How to use Lean Data to get better feedback

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We need feedback to improve how we do things – but what is a good way to get it?

At one extreme in the feedback spectrum is Big Data – if we collect as much information as possible and feed it into clever machines that can learn and have artificial intelligence (AI), we’ll get deep insights into customers, organisations and systems.

At the other extreme is the collection and processing of information that seems important to people that matter – bosses, shareholders and government.

These focus on output metrics that are important.

In local government, for example, the number of jobs created is the main question asked of any project and so, unsurprisingly, most proposals will bump up the number of jobs projected to numbers that may never be realised.

The Big Data approach has a barrier to entry made up of knowledge and systems.

The output based approach makes the numbers look good but may not reflect reality.

So, is there a middle way that is simpler and cheaper to do?

Sacha Dichter and Tom Adams at Acumen, a non-profit that looks at innovative ways to reduce poverty, and Alnoor Ebrahim at Harvard Business School have used a new approach to measurement that combines lean design principles with quick and inexpensive data collection methods that they call Lean Data.

Lean data has two goals:

  1. Make measurement cheaper.
  2. Increase the value to enterprises of collecting data.

In the non-profit sectors that Acumen studies things change quickly, there is little money, there isn’t anyone in the organisation with deep data experience and the systems to collect and keep data aren’t there.

These problems aren’t limited to non-profits, however. Virtually all organisations will face the same issues.

Acumen have come up with an acronym – BUILD – that we can use to create a measurement system that works.

Such a system will be:

  • Bottom Up: created after listening to customers so that it addresses what they need.
  • Useful: What comes out of it helps us to make decisions.
  • Iterative: We won’t get it right first time – we need to iterate and continuously improve it.
  • Light touch: We need to be able to use cheap tech that needs little time or money to get going.
  • Dynamic: Things will change – and we need to be able to change as they do.

A problem with many management systems is that although they are designed around principles of test and learn and continuous improvement, they quickly degenerate into compliance activity with box ticking and paper processes that don’t match the real world.

A truly lean approach may help us cut through that and look at the underlying reality with fresh eyes.

We are certain to improve performance if we improve the quality of feedback.

Why we find it hard to make decisions about the future

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One of the oldest and best known proverbs is a bird in the hand is worth two in the bush.

This way of thinking is so ingrained in us that we accept it unquestioningly.

We give more importance to what is happening now than what may happen in the future when making decisions.

The simple instance of this is that many people will take a small reward now, say £10, in preference to a larger reward in a year’s time – say £20.

Given a choice between waiting a year for £10 and two years for £20, they will often choose to wait the two years – what’s the difference between a year or two?

It’s a struggle to pass up chocolate now to avoid the weight gain that may accumulate in a year’s time.

This way of thinking is endemic in business.

Managers spend a lot of time focused on the short term – cutting costs and deferring spending now to protect short term results.

In the long term the costs are almost always higher as we take action then only when compelled to by a failure or catastrophe – at which point we are forced to pay whatever it costs.

The economics of this approach is summed up in a term called discounting.

We discount the future – on a linear basis for accounting, on an exponential basis for investing and on a hyperbolic basis (possibly) for impulse purchases.

Let’s spend that money on a new telly now because putting that money in a retirement pot is so much less appealing.

Our future self, sat in a retirement home, will appreciate that telly so much more when sat waiting for the weekly visits from our family.

But that’s simply over-dramatic. We could die tomorrow – we should enjoy things while we are still around.

But we probably won’t die tomorrow.

In many parts of the world the chances are that we will live longer than previous generations and, for the first time, we may be poorer than our parents when we grow old.

Future generations will think up new ways and come up with the technological solutions they need, so we should put ourselves first and the earth could be destroyed by a comet at any time.

Except we can be pretty confident that a future will arrive – and if it’s accompanied by climate change on a vast scale – the amount of investment and technology required to deal with it may be beyond the capabilities of those future generations.

Think about it this way – a discount rate of 5% over 500 years results in a number that looks like this: 39,323,261,827.21.

That’s 39 billion, give or take a few 100 million.

A pound we spend now on ourselves, at that rate, would be worth £39 billion to someone 500 years from now.

That perhaps still doesn’t compute…

The point is that the decisions each of us makes now impact the lives of billions of people some time from now.

We need to try and make wise ones.

How to really make change happen

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It shouldn’t surprise anyone that what we say and what we do often don’t match up.

For example, this paper exploring the green consciousness of Egyptian customers says that more and more people say they are concerned about the environment and want to contribute to protecting it by buying green products.

