How ethically are people likely to act?

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What makes people act the way they do?

Is it the organisational culture around them? The people in their team? Their own values?

The 10/40/40/10 principle suggests that in any group 10% will take advantage if they see the risks as being low.

40% will go along with the group.

40% will try and figure out what the organisation is trying to achieve and do it.

10% will push for their personal beliefs and values.

This breakdown is apparently based on Lockheed Martin research, but it’s not clear whether there are any substantive studies that confirm this – it might just be a convenient rule of thumb that matches the 80/20 principle.

The point is that people are complicated – and the situation they are in will influence how they act.

For example, once we know that this is the expected breakdown, will we do the same as before?

Is it possible that awareness might cause us to change the decisions we take?

Or take a thought experiment.

Let’s say we’re in a ship that ran into trouble and everyone had to abandon ship and get into lifeboats.

We’re in the boat with six others and there is still one person in the water.

The leader says that as all of us are now in, and there is enough food and water for the group, the best option is to leave the last person in the water.

That way all of us can survive.

What would you do?

Perhaps we’d like to think that we’d stand up to the leader and insist on helping.

Or we might follow the group.

But many would be appalled at the idea of leaving one struggling person in the water.

But, when we take that principle up a few levels, to the question of a country and how it treats refugees, how do our thoughts and actions change?

What is clear is that as a species, humans are biologically and socially inclined to go with the group – for better or worse.

Which makes it all the more important for individuals to try and have a mind of their own.

How to expect the unexpected

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History is littered with examples of when well intended action has resulted in unexpected outcomes.

Steven Levitt and Stephen Dubner in Superfreakononics write about the General Hospital in Vienna, where in the 1840s, women in labour where more than three times as likely to die in hospital than at home with a midwife.

It turned out that the doctors – who were trying to save their patients – were actually transferring germs to them because they were examining them without first washing their hands.

A simple fix – washing their hands – put an end to the deaths.

Politicians are forever coming up with programmes to incentivise changes in behaviour.

Often, however, things don’t go the way they expect.

For example, subsidies for wind power around the world have, quite literally, led to a windfall for landowners who have large tracts of property in windy areas.

People who are probably already relatively wealthy have been offered guaranteed subsidies for many years to site turbines on their land.

People who don’t have access to land or utility connections face relatively higher costs.

And the poorest, who have no way of reducing their costs or investing in their own generation systems face the prospect of increased bills to pay for the subsidies for the wealthy.

This happens again and again in different situations.

Levitt and Dubner put forward examples where laws aimed at preventing discrimination result in increasing it, or laws aimed at reducing waste result in more illegal waste dumping.

And, when it comes to things like tax codes, there are entire industries devoted to figuring out and working within loopholes.

So, how can we improve the way in which we plan actions and deal with outcomes?

The systems around us are complex and any intervention is likely to work through feedback loops rather than a simple cause and effect approach.

In addition to the intended result, we need to think about what we will do if the outcome results in a windfall, a detriment or a perverse result.

In essence, we need to make sure we model more than just one scenario.

What makes an innovation likely to succeed?

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When we try and create something new, whether for a new startup or as a new product line in an existing company, what questions should we ask ourselves to increase our chances of success?

Everett Rogers, an eminent sociologist, came up with the diffusion of innovations theory that tried to show how ideas and innovations spread through societies.

He argued that there were five characteristics that people looked for when considering a new product – in essence asking themselves five questions.

1. Relative Advantage

People start by looking for relative advantage.

Is it better in some way than what we are doing now?

Does it help us do something faster or make it easier to do a complex task?

2. Compatibility

Many innovations aren’t adopted simply because they aren’t compatible with existing systems and processes.

One of the reasons Software as a Service (SAAS) appeals to people is because all you need to get started is a web browser.

There is no need to get IT involved and get permission to install new software, or worry about which operating system or hardware drivers are needed.

3. Complexity

If something is seen as too complex or too hard to do, people will be put off.

When a product needs extensive training before it can be fully used, then the chances of widespread adoption start to fall.

These days, if something needs to ship with a manual it’s probably too complicated.

4. Observability

Are the pros and cons of the innovation clear to anyone looking?

Quite often, we still work through a list of positives and negatives when considering a purchase.

Decision makers want to be able to see clearly what return they will get on an investment – a fuzzy set of benefits will probably make them nervous and less willing to commit.

5. Trialability

Can we try before we buy?

This is almost a given for most organisations now.

Very few companies have the reputation and market dominance to simply put an innovation out there and expect customers to buy it.

Most need to provide a trial or pilot period where customers can test and use the innovation before signing up for a contract.

