The current retail model is a constructed environment and shopping a learnt experience. This model is a response to the creation of mass market products and supply chains.
The model is build on there pillars: customers identifying a need, searching for a solution to the need, and then transacting with a merchant that they may not know or trust. Money – cash – facilitates this, as it enables us to transact with someone we don’t know and may never meet again.
However, a number of trends we saw in FoEV suggest that this model might be breaking down. The mid-market dies, consumers seized control of the customer-merchant relationship, peers replaced brands, value is now defined by the consumer rather than the producer, payments are moving away from the till, and shopping is becoming increasingly impulse driven.
What will retail look like in a world where need is never fully formed, search is irrelevant, and transactions are seen as distasteful? What is the new trust architecture?
See what you think of the presentation and feel free ping us if you have any thoughts.
The two reports mentioned in the presentation are:
I’ve been watching the Bitcoin scaling debate with some amusement, given that my technical background is in distributed AI and operational simulation (with some VR for good measure). Repeatedly explaining blockchain’s limitations to colleagues has worn thin so I’ve posted a survey of the various scaling approaches on the Deloitte blog,1)Peter Evans-Greenwood (5 May 2016), Blockchain performance might always suck, but that’s not a problem, Deloitte Australia blog. Available at <http://blog.deloitte.com.au/greendot/2016/05/05/blockchain-performance-sucks-not-problem/> pointing out why they won’t deliver – either separately or together – the 10,000 time improvement everyone is wishing for, and why this is not a problem. This post is the short version, one not intended for the general audience of the Deloitte blog has.
In the FoEV we looked at cryptocurrencies and Bitcoin, however we had to set aside a discussion on the technologies that underpin cryptocurrencies and their broader affect on society as the report was already quite long.
With Bitcoin, Blockchain & distributed ledger we pick up where we left off, and take close look at the opportunities and problems created by these technologies, and their regulatory implications.
We didn’t want this report to be yet-another explainer, since there’s already a lot of those out there, with the ensuing arguments over technology that invariably seem to follow them.
So rather than focus on the technology – the solution space – we focus on the potential applications – the problem space, to try and understand not just what is possible, but what is practical. This is resulted in a fail compact and pragmatic report focused on a few key areas, as we point out in the report’s introduction.
In From Bitcoin to Distributed Ledgers, we compare the Bitcoin’s ledger with the more familiar physical ledgers that preceded it, and develop the concept of a distributed ledger7 de ned in terms of the problems solved rather than the technologies used.
In A map of the distributed ledger landscape, we identify questions that should be asked when considering a new distributed ledger, creating a map of the solution landscape.
In Regulation, we explore the potential regulatory implications of these solutions, though we only focus on what is different with distributed ledgers. How does one regulate something no single person or organisation is accountable for?
In Applications, we review the strengths and weaknesses identi ed in the previous two sections to develop an understanding of what a distributed ledger can be and what it can’t be.
Finally, in Conclusions, we look at the technology’s potential and what the future might hold.
Computers are at the heart of the economy, and coding is at the heart of computers. Australia’s prosperity depends on equipping the next generation with the skills they need to thrive in this environment, but does this mean that we need to teach everyone how to code? Coding has a proud role in digital technology’s past, but is it an essential skill in the future? Our relationship with technology is evolving and coding, while still important, is just one of the many new skills that will be required.
Bitcoin’s ledger is protected by an indirect consensus process. Rather than voting on which ledger is correct, with Bitcoin we prefer the ledger (the version of the truth) that has contains the most “embedded work”, as this should be the ledger with the support of the largest proportion of the mining community.
Bitcoin’s definition – its consensus process (protocol in geek, the whole transaction definition, proof-of-work thing) – is protected via a similar mechanism. Miners are free to adopt any version of the consensus process they chose; big blocks, small blocks, etc. We should also remember that there is no restriction on who can offer up a version; they don’t need to be from the “core team” or other blessed group of individuals.