In reality, they don’t do this consistently.

Many companies in Europe are going to start doing energy audits over the next couple of years to try and find opportunities to save energy.

A lot of these will result in recommendations that go into a report, which then sits on a shelf because going from what we should do, to doing it is not easy.

When trying to change behaviour we can easily think of all the things we can do to change things.

At home, we could turn lights off to save energy, control the amount of chocolate we eat to lose weight or set the alarm a half hour earlier to get up and go running.

If we rely on willpower, however, then the amount of willpower we have left determines whether we do something or not.

After a tiring day at work and a late night with the kids it takes superhuman effort to get out and go for a run when there is sleet and rain falling out of a dark sky.

So, how can we make a change?

The research talks about the market as the intermediating factor

When we’re trying to do something new, say a new gadget, the worst question we can ask people is whether they would like a gadget like the one we have.

That’s called an ice cream question – because no one says no when you ask them would you like some ice cream?.

A better question to ask is what gadgets have you bought recently?. Maybe they’d love to have the latest things but can’t justify buying them.

Or they’d love to save energy, but consistently leave lights on in at least five rooms in their house in the evening despite spending all their time in just one.

To find a change pathway that people and organisations will adopt, we need to understand how they change now – and that change is best seen in the way they have been spending money so far.

The case for change needs to be made in the same way previous cases were made and approved to have a chance of succeeding.

We have to make it easier for people to get their money to where their mouth is.

How to do more with less – frugal innovation

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2009 Kenya. We might see a man pushing a cart full of produce on his way to the market looking at his mobile phone – one of the old ones with buttons.

The chances are that he was checking the forward price of the items in his cart and agreeing transactions as he pushed, so that once he arrived at the market all he had to do was deliver the items, get the money and complete the deal.

Developing regions always have two problems – there are too many needs and there aren’t enough resources.

They have had to innovate with scarce resources – something now called frugal innovation.

Take keeping food fresh, for example.

In the West, a fridge is a as much a part of a house as the walls or roof.

Our lifestyles are built around going to supermarkets, getting food and keeping it in fridges or freezers.

If the power went off for a couple of days or a week we wouldn’t know what to do – all that food would spoil and the system would break down.

Mansukh Prajapati from India came up with a fridge made from clay that works using evaporative cooling.

Food can be kept fresh for a few days and it works without electricity so can be used anywhere.

Ingenious solutions can be high-tech or low-tech – the key is finding opportunities and coming up with creative ideas and approaches.

And some problems can be quite complicated.

Take energy consumption, for example.

In 1885 William Jevons noticed that when technology made coal usage more efficient, the amount of coal being used increased dramatically.

Appliances today are much more efficient in the way they use electricity but as we move to driving more electric cars, the total amount of power we need is going to increase.

Clearly we’ll use less fossil fuel as a result – but pour more chemicals into batteries that last three years and then have to be thrown away or recycled.

And we don’t really know if that will be good overall in the long run or not.

A simple solution would be to get people walking and cycling more.

But that requires fundamentally changing the way we work and live our lives – and while we are taking steps in that direction there is still a long way to go.

Frugal innovation also relies on the principle of leverage.

Why reinvent things when you can use what is already out there?

The GNU/Linux system has “free as in freedom” software that allows people who could never afford commercial software to access, learn and use computing tools.

Why build a new proprietary application when we already have existing capability out there – why not build on that instead?

Economies based on consumption prosper – encouraging people to want and spend and buy has unleashed innovation on a vast scale – benefitting people with the ability to pay.

Frugal innovation may be a way to benefit everyone else.

What is Artificial Intelligence and can we use it yet?

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Artificial Intelligence (AI) has been just around the corner for a long time – but looks like we have now arrived.

Computers can beat us at Chess and Go and respond to voice commands.

Navigation systems are so good many of us now have never really learned to use a map.

There are so many ways of looking at and classifying the field of AI and machine learning that it’s almost impossible to get a sense of the field.

But we can start by looking at some broad domains – what do humans do a lot of the time?

We sense things – taking in vast quantities of visual, auditory and tactile information and responding to our environment.

We can detect the edges of things, work out which way is up or down and work out what is near us and if we are going to bang into them.

A particularly human thing to do is reason. Our brains are essentially prediction machines – we can think about what has happened and use reasoning to work out what we should do next.

But we don’t exist in isolation – as social creatures we interact with others – listening, speaking and responding.

