has accelerated and this appears mainly to do with increased communication especially as means of communication become more commoditized. This is not a new phenomenon.

For example, on the 1st May 1840 a revolution in communication was started by the introduction of the Penny Black. This simple postage stamp caused a dramatic explosion in written communication from 76 million letters sent in 1839 to 350 million by 1850. It wasn't a case that postal services didn't exist before but the Penny Black turned the act of posting a letter into a more standard, well-defined and ultimately ubiquitous activity.

The introduction caused a spate of copycat services throughout the world, with the US introducing their first stamps in 1847. The 125 million pieces of post sent through their system in that year mushroomed to 4 billion by 1890. From stamps to street letter boxes (1858) to the pony express, railway deliveries (1862), money order and even international money orders by 1869. A vast array of new activities were created that quickly spread.

However, the lasting effect appears to have been that as the speed of communication accelerated the rate of evolution of other activities correspondingly accelerated. The printing press, postage stamps, telephony and the Internet have all accelerated the general rate of evolution of all other activities by increasing communication and participation.

So whilst, we as a species may not have become more “innovative”, the speed at which new activities evolve and new ages begin certainly appears to be accelerating.

On Evolution, Energy, Entropy and Vulnerability

The constant snake like progress of our economy through those Kondratiev waves of the industrial age, the age of steam, the age of electricity each with its own time of wonder, peace and war is driving us further up the value chain with higher order systems continuously evolving from chaotic to linear (see figure 28).

Figure 28 – Evolution and Entropy

We are continuously moving away from a disordered, primitive and information poor position to a highly ordered, sophisticated and information rich position. Now ignoring the fact that we waste energy in abundance, the shift to a more highly ordered position means decreasing entropy and hence always requires more energy than the previous position. Even if we could eliminate waste, our economic progress would continually demand more energy.

However, there is another consequence. As we move to a higher order environment, we are not only dependent upon the lower order systems but they become increasingly less visible. This exposes us to all forms of new vulnerabilities as our complex environments depend upon these underlying components and we make assumptions of their availability. For example, the solar storm of 1859 known as the Carrington event had fairly minimal impacts on the society of its day. A similar storm today would impact many of those invisible, taken for granted, lower order subsystems that our society relies upon for its supply chains, production and computing. It would have a far greater impact.

However, this dependency and potential doom also walks hand in hand with our salvation. It is those very same higher order systems that are exposed to these vulnerabilities that enable us to identify and potentially negate threats whether it’s the Apophis asteroid, detection of solar storms or exceeding the carrying capacity of world agriculture etc.

Evolution is the constant progress towards higher ordered systems that create new capabilities, consume more energy, exposes new vulnerabilities and protects against threats.

On Prediction and Disruption

The final comment I wish to add is on the commonly perceived random nature of “innovation” and disruption. The genesis of novel activities is chaotic and uncertain. These can’t be predicted with any degree of accuracy, any more than the gas lamp lighter could have foreseen the creation of television and the broadcast industry. Since the activity is new we generally don’t describe this as disruption as there is nothing to disrupt.

The substitution of one product with another product because of some changed characteristic is also extremely difficult (though not impossible) to predict. An example of this would be the substitution of gas lamps with electric lights or one format of hard drives with another. In this case, pre-existing suppliers are often dismissive of the change that will subsequently disrupt them. They are caught flat-footed by the change in the market because they focus too much on existing needs. This is what is commonly known as the Innovator’s dilemma.

However, the evolution of an activity from product to commodity and utility services is entirely predictable. For example, the changes as a result of cloud computing were first described forty years ago in Douglas Parkhill’s book on the challenge of the computer utility. Key to identifying when this will happen is how commonplace and well defined the activity is.

Nevertheless existing suppliers are still disrupted by this change despite it being entirely predictable because of the inertia they have due to past success. It is important to understand that unlike the classic case of disruptive change (Christensen) where the market moves in an unexpected way, this form of disruption because it is entirely visible should be prevented. In the case of cloud computing this has been clearly signposted for over a decade.

Whilst people claim this is an example of the innovator’s dilemma, this is not the same precisely because the change is not unexpected. The role of the CEO is to see this clearly visible storms coming and move the organization out of its path. Disruption in this instance is simply a matter of failure in corporate strategy and is entirely preventable.

In the next section, we will revisit that first Fotango map and we will use all we have covered to explain its meanings.

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Post 12 of 200

Next post in series ... Revisiting that First Map.

Previous post in series ... Inertia

Beginning of series ... There must be some way out of here

Inertia

In the previous section, I discussed the importance of interfaces for activities that commoditize and become components of higher order systems e.g. standard electricity supply, standard units of currency. There is a significant cost associated with changing these interfaces due to the upheaval caused to all the higher order systems that are built upon it it e.g. changing standards in electrical supply impacts all the devices which use it. This cost creates resistance to the change.

