Category Archives: Digitalization

Analytics for Strategic Management

I am starting a new executive course, Analytics for Strategic Management, with my young and very talented colleagues Alessandra Luzzi and Chandler Johnson (both with the Center for Digitization at BI Norwegian Business School).


Alessandra Luzzi


Chandler Johnson

The course (over five modules) is aimed at managers who want to become sophisticated consumers of analytics (be it Big Data or the more regular kind). The idea is to learn just enough analytics that you know what to ask for, where the pressure points are (so you do not ask for things that cannot be done or will be prohibitively expensive). The participants will learn from cases, discussions, live examples and assignments.

Central to the course is a course analytics project, where the participants will seek out data from their own company (or, since it will be group work, someone else’s), figure out what you can do with the data, and end up, if not with a finished analysis (that might happen), at least with a well developed project specification.

The course will contain quite a bit of analytics – including a spot of Phython and R programming – again, so that the executives taking it will know what they are asking for and what is being done.

We were a bit nervous about offering this course – a technically oriented course with a February startup date. The response, however, has been excellent, with more than 20 students signed up already. In fact, wi will probably be capping the course at 30 participants, simply because it is the first time we are teaching it, and we are conscious that for the first time, 30 is more than enough, as we will be doing everything for the first time and undoubtedly change many things as we go along.

If you can’t do the course this year – here are a few stating pointers to whet your appetite:

  • Big Data is difficult to define. This is always the case with fashionable monikers – for instance, how big is “big”? – but good ol’ Wikipedia comes to the rescue, with an excellent introductory article on the concept. For me, Big Data has always been about having the entire data set instead of a sample (i.e., n = p), but I can certainly see the other dimensions of delineation suggested here.
  • Data analytics can be very profitable (PDF), but few companies manage to really mine their data for insights and actions. That’s great – more upside for those who really wants to do it!
  • Data may be big but often is bad, causing data scientists to spend most of their time fixing errors, cleaning things up and, in general, preparing for analytics rather than the analysis itself. Sometimes you can almost smell that the data is bad – I recommend The Quartz guide to bad data as a great list of indicators that something is amiss.
  • Data scientists are few, far between and expensive. There is a severe shortage of people with data analysis skills in Norway and elsewhere, and the educational systems (yours truly excepted, of course) is not responding. Good analysts are expensive. Cheap analysts – well, you get what you pay for. And, quite possibly, some analytics you may like, but not what you ought to get.
  • There is lots of data, but a shortage of models. Though you may have the data and the data scientists, that does not mean that you have good models. It is actually a problem that as soon as you have numbers – even though they are bad – they become a focal point for decision makers, who show a marked reluctance to asking where the data is coming from, what it actually means, and how the constructed models have materialised.

And with that – if you are a participant, I look forward to seeing you in February. If you are not – well, you better boogie over to BIs web pages and sign up.

Norway and self-driving cars

(This is a translation (with inevitable slight edits) from Norwegian of an op-ed Carl Störmer (who, in all fairness, had the idea) and I had in the Norwegian business newspaper Dagens Næringsliv.)

A self-driving future

Espen Andersen, BI Norwegian Business School and Carl Störmer, Jazzcode AS

Norway should become the world’s premier test laboratory for self-driving cars.

Norway needs to find new areas of development after oil – and we should go for something the whole world wants, where we have local advantages, and where we will develop deep and important knowledge even if the original idea does not succeed. We suggest that Norway should become the world’s premier test laboratory for self-driving cars – a “moon landing” we can develop far further than what we have been able to do from our expertise in sub-sea petroleum extraction.

1280px-tesla_model_s_26_x_side_by_side_at_the_gilroy_superchargerSelf-driving cars will do for personal transportation what e-mail has done for snail mail. Tesla-founder Elon Musk says Teslas will drive themselves in two hears – they already can change lanes and park themselves in your garage. The “summon“-function (a “come here”-command for your car) could, in principle, work across the entire USA.

