Archive for the ‘Web2.0’ Category
When an employee leaves one organisation and moves to another, they are not allowed to take the property of their first employer with them. That includes lists of customers, algorithms or other intellectual property. It doesn’t, however, stop employees from taking what they’ve learnt and applying it in their new role. The rules around what is fair (and legal) have developed over many years. We are just starting to explore the same questions now with robots powered by machine learning.
It is worth a reminder on the two main types of robot. The first, and the origin of the term, are those that manipulate the world around them supporting tasks like manufacturing, cleaning and an increasingly wide range of other real world physical tasks. The second are virtual agents that mimic real world user activity online, such as filling in forms, responding to emails and conversing on chat tools. Although the conventions are still forming, online agents are generally referred to as “bots” (derived as a shortened form of robot).
A debate on the role of bots (and robots more generally) moving between organisations isn’t academic, as most robotic process automation (bots replacing people in routine, often “cut and paste”, processes) are provided by third parties through the cloud. When a bot finishes with one organisation, what does it need to leave behind and what can it take with it?
There is no doubt that the data a bot deals with belongs to the company that created it. However, bots use artificial intelligence (AI) to get constantly smarter. The question is whether this AI-powered machine learning is deemed to be a form of data that is derived from the data that supported its learning.
It would be very easy to descend into a legal debate. My intention is to focus on what the right answer is to these important questions. Lawyers, guided by business, can then direct the development of contracts that support these positions.
If a business wanted to play to its own maximum advantage, it could insist that any machine learning done on their data was only to be used for their advantage. Taken to its logical conclusion, the consequences of such an approach would extend beyond bots to learning algorithms such as search engines. Search providers would actively resist attempts to isolate the activities of individuals in particular organisations from the constantly improving results they deliver for all their users.
Even if this position were possible to enforce, it would not be in any organisation’s favour unless they were the only ones that were applying such a rule. Any economy that allows the free flow of capability is better and more productive as a result. We all benefit by sharing as the machines we deal with get smarter.
However, taken to the other extreme, a robot that learns the secret algorithm behind the pricing or apportionment of a business should not be taking that knowledge to another organisation.
The difference, of course, between machine and human learning is the recall of the former. When a machine encodes something, it has total recall. By comparison, if a human sees a list of customers and their phone numbers their accurate recall would be close to zero!
The argument in favour of limiting machine learning derived from an “employer” would be that learning is at best an analogy rather than an exact analogue. The argument against is that everyone benefits as the pool of machine “employees” improves, a little like competing employers actively working together to improve the quality of professional education.
In my view, organisations overestimate the exclusivity or differentiation of their intellectual property. I also believe that they underestimate the power of working as part of a community that grows the whole economy. The most successful organisations grow the size of their market rather than treat it as a zero sum game. That doesn’t mean that businesses don’t have secrets that provide them with unique advantages, but rather that there are few that are genuinely valuable, they expire quickly and they are generally less valuable than having access to more capable people and machines.
Bots that learn across a community of businesses can actually make the whole economy stronger, no-one needs to lose in that equation!
We live in times of rapid change when businesses that assume they have a secure market are suddenly having their world turned upside down. With the most substantive impact coming from technology, many have assumed that large investments in IT and digital would act as a protection. In fact, many of the businesses who have made the largest investments, such as some retailers, are actually the ones experiencing the greatest disruption to their operations.
It is hard to describe disruption in a meaningful way, but I like Jack Welch’s famous quote “if the rate of change on the outside exceeds the rate of change on the inside, the end is near”. A disruption index can be described in terms of the ratio of the external and internal rates of change. But, how do you measure change and the transformation within your organisation (the numerator and denominator of this ratio)?
When I was putting the finishing touches on Information-Driven Business, I had the opportunity to share an editor with Douglas W. Hubbard who wrote How to measure anything. This book is a wonderful reminder that the only limit to putting a numerical value on any business problem is our imagination! Whenever someone argues that their change, driven by transformation, is too hard to measure, I’m reminded of this book.
