Author: Karthik Suresh

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How to connect management, measurement and focus

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There are three questions that many businesses will need to address in 2018:

  1. How can we create business operations that have fewer emissions while creating more value?
  2. How can we protect the value we create – the intellectual capital – from pirates?
  3. How are we going to ethically use personal data?

These three questions are important because there are regulations and rules that need to be looked at now.

The Energy Savings Opportunities Scheme or ESOS requires large companies to audit their energy and transport use and look for savings.

The Cyber Essentials programme, run by the UK’s National Cyber Security Centre, aims to help all businesses and consumers become more secure.

And the General Data Protection Regulation or GDPR tells us how to manage personal data and keep it private.

So, what are the links between management, measurement and focus?

First, the standard approach to complying with requirements like these is to put in place a management system.

Most management systems, especially those that follow an ISO type standard, are based on the Deming Cycle – named after the engineer who helped transform Japanese manufacturing after the war into a lean powerhouse of quality.

The essential elements of the Deming Cycle are based on the principles of scientific enquiry and are:

  • Plan: Look at the situation and come up with an approach to manage it.
  • Do: Do the things in the plan
  • Study: Study what has happened and learn how to improve things. Some people use check instead of study – but Deming thought study was better as check is more about inspection, while study is about learning.
  • Act: Change the plan based on what has been learned and go through the cycle again.

This approach works well for tangible things such as reducing process waste or figuring out which items of kit to replace to reduce energy use.

But… things can get messy.

All too often a management system becomes an exercise in paperwork rather than a real effort to improve something – we do it to comply or pass the audit rather than actually becoming a better organisation.

And that’s because the technical element is only one part of an organisational system.

The intangible elements are just as important to get right.

The Balanced Scorecard is a method created by Robert Kaplan and David Norton that tries to look at the organisation as a whole.

Clearly, the way in which success is measured by an organisation is in the financial returns that come from doing a project.

Customers, on the other hand, focus on what they get out of the partnership.

In order to keep customers happy, the organisation needs to have the right internal capability to do the right things.

And that only comes when the people in the organisation are given the right learning and growth opportunities.

So, once again, how does all of this connect to management, measurement and focus?

It’s because it all starts with culture.

If the people in an organisation know and get with the vision and strategy – whether it’s becoming cleaner and greener, more secure or more ethical – then we’re starting with a firm base.

We can create a strategy using the Balanced Scorecard approach that works out what people need to know and helps them learn, puts the internal processes in place that are required, shows customers how this makes us better service providers and also gives us the financial returns needed to keep shareholders happy.

Our of the Balanced Scorecard comes a set of objectives, measures, targets and initiatives.

Otherwise called a plan.

We can implement the plan using the Deming Cycle and continuously improve our organisation – and that’s where quality comes from.

That’s sort of management and measurement covered, but where does focus come in?

Well, with all these kinds of things, there are two approaches we tend to take.

Either we look for the lowest cost compliance approach – because really none of these things are really that important and the existing financial pressures and priorities don’t leave any time for the hard work of business transformation.

Or we believe that we need to change to survive – and so the work involved in really becoming cleaner, safer and more ethical is worth doing.

And the results we will get in the next year will depend on which approach we focus on.

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What does the UK’s clean growth strategy focus on?

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The UK’s clean growth strategy, published in October 2017, sets out how the UK can grow while cutting greenhouse gas emissions.

The economic opportunity in clean business is huge.

Countries and companies need to invest around $13.5 trillion in the energy sector alone if we are to meet the Paris committments to keep the rise in global temperature below 2 degrees.

At the same time the UK wants to keep bills low – so the reduction in emissions needs to come from the cheapest technologies, processes and systems.

The UK plans to invest £2.5 billion from 2015 to 2021 in low carbon innovation.

33% of this will go into transport, with petrol and diesel cars killed off by 2040, a shift to electric and ultra low emission vehicles (ULEVs), more cycling and walking and improved logistics.

25% will be spent on power with a focus on smart systems, nuclear, price controls and ongoing work in renewables.

4% will go towards land use and waste, with new support mechanisms after the UK leaves the EU, planting new forests, having zero avoidable waste by 2050 and investing in agri-tech, land use, greenhouse gas removal technology.