An electrical self-driving vehicle will seldom par, choose the fastest or most economical route, always obey the traffic laws, and emit no pollutants. A society with self-driving cars can reduce the number of cars by 70-90%, free up about 30% more space in large cities, reduce traffic accidents by 90%, and drastically reduce local air pollution.

Google’s self-driving carsgoogle_self_driving_car_at_the_googleplex have driven several million kilometers without self-caused accidents, but there are still many technical problems left to solve. The cars work well in the well marked and carefully mapped roads of sunny California. The self-driving cars drive well, but the human drivers do not. But we cannot execute a sudden transition – for a long time, human and automated drivers will have to coexist.

Norway has unique advantages as a lab. In Norway, we can develop our own self-driving cars, but also be the first nation to really start using them. We do not have our own car industry to protect, we are quick to purchase and start to use new technologies, we are such a small country that decision paths are short, and should an international company make a marketing blunder in Norway, the damage will be limited to a very small market. We can easily change our laws to allow for testing of self-driving cars: Oslo, Bergen, Trondheim and Stavanger has enough traffic issues and large enough populations to suffice for a serious experiment. As a nation, we are focused on environmental issues, innovation and employment.

Norway’s bad road standard is an advantage. Norway has plenty of snow and ice, bad weather and bad roads. Today’s self-driving cars need clear road markings to be able to drive safely. But Norway has world leading capabilities in communication and coordination technology: The oil industry has learned how to continuously position ships in rough seas with an accuracy of about five centimeters. Telenor is a world-leading company in building robust mobile phone networks in complicated terrain. Technology developed for Norwegian conditions will work anywhere in the world.

Norway needs self-driving cars more than most nations. Norway is the world’s richest and most equal country, creating a modern welfare state through automation and technology-based productivity improvements. The transportation industry is over-ripe for automation. The technology can maintain productivity growth and offer a new life for many people – the blind, the old and the physically handicapped – who do not have access to cheap and simple transportation today. It will create many jobs – think before and after the smart phone here – that can be created based on abundant and cheap transportation.

Norway will win even if we don’t succeed. Lots of new technology has to be developed to make self-driving cars from experiment to production: For instance, software has to be developed that can handle extremely complicated situations when autonomous cars will have to share the road with tired human drivers. More importantly, lots of products and services can be built on top of self-driving cars, business models have to be developed, and many industries will be impacted. The insurance business, for instance, will have to adapt to a market with very few accidents. Even the donor organ market will be impacted – though traffic accident organs by no means make up the majority of organs available, there might be a shortage of available organs.

Norway has faced tremendous changes before. We have transited from being harvested ice to electric refrigertation (in the process enabling our large fishing and fish farming industries), from sail to steam shipping, from fixed line telephony to mobile phones. Our politicians have, quite wisely, created an electric car policy ensuring that we have the highest density of electric cars in the world (10% of all Teslas are sold in Norway.) Norway has everything to earn and very little to lose by going all in for self-driving cars.

Let’s do it!

Does someone have to die first?

double-classroomBlogpost for ACM Ubiquity, intro here:

Digital technology changes fast, and organizations change slowly: First using the technology as an automated, digitized version of the old way of doing things, then gradually understanding that in order to achieve productivity and functional breakthroughs. We need to leave the old metaphors behind. For this to happen, we need new mindsets, unfettered by the old way of using the technology. I wonder if my generation has the capability to do it.

Read the rest at ACM Ubiquity: Does someone have to die first?

Computational thinking notes

Grady_BoochNotes from Grady Booch‘ presentation on Computational Thinking, and ACM Webinar, February 3, 2016 (4617 people attended, in case you wondered.)

Note: This is real-time notetaking/thought-jotting. Lots of errors and misrepresentations. Deal with it.

