In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics. As data science is a broad discipline, I start by describing the different types of data scientists that one may encounter in any business setting: you might even discover that you are a data scientist yourself, without knowing it. As in any scientific discipline, data scientists may borrow techniques from related disciplines, though we have developed our own arsenal, especially techniques and algorithms to handle very large unstructured data sets in automated ways, even without human interactions, to perform transactions in real-time or to make predictions.
1. Different Types of Data Scientists
To get started and gain some historical perspective, you can read my article about 9 types of data scientists, published in 2014, or my article where I compare data science with 16 analytic disciplines, also published in 2014.
The following articles, published during the same time period, are still useful:
- Data Scientist versus Data Architect
- Data Scientist versus Data Engineer
- Data Scientist versus Statistician
- Data Scientist versus Business Analyst
More recently (August 2016) Ajit Jaokar discussed Type A (Analytics) versus Type B (Builder) data scientist:
- The Type A Data Scientist can code well enough to work with data but is not necessarily an expert. The Type A data scientist may be an expert in experimental design, forecasting, modelling, statistical inference, or other things typically taught in statistics departments. Generally speaking though, the work product of a data scientist is not "p-values and confidence intervals" as academic statistics sometimes seems to suggest (and as it sometimes is for traditional statisticians working in the pharmaceutical industry, for example). At Google, Type A Data Scientists are known variously as Statistician, Quantitative Analyst, Decision Support Engineering Analyst, or Data Scientist, and probably a few more.
- Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers. The Type B Data Scientist is mainly interested in using data "in production." They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results). Source: click here.
I also wrote about the ABCD's of business processes optimization where D stands for data science, C for computer science, B for business science, and A for analytics science. Data science may or may not involve coding or mathematical practice, as you can read in my article on low-level versus high-level data science. In a startup, data scientists generally wear several hats, such as executive, data miner, data engineer or architect, researcher, statistician, modeler (as in predictive modeling) or developer.
While the data scientist is generally portrayed as a coder experienced in R, Python, SQL, Hadoop and statistics, this is just the tip of the iceberg, made popular by data camps focusing on teaching some elements of data science. But just like a lab technician can call herself a physicist, the real physicist is much more than that, and her domains of expertise are varied: astronomy, mathematical physics, nuclear physics (which is borderline chemistry), mechanics, electrical engineering, signal processing (also a sub-field of data science) and many more. The same can be said about data scientists: fields are as varied as bioinformatics, information technology, simulations and quality control, computational finance, epidemiology, industrial engineering, and even number theory.
In my case, over the last 10 years, I specialized in machine-to-machine and device-to-device communications, developing systems to automatically process large data sets, to perform automated transactions: for instance, purchasing Internet traffic or automatically generating content. It implies developing algorithms that work with unstructured data, and it is at the intersection of AI (artificial intelligence,) IoT (Internet of things,) and data science. This is referred to as deep data science. It is relatively math-free, and it involves relatively little coding (mostly API's), but it is quite data-intensive (including building data systems) and based on brand new statistical technology designed specifically for this context.
Prior to that, I worked on credit card fraud detection in real time. Earlier in my career (circa 1990) I worked on image remote sensing technology, among other things to identify patterns (or shapes or features, for instance lakes) in satellite images and to perform image segmentation: at that time my research was labeled as computational statistics, but the people doing the exact same thing in the computer science department next door in my home university, called their research artificial intelligence. Today, it would be called data science or artificial intelligence, the sub-domains being signal processing, computer vision or IoT.
Also, data scientists can be found anywhere in the lifecycle of data science projects, at the data gathering stage, or the data exploratory stage, all the way up to statistical modeling and maintaining existing systems.
2. Machine Learning versus Deep Learning
Before digging deeper into the link between data science and machine learning, let's briefly discuss machine learning and deep learning. Machine learning is a set of algorithms that train on a data set to make predictions or take actions in order to optimize some systems. For instance, supervised classification algorithms are used to classify potential clients into good or bad prospects, for loan purposes, based on historical data. The techniques involved, for a given task (e.g. supervised clustering), are varied: naive Bayes, SVM, neural nets, ensembles, association rules, decision trees, logistic regression, or a combination of many. For a detailed list of algorithms, click here. For a list of machine learning problems, click here.