Not only do I think that the change associated with any transformation can be measured, I also think that the first measure you think of is unlikely to be the best. For example, customer-service focused transformations often default to net promoter score as the main measure while overhead-driven transformations frequently rely on measuring the cost or headcount taken out of the business.
These are good measures, and should play a role, but they aren’t great denominators for the disruption ratio. What we really need to measure is sustainable strategic change in an environment where the very nature of corporate strategy is changing. On the one hand, top-down one-off strategy work is making way for ongoing experimentation combined with a small number of “crossing the Rubicon” moments. On the other hand, too little focus on the Rubicon leads to worrying about horse carcasses in growing cities, something I discussed when I wrote about the difficulty of seeing past today’s problems.
Customer transformations that rely too heavily, for example, on net promoter score, lend themselves to disruption by a better offer. I’ve seen numerous organisations get customer feedback after each interaction only to find it a poor correlation to customer churn. The issues are many, but can include a metric-driven incentive for customer service agents to provide exactly what the customer wants to hear but without any realism that it can actually be delivered.
When we talk about customer loyalty, that really means a build-up of value. Really thinking about this could result in some form of balance sheet recognition. Each time there is a genuine discount to the market, a real solution to a meaningful problem or a deeply insightful interaction there is value. Similarly, the balance sheet value of employee-generated IP is as much a meaningful measure of employee satisfaction and inventiveness as any engagement score or innovation survey.
A great resource which combines employee and customer engagement is Zeynep Ton’s work on The Good Jobs Strategy. Ton’s research very nicely identifies the relationship between the cost of staff, investment in their capability and the loyalty of customers. From here can come an approach to measuring a sustainable transformation.
Like many researchers, Ton has identified that transformation is as much about what you take away as what you add. Simply targeting the creation and launch of new products ultimately destroys organisational agility and adds complexity which stymies both customer service and future innovation. Radical decommissioning is one approach, but another is to measure complexity and target its gradual reduction as I’ve previously suggested by Trading your way to IT simplicity.
Regardless of whether it is customer service, supply chain, human resources, costs or products that you are trying to transform, the challenges are similar. While the strategic goals might be easy to describe, the real work happens when you try to design measures. Rather than setting once and assuming the measure is right, constant experimentation and confirmation is essential.
The attribute of a great transformation measure is that it doesn’t just correlate with the outcome you want, it is intrinsic to it. Given the complexity of changing a business, it is very likely that these outcomes will be complex and the measures you need equally sophisticated.
An alien relying on TV for their knowledge of humanity might watch a few ads and assume our closest emotional relationships are with banks, utilities and retailers. After all, they all claim to be your best friend, look how many ads talk about “falling in love” with your service provider!
It is popular to talk about the relationship between customers and the businesses that serve them. Banks, airlines and utilities all seek to be best friends with their customers. This is probably understandable given that most of us are passionate about the businesses we work for and we want our customers to be as well.
In building such a relationship, marketers can point to great examples such as airline loyalty schemes, social media and even the account balance page of internet banking sites,. In each case, there are individuals who interact daily, even hourly, with these services and look forward to each touchpoint.
Such a strong relationship is hard for most businesses to maintain with the majority of their customers. After all, most people don’t get excited looking-up their electricity prices, mortgage rate or recent phone numbers they’ve called.
The common attribute of the businesses we care about seems to be the information they provide. Many people can’t imagine why they would care deeply about a bank, yet a small number of people check their bank account balances multiple times in a day. Anecdotally, those repeat checkers are dreaming of a saving goal which provides a halo effect for the bank.
Similarly, many travellers love to track their frequent flyer status which they see as a reward in its own right. The airlines create portals that engage their premium passengers and offer a regular sense of progress and engagement.
Uber has a fascinating screen on its app showing all the cars circling locally while eBay has nailed the search for a bargain. Some fintechs are attracting customers by creating a “fiddle factor”, letting them earn small rewards in different ways.
At the same time, it doesn’t seem that people care too much whether they love their basic services. Most people just want their savings to be safe, their lights to stay on and their phones to ring. The only problem is that in an environment where they can change providers easily, this lack of loyalty means that they are more likely to make a switch.