Consequently Bitcoin governance – just like the state of the ledger – is based on the consensus of the miners. This is quite different from the governance models we’re used to in industry or government. It’s also a long way from the traditional open source world.
What we’re seeing is a bunch of high profile individuals getting in knots as they realise that they don’t have any real control over Bitcoin, which is working as designed.
This report started life as a followup to a report we published in 2012. As we say in the current report:
Our findings in that report centred on the realisation that we were reaching the end of the initial build-out
of a digital payments infrastructure. The task of provisioning the infrastructure merchants require to accept real-time digital payments, or for two individuals to settle a debt, was largely complete. Consequently, our focus had shifted to streamlining the buying journey – from the pieces and parts to the whole.
Our key point then was that the future of exchanging value would be shaped by social forces – how payments fit into the end-to-end consumer experience – rather than the technological challenge of deploying yet-another generation of payments solutions.
This new report, which was intended to be a short update, when in an entirely different and much more interesting direction.
Our key insight this time is that we’re all thinking about money the wrong way.
It’s common to assume that we use money (cash, currency…) to build trust relationships. This assumes that our adoption of money stems from the coincidence of wants. I need shoes. You have shoes. You want a fish. I have a chicken. We use money to bridge the gap.
The problem is that this assumption is incorrect. As David Graeber points out in Debt: The First 5,000 Years, debt came before barter and the coincidence of wants. Most folk in antiquity didn’t need money. They knew everyone they interacted with, and could rely on the community to enforce the collection of a debt if need be. Money’s first use was as a measure of value, typically to help calculate damages in a criminal or civil manner. Communities had carefully drawn up lists to capture exactly what you owed, in a convenient currency, someone if you destroyed their house, stole their food. In Somalia, for example, they use camels (commodity money). The other uses of money – as a medium of exchange and store of value – came later.
This is a fascinating fact, is it points out that we have the consumer-merchant relationship backward. We’re focusing on the transaction when we should be focusing on the relationship. The future of payments is not micropayments and tap-and-go. Indeed, the future of payments might be to use a loyalty scheme (a complimentary currency) to anchor the relationship and then move the transactions from the centre of the relationship to the edge. This ties is cultural preferences that we have, and which equate money and transactions as “dirty”. The future of payments might be not to have payments at all.
Bitcoin and the whole cryptocurrency thing is influenced by this too. There’s a huge amount of noise in this area at the moment, and everyone one is waiting for the killer app that will drive Bitcoin (or another cryptocurrency) into mass adoption. If, however, you view Bitcoin adoption as a cultural problem, rather than the search for a killer app, then you end up at the conclusion that no cryptocurrency will become much more than a large niche. The best equivalent in the current environment that we’re all familiar with would be a large frequent flyer scheme. It’s hard to scale trust, even with technology support, and these frequent flyer schemes seem to up near a nature limit.
There is one use case for currencies growing larger, though: when a sovereign nation mandates that you pay taxes in a specific currency. This trick is behind all the major currencies, and was used by the colonial powers to pull conquered land into their monetary system. Acquire currency to pay tax, or we send the bruisers around.
We conclude in the report that the best analogy for cryptocurrencies is rum and cigarettes. Rum was used in Australia’s early days when there wasn’t enough government issued currency to go around. Cigarettes were used by prisoners or war as they had few other options.
We can expect cryptocurrencies to see some adoption in countries where the population doesn’t trust – or can’t access – the national currency. Argentina springs to mind. Cryptocurrencies are mush less useful in other countries with mature and stable economies.
A similar argument can be made against cryptocurrencies as internal reserve currencies. (And that argument is in the report.)
I have a new post up on the Deloitte Strategy blog.It’s the result of a chat I was having the other day with an economist colleague who opined that “platforms are an essential part of the sharing economy”.
As I point out in the post:
These platforms might be sufficient to kick-start the sharing economy, but they’re not necessary for its long term survival. There are alternative approaches to creating sharing economy solutions that do not rely on a centralised platform.