All of this is a subset of data science. When these algorithms are automated, as in automated piloting or driver-less cars, it is called AI, and more specifically, deep learning. Click here for another article comparing machine learning with deep learning. If the data collected comes from sensors and if it is transmitted via the Internet, then it is machine learning or data science or deep learning applied to IoT.
Some people have a different definition for deep learning. They consider deep learning as neural networks (a machine learning technique) with a deeper layer. The question was asked on Quora recently, and below is a more detailed explanation (source: Quora)
- AI ( ) is a subfield of computer science, that was created in the 1960s, and it was (is) concerned with solving tasks that are easy for humans, but hard for computers. In particular, a so-called Strong AI would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic, and includes all kinds of tasks, such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc.
- NLP ( ) is simply the part of AI that has to do with language (usually written).
- is concerned with one aspect of this: given some AI problem that can be described in discrete terms (e.g. out of a particular set of actions, which one is the right one), and given a lot of information about the world, figure out what is the “correct” action, without having the programmer program it in. Typically some outside process is needed to judge whether the action was correct or not. In mathematical terms, it’s a function: you feed in some input, and you want it to to produce the right output, so the whole problem is simply to build a model of this mathematical function in some automatic way. To draw a distinction with AI, if I can write a very clever program that has human-like behavior, it can be AI, but unless its parameters are automatically learned from data, it’s not machine learning.
- is one kind of machine learning that’s very popular now. It involves a particular kind of mathematical model that can be thought of as a composition of simple blocks (function composition) of a certain type, and where some of these blocks can be adjusted to better predict the final outcome.
What is the difference between machine learning and statistics?
This article tries to answer the question. The author writes that statistics is machine learning with confidence intervals for the quantities being predicted or estimated. I tend to disagree, as I have built engineer-friendly confidence intervals that don't require any mathematical or statistical knowledge.
3. Data Science versus Machine Learning
Machine learning and statistics are part of data science. The word learning in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters. This encompasses many techniques such as regression, naive Bayes or supervised clustering. But not all techniques fit in this category. For instance, unsupervised clustering - a statistical and data science technique - aims at detecting clusters and cluster structures without any a-priori knowledge or training set to help the classification algorithm. A human being is needed to label the clusters found. Some techniques are hybrid, such as semi-supervised classification. Some pattern detection or density estimation techniques fit in this category.
Data science is much more than machine learning though. Data, in data science, may or may not come from a machine or mechanical process (survey data could be manually collected, clinical trials involve a specific type of small data) and it might have nothing to do with learning as I have just discussed. But the main difference is the fact that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. In particular, data science also covers
- data integration
- distributed architecture
- automating machine learning
- data visualization
- dashboards and BI
- data engineering
- deployment in production mode
- automated, data-driven decisions
Of course, in many organisations, data scientists focus on only one part of this process.
All sectors of trade and of the economy are facing more or less to the massive development of the digital technology. The industry of the luxury is not being spared. In this sector where the human relation between the seller and the customer is favored, how to integrate artificial intelligence into products which are naturally very far from this connected notion? One thing's for sure: the buyer of the 21th century is more and more demanding. So enriching the relation with clients, anticipating their expectations, while maintaining a personal and human link is all the challenge of Big Data that must make itself the interpreter of the desires of the customer.
Behavioral data, purchasing history, satisfaction survey are so many keys which will allow to better understand the needs of the buyer. So, in a concern of personalization, the group L'Oréal already collects named specific data through the visits of the customers who buy by internet. This trend becomes widespread in the prestigious brands:
the idea is to address individually every customer, but without ever being considered as intrusive.
It is a question before any accompanying the customer in a unique and exclusive experience, of valuing him and making him feel the feeling to belong to the same family.