We make plans – choose between alternatives or options – that range from what to eat to how to get somewhere.

We are also teleological – the conscious part of our brain helps us do things with purpose.

Our brains have evolved to be the way they are – but how would we go about creating an artificial one?

We could start by writing down all the rules we follow.

For example, doctors get to a diagnosis by considering and eliminating possibilities based on the symptoms they see and the measurements they take.

Rule based or expert systems take all this knowledge and use it to create if-then rules – if the temperature is above X, check Y next.

These systems are now pretty effective – and help us select the best flight, the cheapest online store for an item and schedule calendar entries from text in emails.

If there is too much data and variation to come up with rules, then we might use probabilistic approaches.

For example, we can run weather simulations that are probably accurate over hours or days but less so over weeks and months.

We can look at the distribution of a time series and use that to predict the range of probable future values – which then lets us pick out values and events that fall outside expected levels.

The rule based and probabilistic approaches are pretty easy to build and many systems in use now will be based on them.

A more complex approach is pattern matching, where a learning algorithm adjusts itself and learns from the data that goes into it.

For example, every time we type a search term into Google, we are training its AI engines. If we type in the word “eagle” and then click on a picture of an eagle, Google can learn what eagles look like and eventually predict that a picture contains an eagle.

With pattern matching, the more information we have the better our algorithm gets – and so it’s a winner take all situation where the systems we interact with most will learn the most and pull away from the rest.

But where can we use this technology now?

Three areas that are of interest in the energy sector are forecasting, scheduling and trading.

The energy system is all about balancing supply and demand, whether at the grid level or the domestic level.

If we know when the wind is going to blow, then we can make a call on the number of fossil fuelled power stations we need.

If we can see when demand or prices are high, we can schedule when we do work to avoid costs or take advantage of high prices.

We could even trade between ourselves – selling or buying electricity from the grid or a peer-to-peer network for a profit.

An interesting thing that happens with AI is that as it gets cleverer we tend to dismiss the things it does as simply something a machine can do.

As a result it is quietly augmenting how we do things without us really noticing. For example, how many of us now choose a different route based on Google’s recommendations first thing in the morning?

Many AI applications will be almost unnoticed, simply transforming the essential building blocks of our economic system.

Eventually, one hopes, AI will free humans up to do more creative fulfilling work and leave the mundane to the machines.

How to take a planetary health check

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How do we know if things are really getting hotter? Is the Earth really running a temperature?

NASA can answer that. They publish annual data on global temperatures and climate trends that can give us an idea of how things have changed over time.

Take carbon dioxide, or CO2 for instance. Since 2005 the concentration of CO2 in the atmosphere has gone by nearly 8%, from 378.21 to 407.62 parts per million.

CO2 has gone up and down over time, but in the last 400,000 years it has stayed below 300 parts per million – but the current levels were reached in the last seventy years or so.

The global temperature rise seems pretty benign – just up 0.9 degrees.

There is consensus, however, that a 2 degree rise is too much, a 1.5 degree rise would help us survive and much more than two would lead to climate catastrophe.

There still isn’t too much urgency it seems.

That’s perhaps because we’re focusing on the wrong thing.

The amount of sea ice is falling dramatically. The rate of change led James Lovelock to suggest in 2007 that the Arctic could be ice free in 15 years, while the IPCC thinks it could be more like 2050.

Whenever it happens the point is that all the sun’s energy pouring onto the Earth that causes the ice to melt conceals the true warming going on.

It takes a fair amount of energy to turn ice into water and all that ice also reflects heat back into space. With the ice gone, dark sea water will absorb the energy much faster.

So, sea level rises could be a better measure of the extent of warming – with some coming from melting ice but a lot from the expansion of water as it heats up.

The effects of this warming trend are unpleasant – and include droughts, hurricanes, crop failure and insect outbreaks.

It’s also not at all certain that we can stop the trend – in a complex feedback system like the Earth once a trend starts it will only stop when a new equilibrium has been reached.

Any action we take now may be too late.

Conversely, some actions that seem positive may be harmful.

Reducing haze that results from pollution and creating clearer skies may allow the sun’s rays to pass straight through and deliver more heat.

Despite this – the facts are that the planet is warming and that is going to have an impact on the way we live around the world.

We need to do what we can because, as Dee hock said it’s far too late and things are far too bad for pessimism.

Why we all need to experience the overview effect

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What happens to us if we see our planet from space?

The overview effect is a mind shift experienced by some astronauts when seeing the Earth firsthand from space.

