One of the catch-cries of the Smart City movement is that a smart city should be “data-driven”. While this catch-cry rolls off the tongue very nicely, it is incredibly unhelpful for a variety of reasons.

• It’s back-to-front;
• It creates a harmful tunnel-vision and mindset for smart city leaders and practitioners that blocks creative thinking and innovation;
• It puts data in the driver’s seat;
• It’s bad messaging for public relations.

Back-to-Front

Imagine you were attending the first meeting with an architect to build your dream house and the architect starts the discussion by talking about the new type of nails and nail gun she wants the builder to use. You would be rightly confused and unimpressed. When it comes to house design you don’t start with a discussion on tools. Rather, you start with the desired outcomes and user experience. Its the same with any design process and for any smart city idea or project. Data is a simply tool and enabler — nothing more. Starting a design discussion with a tool discussion is back-to-front. Data comes last! Rather, the requirement for data is driven by a hierarchy of design requirements. (Not the other way round!)

The requirement for data is driven by a hierarchy of design requirements.

Should your mindset to be input-driven? The government disagrees, and for good reason. The government funding programs that I’ve been involved with both as applicant and assessor, prefer projects that focus on outcomes and results not inputs on the (reasonable) premise that this focus increases the chances of success. But a data-driven mentality is very much an input focus, i.e. back-to-front. Focus on outputs not inputs instead.

Focus on outputs not inputs.

Further Proof

There is further proof that adopting a back-to-front, data-driven mindset is a problem that needs fixing. These two statements are now common in the smart city domain:

• “Data should have a purpose.” That is, you must be able to explain why you are collecting the data — its benefits to citizens for example. That is, we never collect ‘data for data’s sake’.
• “Data must be fit-for-purpose.” That is, the data types, quantity and quality are suitable for your application so that after processing you can get meaningful results and therefore can make meaningful decisions. That is, we want avoid a situation of ‘garbage in, garbage out’.

If you have a ‘data-driven’ mentality you’ll need to keep repeating these statements to staff and stakeholders!

Both statements are true. But…

Both statements are true. But they exist because of a need to undo some of the misconceptions and pitfalls brought about by the ‘data-driven’ mindset! However, if you follow a proper vision- and goals-driven design process then these two statements become redundant. Why? Because the data ‘purpose’ is determined up-front at the process (re)design stage. Your design blueprint will drive the requirements to collect data that is ‘fit-for-purpose’.

Tunnel-Vision blocks innovation

Naturally, a data engineer and enthusiast will want to remind you that data is vital to a smart city: “Data is the life-blood”, “Data is the new oil”; but is this the right way to think about data and it’s relationship to a smart city?

Let’s take an example from the transport domain. How would you answer the question, “Is petrol vital to the operation of a car?” The petrol expert will claim it is vital and extol the virtues of “petrol-driven” engine in the same way a data enthusiast might extol the importance of data, and the “data-driven” city. And similarly the suspension expert will say the suspension is vital to a car in a similar way that a privacy expert might emphasise the importance of privacy to a smart city.

But if your job was to innovate next-generation cars, an emphasis on petrol’s vital role will probably make you feel uneasy. Why? Because you know that the next-generation cars may not use petrol at all, but hydrogen or electricity instead. The tunnel-vision, narrow focus on petrol ruins our creative and innovative thinking by blocking alternatives.

This is a common flaw in Smart City thinking. Practitioners focus on the data they have (the old ways) without identifying the data they want (paradigm shifts and process re-design).

Know the data you want.
The data you want is driven by vision, goals and paradigm shifts.

Such practitioners will then add better tools to their processes, e.g. Artificial Intelligence, to try and extract more value from existing data sets instead of rethinking process and outcomes to seek better data sources, e.g. sourcing data direct from willing citizens.

The smarter city always needs to have the bigger picture in mind and be driving the paradigm shifts and excellence in customer outcomes.