Following the example of the group LVMH which proposes to its most faithful customers experiences top of the range in domains such as the oenology, the jewelry(jeweler's store), or of these hotel chains top - of range which exploit the data of consultation of the customers on Web to anticipate their desires.
Big Data, in the industry of the luxury, aims to be above all “smart "and "small ".
By INI Consortium/ November 2017
Each second a plane takes off in the world. Each of these planes today is equipped with an impressive quantity of chips and other digital sensors. An engine can soon generate 5 000 signals to be checked every second. Others can produce 10 To of data by flight.
Aircraft manufacturers, motoristes, equipment manufacturers, airline companies, all are on the outlook for any megadata which allow to anticipate efficient maintenance of all devices. Thanks to Big Data, we can make today speak the parts of the plane: its wear, its reaction in the conditions of atmospheric pressures, in the vibrations, in the magnetic field …
Aircraft manufacturers, engine and equipment manufacturers, airline companies, all are on the lookout for any megadata which permits to anticipate the aircraft maintenance. Thanks to Big Data, we can now make sens with collected data on devices of the plane: its wear, its reaction in response to certain atmospheric pressure, to vibrations or to magnetic fields …
The predictive maintenance is one of the most important axes of development in the aircraft industry: following the example of Safran which developed an entity dedicated to Big Data, Safran Analytics, leading the airline companies to reduce their fuel consumption, thanks to on-line data analysis of the engines during the flights.
Similar enthusiasm for Airbus company which has set up a data basis to evaluate and experiment Big Data projects or for GE which has spread a European platform allowing its manufacturers to continuously check their installations so that they can give real time advises to their customers.
The exploitation of Big Data is also an advantage for pilots who can rely on better indications of meteorological events and soon for the travelers themselves: 44% of companies anticipate to set up a geolocation of luggage by 2018.
By INI consortium/ November 2017
Thanks to evolved systems of counting and thanks to new technologies of measurement, the network managers of electric distribution can follow at time T the specificities of the consumption at various scales, whether it is at a country level, or at a region area or at village level. ERDF made its first steps in "smart grids" ("Intelligent" said distribution network of electricity) with the intelligent meters Linky with which all the French households will be equiped by 2020. Counters communicate data every ten minutes, that is 1 800 billion recordings stored every year by the distributor!
All these data are stored in a System of Treatment of Measures ( STM), the real warehouse of the data for the electricity supply, which supports a strong diversity types of data (measures, meteorological data, description of the network, etc.).
The technologies of Big Data allow to intervene on the electrical distribution system itself too.
GE Energy, Alstom, Siemens or still Schneider Electric spread Big Data infrastructures which collect the data streaming from equipments of distribution. Objective: optimizing the electrical distribution according to the request. Smart Grid also allows to reinsure the network against possible hazards, particularly climatic one .
At our Italian neighbors, the deployment of the intelligent meters was launched from 2001 by the main distributor of electricity, Enel, with a main vocation: fight against the fraud, very important in the country.
By INI consortium/ October 2017
The bank is certainly one of the sectors where companies hold the most information on its customers: private situation, salary, spendings, propensity of savings, favourite retailers… But not only. Thanks to the mixing of structured and non structured data (publications on the social networks, emails, history of purchases on e-commerce web site, etc.), Big Data enables the banks to have a true analysis of the life of their customers day after day, for to better understand their behavior and to anticipate them.
When the customers complain about too much standardized products, the banks can propose to them then effectively accurate, ultra-personalized advices, and are able to recommend to them financial products targeted according to their specific needs. We pass from the classic marketing based on partial and often obsolete information, to the predictive marketing based on "fresh" data.
But the use of Big Data has to be much more than a simple alternative of the transactions made in agency. Banks must also know how to build a successful, responsible, and ethic exploitation of the collected personal data. The data of the customers have to be used for optimizing marketing or otherwise to detect risk behaviors for the bank.
To note: more and more software of fraud detection have integrated Big Data into their strategy.
By INI consortium/ October 2017
Cities that can clear away issues that prevent smart city growth have an advantage over other municipalities in the race to become smarter.