Platforms solve what we might call the discovery problem. When we’re creating a market it needs a mechanism for buyers and sellers to discover each other.
Rendezvous – where buyers and sellers meet at a common location – is probably the most common solution to discover. It’s also the one that firms prefer as it’s the easiest to monetise.
As I point out later in the post:
The recent emergence of blockchain – a distributed ledger solution – from the shadow of Bitcoin might be a sign that something has changed in the environment, something that is tipping the advantage away from centralised solutions and toward distributed ones.
This could be a big deal, as it blows a rather large hole in the business models of the sharing economy firms.
The following are the notes I pulled together for the first panel in ADC‘s Future Summit on Monday September 28th.
The major opportunity for Australia is to find and exploit new production systems and consumption models that are cheaper, simpler and more “digital” than the highly entailed product-creating systems that are the legacy of the previous industrial era. We also need to see this as socially driven change, rather than a technologically driven change.
Two quick examples of this in action.
There’s a lot of talk at the moment around self driving and electric cars. Tesla has built an expensive but unprofitable electric car on the back of over USD 4 billion of government grants, while Mercedes, Google et al are out there with prototypes for self-driving cars that look like a technoutopian’s fevered dream.
In the case of Tesla, on the production side, the firm is better thought as the ultimate expression of an industry structure established roughly 100 years ago by Henry Ford; but it might not be an exemplar of how we will build cars in the future. A better example of where car manufacturing might go is iStream by Gordon Murray Design in the UK. iStream is a new production process, one based on established and well understood technology, but which removes 80% of the cost from the factory, slashing the cost of cars in the process. The production process Ford, Toyota et al are using needs 150k cars from a single model to be profitable, which means that Austrlia was lucky to have an old skool car industry for as long as we did. iStream is profitable on 12,000 cars, and would be commercial viable in Australia.
On the consumption side, viewing self-driving cars simply as autonomous versions of manually operated cars ignores changes in consumption patterns where consumers are preferring to consume many products as (value-added) services (think Spotify et al). The car equivalent is Flexicar or GoGet (car-by-the-hour).
If we put the two of these together it’s possible to imagine a new public transport model based on cheap and flexible, locally built and supported, autonomous cars. Some of the cars might be contributed by the government. Some by private operators (Flexicar et al). Some might be from individuals who are contributing their cars to the common pool when they don’t need them (during the week when they work, or when on holidays) via something like Uber.
Second, a local example: the transformation of the building industry.
Building mid-rise buildings—office blocks, hotels, apartment buildings, &c.—is currently a craft-based process. Design a building, create holding company, buy land, put together consortium, get funding from bank(s), and then go onsite and incrementally add value to the land by hammering in nails, pouring cement, running wires etc. There’s a lot of talk about new technologies “disrupting” building, such as 3D printing. This is unlikely. Buildings are complex structures with many interwoven parts. You might be able to 3D print a wall, but you still need to integrate the services, render it &c. While these new technology might make elements of the process more efficient, they’re incremental improvement at best.
Enter Unitised Building (UB), based in Melbourne. UB have created a new production process that enables them to build a mid-rise building in a fraction of the time at less than half the cost. A good example is 3:East, built in 11 days. UB takes a complete 3D model of the building—including services &c.—and uses digital tools to split the building into a number of units (the model has been “unitised”). A second layer of digital tools takes that unit models and splits out the files required by CNC machines. The units are built in a factory and then transported to the site where they are lifted into place (one every 8 minutes) and snapped together. The only requirement is that you need a crane on-site, which, practically, means that the UB approach is dramatically faster and cheaper once you hit 3 floors (and need a crane regardless).
What UB have done is create a new process that moves the complexity of building from the physical world to the digital world. Indeed, their CNC requirements are quite light and they need few machines, so their factory (in Brooklyn, in Melbourne) has a very small footprint by manufacturing standards. They’re even exporting by finding contract manufacturing facilities overseas and transmitting the digital files to the CNC machines in the remote factory.