Now notice that our petrol-driven car example still has a major flaw — the focus on the car itself! In the transport domain it is now common to talk about ‘future mobility’ — getting from one place to another — not ‘future transport’. Mobility encompasses all modes of transport plus more: buses, cars, bicycles, roller skates and even simply walking. And who knows?.. in the future it may even include hover boards and teleportation! With this big-picture, forward-looking concept of mobility, a tunnel-vision focus “petrol-driven mobility” is particularly absurd and limiting.

Similarly, in the smart city domain, the oft-promoted ‘data-driven’ mindset is an example of back-to-front thinking and tunnel-vision that can block creativity and innovation. But if, for example, you limit yourself to only thinking ‘open data’ you are guilty of the petrol-driven mobility mistake.

At a workshop a local government employee explained to our group that their council had lots of unreleased data then repeated the misconception that data was of high value and could be exploited. I explained that in reality some of that data will be gold and other will be dross. The only way to know the difference will be determined by it’s eventual application, and those new applications, driven by visions for new customer experiences, should be the initial focus of his smart city thinking and creative effort. While indeed some data may have hidden value, with a bit of futuring and co-design effort the value of most data will become plain, as we allow vision and goals drive the requirements for data. Before you start searching amongst data you have, know the data you want.

Before you start searching amongst data you have, know the data you want.

Data in the driver’s seat

Some people sit back, look at the smart city, and see data feeding all the new applications and solutions. Like the petrol-driven car they see data as the new oil driving the smart city engine and outcomes. But who creates the great driving experience of a great car? The petroleum engineer? Not at all. The car designer does that. And who puts a car to good use and extracts the real value out of the car? The driver does that, not the petrol!

Neither the car designer or the car driver give much thought to role of petrol other than trust the petroleum engineers have done their jobs properly. If you are a smart city leader, innovator or involved in city operations your focus must be the same; on new outcomes and possibilities, not on the data.

But, you may (rightly) say, “But petrol is old technology in a car, it doesn’t need any special attention. Data is new in a smart city context. It’s a new enabler. It is naturally the centre of attention.” OK, so let’s revise our analogy to the electric car instead. It too is relatively new and revolutionary.

So, how much does the car designer care about the car engine being electric and not petrol-driven? The answer is some. Of course the designer factors in the advantages of the new technology (no gear box, great torque) and the disadvantages (weight, size, less range). But the designer’s main focus is always on delivering a great customer experience. If she doesn’t she will fail.

If a car designer doesn’t deliver a great customer experience he will fail.

Similarly the driver must factor in the advantages and disadvantages of driving an electric vehicle to arrive safely, on time and to enjoy the driving experience. The driver’s vision, goals and outcomes are not determined by the power source.

City leaders and innovators must focus on outcomes, and the design decisions that determine those outcomes. Advances in data and technology and opportunities of direct collaboration with citizens, enable us to rethink every process and outcomes of the city. They totally reset our vision and goals and expectations of citizen experience. This new mindset is what drives value in a smart city — not the enablers like data.

City practitioners involved in city operations make the city function effectively and cost efficiently. They too are innovators and must rethink every existing process in the light of what advances in data, technology and citizen participation enable.

Changing mindset is what drives value in a Smart City.

Don’t be distracted by the ‘bling’ of data and technology.

Bad Public Relations

Dear data enthusiast, believe it or not, many citizens are not that interested in data! In fact they are wary of it. For some, ‘data collection’ has connotations of ‘surveillance’. Both an emphasis on data, as well as on being a “data-driven smart city” is bad for PR because it sends the wrong message to citizens. Here are two examples.

Sidewalk Labs Toronto

Google’s now abandoned Sidewalk Labs concept in Toronto was unashamedly built on the premise that data was an innovation enabler and we should collect lots of it. It was very much a data-first approach with clever applications following after. Their approach is absolutely fine if your city or precinct aspires to being a scientific research laboratory where the focus begins with scientific investigation where the likely outcomes are unclear. In a research lab (where I’ve spent much of my career) you essentially: arm your scientists with lots of tools (in Sidewalk’s case lots of data); give them a general direction to investigate; and let them loose. As such, Sidewalk’s initial plan was to collect lots data without clearly defined applications in mind — (deliberately) data without a purpose.