A similar cost also occurs with practices when they co-evolve with activities. From the earlier example on computing infrastructure, if the consumers of large powerful servers had developed estates of applications based upon the practices of scale-up and N+1, then as the activity evolved to more utility services those consumers would incur significant costs of re-architecture of the “legacy estate” to the new world. This cost creates resistance to the change.

You also find similar effects with data or more specifically our models for understanding data. As Bernard Barber once noted even scientists exhibit varying degrees of resistance to scientific discovery. For example, the cost associated with changing the latest hypothesis on some high level scientific concept is relatively small and often with the community we see vibrant debate on such hypotheses. However changing a fundamental scientific law that is commonplace, well understood and used as a basis for higher level concepts will impact all those things built upon it (see figure 23) and hence the level of resistance is accordingly greater. 

Figure 23 – Graphical illustration of Scientific Resistance.

Such monumental changes in science often require new forms of data creating a crisis point in the community through unresolved paradoxes and things that just don’t fit our current models of understanding. In some cases, the change is so profound and the higher order impact is so significant that we even coin the phrase “a scientific revolution” to describe it.

The costs of change are always resisted and past paradigms are rarely surrendered easily – regardless of whether it is a model of understanding, a profitable activity provided as a product or a best practice of business.

As Wilfred Totter said “the mind delights in a static environment”.  Alas, this is not the world we live in.

So what makes up inertia and this resistance to change in Business? That depends upon the perspective of the individual and whether they are a consumer or supplier.

The Consumer

From the perspective of the consumer of an activity, a practice or a model of understanding that is changing then inertia tends to manifest itself in three basic forms - disruption to past norms, transition to the new and the agency of new. 

To explain this, let us consider the evolution of an activity (driven by user and supply competition) from being provided as a product to one of utility services. Examples of which could include the past shift of electricity provision or more recently the Cloud and the evolution of components of IT to utility services.

The typical concerns regarding the disruption to past norms include: -

• Changing business relationships from old suppliers to potentially new suppliers.
• A loss of in financial or physical capital through prior purchasing of a product e.g. the previous investment needs to be written off.
• A loss in political capital through making a prior decision to purchase a product e.g. “what do you mean I can now rent the billion dollar ERP system I advised the board to buy on a credit card?”
• A loss in human capital as existing skillsets and practices change e.g. server huggers.
• A threat that barriers to entry will be reduced resulting in increased competition in an industry e.g. even a small business can afford a farm of super computers.

The typical concerns regarding the transition to the new include: -

• Confusion over the new methods of providing the activity e.g. isn’t this just hosting?
• Concerns over the new suppliers as relationships are reformed including transparency, trust and security of supply.
• Cost of acquiring new skillsets as practices co-evolve e.g. designing for failure and distributed architecture.
• Cost of re-architecting existing estates which consume the activity. For example, the legacy application estates built on past best practices (such as N+1, Scale-Up) and assume past methods of provision (i.e. better hardware) and will now require re-architecting.
• Concerns over changes to governance and management.

The typical concerns regarding the agency of the new include: -

• Suitability of the activity for provision in this new form i.e. is the act really suitable for utility provision and volume operations?
• The lack of second sourcing options. For example, do we have choice and options? Are there multiple providers?
• The existence of pricing competition and switching between alternatives suppliers. For example, are we shifting from a competitive market of products to an environment where we are financially bound to a single supplier?
• The loss of strategic control through increased dependency on a supplier.

These risks or concerns are typical of the inertia to change we see with Cloud today. But it’s not just consumers that have inertia but also suppliers of past norms.

Suppliers of past norms.

The inertia to change of suppliers inevitably derives from past success. For example, let us focus on the part of the cloud which represents the shift of computing infrastructure from products to utility services i.e. infrastructure as a service (IaaS).

Amazon started this industry change in earnest in 2006 with the launch of Amazon EC2. As a company, Amazon had no prior business model in the sale of hosting (rental model of products) or servers (products) to consumers. As such, Amazon was not encumbered by any past business models and had no inertia to introducing this change. However, hosting companies and hardware manufacturers had significant inertia to the change caused by their past and successful business models. 

From Figure 24, the shift from product to utility services is a shift from high value to one of volume operations and declining unit value.  Hence the existing suppliers would have needed to adapt their existing and successful high margin business models to this new world in order to initiate it. 

Figure 24 – Changing from Product to Utility Services

Such a change is problematic for several reasons: -

• All the data the company has demonstrates the past success of current business models and concerns would be raised over cannibalisation of the existing business.
• The rewards and culture of the company are likely to be built on the current business model hence reinforcing internal resistance to change.
• External expectations of the financial markets are likely to reinforce continual improvement of the existing model i.e. it’s difficult to persuade shareholders and financial investors to replace a high margin and successful business with a more utility approach when that market has not yet been established. 