Creating these sorts of system changes has a couple of problems.
First, the old industry / sector structures we use to frame regulation and government support make no sense in this new world, as these new solutions span industry sector boundaries and have different requirements. (Supporting manufacturing, for example, has traditionally been a question of ensuring that the manufacturers have lots of land, but the new generation coming through don’t need much land, while they do need access to lots of network bandwidth.) This miss-match between the demands of the new and how government frames public policy makes it difficult for the two to engage.
In the case of UB, two of their challenges have been getting the banks to fund buildings when the current building risk model (based on incremental value creation on-site and quantity surveying) doesn’t match their building process, and the challenge of accessing government support when they don’t fit in any particular sector/industry (Are they a builder? Or a manufacturer?). These new firms span sectors / industries, deliver products as services, and do a bunch of other things that don’t fit with the old industry models. If we’re to frame policy and regulation for the future then we need to set aside the old industry/sector-based view of the world. Fundamentally, we need to stop muddling through as incrementalism won’t fix this problem. There are signs of change though, such as UB winning this year’s “Victorian Large Manufacturer of the Year” award.
Second, we need to acknowledge the these innovations are not the result of light-bulb moments or heroic individuals—they’re the product of trial-and-error and collaboration. By definition, they’re a social process. There’s a tendency—particularly among the technology crowd—to frame the debate in terms of technological determinism. Or, put another way, futurism has a technological blindspot. Just because we invented nuclear reactors doesn’t mean that we’ll have one in every home, or every car. No technology has ever survive contact with society intact.
We need to acknowledge that while the shape of society will change in response to technology (just look at what the modern smart phone is doing to our sense of identity!), society will, in turn, shape the technology it adopts (note that many people now find phone calls rude as they interrupt the recipient, whereas messaging is async).
The current obsession with disruption is a case in point. (And first we must acknowledge that Clayton Christensen’s “disruption theory” is looking less like a theory and more like an interesting idea.) There’s cries that we should let these disruptors usher in the brave new world by allowing them to skirt existing regulation. This assumes that all regulation is bad, or the more nuanced version, that techniques such crowd sourced recommendations are superior to regulation in many instances (why have certification when you can have ratings?) This point of view ignores the fact that regulations are one of the tools we use to encode what we see as the socially acceptable uses of technology. Nuclear power is a really cool technology, but do we want people driving around with small nuclear reactors under their bonnets?
With regard to Uber, and the taxi industry, it’s worthwhile considering the following:
allowing taxi licenses to be transferable and limiting their number was probably a mistake, however
we provide taxi vouchers to pensioners, partly to to encourage them not to drive, and partly to help them stay mobile and engaged with society: should we compel (i.e. regulate) Uber et al to accept taxi vouchers, or will we allow the death of the taxi industry to disenfranchise these pensioners?
Uber separates the payment from the provision of the service, and some parents are using this as an opportunity to give their under 18 (even under 13) kids Uber accounts so that they can get themselves home from school &c. rather than need mum or dad to pick them up: does this mean than we need to compel (i.e. regulate) all Uber drivers to have Working with Children checks?
It’s best to think about three types of policy:
Enablers, what do we need to put in place to enable the society we want. One of the biggest boosts to start-ups in Silicon Valley, for example, was Obama Care, as it means that individuals in startups could now access affordable health care. We undervalue policies such as Medicare and HECS as tools to enable as many people in society as possible to engage in the trial-and-error innovation process.
Drivers, how can we encourage new developments / ideas that create new value, given that government has a poor record of picking winners? This comes down to how do we use policy support the demand-side to help society to pull in the technology it wants in the way it wants. Admitting that we will regulate driver services, and we will require these services to accept taxi vouchers, and their drivers to have working with children checks, are good examples, as is the policy in Tasmania to provide interest free loans to individuals who want to by bespoke products from makers. Germany’s high feed-in tariffs for solar are another example.