How can a brand that provides a capability that people need, but lacks passion, align with a brand that everyone cares about? This is the power of the API economy where it is easy for businesses to partner seamlessly.
Banks and airlines were pioneers in partnering, bringing together credit cards and air miles. Similarly, phone companies are partnering with music and movie streamers to dramatically increase engagement with their services. In coming years we can expect to see social media, fashion brands and travel businesses join with the everyday services that meet our basic needs.
To be successful, partners need to make sure they understand what elicits a strong affinity. To-date, brands have largely taken the same approach for all customers. For example, “daily-deal” style retailers are highly attractive to some customers and highly annoying to others. Basic services, such as insurance, who choose to partner with businesses like these need to be very targeted, otherwise they risk alienating as many customers as they delight. Too many marketers have made this mistake and have potentially damaged their brands.
The key to a meaningful relationship is tailoring the partnerships to offer the customer something they genuinely want to engage with. Talking to their customer community and offering them choice is a very good start, giving the winners in the race to pair more opportunities to generate genuine friendship, if not love!
Negotiation is one of the oldest human activities and is an important part of our economy. It is essential for sharing resources between people and groups. However, as our organisations have become more complex, the outcomes that we are achieving are getting worse not better.
The usual objective of negotiation is to match the needs of two parties. In business, this is often bringing together the provider of goods or services with someone who is in need of the resource or to get the holder of budget to release it for a given initiative.
Negotiations have three potential outcomes: win-win, win-lose or lose-lose. Amazingly, talking to many businesses over many years, I’ve come to the conclusion that lose-lose is increasingly the most common outcome. That is because in the absence of confidence, people opt to avoid loss and choose not to act. But in choosing not to act, they are adding friction into their businesses and missing out on the return on risk they should be taking.
As business has become more complex, so it has become much harder for buyers to know what a good outcome is. Worse, this is becoming a major cause of stress in business as both buyers and sellers have a huge amount at stake and lessening ability to navigate to a good result.
To illustrate the problem, consider this case we can all relate to. You are in the market for a car, but have limited time and need to buy now. You know the car make and model you want and know the recommended retail price. You walk into a dealer who makes an on-the-spot offer for the car that matches your requirement at 10% less than the listed price. Do you sign?
The dealer could be offering you a great deal, or they could be holding out and most customers could be getting 15% off. You just don’t know. Meanwhile, if it is the former the dealer is frustrated and has less incentive to offer a good starting price for future customers.
Independent brokers are sometimes a solution for this dilemma, having knowledge of the market and knowing which price is actually a fair one.
Few executives have any more time available than our hypothetical car buyer. Also like the car buyer, most things they are seeking to acquire are outside of their day-to-day experience.
It’s relatively easy when commodity items (like replacement parts for machinery) are needed, procurement experts can negotiate against a pricelist. However, when it’s a complex product, such as a computer system or an internal budget allocation for a new service, then there are few points of reference within the organisation.
I lamented in a past post about the lack of productivity growth resulting from our transition to a digital economy (see Where is the digital-fuelled growth?). Where new approaches to sharing knowledge takes friction out of the systems, there is a boost to productivity and this is where much of our effort should go. Negotiations are a prime candidate for this focus.
Increasingly digital solutions are allowing for the creation of anonymous or semi-anonymous benchmarks. But complex procurement and negotiations require more than simply finding a fair price. Factors at play often include risk, time, quality, the competitive landscape and so many more.
Artificial Intelligence and Robotics
The two technologies that could make a material change here are Artificial Intelligence and Robotic Process Automation.
Artificial Intelligence, or Cognitive Computing, is a form of very advanced analytics. In negotiations between parties, the primary objective is for the person who wants to acquire the resource to work out what a fair trade would look like. Even when it is just the allocation of internal budget for a new capability, there is still a need to know whether the return is commensurate.
Where these technologies come in is their capability to find things that are similar based on a wide range of criteria. For example, anyone who has seen how well search engines can group similar questions, worded completely differently, has some idea of how Cognitive Computing can bring together the right answers from disparate sources.