Public perception was not flattering: see The City of the Future Is a Data-Collection Machine; and Project with Google’s Sidewalk Labs comes under increasing scrutiny amid concerns over privacy and data harvesting. Not surprisingly many citizens were concerned with what data was to be collected and exactly how it was to be used. Google tried to respond with a Civic Data Trust concept to manage data responsibly on behalf of citizens but this was and uphill battle, e.g. Why Does Google Want to Hand Its Smart City Data to a Third Party ‘Civic Data Trust’?

It is also poor PR to inadvertently imply that your citizens might be ‘lab rats’ in your smart city laboratory! I’m a big advocate of Living Laboratories where students and citizens directly participate in experiments with new smart city technologies and solutions. But it is vital to get their buy-in first. Not ‘lab rats’ but bold pioneers and partners who are willing part of social and smart city trials!

City of Darwin

The City of Darwin in Australia installed CCTV cameras as part of a smart city safety and security project. They suffered considerable citizen backlash because of the perception that the surveillance cameras may be used for face recognition. Though the city claimed no face recognition capability would be ‘switched on’ citizens were highly sceptical. You can read about these concerns here Darwin’s ‘smart city’ project is about surveillance and control. According to the Darwin’s smart city leaders, it took a lot of work to gain the trust of citizens for the CCTV system to gain acceptance. Afterward they have openly acknowledged the importance of citizen consultation as early as possible [at the design stage].

In cases such as this, it is important not to skip citizen involvement in the co-creation process and jump to technology installation and data collection. A proper design process driven by vision, goals and benefits builds trust and helps all stakeholders understand the pros and cons of any technology application before committing.

Signs of the Data-Driven Problems

It is possible to sail through a successful project with a data and technology focus from beginning to end. But the risks of failure are high. The domains of research, innovation and start-ups are well aware of this trap but not so much in the domains of government and Smart Cities. Many Smart Cities that have had successes are still making beginner’s mistakes and learning the hard way. Here are some signs that your smart city initiative suffered a data-driven mentality:

Unexpected privacy issues

If you have a surprise backlash due to citizen privacy concerns then your design stage failed to engage citizens to either: get them on-board with the data and technology your design requires; or alter the design to one that requires data that has no privacy concerns.

CCTV can be used for a wide variety of Smart City applications, e.g. people counting, amenities usage, smart parking, public safety and security. Deployment of CCTV cameras is an example privacy are likely to be raised depending on their use.

Interoperability problems

While difficult to address, interoperability challenges, i.e. problems sharing data between systems, should be recognised and addressed at the futuring/design stage of a smart city project. Recognition of the future importance of interoperability might encourage you to seek out a relevant standard to adopt, or failing that, to collaborate with others to develop your own open ‘standard’.

All too often interoperability barriers are hit after a project is well underway or complete.

Failed processes

Ultimately Smart Cities are about doing things differently. The old ways are replaced or revolutionised by totally new paradigms. New services and enhanced customer experiences. The processes — the way we do things — are fundamentally changed. The biggest risk of a data-driven (or technology-driven) mindset is to divert attention from co-designing and user testing the new way of doing things — the new process. The the engineering challenges associated with implementation of new data and technology are surmountable — the challenges with customer and user rejection of new processes may not be!

Technology challenges are surmountable…
The challenges associated with customer and user rejection of new processes may not be surmountable!

For example, at Communities of Practice meetings in Australia some cities have admitted that Smart Bins and Smart Irrigation are examples of projects that have ‘struggled to bring benefit’ due to the need to rethink and redesign the processes and procedures of waste and grounds maintenance departments for them to succeed. The data collection alone was insufficient. Staff and departments must be willing to change their processes.