This will be a different way of thinking – and perhaps to think differently about the profession of software development. Recommends Yuval Harari Sapiens, talks of the cognitive revolution, the agricultural revolution, the scientific revolution. Babbage as citizen scientist, begin to see a new way of thinking: Computational thought. Boole had a similar set of ideas, took it from mechanization to laws of thought – tries to investigate the operations of the mind by which reasoning is done.

I can’t shoe a horse, but can build a 3D rendering of one, and then produce a virtual horse in Avatar. Why? Our ways of thinking addresses what is necessary to survive in the world we live in. We have different relationship to time: With the cognitive revolution, we had slow ways of measuring time, such as seasons, the scientific revolution gave us theories of time – and a frantic obsession with ever smaller measures of time. If the ways of thinking we had in previous lives where appropriate then, what are the ways we should think now?

Jeanette Wing – introduced computational thinking in CACM: Computational thinking as the thought processes that are involved in formulating a problem and expressing a solution in a way so that a computer – human or machine – can carry it out. To be able to do that will be increasingly important to succeed in today’s world – it will help you shape the world and live in this world.

Computing started out as human computers (mostly women), then a gradual mechanization and, indeed, industrialization of computing with ever more rigid processes, eventually digitalization of it (via punch cards). Businesses gradually starting to reshape itself as a result of computational thinking – and businesses changing computation. Sciences beginning to use computational thinking. Around WWII it also began to change the ways we went to and won wars. (Again, many women, see the documentary “Top Secret Rosies“.) The computational thinking drove our imagination beyond what the computers could do, beyond what we do in the present.

In the 60s and 70s, computational thinking started to reshape society – but it was compartmentalized – the “programming priesthood.” The SAGE was one of the first personal computers, example of interfaces learning from war. Largest systems of its kind, forced us forward in UI, hardware and software. The 360 and others broke computational thinking out of the chosen few – Margaret Hamilton coined “software engineering”. Finally personal with the PC – representing a state change. omtroducing devices that forced people to think in computational ways, forcing us to adapt to the machine. Current state: Outsourcing part of our brains to smartphones – computers that happen to have an app for dialing – computational going from numerical to symbolic to imaginatined realities. Computational thinking is beginning to erode our thinking about old imagined realities, such as governments and organizations.

I think the idea of the singularity is fundamentally stupid – when and if it comes, we will have become computers ourselves anyway, according to Rodney Brooks. This forces us to think about what it is to be human. How does computational thinking change how we look at the world.

In terms of software development, the changes has been from mathematical to symbolic to imagined realities. We are not only building imagined realities, but stepping inside them and living in them.

The fundamental premise of science is that the cosmos is understandable; the fundamentalt premise of our domain is that the cosmos is computable. We enter the world with the understanding that anything we can dream, we can compute.

Gödel taught us that there are things that are unknowable, but that does not diminish the importance of scientific thinking. There are similar things that are uncomputable, the computational thinking is still powerful and can push the world forward. The scientific process suggest that we have a trajectory towards a simplified, standard model. In computation, we go the other way: Start with something simple and make it incredibly complex.

What does it mean to see the world as computable? The first assumption is that the cosmos is discrete, or at least computationally finite. I can make reasonable assumptions about reality that means I can do powerful things. It may not be totally, but near enough that it is useful.

Secondly, I assume the world is based on information, which means I can look at the world through data. DNA and cellular mechanisms can be computed. The lens of information allows us to derive powerful theories. The dark side what is happening in CRISPR, genetic manipulation without knowing the consequences. Incredible power but incredible responsibility.

Third, data is an abstraction of reality. We can use all these powerful tools, but in the end we are building an abstraction of the world. Can build them and begin to rely upon them, but the other side of computational thinking realizes that this is not reality, it is just our view of it. A model is a model.

We use algorithms to form abstractions, but now can hand over without waiting, because we can depend on our ability to generate an algorithm to represent the world. Look at BabyX, from University of Auckland.