10% is targeted at smart systems including storage, demand response, nuclear and offshore wind.

Homes will get around 7% to upgrade home energy efficiency measures, smart meters, a roll out of low carbon heating and new requirements for control systems.

Business and industry will get 7% spent on them to develop a package of measures to support them in increasing “energy productivity” by 20% by 2030.

This includes minimum standards on energy efficiency, helping businesses identify where they can cut bills and industrial schemes to help large companies install energy efficiency measures and support heat recycling.

Innovation will also be supported through the ongoing Energy Entrepreneurs Fund.

The purpose of this document is to set out a framework for action.

It’s then time for businesses, investors and innovators to go after the economic opportunities out there…

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How to predict the future

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Peter Drucker, who died in 2005, was one of the most influential scholars of management theory – writing 39 books and his quotes and sayings litter the field.

He came up with the term knowledge worker in 1959 – well before the Internet turned us all into knowledge workers.

He also explained how he was able to predict the future. It’s easy – he said.

All we have to do look around, report what events we see happening and what we think that obviously means for the future.

Some people think that’s prediction, but it’s really just having our eyes open and taking the time to think.

So, how can we use what Drucker write about to come up with a clear strategy for the future.

First, we start by looking at the opportunities out there that we want to go after.

This is the world from our point of view – the things that we think are important and material and that should matter to everyone else.

Second, what are the opportunities that our organisation can deliver?

We need to be able to innovate – to create and deliver a product that matches the opportunity we select.

Without an organisational structure in place – with people, equipment and processes and a supply chain, we can’t deliver anything – or convince anyone else that we can.

Third, we need a customer.

Drucker said “The purpose of business is to create and keep a customer”.

Without a customer, who needs, wants and values the product that our organisation creates to address the opportunity – we’re simply setting ourselves up for failure.

And who wants that… especially when we’re starting a new year.

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What are the trends to watch in 2018?

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Forecasts are usually a waste of time

At the start of the year, a number of predictions are made about what may disrupt life and business in the year ahead.

If we start, instead, by taking a look back, some of the biggest stories of 2017 came as a surprise.

Was anyone surprised by Trump’s policies?

The policies of the Trump administration are perhaps more of a series of road bumps to be endured rather than a revolution in how things are done.

Despite the US withdrawal from the Paris accord, it remains a leader in electric car technology and its technology companies still dominate global markets.

Attempts to support old industries will probably result in the money going to executives at large oil, gas and coal firms rather than actually helping people and communities with cleaner energy and new opportunities.

And the effect of the policies may well be to hand the Chinese a leadership position in the actual manufacturing of clean energy technology as well as increasing dominance over emerging markets.

The Brexit process was triggered

The UK will leave the European Union.

How and when and how much it will cost and who will win and lose are still being discussed.

The UK is not in a strong position.

It faces a juggernaut which is its main export and import partner and takes a long time to decide anything.

The EU can take its time – the UK needs decisions to be made soon.

And that pressure means the UK may have to settle for less than it hopes for.

Cyber-security and hacking are making headline news

2017 was full of news about Russian involvement in the US election and the extent to which foreign nations are carrying out cyber-warfare.

At the same time, criminals are targeting individuals and businesses.

Government and business are increasingly conscious of the risk of being hacked – but are still working out what to do in response.

The Grenfell fire caused 71 deaths and will result in changes to building regulations and fire safety

As more of us begin to live in cities and high-rises, the Grenfell Tower Fire has to spur action to improve safety in such buildings.

Better, more energy-effient infrastructure is needed to both prevent electrical fires from starting in the first place and stopping them quickly when something fails.

Electric cars may have reached a tipping point

Electric cars sales are still low, but their public perception as a viable alternative to petrol and diesel cars may have reached a crucial point.

Falling battery prices and increasing government support, including long-term targets to make electric cars the norm are helping the industry.

Bitcoin – bubble or not?

It’s probably one…

So, what could happen in 2018?

Security should be on our minds.

Recent news about hardware and software vulnerabilities should not be a surprise.

There are always bugs in systems.

The weakest point, however, is usually people.

Many organisations still need to really get to grips with digitisation and what it could do for them.