6. Social and legal considerations

A sixth point, and one that is increasingly important, is around the social and legal aspects of the innovation.

Socially responsible products and businesses are likely to be preferred by buyers that are looking for a long term partnership.

And modern technology creates new legal challenges – for example, it’s easier to take pictures but which ones are legally acceptable to share on social networks?

Summary

In summary, if we can say yes to these six questions, we should increase our changes of succeeding with a new idea, innovation or product.

Do you have what it takes to be a hacker?

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A hacker, in the eyes of many, is a criminal – someone who breaks into computer systems and steals information or worse.

Real hackers, however, don’t see things that way.

They call criminals “crackers”. Hacking is something much more creative.

Too many of us are bound by the rules, the way things are done and have always been done.

This is why we spend time entering data into mind-numbing spreadsheets, making cold calls, going into a physical office every day and following processes laid down by administrators.

That’s business as usual.

A hack is simply a cool new way to do something.

It’s that shortcut we figured out, a piece of code that can take something that takes 2 weeks to do at the moment and gets it done in 2 minutes, the one change in our daily routine that saves us an hour of effort.

The main characteristics of a hack are that it is simple – almost obvious in retrospect, and masterful – something that bubbled up from all the expertise and work we had already put in.

It is also illicit – it doesn’t follow the rules set out by the system.

Just because managers have said something should be done in this way doesn’t mean it’s the best way to do something.

If we all accepted the status quo all the time, there would be no innovation, nothing would change.

One of the greatest “hackers” of all time, Richard Stallman, would never have built his free software and associated free software license that went on to transform the world of computing, creating a real alternative to the corporate giants – Microsoft and Apple.

Sometimes, to do something better, we have to ignore imposed rules – and act illicitly.

The problem, of course, is when this turns into illegal activity – and that’s what worries the general public.

The idea of hacking – the good way – is behind sites such as lifehacker, that provides tips and tricks for getting things done, or the concept of growth hacking – unconventional ways to acquire customers and rapidly grow a company.

In today’s technology dominated economy – innovation comes from looking at things differently and coming up with simple and masterful ideas that transcends conventional wisdom and conventional rules.

The hackers in the machine are the ones doing innovative things and creating value – whether it’s in software, business systems, marketing or operations.

Being a hacker, in essence, can make what we do more interesting.

How to set up a knowledge work space

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The 5S method is a way to organise work spaces in manufacturing – and is a core part of the concept of lean.

It uses 5 Japanese words: seiri, seiton, seiso, seiketsu and shitsuke. They can be translated as sort, set in order, shine, standardise and sustain.

It’s easy to see that having an organised workspace makes a difference in physical work.

But what about knowledge work? Can it be used there as well?

Bradley Staats, David Brunner and David Upton looked at whether lean principles could be embedded into a software company.

Staats and Upton write in the Harvard Business Review that lean projects don’t necessarily produce better quality work but do come in on time and under budget.

Knowledge work has specific aspects that make it different from manufacturing, such as uncertainty over the tasks that need doing, the fact that a lot of knowledge may be inside people’s heads as tacit understanding and that how things are done may change during the projects as requirements evolve.

In manufacturing, we usually know what we need to do, how to do it and in what order we should do it.

An assembly line designed to make cars doesn’t usually end up producing pizzas. But that seems to happen quite often in knowledge work.

So, how might we apply 5S in knowledge work?

Sort is all about removing obstacles. It means getting rid of piles of paper, books that we are never going to read and clutter that gets in the way.

The things that are in front of us or on our desks should be the things that we use every day. Everything else should either be discarded or be put somewhere where we can get them when we need them.

Set in order is then about arranging things in the way that makes it most efficient for us to use them.

For example, if you are right handed and your office phone is on your right side, the chances are that you’ll pick up the phone in your right hand, and then move it to your left to take a note.

When you put the phone down, it will probably introduce a kink into the cord – and that’s why so many office phones have such twisted wires.

Putting the phone on the left should sort this out.

Shine is about keeping our desks and workspaces clean.

Standardise is about doing things in a particular way – that’s crucial in manufacturing so that variation is minimised and everything comes out the same.

In knowledge work this is sometimes taken to mean that everything should be documented and made explicit so that people can follow a set of instructions and do things.

This is something I disagree with. If you can make something into a procedure then it should also be possible to automate it and remove the human element.

Instead, standardise in the context of knowledge work should be more about getting ourselves in the right frame of mind to do creative and innovative work.

We should aim to set ourselves up to get into flow with what we are doing, so standardisation should really mean things like checking email at set times, doing timed pieces of work, creating spaces for deep work without interruption and so on.

Finally sustain is about what we do every day.