The importance of scale, from Feynman‘s Room at the bottom article. But we can also build imaginary realities that are larger than the universe itself. Computing is universal – can be used everywhere, spreads to any manifestation of execution: computational physics, chemistry, biology, psychology, sociology … and gradually computational philosophy. Has spread itself in ways that has changed everything – but maybe this way of thinking is just the threshold of the next way of thinking?

The earliest ways of thinking evolved as a means of bringing more certainty and predictability to an uncertain and unpredictable world. Scientific thinking evoleved to understand the world. Computational thinking has evolved as a means of controlling the world at a level of fidelity once reserved for gods.

Computational thinking has changed how we look at the world. That is to be celebrated, and we should encourage non-programmers to understand how it works. But let’s not forget what it means to be human in this world.

Some questions:

  • Are we falling into the “modelling the world in terms of current technology” trap? Yes, let us be self-aware of the limits of this thinking. We are assuming that evolution is computation on DNA, but it is only an abstraction – what if there is something wrong and it is not the correct model. BTW: Nick Bostrom and intelligence – I disagree that computation can create life, but lets explore it.
  • How does new forms of teamwork (as with email) change our ability to solve problems? Not a sociologist, but fascinating that the same social structures show up in our imagined worlds. 10K years out? Don’t know, but some adaptation may have happened. No matter what, we need trust – the degree of trust forms the basis for any organization and what you can do with it. I believe that anything we do in this space is shaped by human need.
  • What about genetic programming – will computers be able of compuational thinking? First off – computers write their own programs now, including manipulating their environment. But most of the stuff in neural networks is dealing with the perception side of the world, we can’t go meta on those neural networks. Second – is the mind computable? Yes, I believe it is, but see one of the computing documentary we are making.
  • Can computing create art with meaning? Listen to the classical pianist Emily Howell, but Emily is an algorithm. Computers can create art, but we create our own meaning.
  • Does outsourcing your brain to smartphones inhibit our ability to do computational thinking. See Sherry Turkle, it does change our brain, refactors it. It is a dance between us and our devices, and that will continue for a long time.

Recording will be at


Accenture and connected health

(Notes from an Executive Short Program called Digitalization for Growth and Innovation, hosted by Ragnvald Sannes and yours truly, in Sophia Antipolis right now.Disclaimer: These are my notes, I am writing fast and might get something wrong, so nothing official by Accenture or anyone else.)

Andy Greenberg is relatively new to Accenture, having a background in various technology companies involved in health and fitness monitoring.

The Internet of Things is the next era in computing, we are moving to the second half of the chessboard, Moore’s law is still active. Everything gets faster all the time, sensors cheaper, more and more connections and kinds of connections becoming available. A lot of the data growth has been driven by sensors. Smartphones everywhere, but can’t be assumed in the health space.

We need to capture the data, and we can’t send it all away – so we have to do data analytics on the edge, i.e. do analysis right away. You have to think about some things, such as engineers designing for engineers is not a good thing, and that if you can do something – such as connecting a device – it does not necessarily mean that you should do it. However, there will be 25 to 50b connected devices in the next few years – and it can deliver value. Tesla, for instance, can update its cars  instead of recalling them, improving customer satisfaction and saving money. An Airbus can send messages about needed parts, in the future they will be 3D printed at the airport before they land. There is a large gap between how many CEOs think IoT is important and how many have any kind of capability to do it.

IoT has enormous potential in health care. We have an aging population – and that is true of the health providers as well. Patients have different expectations: “health consumers are becoming consumers, comparing their experience not to the last doctor’s visit, but the last time they bought something on Amazon”. Spending on healthcare is increasing, as is the number of connected and connectable devices.

IoT enables connected health services, including merging the experience at home and at hospital, feeding data from home and feeding treatment from hospital to home after a hospital stay. The key is to understand the complexity of where people are at different times and manage accordingly, as opposed to thinking that they are either one kind of patient and another – we are all different types of patients at one time or another. Key is to focus on preventing readmission to hospital, but there might be more value in managing the healthy population – focusing only on the high risk patients may not be the right strategy. (Dee Edington – Zero Trends). It is not just about getting the ill well, but keeping the well well.