Powerful trends are pushing the global community to develop more smart cities, as the world population increases and more people move to urban environments.
By 2050, there will be approximately 2.5 billion new urban residents. "Every month our urban population grows by about 5 million between births and migration. So that's roughly a million new housing units we need every month, and then the requisite number of hospitals and schools and roadways and other infrastructure," said Jesse Berst, chairman of the Smart Cities Council, during Smart Cities Week in Washington, DC in early October 2017.
As new infrastructure is built to support the growing population, it is essential that it is smart infrastructure to improve the lives of citizens.
"This is not just a trend. It's a race. The world is not waiting for us. It's a race for a connected lifestyle," Berst said.
However, there are five major infrastructure roadblocks that cities across the globe have to deal with in order to become a smart city. Different regions might have different priorities, but almost all cities face these five issues:
- Stakeholder engagement
Roadblock #1: Technology
The Smart Cities Council has surveyed city officials, and they consistently say that understanding which technology they need is their top concern. Berst said he hears from city officials questions such as, "How do I do it in a cross-cutting way? How do you actually do it in real life, and what does that mean, and which vendors do you use?"
Berst said, "This is actually the easiest of the problems, and I'm not saying it's trivial, but that's technology stuff that we've got figured out, if they could connect to the right companies."
Many cities have policies in place that worked for the last century, but are now obsolete. It might be anything from a policy that prevents utility companies from installing solar rooftops or even a simple signage policy, he said.
"I was visiting Pittsburgh and they created an app that would tell people how many free parking spaces there were in the city parking structures, and then point them to the next one if this one was full, and they weren't allowed to attach that sign because of some signage regulation, so they had to go and build these 'temporary' things that weren't attached to anything," Berst said. It took the city of Pittsburgh an extra six months to overcome the existing signage policies.
Roadblock #3: Financing
Financing might be the biggest thing holding cities back from adding smart city infrastructure, which is particularly frustrating because there are many creative financing options to choose from, Berst said.
"Many cities don't know about these other ways, and they're still kind of locked into municipal bonds and capital projects. There's also some psychological resistance. There's lots of people who are really nervous about, or dislike the idea of public/private partnerships, and for some reason it's fine in their mind for Google to have a monopoly, or Facebook to have a monopoly, or Microsoft to have a monopoly and reap huge financial rewards, but if a company wants to come and build some public infrastructure, they don't want them to make a profit from that," Berst said.
Some cities also have problems with moving items from the capital budget to the operations budget, which would be necessary to install items such as streetlights or a smart grid "as-a-Service" where a city doesn't pay anything up-front, but just pays per month to the vendor.
For many cities, "it's easier for some of them to build a big data center than it is to pay a much smaller monthly fee for cloud. They're just not set up that way, and they have policy issues and so on," Berst said.
Roadblock #4: Stakeholder engagement
Figuring out how to get stakeholder involvement both internally and externally is a major problem for some cities. Stakeholders include citizens, businesses, universities, and city employees.
"If I had to pick a problem that I see cities making most often, it's failing to assemble the right team, up-front—a team that includes all of your internal stakeholders, but also all of your key external stakeholders. Internally, it's the department heads, so they can say, 'Oh, you need a network? We need a network, too,' or, 'You want to do video? We need to do video, too.' But then, if you're not also including your utilities in the conversation, if you're not also including your low-income advocates, and your advocates for the handicapped, and your university, you're just crazy," Berst said.
"Now, I understand the temptation to go, 'Oh, God. Everybody's going to complain. Let's just go over here. We'll do it. Before they notice it, we'll have this done, and they'll like it.' Actually, that almost always backfires. I'm not saying you have to have all of these people with you every day on your working group, but once a month, once a quarter, you need to be checking in with them. You need to be hearing their ideas, and you need to be hearing their objections," Berst said.