In our many Transport and Logistics Living Lab workshops it was often recognised that technology was not the biggest challenge to innovation, human factors were. In particular the fear of what change my bring.

Human factors and fears are the biggest risk to Smart City projects!

People must be willing (or convinced) to change!

Citizen push-back

A focus on the data and technology means taking your eye off the true goals and the biggest project risks. Unexpected citizen push-back is a warning sign of a data- or technology-driven mindset.

If you have a proper design process featuring customer engagement and co-design, there should be no surprises such as unexpected citizen push-back later on.

Both the abandoned Google’s Sidewalk Labs in Toronto (at the design stage) and Darwin’s CCTV camera initiative (at the implementation stage) were forced to take reactive approaches to citizen push-back. A proactive goal-driven co-design approach (where customers, citizens and stakeholders are involved from the onset) is the best insurance against unexpected citizen push-back.

Summary

I hope after this you don’t want to be seen as being a data-driven smart city. I hope you carefully reword your promotion documents to remove the emphasis on data and data collection and focus on vision, goals, outcomes and benefits.

I hope that also you’ll want to put some proper co-design processes in place that focus on the future and are driven by vision and goals for excellence in future customer experience. These will drive your requirements for data.

But remember:

The true driver of value of a Smart City is changing our mindsets.

We need to be vision-, goals- and outcomes-driven.

Caveats

You can have a successful Smart City program and focused on data and technology the whole time. If you did you were lucky. And there is a good chance your projects were more costly than they needed to be. They were definitely higher risk. Also, they may lack future-proofing and suffer problems further down the track. You chances of success are even higher if you hold on to your data and technology skills then readjust your aim so the data and technology doesn’t dominate or drive your thinking.

Readjust your aim

You may be in a city that had strong effective customer engagement as part of its design process and has avoided the pitfalls of a data- and technology-driven focus. Never-the-less your CEO or your marketing department may be caught up in the Smart City data-driven hype and feel a need to be perceived as data-driven. For all the reasons I’ve given above, I suggest you gently push-back! Don’t be derailed.

Evidence-Based Decisions

Cities can sometimes find a need to emphasise the role of data in decision making as opposed to gut-feel, i.e. ‘data-driven’ decisions as opposed to ‘gut-driven’ decisions. This distinction is particularly important in the contexts of COVID and climate science for example — challenges where wishful thinking and gut feelings should have no place in decision making. In this case, a better term than ‘data-driven’ decisions is ‘evidence-based decisions’. You can expand this messaging to something like this COVID example:

“We are driven by a desire to get our lives back to normal with the least suffering. We are making evidence-based decisions based on the best (scientific) modelling and best data we have available”.

Dashboards and Data-Driven Change

A Smart City can present decision-makers with information they have never seen before. Perhaps, for example, we present the mayor with a dashboard showing the mental health of female teenagers. The mayor may say, “I never realised that we had such a problem, let’s establish a program to do something about it.” In a sense, the availability of data has ‘driven’ the mayor’s ability to gain and insight and make an evidence-based decision. But like the ‘petrol-driven car’ this is a poor way to think about the relationship between data and outcome. And it is a poor way to think about and promote the value of smart cities.

A better, ‘smarter city’ thought process for leaders and innovators goes along these lines:

• Part of our vision and goals is better mental health in our city.
• Anecdotally we’ve heard there is a problem with teenage mental health.
• Let’s start a design process involving teenagers to look at the topic. We’ll look at it from a perspectives of: likely futures; ideal end-states for health management, data and insights; and identifying future-proof data elements.
• Our brainstorming and creative design process will tell us what data we’d like to collect and how to collect it.
• Then we’ll search for alternate data sources — perhaps not as valuable but already available. Accordingly, we may need to revisit some of the above steps.
• Let’s collect and examine the data and get an initial insight.

Notice how this thought process turns our thinking around from data-driven to vision- goals- and outcome-driven. The focus is clearly on the outcomes and not the data inputs.