Where Robotic Process Automation (RPA) can be most effective is in taking the emotion out of negotiation and pushing to get the best outcome based on relative criteria. There is an argument that the political and emotional process is an important part of getting to the best possible outcome. The problem with this is that as the environment has become more complex, negotiations have become simplified on a subset of dimensions meaning that it isn’t the best argument that wins but, all too often, no argument that wins.
Most negotiations are a manual process with lots of spreadsheets and lists of points to push on and others to give on when pushed. This is exactly the sort of complex process that RPA is ideal at and makes it an ideal target for the technology.
Our robots can be programmed to provide a win for us all. While some negotiators are happiest with a win-lose, win-win puts the right incentives in the system for the long-term. If we program our robots that way we can be freed-up for the creative task of getting the right job done with the right resources.
In a future economy, where robots act on our behalf to find winning combinations, everyone could win.
Business is both complicated and structured. Our education, training and professional lives all teach us to think inside the box. Before rampant automation, and when problems sat inside the same box, this was ideal. The business world we are dealing with today needs a new approach.
It is increasingly popular to approach strategic questions using the power of games which encourage people to leave their assumptions behind. I’ve talked before about the role of games more broadly (see Turning decision making into a game).
While games are great, they still keep decision making within a frame. Games are a competitive activity within the confines of a set of rules. Every lunchtime kids launch into all manner of ball games in schoolyards around the world. Most games follow structure, build teamwork and have a win/lose outcome.
Sometimes, though, rather than play a defined game, children feel free to make up their own rules and migrate to free play.
To test this, give a group of kids a ball of any shape or size and tell them to make up a game. Watch what happens as they play and explore different approaches. Free play is really important for children to learn about the world around them. For children the world is far more full of mystery than known boundaries and rules-based learning doesn’t work until they have a better handle on their surroundings.
Many parents would know about the Reggio Emilia approach to preschool teaching. The idea of learning through exploring the world around you. Watch a child and they explore everything with an open mind.
It is interesting that modern sports went through an intense period of development in the eighteenth and nineteenth centuries with free play exploring different sets of rules that might make for great games. Largely (and there are, of course, exceptions) today’s most popular games have had stable rules for many decades.
In our world of disruption, we can argue that in many business settings the world around us is full of more mystery than known parameters. The sport of business that seemed so well defined is now up for grabs. No wonder a structured approach seems to limit us to thinking inside the box.
It is hard to find a consistent definition of play, but it does seem to be an activity conducted for pleasure, with the journey being the goal rather than any end and it is self-directed with minimal rules. It seems that play is far more important to our wellbeing than we ever realised, as described by psychiatrist Dr Stuart Brown in his TED talk.
When we’re looking for new employees, it makes sense to interview for the skills of the job they will tasked with. The trouble is that the return on the investment is unlikely to come with the first task that they complete but rather the job they will do over a number of years. Increasingly, that job hasn’t even been invented yet!
My personal view is that the characteristic that really matters in future employees is a curiosity about the world around them and a willingness to play for its own sake. In my own field of management consulting, I regard this as the renaissance consultant.
Elon Musk gets a lot of press around his intensity, but he does embody the idea of the renaissance with his wide range of interests (rockets, electric vehicles, batteries et cetera). Like Leonardo da Vinci, the best of our next generation will be interested in everything from science to music and much that goes in between.
There is a lot of discussion at the moment on the role of STEM (Science, Technology, Engineering and Maths) in education and our future workforce. Some are arguing that many STEM graduates are struggling to find work, while the reality remains that there are hundreds of thousands of jobs that can’t be filled that require these skills.
The problem isn’t with STEM, rather it is that not all STEM pathways are equal. It isn’t any one skill that is needed, but rather it is a flexibility and willingness to learn. Even more important, it is the combination of STEM foundational skills with a natural curiosity and willingness to explore.
In a world that is changing fast, none of us can assume any existing approach to our work will serve us well even into the near future. We need to be willing to play in order to find the new rules that are going to define the business answers for the coming years.
The great news is that there is a child in all of us!