Moore’s law works both for fitness devices and medical devices. For fitness devices, wireless offloading of data makes a real difference, the holy grail is when the data offload disappear completely, if something monitors you all the time and alerts you to do something then you are more likely to use it. Medical devices have been more about diagnosis, now moving into monitoring and adherence. Proteus Digital Health, for instance, has a smart pill that monitors that it is being taken, for instance. Problem is that you need to wear a patch, and the first drug it is being applied to is one for schizophrenia – in other words, the patients that are most likely to be paranoid… There is also work done with smart devices, such as asthma inhalers, which can track how much it is used, geolocate, match to other people using inhalers same day, track pollen count etc. Find covariation from individual and communal data.

Healthcare players need to understand consumer expectations – Disney spent more than one million on wristbands to make the interaction with their parks much more frictionless. Healthcare providers should do the same thing – help their patients navigate through their services – including hospitals – to make the experience more seamless. This is happening in the pharmaceutical space: About half of all presecriptions are either not filled or taken incorrectly. Some names: Gecko Health, Propeller Health, Adherium, Inspiro medical.

When you add connectivity to the mix, it changes everything. One challenge is that even though the value is clear, the person receiving it may not be the one paying for it. This means that many device innovations are seen by their creators as a way to be unique, that will change over time because the value is much bigger of things being standardized and more widely distributed. You also need to standardize to the lowest common denominator from a connectivity perspective. Security is obviously an important issue as well.

Q: How do you make a secure app, how do you handle security?

Andy: Only a minority has a code on their phone, so you need a separate login. Security has be be part of the design from the very beginning. The biggest piece of guidance is to understand that.

Q: Can you see health care become completely digitalized?

Andy: Health care will always have a large human element, and there are huge hurdles in interoperability, but in between 5 and 10 years we will see significant action. The technology is not the problem any more, it is all about adoption.

Francis: We are stuck in a fee for service model that is, in my opinion, broken. Should move to a value model, and digitization can help with that.

Q: Where will we see the first real use of it?

Andy: Already seeing that, pockets of it. Maybe the most interesting and recent adaptation is the use of telemedicine about mental health. The VA hospitals are doing that to allow face-to-face conversations with clients with mental health issues. The key here is having payers pay for this as legitimate treatment. Remote monitoring is coming along. Change in payment models and health plans that change prices if you carry a device also drives this.

Q: The nordics are a bit of digital laggards – what will happen here?

Andy: The nordics tend to be ahead in technology and behind in business models. The aging population is a driver and Asia is a big area for that. Regulatory constraints are going to be a big hurdle, some countries are so high on privacy that they make it almost impossible to even try. Payment is important – if governments say they are willing to pay for making the elderly stay home longer, then it will come.

Accenture on Cognitive Computing

(Notes from an Executive Short Program called Digitalization for Growth and Innovation, hosted by Ragnvald Sannes and yours truly, in Sophia Antipolis right now.Disclaimer: These are my notes, I am writing fast and might get something wrong, so nothing official by Accenture or anyone else.)

Cyrille Bataller is a managing director and the domain lead for Intelligent Computing, with the Emerging Technology group at Accenture. He leads Accenture’s exploration of the Cognitive Computing field

Cognitive Computing

Skilled work increasingly done by machines – doctors, lawyers, traders, professors etc. Large potential impact, also societal, but exciting new technology. Cognitive computing is IT systems that can sense, comprehend and act – and are perhaps the most disruptive technology on the horizon. They can interact with their environment, learn by training instead of coding, and can analyse and use enormous amounts of data. The quest for cognitive computing has a long history – and has had a varied history, with periods of advances and periods of setbacks, or at least less interest. We differentiate between “strong” AI – achieving consciousness – and “weak AI” – having the machine mimic certain capabilities of humans.