For the reasons above, the existing business model resists change and the more successful and established it is then the greater the resistance. This is why the change is usually initiated by those not encumbered by past success.

This resistance of existing suppliers will continue until it is abundantly clear that the past model is going to decline. However, by the time it has become abundantly clear and a decision is made, it is often to late for those past incumbents.

Why we’re often too late in making decisions to change

In the case of product competition (i.e. during the transitional phase) where competitors replicate a new feature, change is often a case of constant gradual improvement. In this environment sustaining change tends to exceed disruptive change and a method such as fast following is appropriate.

However the shift from product to utility i.e. the crossing of the boundary from transitional to linear is a significant shift. In this case, disruptive change tends to exceed sustaining.

For the existing supplier, they not only have to contend with their own inertia to change but also the   inertia their customers will have. Unfortunately, the previous model of competition (e.g. one product vs another) will lull these suppliers into a false sense of gradual change, in much the same way that our existing experience of climate change lulls us into a belief that climate change is always gradual. This is despite ample evidence that abrupt climate change has occurred repeatedly in the past, for example at the end of the Younger Dryas period, the climate of Greenland exhibited a sudden warming of +10°C within a few years.

We are as much a prisoner of past expectations of change as past norms of operating.

Hence suppliers, with pre-existing business models, will tend to view change as gradual and have resistance to the change which in turn is reinforced by existing customers (see figure 25)

Figure 25 - Inertia and Past Experience

Unfortunately for these suppliers, the shift towards utility services has significant competitive effects for customers: - 

• Increasing efficiency for provision of an activity through volume operations (economies of scale)
• A shift from capital to operational expense and payment for that which is actually consumed
• Provision of high volumes of more standard, good enough components that enables high rates of agility and genesis of new activities (componentization)
• Flight of capital to the new higher order systems that are enabled and represent the future source of value (creative destruction)

As companies compete in ecosystems with others, then as any activity evolves they will need to adapt accordingly. The above effects of increasing efficiency, agility, innovation (as in genesis of new activities) and new sources of wealth for the consumers of the more evolved activity will tend to turn a trickle into a flood as the pressure to adapt increases as more competitors adopt the change. 

In the case of Infrastructure as a Service, we see all of these effects.

• A change in industry from products to utility services initiated by a company not encumbered by a pre-existing model e.g. Amazon.
• High levels of resistance to the change by existing consumers (i.e. businesses) because of past norms of operating and existing legacy estates.
• Rapid growth in new high value activities based upon these utility services and a shift of capital towards this. Most VC’s now expect new companies to build with cloud services.
• Increased awareness of the competitive benefits from agility, rapid creation and efficiency of using the services.
• Exponential growth in Amazon EC2.

It’s the exponential growth part that catches most past suppliers out and that’s due to this expectation of gradual change due to the previous competitive stage (i.e. product vs product).

To explain this, I’ll use an analogy from a good friend of mine, Tony Fish.  Consider a big hall that can contain a million marbles. If we start with one marble and double the number of marbles each second, then the entire hall will be filled in 20 seconds. At 19 seconds, the hall will be half full. At 15 seconds only 3% of the hall, a small corner will be full.

Despite 15 seconds having passed, only a small corner of the hall is full and we could be forgiven for thinking we have plenty more time to go, certainly vastly more than the fifteen seconds it has taken to fill the small corner. We haven’t. We’ve got five seconds.

Hence for a hardware manufacturer who has sold computer products and experienced gradual change for thirty years, it is understandable how they might consider this change to utility services will also happen slowly. They will have huge inertia to the change because of past success, they may view it as just an economic blip due to a recession and their customers will often try to reinforce the past asking for more “enterprise” like services.

Worst of all, they will believe they have time to transition, to help customers gradually change, to spend the years building and planning new services and to migrate the organization over to the new models.

Alas, Amazon alone is estimated at $2bn in cloud revenue for 2012 and predicted for almost $4bn in 2013. If that growth rate continues then by 2016 they will be in excess of $30 billion in revenue. They also have rapidly growing competitors such as Google.

The cold hard reality that many existing suppliers probably don’t comprehend is that the battle will be over in three to four years and for many the time to act has already passed. Like the rapid change in climate temperature in Greenland, our past experience of change does not necessarily represent the future.

In industry, we have a long history of such rapid cycles of change and inertia is key to this. These cycles we call “revolutions” as in industrial, mechanical and the revolution of electricity. During these times, change is rapid not gradual and disruption is widespread.

In the next section we will explore these "revolutions" more after which we can take all that we have discussed and apply it to that first Fotango map.

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Post 11 of 200

Next post in series ... Revolution.

Previous post in series ... Evolution Begets Genesis Begets Evolution

Beginning of series ... There must be some way out of here

Evolution begets Genesis begets Evolution

I’ve talked about the genesis of activities (i.e. new things) and how they evolve to more of a commodity but does any relationship exist between the two? The answer is yes.