They immediately get that we all live on the same small, fragile planet protected from the void by nothing more than thin air.

And the things we do to mess things up here – from conflict to resource exploitation – are simply stupid and what is needed is the united will to do something about it.

But – less than 600 people have travelled space and that probably isn’t enough to create a movement.

As a starting point, perhaps we can look at some pictures.

Benjamin Grant started a project called Daily Overview that curates stunning images of the Earth from space and our impact on it.

These images capture how we have completely taken over the planet and the scale of activity and building we carry out – from deforestation in Bolivia to iron ore mining in Australia.

A related set of images called juxtapose shows images of before and after – how Dutch tulip fields bloom or how a patch of desert turns into a refugee city.

Contrast “England’s green and pleasant land” with the Zaatari Refugee Camp in Jordan that holds refugees fleeing from conflict in Syria.

Grant was inspired by a film from the planetary collective, available on vimeo and now viewed nearly 8 million times that explores the overview effect.

We often think that solutions to problems like climate change have to be technological – we focus on things like reducing carbon through more efficient technology or processes.

To really get people engaged we should help expand their perspective and see things from a different point of view.

And you don’t get much more different from the moon – imagine seeing an Earthrise the way the first astronauts did.

Unless you travel to the edge of the solar system.

From 6 billion kilometres away the Earth is simply a pale blue dot, the size of a pixel on a screen.

Although most of us won’t get a chance to travel into space, perhaps technologies such as virtual reality and augmented reality can help us have the same experience right here on Earth.

Maybe the technologies we have have created to escape reality can also help us protect it.

How to give people feedback on their eco-behaviour

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Many of us assume that if we just give people the information they need, they will act in the right way.

This assumes that households and businesses are run by micro-resource managers who make decisions about how to act after looking at the costs and benefits of all their options.

To change behaviour all we need to do then is provide things like smart meters and everything will be better, won’t it?

It turns out that isn’t the case because there are a number of problems we face.

For starters, not everyone understands the language of energy or water as shown on a display or report.

It’s hard to visualise 140 litres of water, for example.

It may be easier to grasp when it is translated into something that people can relate to – for example a one litre bottle for every child in a small primary school or the amount of water that goes into making one unit of product.

Even when people understand it, they may not feel they have any choice about using energy.

No one wants cold tea – and we would rather wear clean clothes. That means using the kettle and washing machine.

We can’t turn off energy if we have to do things – we have no control really.

A related problem is focusing on visible things and forgetting the stuff running all the time in the background.

For example, using the kettle will turn displays red with a power spike – but it only lasts a small time.

Or many organisations focus on replacing lights, because that is a very visible way of showing that they are reducing energy – but miss out on larger savings.

Then there are traffic light systems – which have been shown to improve behaviour by getting people to try and stay in the green.

At the same time, they make it ok to do things as long as the display is green – consuming more power overall over time.

A major stumbling block is simply what we want. We’d like a bigger telly, even though the old one works.

Christmas is about shopping – and we’re not going to deprive the kids of stuff even if it’s mostly plastic.

All these problems and more mean that getting people to change behaviour is a bit of a challenge.

So, how should we look at solutions? We can take some lessons from the designers of better eco-feedback solutions.

First, we need to provide better information.

For information to work, it needs to be easy to understand, attract attention, have a social component and be provided at the right time – the EAST framework.

An example is making sure there are labels that say when and who something must happen – like a sticker on light switches saying the last person out should turn off the lights.

Then there are goals.

Companies that commit to targets like Science Based Targets will give people the responsibility and permission to take action.

Government targets can ripple through economies and cause changes in behaviour in order to comply.

There is some evidence that comparisons work – people like being on top of league tables, for example.

After a certain point, however, it may not be possible to eke out further savings, and so comparisons become less important as people feel they are impossible to achieve.

Incentives and rewards, even small ones, can affect behaviour.

Simply having a star after the names of people can change how the group views and engage with the activities required – either to get their own star or keep one they already have.

For eco-feedback to work, it needs to follow the Goldilocks principle.

Not too much, not too little, but just right.

What is a smart contract?

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We hear a lot about smart contracts these days.

But what exactly is one, how do we create it and when can we use it?

The term was invented by Nick Szabo, who applied his interests in computer science, law and cryptography to create a cyberspace equivalent of gold – and came up with the idea of decentralized digital currency.

The point of a currency, however, is that it acts as a store of value and a medium of exchange – but when do we actually need it?