"You get better ideas when you've got a broad group of stakeholders, and diversity helps you get better buy-in because when people help plan, even if they end up with the same plan you had in mind, they've now bought in emotionally, and then you hear about objections and problems early. We had a number of instances back in the days of the Smart Grid, when utilities just imposed this top-down. The first time a person would hear about it was a [person knocking on the door], and, you know, 'We'll be by next Tuesday to swap out your meter. By the way, your rates are going to go up by $100 a year.' And they got huge pushback. We don't want to have that kind of situation," Berst said.
Chicago experienced a similar situation when it installed environmental sensors and cameras on smart streetlights and citizens worried they were being watched. "It started out as, 'Wow. Look at all this great stuff,' and then it became, 'Big Brother is watching us,' and then it became, 'Oh, my God, they're not secure, so anybody can tap into it, now everybody's watching us.' It took them a good year, if not longer, to get through that," Berst said.
Roadblock #5: Governance
Getting departments to work together is a problem in smart cities. Government departments have traditionally operated as silos, and overcoming that mindset is difficult for many cities. Using data as an example, Berst said it's important to know, "Who is allowed to have access to the data? Who is allowed to update the data? Who is required to keep the data up to date?"
The change in management style is part of governance. "How do I bring our people forward? People are tribal. People don't like change. People specialize. And, all of that's normal and natural, but we have to break through that to have a smart city because it is about data from all the departments, and it is about all of the departments serving their citizens digitally," Berst said.
By Teena Maddox | October 9, 2017 on www.techrepublic.com
To survive the next decade, businesses will need to be able to utilise Big Data.
Buzzword or not, it’s probably the most important feature of the fourth industrial revolution, a new economic asset that will define the near future of commercial evolution.
Structured data has been used by companies for a long time, but Big Data is distinct from this in three main ways: volume, velocity and variety. In simple terms, there is a lot of it, being produced at an extremely fast rate, and occurring in many different forms.
Businesses will need to evolve in their practices of data generation, acquisition, storage and analysis. Their success in these skills will dictate the new hierarchy within almost every industry.
Big Data is already transforming the way businesses operate. It is the key to the success of super-platforms such as Google, Amazon, and Facebook, and, via Internet of Things technology, will soon enable great improvements in transport, energy distribution and healthcare, as well as profound changes to the way we live.
Any future-facing business will need to become adept at collecting, processing and analysing data if they wish to survive the next decade.
Several studies have aimed to measure the impact that data-driven decision-making (DDD) has on the performance of a company. MIT research this year, which included 330 US companies, found that businesses in the top third of their industry for DDD were “on average, five per cent more productive and six per cent more profitable than their competitors”.
In a study of Big Data technologies’ impact on businesses, the economist Prasanna Tambe identified “significant additional productivity growth”. The research found that “one standard deviation higher utilisation of Big Data technologies is associated with one to three per cent higher productivity than the average firm”.
Think of Amazon’s hugely effective automated recommendations as an example, which harness the masses of data they collect about their customers. The US giant Sears has started doing the same, and, with the help of the data storage and processing software Hadoop, was able to reduce the time needed to generate personalised promotions from eight weeks to one.
A major US airline used Big Data technology to improve ETA predictions. Their savings at each airport are reported to be several millions of dollars each year. Visa uses Hadoop to process 73bn transactions in 13 minutes, having previously required a month.
The advertising industry has naturally been one of the forerunners in embracing Big Data. Real time response in digital advertising enables precise measurement of the performance of an ad, and this plethora of information can be complemented by data collected by websites and third party providers.
Digital advertising technology has enhanced the targeting, optimisation and measurement of marketing to an extent that was inconceivable in the previous century.
Advertising is, in some ways, a vision of the future for every industry. The only relevant ad agencies are those that have developed their ability to process and analyse data. The days of the HiPPO (Highest Paid Person’s Opinion) leading decision-making in this sector are long gone, and a far more scientific era has taken its place.
Big Data’s benefits to other sectors will be just as profound. McKinsey and Company researched the potential value of Big Data to five core industries in the US. As an example, they predicted savings of $300bn for US healthcare, if Big Data was used effectively.
In 2009, Google famously used Big Data to help identify people who had been infected during the flu pandemic. Based on users’ search behaviour, Google was able to forecast where the disease might spread to and predict where it had spread from.