We’ve had about 50 years of computing in business and about 20 years of the digital revolution. How are we faring on the question of digital fuelled growth and productivity? Many economists are coming to the surprising conclusion that technology may not be providing the boost we had expected.
This question really matters as politicians around the world are grappling with a voter backlash at disruption to industries and the promise of growth providing new jobs seems to be wearing thin. The population wants jobs but many fear that the new employment, relying on technology, are not going to be relevant to their individual skills or geography. New tech jobs are ending-up being concentrated in a few locations and requiring skills that are out-of-reach to those that have been displaced by global trends driven by digital channels.
Robert Gordon (author of The rise and fall of American Growth) splits productivity into three industrial revolutions: 1770-1840 (steam and transport), 1870-1920 (electricity, cars, city infrastructure, chemicals and working conditions) and 1970- (ICT). He argues that the second revolution provided about three times as much productivity growth as the other two. Worse, when he breaks-down the third revolution he argues that productivity growth has stagnated since early in this century.
The last part of the 20th century saw almost universal growth driven (arguably) by mass liberalisation of trade and the opening of new markets. Many assumed that technology was providing a virtuous boost. It seems that the rise of the web, digital technology and the smartphone have driven consumer demand but more economists like Gordon are questioning whether it has made the supply of that demand any more efficient.
So where has the productivity gone? I’ve argued before the IT has become too complex and expensive. In addition, we’ve lost some of the traditional ways of encouraging organisations to leverage their investments. Many of the online tools that we all use (such as search, collaboration and workflow) are fantastic but they don’t cost very much (and are often free) resulting in little governance to make sure that the benefits are realised.
Without a clear focus on realising productivity as the main aim of technology, many benefits are pleasing but of little benefit to the economy. For example, is there a real gain for the economy being able to check-in to your aircraft in half a dozen different ways? What about buying soap with a QR reader?
Ergonomics matter but much of what we implement is about gimmicks that are pleasing but don’t improve society.
That doesn’t mean that productivity growth for our economy isn’t coming, rather just that it may not be as easy or clear cut as we had expected. As we approach a new generation of robotics and artificial intelligence what do we learn? The problem is that the combination of genuine displacement of people without economic benefits mean there aren’t resources available to grow the job pool in other ways.
There have been thousands of words written about the threat of automation and I’ve previously given my view that our machines won’t outsmart us. I’ve also written about why we haven’t lost jobs yet.
We need to pivot our focus from whether jobs will be lost (they will, but new ones can be created) or whether machines will lead us into a terminator style future (they won’t), but rather how we change the trend on productivity.
The last 200 years have been amazing. Angus Maddison was an eminent economist who estimated the world’s long-term economic growth to be surprisingly small. According to Maddison’s work, from the Middle Ages through to the Industrial Revolution, the normal annual growth was less than 0.07%, far less than the numbers we assume today.
Without a change to the status quo, including new approaches to technology which unlock productivity growth, it could be that we are heading back to a world where growth is near zero. By the middle of the century, even population growth won’t help the world economy.
This is so important that it may be that there is a role for government regulation to ensure investment in technology results in productivity that is seen in the economy. It is in all of our interests to change the equation and find a way to turn our digital revolution into a new wave of productivity and wealth that everyone can share in.
One of the most exciting features of the Internet is the ability to get the voice of the crowd almost instantly. Polling of our organisations and society that would have taken weeks in the past can be done in hours or even minutes. Ideas are tested in the court of public opinion in real time. This is potentially a huge boost for participation in democracy and the running of our businesses, or is it?
Our governments and businesses have long worked with a simple leadership model. We select our leaders through some sort of process and then give them the authority to act on our behalf. In the past, we asked our leaders to report back on a regular basis and, most of the time, we left them to it. In democracies we used elections to score the success or failure of our leaders. If they did well, we gave them another term. In business, we relied on a board selected by shareholders.
This really started to change with the advent of the 24 hour news cycle. Rather than curate 30 minutes of news once a day, the TV needed to find stories to fill all of the day. Unlike newsprint which had time for analyse, speed to air was a key performance metric of reporters and an initial, even if uninformed, sound bite was enough to get something to the public.