Accenture’s ambition has been to create a toolbox of cognitive computing capabilities under a single architecture, such as text analytics, research assistants, image analysis, multimedia search, cognitive robots, virtual agents, expert systems, video analytics, identity analytics, speech analytics, data visualisation, domain-specific calculations, recommendation systems and self-adjusting IT systems. These are all examples of human traits and capabilities as well…

Some detail – image analysis is about deep learning, using neural networks to mimic how the brain works. Use layers of increasingly complex rules to categorize complex shapes such as faces or animals. In one case, they used neural networks to recognize and classify images of cars as undamaged, some damage or totalled to an accuracy of 90%. By applying text analysis as well, this could be used by an insurance company to analyze insurance claims. Google has acquired a company called DeepMind to look at images on the web, eventually recognizing cats.

Another example is use of robotics in business operations – from minibots, companies such as XL and AutoHotKey to automate application software, to standard robots to using virtual assistants to do user support, for instance. You can have a team of robots in the cloud to work alongside your real back office. These robots can observe and learn and gradually take over or optimize these activities, such as updating documents (powerpoints, for instance). Could be used to observe how an accountant works, for instance, and issue certification based on following proper procedures. You could have 20-30 people in the back office and 100s of robots learning from them and doing the regular stuff.

Virtual agents: Amelia, handling a missing invoice situation. Amelia can understand, learn and solve problems. Generates a flowchart which can then be optimized and analyzed.

Video analytics is another application that is receiving lots of attention. One problem with CCTV videos is that they are almost only used forensically – checking a certain date and time – because nobody has time to watch the videos themselves. Can be used to count cars and recognize events, measure queuing times at customs and immigration, measure service times, monitor for lost objects on a train, analyze the age distribution on public transport, detect emotions, understand the hot and cold zones in the shopping center, find leaks in an industrial setting, etc.

The following framework (from the perspective document referenced below) shows a four-quadrant framework that can help guide companies in choosing the approach towards cognitive computing:


See for more information and a point of view document, written by Cyrille Bataller and Jeanne Harris.

Accenture and Smart Cities

(Notes from an Executive Short Program called Digitalization for Growth and Innovation, hosted by Ragnvald Sannes and yours truly, in Sophia Antipolis right now. Disclaimer: These are my notes, I am writing fast and might get something wrong, so nothing official by Accenture or anyone else.)

Smart Cities – a discussion with Simon Giles.
Simon Giles is the global lead for global cities initiative in Accenture. He is located in the Health and Public Services group, and
– work with large, established cities on the process of digitalization
– work on digital masterplanning on new cities (Asia, Middle East) or fundamental redevelopment
– work with large organizations on understanding the consequences of increasing urbanism

The Global Cities team has worked in more than 80 cities around the world, they are all unique (and that is very important when working with them – not just the government, but also physical and demographic legacy. Huge differences even within Norway, for instance.) However, some common themes emerge:

  • digital transformation everywhere: The increasing IoT and addressable devices gives enormous rise in data amounts and sources. Can the cities deal with it? The rise of citizens digital service delivery expectations paired with a data explosion is pushing the IoT agenda forward. Example: Landing in Sophia Antipolis, firing up the Uber app, immediate booking and invoice submission, 30% cheaper than regular taxi. Some consequences: Digital privacy and security is going to be a huge hurdle and has to be taken seriously. As for democratisation, people both have to understand their rights, but also the benefits
  • mobility of talent. Digital talent can work anywhere, so how do you attract them? There is a war for talent, especially cities transitioning from a post-industrial to a knowledge economy. How do you make the city as attractive as possible for the people you want to attract? Healthcare, education, job prospects…
  • resource stress. Availability of resources (water, power, waste management, pollution etc.) increasingly a problem, need to understand flows and dynamics at a much more sophistication, digital technology can help shed light on what literally used to be a dark system. Moving from mitigation to adaptation – energy efficiency, transition from hydrocarbon mobility to electromobility. Differs between cities – water stress in California, for instance.
  • constraints on public spending. Need to reduce cap ex and op ex spending, as well as increase productivity. But not all investments are equal – a $400m bridge is seen as normal, a $400m investment in digital infrastructure as profligate.
  • emerging prosumers Citizens increasingly have a voice through digital tools and participate more actively. Need to think creatively about how to use the data created by citizens to make better decisions and services.