Componentisation

In the Theory of Hierarchy, Herbert Simon showed how the creation of a system is dependent upon the organisation of its subsystems. As an activity becomes commoditised and provided as ever more standardised components, it not only allows for increasing speed of implementation but also rapid change, diversity and agility of systems that are built upon it.

In other words, it’s faster to build a house with commodity components such as bricks, wooden planks and plastic pipes than it is to start from first principles with a clay pit, a clump of trees and an oil well. 

Bricks, planks and pipes along with other architectural building blocks have led to a faster rate of house building and a wider diversity of housing shapes. This is the same with electronics and every other field you care to look at. Commoditisation to standard components leads to increased agility, diversity and speed of creation for higher order systems that are built with it. 

The same phenomenon occurs in biology i.e. the rapid growth in higher organisms and the diversity of life is a function of the underlying components, standard building blocks that have evolved allowing higher orders of complexity.

This doesn't mean that change stops with the standard components. Take for example, brick making or electricity provision or the manufacture of windows, there is a still significant amount of improvement hidden behind the "standard".

However the "standard" acts as an abstraction layer to this. Just because my electricity supplier has introduced new sources of power generation (wind turbine, geothermal etc) doesn't mean I wake up one morning to find that we're moving from 240V 50Hz to something else.

If that constant operational improvement in electricity generation was not abstracted behind the standard then all the consumer electronics built upon this would need to continuously change - the entire system would either collapse in a mess or at the very least technological progress would be hampered.

It’s no different again with biology. If there weren’t underlying components from DNA to RNA messaging to transcription to translation to even basic cell structures within more complex organisms then you and I would never have appeared in the time frame.

Now as an activity evolves to a more standard, good enough commodity then to a consumer all this improvement is normally hidden behind the interface. Any changes are ultimately reflected in a better price or quality of service but the activity itself for all sense of purpose will remain as is e.g. a standard but cheaper brick or power supply or wooden plank. 

There are exceptions to this but it usually involves significant upheaval due to all the higher order systems that need to change and hence Government involvement is usually required e.g. changing electricity standards, decimalisation and the changing of currency or even simply switching from analogue to digital transmission of TV.

Hence, activities evolve to more of a commodity (i.e. linear) and those that become components act as an interface boundary between the higher order systems that consume them and operational improvement to the activity. Change can happen but it’s costly. I’ve summarised this in figure 19.

Figure 19 : Componentisation, Genesis and Evolution

Obviously not everything becomes a component of something else but IT systems often are and IT is no exception to the effects of commoditisation and componentisation. For example, the cloud represents the evolution of many IT activities from product to utility services (commoditisation) and through provision of good enough, standard components it is causing a rapid rate of development of higher order systems and activities (componentisation). 

This isn’t the only effect, there are others.

Volume Effects

In an earlier section, I mentioned Jin Chen’s Entropy Theory of Value and how as activities evolve to more of a commodity they have declining unit value (assuming no effects such as monopolies). The total value tends to be highest during the transition (i.e. product) phase.

This can often be misconstrued as meaning that when something becomes more of a commodity then total revenue declines. This is not the case because of volume effects.

In the 1850s, William Stanley Jevons observed “England's consumption of coal soared after James Watt introduced his coal-fired steam engine, which greatly improved the efficiency of Thomas Newcomen's earlier design"

In other words by increasing the efficiency and hence reducing the cost for provision of an activity (in this case steam power), a large number of new activities which might have once not been economically feasible became economically feasible. This led to an increase in the consumption of the underlying subsystem i.e. coal.

In general, as an activity becomes more of a commodity it can increase in total volume of units produced through a number of routes, including: -

1. There existed an unmet demand for the activity in the market that a lower cost enabled e.g. general price elasticity.
2. As it becomes a standard component it enables the rapid generation of new higher order activities (and industries) that consume the component e.g. transistors giving rise to calculators, computers and a wide variety of electronic devices which all consume transistors.
3. A lower cost of providing or a more efficient use of a component activity enables new consuming activities to become economically feasible e.g. more efficient steam engines cause more coal consumption.

These volume effects above can also be recursive throughout the value chain e.g. evolution of a higher order system to a standard component of other more higher order systems will also lead to increased consumption of any lower order system. 

For example, in 1947 Raytheon introduced the first commercially available microwave oven (the “Radarange”) at an equivalent price today in excess of $50K. As the microwave became more of a commodity and also integrated into other components (today, microwaves are a common component of modern), this has caused a growth in consumption of underlying subsystems – from electric power to microwave generators.