There is a popular movement to open-up government even further with regular electronic plebiscites and a default to open data. At its core is the desire to make the machinery of government transparent to all citizens. While transparency is good, it is the consequence of having “too many cooks in the kitchen” that leads to problems. Having everyone have their say, either through direct contributions or through endless polling means that the fundamental approach to decision making has to change. While fulltime politicians have the time to get underneath the complexity of a problem, the mass of voters don’t. The result is that complex arguments get lost in one or two sentence explanations.
This is happening at exactly the time that our global societies are becoming more complex and need sophisticated responses. Issues such as migration, debt and global taxation are too complex to be boiled down to a sound bite. It is telling that few have suggested turning our judiciary over to the court of public opinion!
H. L. Mencken, a well-known journalist from the first half of the 20th century who wrote extensively on society and democracy, once said “For every complex problem there is a solution that is concise, clear, simple, and wrong.” An overly crowd oriented approach to democracy results in these simple answers which are dumbing down our decision makers.
The danger doesn’t stop at our leaders, it also extends to the running of our organisations. We work more collaboratively than ever before. Technology is enabling us to source solutions from the crowd to almost any problem. This can work brilliantly for many problems such as getting a quick view on whether a brand message is going to resonate, or if a product would appeal to a particular demographic.
Where it can let us down is when we start trying to ask too many people to provide input to complex problems. Great design, sophisticated modelling and radical new product offerings don’t lend themselves well to having a large number of people collaborate to find the answer.
Collaboration and the use of the crowd needs to be targeted to the places where it works best. This is going to be more important than ever as more people move to the “gig economy”, the movement where they use platforms like 99designs, Expert360, Topcoder or 10EQS to manage their work. The most successful organisations are going to learn what questions and problems the crowd can solve for them.
Questions that require a simple, technical answer seem to suit this form of working well. Similarly, problems that can be solved with well-defined technical solutions are well suited to handing out to a group of strangers.
The crowd either completely rejects the status quo (the crowd as a protest movement), with little to offer in terms of alternative approaches or it slightly tweaks current solutions (the crowd without depth). Even the individual sourced through the crowd seems to be unlikely to rock the boat due to a lack of context for the problem they’re trying to solve.
The way we work and solve problems as a society and in our organisations is changing. The one thing we know for sure is that we don’t yet know how this will really work!
Imagine an invention that deliberately wasted resources. Maybe a car that burns oil just to create smoke that is easy to see or an electric light that uses twice as much energy to avoid burning out. That’s exactly what blockchain is doing, consuming large amounts of electricity for no purpose other than making fraud prohibitively expensive.
I recently had the privilege of collaborating with my colleagues from the Australian Deloitte Centre for the Edge on a report looking into distributed ledgers and the blockchain technology. Reading the result, it is striking how far we still have to go to invent our digital business future.
As a quick reminder, blockchain is a technology to support the exchange of value or contracts in an environment where anonymity is important and no one is to be trusted. The best known application of blockchain is in the exchange of Bitcoins, a virtual currency.
Business models for the future
In recent years, all of the talk of digital business has been the creation of new platforms as the success stories, like Uber, Airbnb and Amazon, wield increasing power and value. Of course, platforms aren’t new, banks and credit card providers have long played this role in our financial services sector.
One of the big questions for the future of the internet is whether we want to see more platforms with trusted parties or do we assume the worst of everyone and “trust no-one”. The potential advantage of moving away from platforms is the “democratisation” of business.
Instinctively, there is a lot to like about democratising business and taking the power away from a few platforms. The problem is that such a move comes with a tremendous cost. There are good reasons why consumers tend to gravitate towards these providers who have scale, even when it might not align to their view of an ideal world.
The downside of blockchain
There are usually good reasons to be worried when any technology is over hyped and this has never been truer than with the excitement that currently surrounds blockchain. There are two fundamental challenges that are particularly worthy of highlighting:
The first is that it relies heavily on the cost of electricity and use of computing resources to protect against fraud. Don’t be fooled, blockchain can be hacked allowing fraudsters to gain access to the payload. The most common payload of blockchain, and the product with which it is synonymous, is Bitcoin. The safeguard on the payload isn’t that it can’t be defeated but rather that the cost of fraud in electricity and computing resources is higher than the payoff.