Cities’ role has changed over time – from a meeting place for exchange of agricultural products to a centre for industrial production, introducing sanitation etc. Glasgow and Liverpool became rich because they were on the leading edge of the industrial economy, as market makers. What does that mean in the digital economy? There seems to be an abdication of responsibility from city politicians and managers, thinking digital is completely global – but there are definite winners and losers in that contexts: London and Berlin dominating in Europe, for instance, by making very small changes, such as Tech City in London, relatively small but enough to win the war for talent. Cities need to be more active, governance changing from verticals (water, power, etc.) to a more integrated model. How do we organize for digitization. In Europe, we really are laggards in that respect – companies have CIOs, moving into new technologies etc.. In the US we see city CIOs. In London they did, but did not give that person any power, so he left. The operating model for cities will change.

In the corporate world, we are very articulate about value – shareholder value etc. In the public world, we don’t have that tradition, vocabulary and methodology for doing that. We need to create the equivalent of a business case, focusing on economic prosperity, efficient and effective operations, and high quality public services.

The Global Cities initiative posits the following value proposition for cities:

  • how do you create economic prosperity?
  • how do you create effective and efficient public services?
  • how do you create social and environmental sustainability?
  • how do you create strong financial management and governance?
  • how do you create a good experience for the citizens? (so much is driven by expectations, you have to understand perceptions, no matter what else you measure)

Break this down further into sub-questions where projects can be evaluated to determine the equivalent of a business case – not to measure profits, but outcome to manage the taxpayers’ money as effectively and efficiently as possible.

Also have developed an operating model consisting of processes such as

  1. strategy and planning
  2. performance management (better use of data to drive decisions)
  3. digital citizen (single face to the customer, need single face to the government)
  4. asset management and operations
  5. back office (shared across functions)
  6. digital enterprise (common digital infrastructure, i.e. a common GIS system across all agencies)
  7. agency services

Analysis of data becomes more important. For example: There was a bridge in a city in Brazil, lots of congestion, discussion on whether to build a new bridge, increase ferries, or build a tunnel. Analysis showed that two government agencies that had moved to the other side of the bridge created most of the traffic, moving them back would obviate the need for a new bridge…

Cities are organized as a legacy of the industrial economy, geared to make cities work like machines. Now we are in a digital economy, thinking about becoming a place where things are designed and services are sold. The CIO role that came in the 90s in the US is happening now in cities, moving from provisioning to thinking about where the data is and how we can use it.

In New York anyone can call 311 to get in touch with the city’s public services. They log the requirement, and get back to you. By the way, why should this be phone call, why not an app? In general, public services do not do front desk. Need to professionalize and de-politicize the relationship between the strategy/policy setting and the service delivery.

Case study: Accenture is working with two larges cities in setting an agenda for a more systematic approach, based on sensing and responding to activity, and that might be smart metering or smart transportation, but it is not systematic and not city-wide. A Smart City is not that because the power company has put in 5000 smart meters…you get death by pilots, they never scale. In these cities we are seeing much more systematic approaches, taking a common approach. It does not make sense that individual ministries which inevitably will implement IoT set up their own platforms. In two cities Accenture work with, they go beyond the platform, extracting data from all platforms and an kind of device and make it available for analytics.