Nick, in his original article, says a contract is really a set of promises.

The things that go into a contract have evolved over time – from a contract of marriage to a contract of employment, from a contract of sale to a contract of lease.

All these structures say that the people involved promise to do certain things as set out in the contract.

While the concept of a contract can encompass a blood oath sworn at midnight under a lightning blasted oak tree, normal contracts have four characteristics:

  1. The participants can observe how each party is performing on the contract.
  2. An arbitrator can verify that the contract is performing as set out or is being breached.
  3. The only people involved are the people that need to be involved – their rights are secure and private.
  4. The contract can be enforced – for which we need acceptance of the contract by the powers that be.

A smart contract takes these concepts and recreates them in the digital world.

The difference is that parties enter into a digital agreement instead of signing a paper contract.

This has been done several times before – every payment with a Visa debit card, Ebay purchase with Paypal and software contract we sign without reading are all digital contracts.

The idea of smart contracts when linked with Blockchain technology adds a few ideas to standard paper contract.

The first is that contracts can be expressed in code using an algorithm.

Common algorithms may be if-then or when-do.

For example, if you pay me X, then I will give you an hour of work.

Or when a shipment of copper is delivered in two months time I will pay you the price then that we agree now.

So this means that we need to identify the real life elements that the contract talks about like money and stuff.

For example, payments might be done using a decentralized digital currency like Bitcoin or Ethereum while each hour of work or shipment of copper is tagged with a digital identifier.

So, a second part of a smart contract is the ability to give things a digital ID.

Then there is the question of where to store the smart contract – and that’s where the idea of a ledger comes in – especially a blockchain based distributed ledger.

This keeps the information related to the contract secure by design.

Smart contracts are the way things are going to go – paper agreements have little place in the future.

Many other part of a contract – the promises we make, the reliance on the state to enforce agreements, and the lawyers that create and interpret them – are all still likely be around.

The key, however, is going to be to get the contract algorithm right.

How to think about waste in knowledge work

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A principle of lean thinking is to remove waste – anything that stops us from producing our product or service for a paying customer.

This is called muda in Japan and seven ways to create waste are especially important to manufacturing organisations.

They can also be used to think about knowledge work and if we are doing it effectively or not.

Take transport for example. Are we moving work between locations unnecessarily?

This can be as simple as splitting a process up between teams, so that one team focuses on just one part and the other team on another.

This is a good way of creating silos – where people focus on their bit and forget the overall system and waiting customer.

Having a small group of multi-skilled individuals close together that can handle the entire process from starting the project to delivering to the customer reduces this type of transport and waste.

What might inventory look like in knowledge work?

Could it be the collection of reports, analyses, studies, comments, meeting notes and so on that accompany the simplest of projects or decisions?

Is all that really necessary?

A lean organisation can design and test a product with a customer and iterate to a finished version in the time it takes for another to come up with specifications.

Motion simply adds heat to a process. The best knowledge work gets done when people sit down and work on a single task for a stretch of time.

Flapping between projects, having to check in all the time with managers and getting interrupted break the flow of work, raise stress levels and increase the total time needed to do the job.

Then there is the time we spend waiting. Meetings are places where we wait – where interminable discussions happen to decide what to do.

We spend so much time in meetings that there is little time left over to do any of the work or actions that come out of them.

Or, we often do too much – over-processing the work we need to do.

Modern computer systems are so powerful that we can do almost anything on them – which means we spend ages selecting fonts and sizes and colours and logos and header placement instead of creating content or analysis.

User interfaces that try and make things simple don’t help.

A quote from an old newsgroup says that graphical user interfaces (GUIs) make simple things simple and complex things impossible.

Closely related to over-processing is overproduction.

There is little point creating work-in-progress that has to go through a bottleneck.

It makes more sense to work to the capacity of the bottleneck and spend the rest of the time working on a different project or improving the performance of the bottleneck.

And finally, there are defects.

Defects in knowledge work often result from not understanding the end result well enough and rushing to create something too quickly.

Information degrades quickly it is passed along links in the chain of communication.

For example a customer speaks to a sales person who speaks to an operational manager who asks an analyst to do some work who then creates something.

It is almost a certainty that the something that is created is not what the customer had in mind.

The problem is that knowledge work is not as visible as manufacturing – we don’t see piles of inventory piling up.

We may only see a cluttered desk and be aware of late projects.

We’ll also see the effect of waste in rising stress levels among colleagues.

And all that’s just a waste.