Alternatively, if we examine the last forty years of computing infrastructure then as it became more standardised and lower cost per unit (measured in price per transistor) rather than seeing a decline in revenue we have seen a growth in volume to offset any efficiency gains. Hence today, even though I can buy a million times more compute resource for a $1,000 than in the 1980s, this does not mean that IT budgets have reduced a million fold in that time. In fact, we’ve just ended up doing more stuff that consumed more compute resources. Hence whilst the differential value of computing infrastructure has declined to almost nothing, the volume of consumption has massively increased and the revenue (i.e. total IT budgets) associated with it remains reasonably constant.

Commoditization of activities to component subsystems of other activities can led to rapid increases in volume which can offset any decline in revenue per unit despite the actual component activity having little or no differential value.

To illustrate the point, let’s look at something relatively new - the iPad. The average consumer has no or little knowledge of the vast number of components that make up an iPad. Any differential value is associated with the device itself whereas the components are all, more or less, invisible.  Even the most ardent Apple watcher would be hard pressed to describe the 39 screws (of various standard types) in the iPad 2, nor which of those 39 are common with the iPad mini or alternative tablets such as the Samsung Galaxy Tab 10.1. 

There are however multiple manufacturers of these screws and the rapid increase in the volume of tablets whilst resulting in price pressures on component costs has increased the volume of components and associated revenues both directly and though secondary markets such as Alibaba.com.

There might not be a lot of differential value in small screws but there’s quite a bit of volume and revenue. Hence commoditization results in declining value associated with an activity but if it becomes a component of other higher order systems then volume effects can counter this. 

One final impact that the declining differential value caused by commoditisation has, involves the flight of capital known as creative destruction.

Creative Destruction

Capital tends to chase higher perceived value i.e. higher margin activities that are associated with future value. Hence looking at our change of characteristics from chaotic to linear (see figure 20), then it is those new activities that are moving into the transitional phase. 

Figure 20 – Perceived Value and Evolution

For example, as electricity became provided as a utility (having previously be provided as products such as the original Siemens Generators of the 1860s), the perceived value drifted towards the higher order systems i.e. those things that consumed electricity from televisions to fridges. I’ve combined figures 19 and 20 together in figure 21 to illustrate this point.

Figure 21 – Evolution, Componentisation and Perceived Value.

Equally today, in the world of computing the focus is not on provision of computing infrastructure but instead those new, higher order systems that consume it e.g. Instagram, Netflix, Big Data etc.

This flight of capital, from once industries that were perceived as “value” generating to new higher order systems that are now perceived as “value” generating is known as creative destruction . The commoditization of one set of industries to standard components (destruction of past value) enables these new industries to flourish (creation of future value). In some cases, the destruction is actual and direct, sometimes indirect. To survive this change companies have to adapt.

For example, the commoditization of electricity production from generators to modern A/C based utility services started with Tesla and Westinghouse in 1886. Componentization effects enabled the rapid growth in creation of higher order systems such as electric lighting, radio, television, consumer electronics and even computing. 

These higher order systems became the focus of future wealth generation and capital flowed into these industries whether TV manufacturers or TV broadcasters (Creation).  Electricity itself was viewed as a commodity. Past industries such as the manufacturers of electricity generators were directly affected by a loss of market as existing customers switched to utility provision (Destruction). 

However, being a component of these new higher order activities, the consumption of electricity increased rapidly as those activities themselves evolved e.g. as TV’s evolved from a novelty to commonplace then more and more electricity was consumed (Volume Effects). The manufacturers of generators had to redefine themselves either as components for utility providers or as utility providers themselves or as “backup” systems for customers with concerns over the new suppliers or they had to focus on niche areas (Adaptation)

Other past industries were indirectly affected, often suffering more damage than those who could see the change coming. These included gas lamp lighting companies who were disrupted by the diffusion of electric lighting or music halls that were impacted by television and radio.

Similar patterns to this can found throughout history. For example, more recently the commoditization of the means of mass communication brought about by the Internet has: -

• Enabled rapid generation of higher order systems from search engines to social network sites (componentization) with associated companies that were seen as the new sources of value (Creation).

• Caused direct disruption of companies that had built products that previously filled such roles from local newspapers to media to catalogues (Destruction). These companies have been forced to adapt to the visibly changing environment.

• Caused indirect disruption of companies due to replacement of their services by higher order systems or reduced barriers to entry. For example, electronic retail, booksellers, grocery and holiday booking agents. Many of these companies would not have seen the changes coming as they were indirect.

• There has been a rapid increase in the volume of data communicated (volume effects).

I’ve summarized all the effects we have talked about in this section into figure 22 using the previous example of electricity provision.

Figure 22 – Componentisation, Creative Destruction and Direct, & Indirect Change.

By now, the reader should understand that organisations consist of value chains that are comprised of multiple components all of which are evolving due to user and supply competition.  As the components evolve their characteristics change and they can enable new higher order activities to rapidly appear either extending the value chain or creating new value chains. This changes an industry dynamic through the destruction of past sources of value and flows of capital into new value generating areas that then in due course, due to competition, evolve.