Barriers, where do we draw the line? Do we want nuclear reactors in cars? Do we want full-timeAustralian for-hire drivers earning under the minimum wage?
There is a lot of opportunity out there for everyone and Australia, as one of the most voracious adopters or technology in the world, is in a position to capitalise on these opportunities. However, we need to accept that we’re seeing with “digital disruption” is the leading edge of a massive social change, rather than a technological change. The future will not be determined by the disruptors. It will determined by how we, as a society, choose to engage with this change.
Tyler Cowen has an article over at MIT Technology Review, Measured and Unequal, that discusses how improved measurement of workers might be a fundamental driver of inequality in the workplace of the future.
Consider journalism. In the “good old days,” no one knew how many people were reading an article like this one, or an individual columnist. Today a digital media company knows exactly how many people are reading which articles for how long, and also whether they click through to other links. The exactness and the transparency offered by information technology allow us to measure value fairly precisely.
The result is that many journalists turn out to be not so valuable at all. Their wages fall or they lose their jobs, while the superstar journalists attract more Web traffic and become their own global brands. Some even start their own media companies, as did Nate Silver at FiveThirtyEight and Ezra Klein at Vox. In this case better measurement boosts income inequality more or less permanently.
The assumption behind this sort of piecework measurement is that all the value realised by an article is due to the sweat and toil of a more-talented-than-usual journalist. If your article gets the clicks, then it must be because you are so good at what you do.
Unfortunately the world is not so simple.
We might choose to build our organisations around this sort of idea (and indeed, BuzzFeed et al work this way) but it tends to foster a short term and overly transactional view of work that ignores a lot of the value that workers, or a community of workers might create.
The first problem is the obsessive focus on outputs, on the assumption that the worker is responsible for all the value created. Outputs depend on inputs, and not just the worker’s skills. You can’t make a silk purse out of a sow’s ear, as the saying goes.
While the worker might be skilled, their work is also dependant on the quality of the materials they have to work with. Take the journalism example. A manager somewhere is splitting up the work, either by handing out the story ideas or by allocating topics to individuals. Not all ideas or topics are equal. It’s possible for someone to come from outside this system by finding a new approach—as Nate Silver did with a data-drivern approach—but that’s the exception rather than the rule. It’s more typical for the quality of the value of the outputs to be bound by the quality of the inputs, not the effort of the individual.
We see something similar in sales. It’s easy to sell in a rising market, and a booming market will see many sales people getting large commissions for no reason other than turning up. In a down market, though, its a different story, and we punish some of our best people for working hard just to bring anything into the business.
If we want to reward individuals based on their contribution then we need to quantify the amount of value they added, rather than the amount of value they lucked into. If we don’t then we’ll create a feeding frenzy for the juicy bits of work, while other less attractive (but possibly no less important in the overall scheme of things) get ignored.
Unfortunately it’s surprisingly difficult to measure value-add for many workers as it can be challenging to gauge the quality of the materials that they have to work with. A good example of this are the efforts in the US to measure teachers on the value they add in the class room, efforts which are struggling as it seems nearly impossible to objectively measure the quality of the students that they have to work with. There’s just too many variables.
Second is the problem of cumulative advantage. Success typically brings more success for no other reason than you were successful. Consider the opportunities created when you win an Oscar. The Oscars are an annual competition, so they’re awarded even if the year’s releases aren’t particularly good (such as if there’s a writers strike during most of the past year).
It doesn’t matter how you win the Oscar—either by creating great art and a big box office success, or simply be being the best of a bad lot—the attention that the Oscars garners you brings you to the attention of the world and the opportunities start flowing in. This improves the quality of the materials you can choose to work with. You might break the VW emissions story due to dumb luck, but it results in more story ideas flowing your way. You might not be the best journalist, you might not even be the journalist best positioned to make the most of the idea, but the idea is yours none the less.
