Accuracy Talks Straight #6
For our sixth edition of Accuracy Talks Straight, Jean Barrère presents the editorial, before letting Romain Proglio introduce us to Wintics, the specialist in intelligent video analysis for mobility operators. We will then analyse data in China with Frédéric Recordon and Helena Javitte.
Sophie Chassat, Philosopher and partner at Wemean, will ask the question of whether data has a conscience or not. Then, we will evaluate our data assets with Isabelle Comyn-Wattiau, Professor at ESSEC Business School, holder of the Information Strategy and Governance Chair. Finally, we will focus on the dual transition to energy and digital with Hervé Goulletquer, our senior economic adviser.
As Victor Hugo did for the toilers of the sea, we must start with a homage to all toilers of data.
Observe the chief data officer setting out the fundamental difference between ‘raw data’, ‘information’ and ‘knowledge’ and reminding us of the oh-so complex nature of switching from one category to another.
Watch the CIO mobilising exponential technologies through connected platforms to capitalise on the organisation’s digital and information assets more quickly.
Pause for a moment to appreciate the grace of a Baudelairean gesture. The data scientist isbinfusing data with art: ‘You gave me your mud and I have turned it to gold’, he proclaims!
Pick up speed again with the decision-maker, on the lookout for some form of informational advantage. Embark on a trip with the CEO over rough seas, taking the organisation on a path to difficult data-driven transformations!
The HR director might be in charge of creating dedicated paths to attract and retain these rare profiles, but the head of finance is more interested in the multiple forms of data value: economic, financial, utility, market, exchange… How can we assess this intangible asset?
Time to move on and applaud! At the front of the stage, the politician sets limits to all things digital and tidies the mess made by the use of our private data!
Make room for thought. Behind the curtain, the philosopher disturbs the order of our digital lives and challenges the Data-Being. Is binary now the language of truth? Is it possible to translate all human experience into 0s and 1s?
When a topic as multifaceted as data mobilises so many profiles and so much knowledge, capital and liquidity, intelligence and technique, material to argue for and against… when this dialectic gives rise to so much wealth and so many new forms of living together, it is because there lies, at its heart, an essential debate that must be brought to life.
Created at the end of 2017 by its three founders, and armed with four years of research and development, Wintics positions itself as the specialist in intelligent video analysis for mobility operators. The company markets its analytical products to four types of mobility infrastructure operator: regional public authorities, public transport operators, airports and ports.
For regional public authorities, the start-up has developed a particularly innovative artificial intelligence software solution (called Cityvision), which can connect automatically to any camera, whether optical or thermal, old or new, in order to extract large amounts of data on mobility, the safety of public spaces and urban cleanliness. For example, the software is able to analyse cycle path traffic and use in order to help the city to organise its mobility.
The solution also enables its clients to manage their infrastructure in real time, for example, by transferring the data collected and analysed by Wintics to the trafficlight system, helping to improve the flow of traffic in a highly targeted way.
For transport operators, Wintics provides the opportunity to visualise in real time movement flows and the level of passenger traffic. Airport operators are able, for example, to supervise the various passenger flows arriving on site and to facilitate their movement around the airport thanks to the realtime management of queues at check-in desks and passport control.
Wintics is positioning itself as an innovative and strategic solution to make cities greener by prioritising the development of soft mobility, the attractiveness of public transport and the improvement of travel flows. The camera has become a management tool for an efficient and safer urban environment.
Wintics is an entirely French company that proposes a solution 100% made in France. It won the 2018 and 2019 editions of the Paris Grand Prize for Innovation, was certified by the Greentech Innovation label and in 2020 joined the best artificial intelligence start-ups in Europe in mobility. Together, the Wintics experts (around 15 today) have already completed various projects in over 30 French cities.
Data, one last reason to take an interest in China?
Why should we still take an interest in China? The signals it is sending of a country locked up, tempted to turn in on itself and asserting an alternative model of society are now leading people to understand it in terms of risk analysis. The latest studies from the European and US chambers of commerce in China testify to a significant re-evaluation in the strategies of foreign companies.1
Yet, in this sombre context, for those of us who have been working in the country for over 10 years, China is a country that deserves attention from Europe. The most relevant reasons to take an interest might not be those that come to mind first, however. Some may even prove discomfiting. What if China was ahead of the West? Ahead in the thinking that is shaping the world of tomorrow? In the absence of a commercial El Dorado… ideas!
The source of this Chinese head start is data. The country has numerous advantages: structural – 18% of the global population provides an unparalleled testing ground to explore new ideas; economic – its regulation or even the abundance of tech investments; cultural – the launch of quick & dirty solutions, which are later improved or abandoned, where Westerners only want to launch more finished products.
This article aims to analyse through three different lenses how China considers data. (1) How regulation turns data into a competitive advantage. (2) How data is at the heart of retail transformation. (3) How it uses data to create new business models.
1. REGULATION FAVOURING COMPETITIVE ADVANTAGE
The first thoughts on data as a factor of production started in China at the beginning of the 2000s and continued throughout the following decade with the creation of a regulatory framework to launch a data exchange platform.
The turning point came in April 2020 when data was officially considered as the fifth factor of production, on the same level as capital, labour, property and technology.
This is effectively the birth certificate of a data economy considered as the disruptive accelerator for the growth of Chinese companies.
The first objective of public authorities is to encourage players to structure their data in such a way as to facilitate their sharing. For this, the government has put in place public platforms.
From 2019, the SASAC, the governmental body that supervises state-owned companies, published a list of 28 state-owned and private companies tasked with federating their industries through sectoral platforms.