Commoditisation begets the genesis of new higher order activities that then commoditise begetting the genesis of even higher order activities than then commoditise. Standard nuts and bolts beget generators beget electricity beget computing beget big data. 

Evolution begets Genesis begets Evolution. 

This cycle of change is a constant result of evolution, which itself is a constant result of user and supply competition i.e. if you don’t like change then simply get everyone to stop competing. The same goes with biology. Business, as with life, is a cycle of change.

Whilst I have talked principally about activities, the above applies to practices and data. Alas, despite its inevitability, people and organisations often act as though they don’t like change even when it is clearly visible that it’s going to happen. Which is why in the next section I'm going to look at inertia.

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Post 10 of 200

Next post in series ... Inertia

Previous post in series ... Everything Evolves

Beginning of series ... There must be some way out of here

A Pause ...

Ok, I've already done nine posts on this long journey into strategy and mapping (in total, it'll be somewhere towards 200 posts, the journey has already been mapped) and so there's an awful lot more to come.

Some of the posts I've gone back and improved the writing on, I'll publish all of this at a latter stage. However, for the time being, what goes up will stay there for the near future.

Anyway, any feedback on this so far is welcome. Is it useful? Is it making you think? Is it too damn confusing?

Oh, and apologies for the place holders to all those picking up from RSS ... dumb move on my part.

Kindest

---- Journey so far.

Post 1 : The start of my journey
http://blog.gardeviance.org/2013/01/there-must-be-some-way-out-of-here-said.html

Post 2 : The importance of maps and why we need them in business.
http://blog.gardeviance.org/2013/01/the-importance-of-maps.html

Post 3 : There's too much confusion. My quest for maps in business begins in earnest.
http://blog.gardeviance.org/2013/01/theres-too-much-confusion.html

Post 4 : Evolution, the missing link in my discovery of a means of mapping business
http://blog.gardeviance.org/2013/01/evolution.html

Post 5 : A first map. To the creation of a rudimentary map of value chains vs evolution.
http://blog.gardeviance.org/2013/01/a-first-map.html

Post 6 : The journey from chaotic to linear and how the characteristics of activities change as they evolve.
http://blog.gardeviance.org/2013/01/businessmen-they-drink-my-wine.html

Post 7 : Why one size never fits all, included survey data on the use of mixed methods - from agile to six sigma.
http://blog.gardeviance.org/2013/01/why-one-size-never-fits-all.html

Post 8 : The perils of outsourcing and business alignment.
http://blog.gardeviance.org/2013/01/of-perils-and-alignment.html

Post 9 : Evolution of Practice, Activites and Data. From Cynefin to Inertia 
http://blog.gardeviance.org/2013/01/everything-evolves.html

Everything Evolves

In the previous sections, I’ve mainly concentrated on the evolution of activities in the value chain, however value chains consist of more than this as they also include practices and data.

It turns out, that everything evolves. By everything, I mean: -

• Every activity (what we do)
• Every practice (how we do something)
• Every mental model (how we make sense of it) 

All appear to evolve from chaotic (poorly understood, rare) to more linear (well understood, commonplace) and the data I collected six years ago mapped all three to the same pattern (see figure 15).

Figure 15 – Evolution of Activities, Practices and Data

Hence: -


For activities we have the evolution from genesis to custom-built examples to products (with rental services) to commodity (with utility services). We normally refer to this as commoditisation.

For practices we have the evolution from novel to emerging to good to best practice.

For data we have the evolution from un-modelled (e.g. we don't know what the structure is and hence we tend to call it unstructured) to modelled (i.e. the data and its structure is understood). Even with scientific pursuits we have an equivalent evolution from concept to hypothesis to theory to universally accepted.

It’s important to emphasis that the process of evolution is common to all and can be graphed over the axis of ubiquity vs certainty. The process is also unavoidable because it is driven by user and supply competition i.e. a single actor (e.g. a company) cannot prevent it from happening as it is results from the interaction of all actors in a market.

Around 2009, I was also introduced to Dave Snowden’s Cynefin framework that describes the transition of practices from novel to emergent to good to best practice. The similarity in pattern appeared extremely close and Snowden’s recent and independent work also appears to be converging on a similar axis (see figure 16) and structure.

For example, 

• It discusses the shift from chaotic to ordered which is akin to the shift from chaotic to linear.
• Convergence (in terms of overall group thinking) is similar to ubiquity or how commonplace something is.
• Coherence is the degree to which any need or requirement is structured, defined and understood which is similar to certainty.

Figure 16 – From Chaotic to Complex to Ordered

Whether these two pieces of work will continue to converge is of great interest to me, because cause, correlation and data is one thing but nothing validates work more than independent discovery of common patterns. Which leads me neatly onto my next topic.

