Category: Analytics

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.

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.

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.

Theodore Levitt, a Harvard economist and professor, said People don’t want to buy a quarter-inch drill, they want a quarter-inch hole.

With larger purchases come greater expectations of performance, reliability and longevity.

An approach companies use to deliver these is servitization.

Servitization is a change in mindset from selling a product to selling a Product-Service System.

Professor Andy Neely from the University of Cambridge is a leading researcher in this area and says that the term has been around since the 1980s.

The classic example here is Rolls Royce.

It no longer sells jet engines. Instead, it sells flying hours.

Rolls Royce now delivers reliability – done through planned and predictive maintenance by analysing data from monitoring systems in the field.

Making a move from scheduled repairs to targeted repairs based on performance data enables fleet operators, from the military to councils, to improve things from combat readiness to bus times.

The way we get media has changed with this approach.

Netflix offers media as a service and Amazon offers ebooks.

Servitization has quietly transformed the software business.

It’s hard to buy a software product any more. It’s all software as a service (SAAS) now.

This is not always a good thing.

Netflix has lots of rubbish on it. The good stuff still needs to be paid for.

We get a million books with an Amazon Unlimited subscription – but the good authors still set their own prices and get committed readers.

Servitization in these cases gives the companies a revenue stream and a customer convenience – but the pressures of controlling costs means that the performance is not superior to just buying a really good collection of books and movies.

And software as a service really all-too-often means self-service software.

So, it’s still a product sell rather than a service when we think about it.

According to Professor Neely, servitization is something of a paradox.

Many firms offer product-service systems, but it makes less money and carries higher risks than we first think.

The key thing is a change in mindset – in how we connect a product, service and performance.

This shift starts with the customer going from wanting to own a product to being happy with a service.

Why should we try and use data better?

The short answer is because we can make money or save money using the insights we get from analysing data.

But a more useful answer might be because it helps us see more clearly.

That’s very Zen.

Zen teaches that we normally see the world through a haze – a fog made up of the assumptions, preconceptions and fixed ideas – that is created by our desires.

For example, many people are so involved in the way they do something that they fail to see that the something they do is no longer relevant. Innovation simply passes them by.

Or, if we really want something to happen or believe that an approach is right, we become blind to anything that contradicts that point of view.

The way to see clearly is to look at the world as it is.

This is pragmatic advice, but how do we go about doing it?

The Cross Industry Standard Process for Data Mining (CRISP-DM) is now over 20 years old – but still useful.

There is data everywhere these days, leaking out of sensors, meters, surveys, analysis and social media.

We could analyse everything, but we should probably start with an understanding of the business.

What is special about this particular industry, what is the competition doing, how do customers act and what would the business like to do?

We can then stick our heads in the fog and try to understand the data that’s available – going back to the business to ask questions or get some more.

Is it clean or patchy? Do we have lots of numbers or is it full of words? Is it in an open format or do we need to get it out of databases or proprietary formats?

For example, the way we treat energy metering data is different from twitter comment mining or newsfeed monitoring

A huge amount of work then often goes into data preparation.

The data needs to be bashed and cut and manipulated into a form that we can work with.

All too often, the data preparation takes so long and ends up in another messy form that there is little time left to do any modelling.

But – getting to the right kind of data structures can make the task of modelling much easier.

Models help us create and test theories.

Do we think there is a relationship between variables or a robust way to identify problems?

Then its time for evaluation.

We could do an infinite number of analyses, but we need to focus on the ones that are aligned with what the business needs to know.

Are we providing useful insights that can be used to make the business more effective?

If so, then we can head towards deployment.

There is no point doing analysis once and then forgetting about it.

The law of entropy – things decay over time – means that without management things will simply drift and become worse again.

Ideally, we’d automate the analysis and reports and let people know when things are going wrong so that they can get involved and fix things quickly.

The CRISP-DM is a process we can follow to create a data mining project.

The real value comes from the understanding we get when we see what is actually going on.

Every real businessperson I have met thinks about money in a certain way.

They break things down into what they cost on a daily or weekly basis and then see how much they need to make every day or week to be in profit.

For example, let’s say we wanted to start a taxi service and bought a car for £10,000 that will be run for 3 years – and it’s going to be worth £3,000 at the end.

A business person will think of that as £7,000 of capital spread over three years to make decisions.

The taxi might be in service 5 days a week, 48 weeks of the year for 3 years – that’s 720 days of operation.

So we need to make at least £10 a day to pay for the car. The stuff on top is profit.

It’s as simple as that – once we are making a profit, however, lots of people will want a bite of it – from the government to helpers.

But the basic principle is still just that simple.

Sometimes the more complex financial calculations that we do – from paybacks to internal rate of return – simply confuse the issue.

Especially when it comes to serious projects that have lots of moving parts.

For example, battery storage plus solar is all the rage now.

Around a year ago I made some notes from a podcast by Barry Cinnamon, who uses exactly this method to evaluate the economics of an battery storage plus solar installation.

It’s from 2016, and prices may have changed, but principles don’t.

We are currently going through an infrastructure upgrade process in the UK that staggering in its scope and scale.

We have ageing networks of wires, pipes, sewers, roads and railways, all of which are being upgraded or replaced.

And there are a myriad projects being chased by developers.

For projects in the energy business – it’s once again pretty simple.

There is a price per kWh of energy – and if we can buy it for less or sell it for more than that we can make a profit.

The problem is when we have very complex financial models that help us buy it for more or sell it for less.

As Theresa May (now probably wishing she hadn’t) said, there is no magic money tree.

But there are many, very real, money pits.