There really is no limit to the potential of data to improve how businesses operate. Whatever your industry, you should be doing whatever you can to get better at Big Data.
source: City A.M website - Thursday 31, 2017. By Daniel Gilbert.
City A.M.'s opinion pages are a place for thought-provoking views and debate. These views are not necessarily shared by City A.M.
We are sure: the data will revolutionize the world, but who will really benefit? In 2011, consulting firm Gartner said: "Information is the oil of the 21st century, and analytics is the engine of combustion". In spite of this metaphor, each one legitimately raises the question of who tomorrow will be the tycoons of the given ... More, what will be the methods of the giants of today and tomorrow: will they be, like the oil giants in their time , at the limit of legality? Will there be oil spills? Will there be collusion with our policies?
21st Century Petrolueum
Our society, our economy, our lifestyles will be profoundly altered by the data that now carries much of the growth of Western countries. In this sense, it is the oil of the 21st century. But where a landowner got rich from an oil well on his land, what about our data? What is their value? Will she come back to us? Every day, every moment, each one of us generates a lot of personal or professional data, data belonging to himself, his company or published on the Internet or harvested by third parties.
That's why many companies offer free mail, data sharing services. The datum has value only if it is massive. Having services that allow global users to collect their users' data is vital for GAFAM - Google, Apple, Facebook, Amazon and Microsoft - who are masters in the valuation and monetization of our personal data.
Future data "oil spills"
If the data is the new oil, what happens when the data escapes? Whether it is the work of pirates who voluntarily steal data or are accidental escapes, no one is immune. The news is full of revelations of large-scale incidents. One can cite the theft of 412 million accounts of the site of meeting AldultFriendFinder in 2016 or an error that allowed the dispersion of confidential information on the members of the G20. Other examples could be used to illustrate these massive leaks, but there is a much greater danger to users.
Thus, many sites trade in your login data, but beyond the sale, the information is aggregated from different sources to make them more relevant to hackers. Basic security rules dictate that users do not use the same login and password on different sites. But honestly did not you happen to derogate from this rule?
A strong geopolitical issue
Let us not be naive: mastering the data is a primordial geopolitical stake. There is often a tendency, and large groups are involved, to believe that the Internet, the cloud and all the tools around the data are beyond any notion of nationality. Now, on closer inspection, the United States, and California in particular, are hegemonic about our data. Caricatured to the extreme, to the question "Who benefits from the explosion of our data? ", One answer might be: Silicon Valley.
To convince oneself of the geopolitical aspect of the data, let us note for example the decision of the American justice, confirmed on appeal on April 19 last, to oblige Google to provide the data stored outside the United States. China has also understood this by pursuing a policy of determined protectionism that allowed the BATXs (Baidu, Alibaba, Tencent and Xiaomi) to thrive in the face of GAFAM. The BATX, strongly supported by the Chinese state, now aim to conquer the international market and Europe in particular.
OPEC of the data
The latter seems stuck in an outdated view of computers and the Internet. The beneficiaries of the explosion of data will be numerous in Europe: telephone operators and digital services companies: they will benefit from this revolution. And we will not forget the many start-ups that emerge around the Internet of objects and data analysis. But let's not be mistaken, the only real beneficiaries of the explosion of data will be those who will own them within their data-centers! Will GAFAM and other BATXs create, thanks to their infrastructures, the OPEC of the data that will fix the course of your data on the world markets?
Source: www.theconversation.com - June 14th, 2017
Eric Cassar is an engineer and architect, founder of Arkhenspaces, whose "Habiter l'infini" project won the Grand Prix Le Monde Smart-cities 2017. He calls in this forum to create new ownership models for digital data buildings.
"The development of the Internet is akin to the arrival of new dimensions. Age 1 had accelerated our exchanges, with emails, and then gave us access to a growing number of information and services. Age 2 has facilitated the linking of individuals with other individuals, with social networks. Age 3 is the continuing relationship of individuals with space, through smart-building or smart-city: a physical space in close relation with the digital space thanks to fixed connected objects or Movement, and the generalization of sensors in our cities.