Because knowledge work is so intangible, it’s easy to get bogged down in day-to-day firefighting and forget that we also need to innovate and create.

And that takes time and energy.

Which we might be able to create if we use the 5S method to address the things around us getting in the way and grabbing our attention.

Humanity as a service: The future of work for us

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We work with computers now – that just seems natural.

Many people have jobs that involve using a computer at some point – it’s hard to think of many occupations that don’t.

Thirty years ago – that would have been unthinkable. Fifty years ago, people would still have scoffed at the idea of individuals owning computers.

The way in which we produce work and output in the future is inevitably going to involve technology.

Which means we should ask ourselves where we might fit in?

Robots – AI – all the tech we have – are still essentially logic circuits. They do things they are programmed to do.

If we could take the knowledge inside a doctor’s head, a lawyer’s head and turn it into a series of steps that could be done by a computer using a decision tree, then we’d be able to free up some of that professional’s time.

In many offices, there is someone given the task of comparing columns of figures and picking out the ones that don’t match.

If anyone is still doing that by eye – they need to get more skills – and quickly.

We’re not going to beat robots at tasks that involve calculation or large amounts of numbers.

We will be able to automate them to perform certain tasks – from doing our accounts to managing our heating.

Many systems come with this technology increasingly built in.

NEST can warm your building when it knows you’re coming home. Landrovers warm up your car in the morning ready to go to work.

So, what does that leave humans to do?

What’s left are essentially human tasks.

Things like being creative, using our judgement, having empathy and doing critical thinking.

Some of us will also be needed to clean and maintain the robots that do all the work.

But increasingly, we’ll spend our time doing work that comes up with new and better things and helps others – especially in ageing economies.

In other words – we need to shift to providing humanity as a service.

How to build something that is actually useful

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What should we do when we’re trying to start something new – whether it’s a new product line within an existing business or a startup dedicated to the idea?

All too often, we can come up with ideas for products and services and go quite far down the track of designing and creating them before finding out that the market isn’t that interested.

Alexander Osterwalder came up with the Business Model Canvas – a way to model a business using a one page framework.

This was much simpler than writing a 50-page business plan, and was enthusiastically adopted by the startup community.

A variant of the model by Ash Maurya is the Lean Canvas.

While the Business Model Canvas is designed to address all aspects of a business, the Lean Canvas focuses specifically on new product development.

The Lean Canvas retains five components of the Business Model Canvas:

  • Value proposition: What does the customer get?
  • Customer segments: Who is going to want this product?
  • Channels: How are we going to get to speak to them?
  • Revenue streams: What will they be willing to pay?
  • Cost structure: What will it cost us to deliver the product or service?

It adds four new components.

First – what is the problem we are trying to solve for a customer?

As Theodore Levitt said, People don’t want to buy a quarter-inch drill, they want a quarter-inch hole.

If the product doesn’t address a real problem that potential customers have then it’s hard to justify its purchase.

Then, what is the solution we are proposing?

The solutions needs to be simple – easy to understand. That doesn’t mean it has to be easy to do – or the customer could just do it themselves.

It must be possible, however, to see how the solution solves the problem.

We then need to look at two more components – metrics and unfair advantage.

Success or failure needs to be measured in an objective way and for that we need to select metrics.

Selecting a metric directly influences the activities we do in order to improve our score on that metric – so it’s important to select a few and important ones.

Finally, there isn’t much point spending time and money developing a solution if it can be easily copied or bought from someone else.

We have a competitive advantage only when it is hard for others to compete with us.

So, in summary, in order to build something useful, we need to start with a customer’s problems, come up with a solution, make sure we are doing the right things, and make sure that what we do is unique to us.

An easy list to write – but not a simple one to do.

The rise and rise of Bitcoin

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The price of bitcoin has gone up by over 16 times this year.

At the start of the year, one bitcoin traded at under $1,000. Now it is over $16,000.

What’s going on – and can it continue?

The price of bitcoin is driven, as are most things, by supply and demand.

Supply, in the case of bitcoin, is controlled by an algorithm, and new bitcoins are created by a process called mining.

In theory, the total number of bitcoins will not exceed 21 million by 2040.

According to Bloomberg, around 1,000 people control 40% of the market for bitcoin.

The currency’s elusive founder, Satoshi Nakamoto, is believed to own around 5% of the market, or 1 million bitcoins, worth around $16 billion at current prices.

The price has been driven up by the rest of us, competing to be part of the rise in valuation that has happened over the last year.

A relatively fixed supply and voracious demand are behind the increase.

Bitcoin has no government backing it and has no intrinsic value.

Its valuation is supported purely by the belief its community of users have in it.

That is no different, really, from any other currency though – we have to believe that the country backing it will still be there in the future.