Motivating anonymous participants, “miners” to expend computing resources sits at the heart of Satoshi Nakamoto’s clever invention of blockchain. Of course, Satoshi Nakamoto is a pseudonym with the real author or authors choosing to keep their identity a secret.
Christopher Malmo, writing on the Motherboard site estimates that each Bitcoin transaction uses the same amount of electricity as 1.57 households in a 24 hour period. That is not a function of the immaturity of the technology, it is a feature that protects transactions from fraud.
The second issue facing blockchain is that far from being open, it is the ultimate closed system. While no-one takes ownership of the data, it is deliberately encrypted in such a way as to make transaction details virtually unavailable for aggregation. That means many of the advantages that platforms provide are simply unachievable using an approach such as blockchain. Some of the platform capabilities that are lost include recommendation engines, transaction aggregation and fraud detection.
Potential roles for blockchain
Despite these challenges, blockchain is an incredibly clever solution. The challenge is finding the problem that it best solves.
Faced with the issue of openness or processing overhead, some organisations exploring blockchain have looked at closed communities where there exists a level of trust between the participants. This approach will allow some of the overheads to be reduced and models to be devised to share transaction information. The problem, however, is that once there exists at least some trust in the network it is likely that a platform model will provide greater functionality at a lower cost and complexity.
The strength of the technology comes in low volume, high value environments where no-one is able to be trusted and there are complex rules. This challenge exists when managing assets of many kinds in jurisdictions where there is little trust in the integrity of government or other holders of records. A related opportunity for blockchain may also be to support the trading of new types of assets where there isn’t yet regulatory support.
Maybe the future of blockchain is as a bridging technology while a community waits for a trusted platform.
Is it just me or has the world gone mad for startups and writing software? Don’t get me wrong, I am a big fan of startups and all that they bring to the economy. However, if you read the business papers or listen to investors you’d be forgiven for thinking that they are responsible for all the great innovations of the world.
Even the definition of startups is controversial. In general, investors expect them to focus on things (usually technological) that can be massively scaled. So many businesses calling themselves startups just turn into small businesses that serve a local region with a specific service or product. And these are the lucky ones, with the vast majority just disappearing within a few short years.
One of the reasons people put a priority on startups is the observation that the Global 2000, Fortune 500, or any other listing of businesses, changes every few decades. What is seldom recognised is that for every Microsoft, Google or Apple there are hundreds of other companies in the lists that are simply mergers or spinoffs of existing businesses.
Even established companies are deciding that it is fashionable to get out of their core businesses and become software companies. Whether it is professional services, engineering or retail, there is a strong push to be more like a startup and to work like a software company.
The greatest opportunities of the 2020s are likely to emerge from some of the exciting technologies that are appearing in fields such as robotics, materials science, autonomous vehicles, machine intelligence and genetics. All of these require greater lead times and research than can be invested by the vast majority of internet startups who are trying to be the next big thing by linking communities and tagging social media.
Like the gold rushes of the nineteenth century, when professionals abandoned their vocations for the chance at quick riches, too many companies seem to be willing to abandon their core for the riches of the Internet. The reality is that the majority of these ventures will have the same experience as their nineteenth century forebears.
What the best businesses are doing is looking again at their “core”, that is what makes them unique. For these businesses, it isn’t about turning themselves into software companies, rather it is about understanding their strengths and then using these to contribute to the evolution of key technologies.
If a company is brilliant at engineering, it is unlikely that they will translate into being a social media business but they can invent brilliant new products in these new fields using their existing capabilities that will appeal to a new generation of clients. Real advances come from building on each business’s core rather than turning their back on what made them great and moving to the new cool thing.
Startups play an important role in our economies as they come up with step-change business ideas in sectors that are resistant to evolution or competition. However, if all innovation incentives are directed to this sector, the economy begins to resemble a roulette wheel. The high failure rate of startups is a feature not a problem as they take on risk that established business wouldn’t consider. However, the bulk of wealth isn’t generated by the high risk/high return nature of startup gambles.