Entire careers are built on the back of a lucky break followed by cumulative advantage. While this is good for the few lucky individuals, it’s not so good for the firm as it means that the firm might not be making the most of the materials at its command (though picking winners does make it easier for management). Nor is it much good for the equally talented individuals who weren’t quite so lucky.
Third is the problem of context. It’s rare, these days, to work in isolation. The context we’re in provides us with resources and connections that we couldn’t get elsewhere, or even just a boss that we can work with. While we might thrive in one environment, we struggle in others. One good example is star analysts, who often struggle when they leave the firm where they built their reputation. Some of that value in the outputs created might be the result of a productive work culture or effective management structure and team, factors that are the result of the everyone’s contributions, and not just the contributions the individual creating the deliverable.
Mr Cowen’s problem is that he has mistaken ease for cost. It’s cheaper than ever to measure all sorts of factors associated with work. At the same time, work has evolved making it hard know what to measure. While it might be cheap to generate all sorts of stats on worker activity, it’s not easy to tie these back to productivity.1)Aside, that is, for work situations which are explicitly configured as piece work, such as Uber drivers.
The root cause of this a recent shift (possibly sometime around 2005) from value being defined by the producer, to being defined by the consumer. The emergence of the consumer internet put the consumer in control as it enabled the consumer to have more information on a product than the merchant or producer, and the ability to source the product from any merchant around the globe. This was followed by the more recent emergence of social media, enabling consumers to turn to their peer, rather then brands.
Value used to be defined in terms of product features and functions, and we could measure a worker’s productivity by their contribution to creating these features and functions. Frederick Taylor started the trend by measuring how long it took for a man to unload a cart. The modern version is the basis of Mr Cowen’s article: counting the number and reach of articles carrying a byline, or worker surveillance where everything a worker types at a computer, everything they do is logged, recorded, and measured.
Value today is defined by a customer’s relationship to a product. Value is relative and shifting because it is a function of an expanding choice space for consumers. While all your workers contribute to creating this value, it’s not always obvious how to quantify their contribution.Their contribution might also be different for each customer, as relative value means that each customer could possibly conceive value differently.
Any retailer who heads down the omnichannel path, for example, needs to deal with the challenging of aligning a salesforce measured on their sales with a strategy that has sales skipping across multiple channels and contact points as the customer learns about the firm, develops their own understanding of what value is created, and winds their way to a decision. When you consider this it’s not surprising the Apple’s stores (some of the most profitable in the world) are not measured on sales, and fall under the marketing budget.
In the mean time we have many firms racing to quantify and optimise individual tasks that their workers undertake. This might drive improvements in a short term and overly instrumentalist definition of productivity, and result in a few lucky individuals receiving large pay checks. In the longer term the same strategy is destroying the value created for the customer, and possibly taking the firm’s future with it.
We used to be defined by what we knew. But today, knowing too much can be a liability.
Google, for example, is putting its trust in (potentially uncredentialled) “capable generalists” rather than “experts”.1)Laszlo Bock, Google’s Vice-President of People Operations, at The Economist’s Ideas Economy: Innovation Forum on March 28th 2013 in Berkeley, California. https://www.youtube.com/watch?v=wBRJ01NNKj8 Expertise still matters for narrowly focused highly-technical roles but Google has found that in most instances a capable generalist will arrive at the same solution as an expert, while in some cases they will come up with a new solution that is superior to those proposed by the experts.
Expertise, and being an expert, implies having the hard-won knowledge and skills that make you a reliable judge of what is best or wisest to do. It’s an inherently backwards-looking concept, ascribing value to individuals based on their ability to accumulate experience and then generalise from it, taking generic solutions that have worked in the past and applying them to specific problems encountered today.
This is an approach that worked well in the past when knowledge and skills were expensive and difficult to acquire, and the problems we tackled later in our career were similar to those encountered at the start. Society has spent centuries reorganising work and dividing it into ever more narrowly defined specialisations to enable individuals to focus on, and develop expertise in, specific jobs.