Independence and Co-Evolution

Consider the provision of an ordinary window. While a window is a standardized building commodity, the practices used to manufacture them have evolved dramatically from blast furnace and grinding to Pilkington’s float glass method. In other words, the way we make windows (the practice) has evolved but the window (the result of activity) remains roughly the same. Here we have independence of practice and activity.

However, in many cases as the activity evolves then the associated practices tend to co-evolve. For example, consider computing infrastructure. When infrastructure was primarily a product, novel architectural practices appeared for capacity planning which relied mostly on the use of more powerful machines (‘scale-up’). For system resilience we also had novel architectural practices that heavily relied on ‘n+1’ designs. Theses architectural practices were based primarily on better products (i.e. hardware) and they diffused and evolved becoming emerging, then good then best practice.

However as the activity of computing infrastructure itself evolved to become more of a commodity that is these days provided through utility services (such as Amazon EC2) then novel architectural practices appeared based not upon hardware but on software.

For capacity planning we now had the novel practice of  ‘scale-out’ (i.e. the use of large numbers of small and good enough virtual machines) that started to diffuse and evolve becoming emerging and then good practice. For resilience, the novel practice of design for failure appeared and also started to diffuse and evolve becoming emerging and then good practice.

Hence, as infrastructure has evolved, the practices of infrastructure management have also evolved. This inter-relationship of practice and activity is shown in Figure 17.

Figure 17 Co-evolution of Practice and Activity. 

This co-evolution of practice and activity can create significant inertia to change for consumers of that activity. In the case of infrastructure, if the consumers of large powerful servers had developed estates of applications based upon the practices of scale-up and N+1, then as the activity evolved to more utility services those consumers would incur significant costs of re-architecture of the “legacy estate” to the new world.

Our current way of operating often creates resistance (or inertia) to change due to the costs of changing practices (see figure 18). In many cases we attempt to circumnavigate this by applying the “old” best practice to the new world or we attempt to persuade the new world to act more like the past. Today, cloud computing is an example of this as it represents an evolution of parts of IT from product to utility services and the “legacy” is often cited as a key issue for adoption or for the creation of services which mimic past models.

Figure 18 Inertia due to co-evolution of Practice with Activity. 

By now, the reader should have some appreciation that :-

• Organisations can be described through value chains of activities, practices and data.
• All the components of that value chain are evolving and sometimes co-evolving.
• Plotting value chain vs the state of evolution can create a map of this landscape.
• As those components evolve their characteristics change which is why one size fits all techniques are ineffective.
• Our inability to see and to deal with evolution causes common problems such as the perils of outsourcing and business alignment
• The co-evolution of practice with an activity can create resistance (i.e. inertia) to change due to the costs associated with it.

In comfort to the reader, I will now tell you that we are slowly getting to the good stuff and all of this is necessary to understand it. But before we do, we have a few more hurdles, as we need to explore the relationship between genesis and evolution along with macro and micro economic effects. 

Then we should have enough to start using the map to exploit our position and explain strategy.

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Post 9 of 200

Next post in series ... Evolution Begets Genesis Begets Evolution

Previous post in series ... Of Perils and Alignment

Beginning of series ... There must be some way out of here

Of Perils and Alignment

So far we’ve explored how to create a map and how characteristics and techniques change as components evolve. Before exploring some of the wider impacts of this both in terms of macro and microeconomics along with how to exploit the map to create an advantage, I’d like to take the reader on a short detour to explain some common phenomenon we see today in business.

The two issues I wish to examine before getting us back on track are the perils of outsourcing and the issue of business alignment. 

The perils of outsourcing

Outsourcing is a global practice that is often disparaged in the popular press due to associations with excessive costs and failure.  The problems are generally not with outsourcing per se but instead what is outsourced.

The concept of outsourcing is based upon a premise that no organisation is entirely self-sufficient nor does any have unlimited resources and some work can be conducted by others at a lower cost. The organizational focus should not therefore be on the pursuit of capabilities that third parties have the skills and technology to better deliver and can provide economies of scale. 

This practice is common in all industries; the machine manufacturer doesn’t have to make its own nuts and bolts and can instead buy those from a supplier.  Anyway, that is the theory but what about the practice?

A case study research project undertaken by Lacity which examined data on the success rates of IT based outsourcing projects showed that only 50% of respondents rated their outsourced IT projects as satisfactory or above and over 29% rated dissatisfaction or below. Other studies have shown that only 5% of organisations have achieved high level benefits from outsourcing IT projects.

These failures are not just an IT phenomenon, the manufacturing industry has several high profile cases of where outsourcing component manufacture has led to excessive costs and delays. Boeing’s 787 Dreamliner is an often-quoted example. 

So what is going wrong, the premise seems sound enough?