It’s not just doing things with computers the way they are done on paper.

And it’s not simply moving to the cloud – that is a particular type of solution – the point is what type of solution actually works for us in a particular situation.

Digitisation and security go hand in hand – we should use computers to do more, and we should be able to keep what we do on those computers confidential and secure from competitors and hackers.

The increasing complexity of everything, however, means that specialisation is now normal.

Advanced economies look at Everything as a Service, where we can find organisations and people to help with specific tasks.

It’s more about the specific solution OR the specific person that does something.

And, as no one wants to take the risk of paying up front for something they don’t understand and can’t use, paying to try things out and then use them if they work seems to be the model for the majority of products.

Unless you’re Apple, in which case you can deliberately slow down your old stuff to make people buy your new stuff…

So, for all those services, we really need to start looking at global/local partnerships.

For example, what’s stopping us from working with admin assistants in the Philippines, sysadmins in Brazil or developers in India?

Well, in 2017, nothing did…

The fact that countries are increasingly nationalistic and making it harder for people to cross boundaries, partly as a result of global terrorism, makes less and less difference in a world where everyone is connected by the Internet.

We should be looking for partners and services wherever they are – and select them based on what they do and how much they charge – and how much value we get.

Then there is the mail…

Many of us probably do the majority of our shopping online.

Vans are the fastest growing automotive segment because of all the deliveries being made.

We can order stuff from China or Taiwan on Ebay and have it here in a few weeks. If we pay more, we can have it tomorrow from the UK in the mail.

The mail order business is simply going to grow – shops are turning into places where we go and browse, like catalogs.

And then there will be all the things that no one predicted

As Donald Rumsfeld said in 2002:

Reports that say that something hasn’t happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns – the ones we don’t know we don’t know. And if one looks throughout the history of our country and other free countries, it is the latter category that tend to be the difficult ones.

In 2018 – the unknown unknowns will be the ones that get the biggest headlines.

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How to analyse a dataset

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When we say we analyse data, what do we actually mean?

For many, it means looking at rows and columns in a spreadsheet, with a sense of quiet desperation.

So, where should we start.

The American Institute of Certified Public Accountants (AICPA) defines data analytics (in the context of audits) as “The science and art of discovering and analyzing patterns, identifying anomalies and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling and visualization for the purpose of planning or performing the audit.”

That’s probably as good a place as any.

The first thing we try and do when looking at a dataset is to figure out where to look more closely.

We’re trying to take a journey from data to insight – and that involves a few steps.

Take patterns, for example. Patterns tell us that something happens regularly – and so help us predict the future.

For example, a heatmap of energy use in a building that consistently shows hot spots of high energy usage outside normal working hours is a pattern worth looking at more closely.

The human brain is wired to detect patterns – but sometimes we can fool ourselves into thinking a pattern exists and then convince ourselves by selecting only evidence that confirms our belief.

But, that’s where computers come in, and the ability to visualize and run a correlation analysis should help sort that out.

Once we have an expected pattern, then we have something to compare against when looking at new data values in the future.

Then there are deviations.

Most things – and their associated data points – fluctuate.

They go up and down – sometimes up for a while and sometimes the other way.

Our task is to figure out which movements are significant.

And we can do that by using methods like control charts – where we work out where we expect values to be most of the time and call out the ones that go out of the bands we have set.

Finally, there are outliers.

These are the data points that are simply different from everything else.

They could just be wrong, or they could point to a major problem.

For example, it could be a sign of fraud, or a breakdown of equipment.

A thematic review of audit quality by the UK’s Financial Reporting Council (FRC) finds that a lot of firms talk about their use of data analytics in auditing financial statements.

Not many, however, have the in-house capability to get and process data in this way.

It seems that it makes a lot more sense to have a centralised team that can provide this kind specialised IT capability – from extracting data from other systems, getting it into the right format and carrying out the necessary analysis.

One point to note is that some companies offshore this kind of data capture and analysis – which may become an issue as more governments create controls over data governance, security and privacy.

On a practical basis, however, if we have tools and processes that can analyze patterns, deviations and anomalies or outliers, we’re off to a good start.