Take the case of the Brunels in the 1800s: Marc, who built the first tunnel under the Thames,2)Marc Brunel was, in the early 1800s, the engineer responsible for the first tunnel to be dug under a substantial river. and his son, Isambard, creator of the Great Britain.3)Isambard Brunel built the SS Great Britain, in the late 1800s, the longest ship in the world at her time and the first iron steamer with a screw propeller. Both Marc and Isambard roamed across architecture, and civil and mechanical engineering, designing everything from buildings and manufacturing processes through railways to steam engines and ships, covering most of the technologies we associate with the industrial revolution.
Overtime all these technologies became increasing complicated and entailed, requiring you to acquire more and more knowledge and skills before you could be productive and contribute your own ideas and findings. The ground covered by the two Brunels has been divided into a range of highly specialised disciplines, each with their own narrowly defined education and credentialing process.
Digital technology, however, is changing our relationship with knowledge and, consequently, with expertise. The pithy version of this is “it’s not what you know, it’s what you can google”. By allowing us to easily capture and transmit knowledge, and by providing new means of communicating with our peers, the growth of digital technology is tipping the balance of power from narrowly defined expertise to more broadly defined capability. Knowledge is available on demand via online resources and social media while skills are being captured in software packages, shifting what used to be stocks to flows.4)The shift from stocks to flows @ PEG The generalist is no longer at a disadvantage to the specialist, as most (if not all) specialist knowledge and skills are available on-demand.
I heard a nice example of this a while ago when I was listening to Film Buff’s Forecast5)Film Buff’s Forecast @ RRR. The show was interviewing a director who also lectured at a local university. The director opined that the current graduating class had a lot more sophisticated understand of film, and were more sophisticated in their approach to their work, than he and his class were back in the early seventies. In his view this wasn’t because they current class were inherently smarter. It was because the majority of their time at university was invested in exploring the possibilities provided by film as a medium, and developing an understanding of what they might do within the medium. This is in contrast to the director’s class back in the early seventies, when the majority of a student’s time was spent finding, accessing, and internalising knowledge stocks.
The example the director gave was of a student being directed to some technique that Alfred Hitchcock used.6)Unfortunately I don’t remember which technique was mentioned. Back in the seventies this would have implied many afternoons spent in the stacks at the library looking for film criticism that discussed the technique, so that the student could develop an understanding of it and know in which films the best (and worst) examples could be seen, followed by a search of the rep theatres to find screenings of key films.
That same understanding can be obtained via an afternoon on the couch browsing the internet with the following day spent streaming films from Netflix.
Today we invest our time exploring the problem we’re trying to solve, and the context we’re solving it in, rather pouring most of our effort into finding the information we need.
We’re also increasingly finding ourselves asked to solve new problems, create new products and services, and, in some cases, even rethink how entire industries and sectors of the economy work. This is what we commonly refer to as digital disruption, even though that term fails to capture the full extent of the social change that is bearing down on us.
Take the construction industry for example. Technology has been used to streamline or automate many tasks making today’s construction industry a different beast to the construction industry of our grandparents, but it is still an industry that adheres to a fundamental craft-based paradigm, with skilled trades people working onsite to create bespoke buildings.
A range of technological and social changes are about to transform the construction industry from a craft-based paradigm to a flexible-manufacturing paradigm, skipping over the traditional industrial paradigm in the process.
My favourite example of this is Unitised Building7)http://www.unitisedbuilding.com who have developed a new construction process (as opposed to a technology) that enables them to construct a mid-rise building in a fraction of the time and with a fraction of the money, of a traditional approach. This building system is completely digitised, with the building design in 3D modelling tools before the design is broken down and sent to numerically controlled machines for part fabrication and assembly on the shop floor. Assembled modules are trucked to the construction site where one is lifted into place every eight minutes after which the various connectors snapped together and gaps plastered over. A process that took months now takes weeks and the cost is shafted in the process.