An MIT Technology Review suggests that bitcoin may be at the point where it is as powerful as a government – China’s attempts to ban it have not stopped it.

The main problem at the moment with bitcoin is its volatility and a marketplace for bitcoin futures may help to stabilize its value.

The point is that while the supply of bitcoins is fixed, the supply of crypto-currencies is unlimited – anyone can set up a new one.

And the thing that is drawing people in is not that they really want to hold bitcoins – it’s that they really want to be able to convert the rise in their bitcoins to conventional currencies like the US dollar.

That’s speculation and could be hazardous to your wallet. Or make you very wealthy.

No one really knows.

How can you use analytics to create value?

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It’s not clear for many of us exactly how we can use the data we have to help our organisations.

The systems we have are collecting more data than ever before – from customers, operations, sensors – and there is data being collected around us in social media platforms.

Many organisations are good at looking back – they can tell what happened and to some extent why it happened.

Fewer can see what is happening right now and what could happen next and what they should do as a result.

Some organisations use real time data to increase sales

The recommendation engines used by Amazon are designed to increase sales by showing you what else you might like – and the way in which google or Netflix present related information is designed to keep you using their platform.

But, what should the rest of us do to get started?

The advice from an MIT study is a perhaps a good point to begin.

There is more data around than we probably have the resources to deal with, so the starting point is to go after the big problems – the 20% of things that have 80% of the impact or cause the majority of the problems.

It’s hard to get people to give up personal decision making and rely on data.

Linking the analytics work to big, important things in the business and showing how data can help with those decisions has a better change of getting new methods into general use.

Then, it makes sense to start by asking questions instead of getting lost in the detail of analysis.

We can spend so much time getting data and starting to cut and analyse it that, all too often, there is no time left to see where it can help.

If we start with the big things that the company is interested in – often set out in their goals and objectives, we can then ask questions about what information would be useful to reach those goals.

Those questions will then let us explore what we can do to get answers and start to define the kinds of data and analyses we need to carry out.

The things organisations say are most important to them right now are trends, forecasts and standard reports.

The things that are likely to also become important are dashboards, simulation and scenario analyses, business process analytics and advanced statistical techniques.

We should try and make sure that what comes out of the analysis we do is business friendly and can be layered with other pieces of information and intelligence to make decision making easier.

Next – we build on what has been done before.

All too often, vendors are dismissive of spreadsheets.

In any room, however, it is likely that the vast majority have used and are comfortable with spreadsheets while advanced analytics tools have a steep new learning curve.

Good information systems design will keep what works and build more – perhaps with advanced analysis automated as much as possible or centralised within a support unit with experts that can help.

Finally – we should have some kind of a plan when building our information systems.

An information agenda, signed off by management, is a good way to oversee the process of sharing and using data better.

The study says that top performing organisations use analytics five times more than low performers.

For the rest of us, we should begin by understanding how to get started.

How can our information systems help us be more productive?

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Productivity is defined as output per hour by workers – and it has flatlined in the UK over the last ten years.

Our output depends on the tools we work with – and information systems are a vital part of that toolkit.

There are some who argue that we have not invested enough in technology while others think that recent technological changes have less potential to transform productivity.

So – how can we select and implement information systems that will improve productivity?

William Delone and Ephraim McLean came up with a model to measure the success of information systems in 1993, which they they revised in 2003, called the D&M IS Success Model.

It remains one of the most influential theories in the field, cited in in thousands of papers, and is a useful one to keep in mind when looking at a new system.

The model has 6 dimensions that are linked together with process flows and feedback loops. They influence each other, and in turn some elements are influenced by others.

The model begins by looking at quality – and sees quality as having three dimensions: information quality, system quality and service quality.

Information quality is all about what the system stores, delivers to the user, and produces for the user.

System quality relates to how the system works – does it do what is needed quickly or not?

Service quality depends on the organisation behind the system – are they helpful and is there guidance that is easy to follow or not?

These dimensions need to be looked at independently to assess quality fully.

The next two dimensions are user based.

First there is the user and the system – and this needs to be looked at from two angles.

How does the user plan or intend to work with the system?

How does the system actually use the system.

The quality of the system directly affects these user choices – we may intend to use a system, but if it is of low quality or the information is not good enough, we probably won’t.

We can measure user satisfaction based on their experience of using the system and their feelings about quality.

The last dimension looks to measure net system benefits.

These need to be some combination of saving money, saving time, increasing productivity and increasing sales.

The D&M IS Success Model seems deceptively simple – but it is a “parsimonious framework” that organizes many of the success metrics from research.

If a system scores well on these six dimensions then it should help us be more productive.