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.

How much will BREXIT cost the UK?

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Is there a ZOPA between the EU and the UK when it comes to a divorce settlement?

A ZOPA stands for Zone of Possible Agreement – also called a bargaining range.

In any negotiation we end up with two roles – a seller and a buyer.

The seller has a high price they want to get and the seller has a low price they would like to get.

The amount wanted by the EU has been talked about as being in the range of €100 billion.

Brexiteers in the UK, on the other hand, want to pay nothing.

A ZOPA exists if there is an overlap between the minimum a seller will take and the maximum a buyer will offer.

Refining the numbers, it appears that liabilities for the UK may be in the region of €80 billion but offsetting claims could reduce the total to €60 billion.

Europe may now want around €50 billion euros of £44 billion while the UK might be offering around £20 billion.

Both sides are tight-lipped about the actual amount.

If the buyer’s maximum price is less than the seller’s minimum – £20 billion vs £44 billion, for example, a negative ZOPA exists.

It may be possible to overcome that with other incentives – for example agreement on worker rights, acceptance of EU laws and so on – all of which come with political and legal complexities.

In the end, however, if the two parties don’t reach an amount acceptable to both, there will be no ZOPA and both will have to walk away.

The negotiations continue…

Why you should trust your gut instinct

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We’ve all probably been in a situation where we can’t decide between two options and someone has said to just flip a coin and let it decide.

It turns out that this may be a very good approach indeed – but not quite in the way we think.

For a long time we assumed that people were rational creatures, governed by a conscious mind that made decisions based on logic.

Relatively recent research found that we weren’t as logical as we though we were – behavioural economics showed that we acted in ways that weren’t consistent with logic.

Behavioural economics, in turn, is criticised by some because what happens in the laboratory does not always show up in the real world.

For example, altruism shows up more often than we expect in experiments, as people share when they have no need to do so – but at the same time do they only share because they are being watched in an experiment?

As we became increasingly aware of the vast number of things the brain does without any conscious intervention – useful things like breathing and digestion – some researchers also realised that our body seemed to know things before our minds caught up.

For example, if people played a card game with two sets of decks, one of which was rigged, they eventually worked out that something was wrong with one of the decks.

When researchers monitored players using methods like measuring galvanic skin response – how much they sweated – the found that their bodies seemed to figure out the wrong deck well in advance of their conscious minds catching up.

Somehow their bodies were taking in a lot more information through their senses and using all that to figure out what was going on – and reacting to their findings emotionally – raising heart rates and sweating more.

The work of Antonio Damasio, described in David Eagleman’s book Incognito: The secret lives of the brain, forms the foundation of much of this research.

Damasio, a neuroscientist, found that patients that had suffered damage to their brains and were no longer to process emotion and the kind of signals their bodies were giving them – so called somatic markers – were unable to make good choices.

In the card deck example above, they were unable to distinguish the bad decks, even after they had been told there was one.

In other words, our ability to make wise choices is fundamentally linked to the reaction our bodies have when confronted with the sensory data in front of us – and a completely rational Spock like approach will more than likely fail us.

What this means is that when we have to make a tough decision and flip a coin – instead of focusing on which side lands, we should be monitoring our body’s reaction.

If we have a sense of relief when a particular side lands or we get knotted up with tension – that is our body’s way of telling us which decision is the right one – through gut instinct.

Then, we should ignore the coin toss and go with our gut.

How to avoid being trapped in an echo chamber

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The giant corporations that we interact with on the internet are trying to understand what we like so that they can feed us a processed diet of stuff that is similar.

Amazon, for example, suggests other books we might like. Newsfeeds on the iPhone change based on what we click on. Google creates personalised search results based on our search histories.

Mainstream news channels pick stories with an eye to what other news channels are likely to talk about – creating an increasingly homogeneous mix of information and news.

Many people simply choose to ignore stuff they don’t like or don’t agree with on the internet – creating a system where what they see is more of what they already see.

The main danger with this is that we could get trapped in an echo chamber.

Colin Raney has a nice way to show this in a 2×2 matrix – shown in the picture above.

When we see familiar content from familiar sources – that’s the time to be wary.

If people and sources we know are repeating the same thing it could be true.

Then again, it might simply be an echo, as the same thing bounces off and is repeated and amplified by others in our network.

We may end up with a distorted version of what is happening.

When we see familiar content from new, independent sources that might help confirm a story.

The idea of independence is key. The content needs to have been gathered and checked independently.

If we see new information from familiar sources, especially ones we trust, that might be something to explore further.

We may learn something in these circumstances.

Finally, when we see new content from new sources, we expand our horizons and are exposed to novel concepts, ideas and stories.

The technology and media that surrounds us are trying to understand us better and deliver more tailored content.

Paradoxically, in doing that, they might make it too easy for us to settle into a situation where we believe that what we see is all there is.

Politicians know this.

The strategy all over the world now is to talk only at people that already believe in them or that might be on the fence. The opposition can safely be ignored.

Comfort leads to complacency – instead we might want to be curious, look for facts, be sceptical and look at things from multiple angles.

Do we all need to think like journalists now?