The shift from craft to flexible manufacturing has a dramatic impact on the skills required from the workforce, moving from deep expertise in building to general design, digital modelling and construction skills. The focus has shifted from needing people who can work within the established building system (people with deep expertise who can generalise experience and then apply these general solutions to specific problems) to people who can work to develop and improve a new building system (people with broader skills who can find new problems to be solves, and solutions to these new problems).
A similar trend can been seen across all sectors. We’re moving from working in the system that is a business, to working on the system. The consequence of this is that its becoming more important to have the general capabilities and breadth of experience that enable us to develop and improve the system in novel directions, than it is to have deep, highly entailed experience in working within the current system. There will always be a need for narrowly focused expertise in highly technical areas, but in the majority of cases the generalist now has an advantage over the specialist.
This raises an interesting conundrum. While you might not need to know as much as you did in the past, it’s not clear just how much you do need to know now. This is a particular problem for educators and firms as they want to arm the individuals under their care with the knowledge and skills required to be successful in the workplace. Teaching too little means that the individual will not be effective at what they do. Teaching too much implies that we are wasting the individual’s time (and money, in many cases).
Focusing on understanding how much to teach might be asking the wrong question though. In many cases the only person who can judge how much knowledge is enough will be the individual, as “how much is enough” will be determined by the problem that they are trying to solve and the context that they are trying to solve it in.
We need to break down the problem a bit more if we’re to understand what question we should be asking.
First, we do know that you need enough knowledge to be dangerous; to be conversant in the domain, to be able to understand and describe the problem, and to be able to interact and discuss what you are doing with the others who you are collaborating or working with. That film director mentioned above needs to be able to understand the criticism that they are reading, knowing the key concepts, technical terms and idioms that form the language of film. Similarly for our flexible-manufacturing building system, where you would need to understand the basic language of building, digital design, and flexible manufacturing if you expect to be productive and contribute.
Second, we need to equip the individual with the tools they need to manage their own knowledge and their access to knowledge. If the only person who can determine how much knowledge is enough is the individual, then we need to empower them by providing them with the tools they need to manage knowledge for themselves.
This can be further broken down into the following.
You need to understand limits of your current knowledge (or, put another way, you need to know when to go looking for new knowledge). This may be as simple of coming across new terms and concepts that you don’t understand, through to having the sensitivity to realise that your lack of progress in a task is due to the knowledge (the ideas and skills) that you’re applying being insufficient, and you need to find a new approach that is based on different knowledge.
You need to be aware of what additional knowledge you might draw on, so that you can you can reach out and pull it in as needed. This is a process of eliminating the unknown unknowns: reading blogs, going to conferences, participating in communities of practice, and even having conversations at the water cooled, so that your aware of the other ideas out there in the community, and other other individuals who are working in related areas. You can only draw on new knowledge if you’re aware that it exists, which means you must invest some time in scanning the environment around you for new ideas and fellow travellers.
You also need the habits of mind – the attitudes and behaviours – that lead you to reach out when you realise that you’re knowledge isn’t up to the task as had, explore the various ideas that you’re aware of (or use this awareness to discover new ideas), and then pull in and learn the knowledge required.
Finally, you need to be working in a context where all this is possible. To many work environments are setup in a way that prevents individuals from either investing time in exploring what is going on around them (and eliminating unknown unknowns), taking time out from the day-to-day to learn what they need to learn on-demand, or from taking what they’ve learnt and doing something different (deviating from the defined, approved and rewarded process).
So question we asked at the start of this post – How much do you need to know? – is clearly the wrong question to be asking.
Rather than focus trying to know (or teach) everything that might be relevant (the old competence model) we need to move up a level, focusing on metacognition. This means providing people with the tools needed to manage knowledge their own: fostering the sensitivity required to know when knowledge and skills have run out, creating time and space so that they can invest in their own knowledge management, and encouraging the habits of mind that mean that they have the ability and attitude to do something about it.
Image: Isambard Kingdom Brunel preparing the launch of ‘The Great Eastern by Robert Howlett