Small business is the bedrock of employment in most economies. The policies that will support the development of solid small to medium businesses are very different to those that are needed by genuine startups. Similarly, large long-term investments are best made by big businesses that require very different government incentives again.
We risk repeating the turn-of-the-century dot com bubble. Government policies and investors are leaning towards quick wins and looking for internet startups to take their money. Yet, the bubble always bursts and only a tiny fraction of these businesses, or their ideas, survive. The vast majority of the growth, wealth and advancement of society will come through the success and innovation of existing companies.
Let’s think about the big wins for society in both new technologies and support for jobs. Let’s then balance our policies to encourage the right mixture of software focused startups, small business jobs and big business investment in future technologies. Let’s also encourage a culture where each builds on their core rather than trying to be something that they’re not.
It’s almost impossible to live these days without a plethora of digital identities that enable us to do almost everything. Whether it be our television, gaming, social media, travel or family security, we depend on all of these things to make our lives work effectively.
Pretty quickly our homes have become as complex as almost any business of just a few years ago! Gone are the days when the most complex device in the home was the hi-fi system.
At the same time, the boundary between work and home has almost disappeared and a fragmented personal digital profile flows through to inefficiencies across our personal and professional lives.
While it might be tempting, few people have the luxury of starting their digital lives from scratch. We all have a technical legacy born of our past digital activities across technologies, family relationships and past jobs. No matter how disorganised, fragmented and out-of-control your digital life is, it is never too late to bring it back into order.
The cost of not taking stock leaves you open to security risks, complexity, fragmentation and the loss of opportunity to live the integrated promise of technology. Increasingly this means even more complexity in the relationship between our work lives and our personal technology.
Over future posts I will look at a number of aspects of our digital lives. In this instalment, I’ll tackle some of the foundations that should be put in place to bring our digital world to order.
The foundation email address
You sit at the centre of a number of circles: family, friends and work. There are a large number of systems and information that you share across all of these groups.
At the centre of your circles is an administrative email address. This email has the attribute of being the last resort for password recovery and other core account activities. It isn’t an address that you should share publically, you don’t want it compromised by excessive spam, for instance.
You could make this email address a product of your Internet Service Provider, but it is better to pick an independent and free service. The more independent of the services that you are going to use in the future, the better.
Search the internet for comparisons of the free email account services and you’ll get a range of articles comparing the benefits of each of the providers. Now is also a good time to pick a foundation name for your digital world. It isn’t necessary for this to be meaningful and it certainly shouldn’t be one that you expect your contacts to be using.
Social media identity
Next you need to have some sort of presence on the major social media sites. Privacy settings can be as tight as you want, but the purpose of these is to act as a common login credential. See Login with social media.
Social media is also the main place to manage groupings which we will talk about in future posts. These groupings come in three categories.
The first are your dependants who don’t control their own online presence, typically your children (or potentially elderly relatives). If they are under age you will create some sort of presence (but not a social media account).
The second group are those you are most closely associated with, such as your spouse or adult children. You will be inviting them to your networks but they will be in control of their own credentials.
Your third group are your very close relatives and friends with whom you regularly share content. Keep this to your immediate contacts but the techniques you use here are going to broaden out to be also your work groups that you enter and leave.
Finally, you need a password management tool. Today’s cloud services are poorly integrated and lack consistent identity management. This is a real opportunity for improvement on the Internet, hence the push towards using social media as a tool of integration. However, the goal should be that your architecture is independent of individual services and should last the distance.
There are a number of very good tools out there, just search for password management tools and compare the benefits of each. The important thing is to have a cloud-based solution that is easy to use across devices.
Having a consistent email as a foundation for managing other accounts, social media for signing-in, a defined network of relationships and a tool for managing all of the accounts you work with will set the foundation for your digital life. When I next write on this topic, we’ll build on this foundation to start describing a complete architecture for our digital lives.
TODAY: Sun, April 30, 2017April2017