Cities to watch are Dubai and Singapore – converging city and state, so they have powers to act, and they have ambition. Smart Dubai is a digital platform to create data across the city. Singapore has a Smart Nation program. Rather than having islands of smartness in verticals, they want to be able to look at data across functions, providing new services, and a platform for entrepreneurs and innovators to build on. Government as platform, and as service.

For example: In one of the cities, it rains almost every day in some area, no taxis available. Match data from meteorological services, taxi fleets, and people location (from cell phone towers), create an algorithm to reroute taxis to desired areas in advance of a rainstorm. The city can create this urban information marketplace to make that possible, to allow entrepreneurs to work based on this platform. To do this, you need citizens that are skilled, connected and have a culture for using and innovating with the platform.

Norway should think about setting up IoT as a platform and make it and its services available to the cities. Brisbane is a good case study on consolidation, from around 20 units to 1, has 2 million people. Could deliver much better public services. Political perspective should be about how to deliver more on top of that platform.

Question: What should I do as fresh politician to promote this?

Politicians need to articulate the value proposition, because industry will respond. For example: Immigrant communities can become a cost to society because of limited inclusion and integration. Politicians need to deconstruct that problem, identify root problems (why aren’t they getting those jobs?), and use digital technology to help them. Start with the small things, where you can make a difference. Find problems to solve, describe what a day in the life of an immigrant community will be two years from now, with digital, and make it a stark and dramatic contrast.

Secondly, establish a capability for analysis, make it small, bring in someone for a few months, demonstrate value propositions. Then signal to the rest of the government about what you could do if you had more data… Demonstrate the value proposition, then expand. And don’t do pilots, technical proof of concepts, make sure whatever you do is scaleable.

Question: The sharing economy, self-driving and electric cars?

At the moment, the relationship with the sharing economy – AirBnB and Uber, mostly – is antagonistic. Uber is doing well because they have a better value proposition than the taxi companies, which are ripe for disruption. It will happen at some point anyway because the fundamentals are solid. But taxi drivers are important voters, so politicians will appease them, but the sharing economy will thrive anyway. How do you manage that transition without losing your seat and disappointing citizens. You lose trust with the public if you stop evolution, the voters will move with their feet (and money).

An interesting view on Tesla’s Model X – there are a few design elements there that point towards a strategy. The gullwing doors are great for mums lifting kids and parking, but also gives good access to the front and back of the car when it is self-driving. The self-docking technology with the “snake” means that it can self-dock and pick up people. Think of this in the context of having a fleet of these things. (Search for article on deconstructing the Tesla launch.). More self-driving functionality will be added eventually, everything is there except the LIDAR. Will become increasingly self-driving, Uber has already done a deal with Tesla and might be the first fleet of self-driving cars. What does that mean for public service providers? Will standard diesel buses be there? Interesting things coming out of MIT, ethics of self-driving for instance.

Question – what about the high levels of unemployment?

That’s a pretty big topic, much writing about the future of work and the implications of the sharing economy. Increasing automation, AI, self-driving cars and so on. If left unchanged, you will see massive unemployment, but this has happened before. The economy of New York city in 1905 had many thousands of people working with horses, and they all lost their jobs over the next 15-20 years. I think we will see the development of the “gig” economy, more freelancing and part-time work. This has massive implications about how we think about support – the traditional job centre will be a much more fragmented activity. The idea of a citizen wage is interesting but a long way off, more about having social services where the onus is on you to create value over that. Cities will need to think about upskilling those workers…

Question – how can we rethink the fragmented nature of the Norwegian public structures?

At the basic level, some services need to operate at scale – no reason a municipality should have its own HR system, for instance. At the same time, don’t throw out the baby with the bath water – from a front office perspective, you need the closeness to the needs of the people. But a community of 10000 does not have the capability to develop their own analytics – so what is the role of the central government in providing a platform? The municipality can become a buyer of services from a common platform. Only a few cities large enough in Norway to do something, so you need to demonstrate value there, then have the central government provide it as a platform for everyone.