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How many data-driven business models can you come up with?

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We’re all aware of big data – the ever expanding collection of data points around us.

The data dump piles up daily.

From the tens of thousands of photographs we have to social media postings, from smart meters monitoring electricity usage to databases full of supply chain variables – the amount of stuff around us just keeps increasing.

Which creates new business models and opportunities for old and new firms.

In a working paper published by the University of Cambridge, Josh Brownlow, Mohamed Zaki, Andy Neely, and Florian Urmetzer put forward a framework to think about data driven business models.

They suggested that we need to ask ourselves six questions:

  1. What do we want to achieve?
  2. What is our offer?
  3. What data do we need and how are we going to get it?
  4. How are we going to process and use the data?
  5. Where is the money?
  6. What’s in our way?

The team also put forward a taxonomy to help classify data-driven business models.

An adapted form of this is shown in the picture above, to help see what products, organisations and solutions are already in this space.

So, data comes from broadly three places.

Internal data is generated by individuals and companies.

External data is broadly everything that we haven’t created.

Monitored data is collected and processed in a planned way – like website analytics or electricity metering data.

With data – we need to do three things to turn it into insight and decision support material.

We need to collect it, sort it in some way and then analyze it.

This classification system gives us a way to start thinking about business models in this space.

Also, clearly, models will overlap and some firms will do more than one thing.

So, for example, lots of data sits in comma separated value (csv) files. While we’d like to think databases are everywhere, the csv format is still very useful.

Many companies rely on Microsoft Excel to process their data – most people know how to use it after all.

The gorilla in the room when it comes to collecting information that is out there is Google.

On the other hand, when we want to find someone in business, LinkedIn is probably the place to go. It’s sorted everyone’s professional information rather well.

Now, lets say we want to analyze twitter feeds – IBM’s Watson has a suite of services that might allow us to do that.

Then there is the data we collect on purpose.

Like electricity smart metering – that’s rolling out in households in the UK – which is 20 million more places to read data from.

When it comes to market price data of all kinds, Quandl provides a convenient way to pick up feeds.

Finally, to analyze all this data, tools like Python and R come into their own – as scripting languages that can cope with the size and complexity of analytical needs.

So, the next time we’re thinking of a data-as-a-service type opportunity, this classification may be a useful one to keep in mind.

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Could reality get too dull for us?

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We spend an increasing amount of time in front of screens.

Some are fixed – our TVs, computer monitors. And some are with us all the time – our mobile phones.

A future where our screens can be in front of us all the time is not too far away.

Cher Tan, in an article on placenessness – a world where we can have the same coffee anywhere in the world – draws our attention to Keiichi Matsuda’s film Hyper Reality, a vision of this kind of future.

In this new world, we layer experiences on top of reality.

We start with questions – and questions these days for most people are synonymous with Google.

The thing is that many of the important questions we have, like who am I? and where am I going? are answered quite literally by Google – with name, rank and serial number, and a point on a street map.

It answers the question but misses the point.

Then there is the constant backdrop of what we do with our time.

Many of us play games – and we can do this all the time – on the bus, while walking, during work…

There is also useful stuff – like instructions on where things are, when to stay out of the road, where the nearest emergency centre might be.

When something is wrong, we can speak to someone.

Or something anyway.

Lifelike chatbots can have a conversation with us and help us with any problem we have.

Until they stop working and the system needs to be restarted.

In this world we’re surrounded by information, from how much things cost to how much money we have left.

Although money might be an old fashioned thing by then – we might be much more focused on points, how many we have, how much we can get with them, whether they are safe.

In Hyper Reality, the biggest feeling is one of sensory overload.

It’s like having the lights and action of Times Square superimposed on the daily activities of life – getting on the bus, going shopping.

And, when the lights go out for a second, reality seems rather underwhelming.

Probably much like the feeling we have when we’re stuck on a train without a book and our mobile phone battery runs out.

We want the lights and action back on again.

It’s a relief when the system starts up and we have the screens up and running.

The thing is – in this world we could go days without having to speak to anyone real.

We could be totally connected and yet be completely alone – we need the virtual world because reality is just too crushingly dull.

Could this be a possible future for many?

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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.

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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.

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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.