Our buildings will therefore process an innumerable amount of new data that will produce key information about the functioning of human settlements at different scales: the building, the block, the neighborhood, the city, the territory. Data related to environments (energy consumption, affluence, access) but also attached to new local social networks.
The effective use of this large amount of information will improve the functioning and efficiency of these estates, in particular by correlating supply and demand, distributing needs and resources and then anticipating. It will be able to suggest, initiate or promote social ties of proximity, and increase the number of local synergies.
A precious raw material
Singapore is tiny, beside the giants of India and China; it nevertheless manages to play an important role where ‘smart cities’ are concerned. With its 4.7 million inhabitants, ‘little’ Singapore has established itself as one of the models in the world for the ‘smart city’. The city-state plays a key role in the extraordinary development of the two Asian giants, China (1.4 billion inhabitants) and India (1.3 billion inhabitants). It experiments on all fronts, loves innovations and multinationals, and seduces clients from all over the world.
Playing in part on its Chinese-origin community (75% of the population), Singapore has signed several partnership agreements with China for testing and further development of smart cities. This is the case, amongst others, for the business park in Suzhou and the high-tech eco-island of Nanjing, the former capital, which has a population of over 8 million inhabitants. The experiments are then reproduced in other cities, including the eco-city of Tianjin and the City of Knowledge in Guangzhou. This methodology “provides a platform enabling Singaporean and Chinese firms to demonstrate their capacities in matters of technologies in a holistic manner», explains the INFOCOMM Agency in Singapore.
Data collection on a massive scale
And China is not alone. To develop its high-priority project of creating 100 smart cities by the year 2020, the Indian Prime Minister, Narendra Modi has also turned towards the know-how and investment capacity of the Singaporeans (of whom over 7% are of Indian origin). Singapore scores points when Hong Kong is marking time. Hong Kong is further away geographically and relies on the gamble – made long ago – of betting heavily on Information and Communication Technologies (ICT).
In more specific sectors, the Nation-state positions itself as a sort of life-size laboratory at world level of the city of tomorrow with cutting-edge experiments on autonomous vehicles or ethnic diversity by neighbourhood and even by apartment building. Above all, data collection on a massive scale combined with the predictive intelligence of big data is used in all sectors for modelling projects, planning changes and endeavouring to offer the most innovative serves, whether it be a question of fluidity, of security, of the comfort of buses or the location of child-care centres. The municipality has moreover implemented a measure which is often a source of anxiety elsewhere. It has set up a sophisticated traffic control system, the price of which varies in function of the amount of traffic, the neighbourhood, the time and the day of the week.
The brand knows how to sell
With the support of this strong position, Singapore organises a World Cities Summit each year. In 2016 this brought together mayors and leaders from 103 cities in 63 countries: all either clients already or potentially so. The brand knows how to sell and it sells itself well. It knows how to rely on its powerful financial community and, for implementation, on various private firms, including Surbana Jurong, present all over the world.
In 2014, the Prime Minister launched the Smart Nation programme, placed under the responsibility of the Minister for Foreign Affairs, Vivian Balakrishnan, thus displaying its ambition to look further afield. A cardinal virtue, the government has understood the importance of a systemic vision for improving cities by means of technology.
Both at home and abroad, Singaporeans focus on sustainable development and concern for citizens in the form of provision of quality services. But, overall, their model tends more to the ‘datapolis’ side than to the ‘participolis’ side: it prioritizes collection and processing of data more than the effective participation of citizens.
This model is limited, as Anthony Townsend, a researcher at the University of New York points out. In an article published by the Technology Review, he states that “The perfectly controlled and efficient utopia of a very safe and smart city can work in a place like Singapore. But it would probably never work in New York or Sao Paulo, where expectations in terms of conception and of what make the vitality of a community, are completely different”. Townsend thus confirms that there is no single model of a smart city. This in no way prevents this small nation-state from innovating or from finding clients all over the world.