In IT, it is not uncommon to treat entire projects as single things. For example, we will take the Fotango value chain and imagine that we had decided to outsource the development and maintenance of Fotango to a third party on the assumption that the Fotango system was a single thing and someone else could better provide it with economies of scale.

Being a consumer of these outsourced services, we’d want to ensure that we’re getting value for money and the features we require are delivered when they are expected. Hence the process of outsourcing often requires a well-defined contract for delivery based upon our desire for certainty i.e. we’re getting what we expect and paid for.

As a result both parties will tend to treat the entire activity as more linear and hence structured techniques are often applied with formal specifications and change control processes.

However, looking at the Fotango system through the lens of value chain vs evolution, we can see that whilst some components are linear (e.g. compute resource, installation of a CRM system), other components are clearly not (e.g. image manipulation system).

The more chaotic components will inevitably change due to their uncertain nature and this will incur an associated change control cost. In a review of various studies over the last decade, the most common causes of “outsourcing” failure have been cited as buyer’s unclear requirements, changing specifications and excessive costs.

However, it’s the very act of treating large-scale systems as one thing that tends to set up an unfavourable situation whereby the more linear activities are treated effectively but the more chaotic activities cause excessive costs due to change. In any resultant disagreement, the third party can also demonstrate this by showing that the costs were incurred due to client’s changing of the specification but in reality those more chaotic activities were always going to change (see figure 13). 

Figure 13 – Outsourcing, value chain and evolution

The “excessive cost” associated with these changes should be unsurprising as a more structured technique is applied to a more chaotic activity. A better approach would to subdivide the large-scale project into its components and outsource those more linear components. 

In today’s world, this is in effect happening where well defined and common components such as compute resource are “outsourced” to utility providers of compute (known as IaaS – infrastructure as a service). Or equivalently, well-defined and common systems (such as CRM) are “outsourced” to more utility providers through software as a service.

Those more chaotic activities offer no opportunity for efficiencies through volume operations because of their uncertain and changing nature and hence they are best treated on a more agile basis with either an in-house development team or a contract development shop used to working on a cost plus basis. Outsourcing itself is not an inherently ineffective way of treating IT, on the contrary it can be highly effective. However, it’s important to outsource those more linear components that are suitable to outsourcing.

On Business Alignment.

One of the more popular questions in management articles is how to maintain alignment between IT and the business and other functions of the organization such as marketing or finance. IT departments are frequently described as being “too slow” or “not innovative enough” or “unreliable and inefficient”.

In a recent Forrester study, not only were IT departments found to be considered too slow for the business but a distinction existed on what each group thought the other was measured on (see figure 14)

Figure 14 – IT and Business Development views on measurement.

The problem again appears to stem from evolution and how we treat activities, to explain why let us consider any reasonable sized company with various value chains consisting of hundreds of component activities. The component activities will all be evolving and so the organization is best viewed through the lens of a profile – i.e frequency of activities at various stages of evolution (see figure 15).

Figure 15 – Profile of an Organization

We tend to organize by type i.e. this component is more of an IT activity whereas this component is more of a Marketing activity.  As a result we create departments with each having a profile i.e. contain a spectrum of activities with characteristics varying from more chaotic to more linear.

IT, typically tends to serve other departments i.e. it provides components activities which are consumed by other groups. For example, the provision of compute resource, storage, web server and database for a Marketing micro site or a Financial reporting system.

Now, as activities evolve, new activities appear and this change happens at different rates in different industries but also for different types activities. Hence profiles change over time and the balance between chaotic and linear changes. For example, recently the focus has been on marketing innovation with social media, sentiment analysis and large-scale analytics whereas the Financial and Operational focus is more on cost, reliability and repeatability. Over a decade ago, a quite different focus existed with an emphasis on Financial and Operational innovation through process re-engineering and the introduction of global ERP whereas Marketing was slanted towards regular, reliable activities such as large scale email broadcasting and newsletters.

When a tendency towards a one size fits all approach exists, then these consuming departments will lurch from an emphasis on innovation to efficiency and back again. For a group serving multiple departments then it will constantly be pulled in both directions and should it apply a one size fits all approach it will always be out of alignment with one group i.e. “too slow” or “unreliable and inefficient”.

Today, IT is likely to be pulled towards cost control and reliability by the Finance department and toward Innovation and rapid change by the Marketing and Business development group.

The issue of alignment within these groups tends to be an artifact of the way we organize ourselves (i.e. grouping by type) interacting with the constant evolution of activities and our predisposition to one size fits all.

There are solutions to both problems but for the time being it is enough for the reader to recognize that the perils of outsourcing and alignment issues have a common cause – the inability of some organizations to deal with the impacts of evolution. Neither symptom can be alleviated without first dealing with this cause.

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Post 8 of 200

Next post in series ... Everything Evolves.

Previous post in series ... Why one size never fits all

Beginning of series ... There must be some way out of here