The China Aerospace Science & Industry Corp. is in charge of aeronautics; the CSSC, of naval construction; Haier, via its COSMOPLAT platform, of 15 different sectors (electronic, industrial manufacturing, textile, chemical industry, etc.).
The second objective aims to create a data exchange platform. Led by local authorities (Shanghai, Beijing, Shenzhen, Hainan, Guangzhou), this takes the form of freetrade zones and pilot data trading platforms.
Thus, the Shanghai Data Exchange Centre (SDEC) is similar to a technology exchange guaranteeing the legal conformity of transactions for member companies, whilst the Beijing International Big Data Exchange favours the sharing of public data at national level with the hope of international expansion.
These initiatives show that China has started to lay the foundations of the data economy.
It is trying different things, experimenting with answers to the most crucial question: how can data be transformed into an item of value? A first challenge lies in the multitudes of data – personal, financial, industrial, meta, etc. – as well as their of ten incompatible formats.
Their standardisation and exchange protocols are crucial stakes for leadership in the world of tomorrow. In parallel, we also have the question of valuing data. The SDEC is currently working on these questions of ownership, source, quality, certification and price setting.
We can see it: China has started thinking about the new asset that data has become. It is advancing in incremental steps leveraging public and private economic actors, thus building a gigantic world of possibilities.
2. DATA, AT THE HEART OF RETAIL TRANSFORMATION
‘Today, we don’t know how to monetise data, but we do know that people will not live without data. Walmart generates data from its sales, whilst we do e-commerce and logistics to acquire data. People talk to me about GMV2 but we’re not looking for GMV. We sell purely to get data, and that is very different to Walmart.’3
This is, in just a few words from Jack Ma, founder of Alibaba, the fundamental difference between China and the West:
WHEREAS WE SEE E-COMMERCE AS AN ADDITIONAL DISTRIBUTION CHANNEL, THE CHINESE SEE IT AS A DATA MINE.
Though comparing the combined figures for Black Friday, Thanksgiving and Cyber Monday in the US ($25 billion) with the Chinese Double 11 ($139 billion)4 shows China’s significant lead, it does not take account in any way of this difference of philosophy.
The fact that China is much more connected than the US and Europe, that 99.6% of Chinese internet users access the internet from their smartphones, is hiding what is most important.
Limiting ourselves to quantitative analyses would be to misunderstand the disruptive nature of Chinese retail. The giants of e-commerce have created innovative payment solutions leading to their dominance of retail and their leadership in mobile payments.
This explains the dizzying growth of retail that depends on a fundamentally different approach from traditional players. Alibaba offers the most complete example with its concept of New Retail, defined in 2015. Two characteristics shape this model: (1) Alibaba positions itself above all as an intermediary facilitating exchanges between retailers and customers. (2) Alibaba has modelled a holistic ecosystem, each segment feeding into the others thanks to the data created by the transactional system.
As an intermediary, Alibaba offers retailers its digital tools in branding, traffic generation, etc. as well as its financial services that are highly appreciated by SMEs neglected by banks.
Concerning consumers, Alibaba makes available to them a universal plat form for all their daily needs: social relationships, administrative operations, consumer loans, etc.
Alibaba therefore sets itself apart from its Western equivalents. It operates an ecosystem, the purpose of which is to produce, analyse and monetise data, whilst its Western equivalents remain, despite their latest developments (cloud, etc.), integrated
distributors whose data is only a result. For Alibaba, retail is the support function, in no way the raison d’être. Its leadership relies less on GMV than on its central position in the generation and exploitation of data. Alibaba has come a long way since Jack Ma’s declaration on 16 June 2016 at the China Internet+ Conference 中國互聯網+峰會 that Alibaba ‘doesn’t know how to monetise its data’!
Since then, seeing huge opportunities far beyond its current revenues, Alibaba has transformed its ecosystem and its services. As a result of its perspective on data, China is leading the transformation of a whole industry, potentially paving the way for its Western counterparts.
3. DATA, A SOURCE OF NEW BUSINESS MODELS
Even though the New Retail example illustrates China’s capacity to pivot an industry from the sale of goods to the monetisation of its data, the spectacular development of electric vehicles highlights its ability to create innovative business models from scratch.
This is the example of electric charging stations. An electric charging station essentially differs from a fuel station in two ways. First, the charging time encourages users to charge their vehicles at home or place of work, which translates into very low utilisation (below 5%) of charging stations located in public spaces. Second, as the price of electricity is strictly regulated, operators’ very low margins prove insufficient to generate a return on investment. The solution in China was to shift the focus from the driver (the focal point of the fuel model) to the electric ecosystem.
In order to be successful, a Chinese operator considers itself a service platform for drivers, site providers (i.e. developers), local councils in their town policies, electricity providers, etc. It is not just about selling energy anymore but about optimising flows and prices: traffic, energy flows, etc. The critical point is, once again, data. The start-up X-Charge 智充科技, a specialist in SaaS B2B services, which our Beijing office knows well having worked with them, is illustrative of this business model revolution. It enables charging station operators to analyse their data in real time, adjust their prices by station based on the utilisation rate and road traffic, store electricity under the best conditions and sell it back to electricity providers or building managers during peak periods, etc.
The start-up has developed predictive models of activity and revenues that are highly appreciated by operators. It comes as no surprise that Shell Ventures invested during its Series B; beyond a financial investment, it is a disruptive model that the major company came looking for in China. It is quite obvious that the race to build the world of tomorrow has started and China seems intent on establishing its leadership through innovation guided by the state and relayed by the tech giants. In this strategy, data is clearly considered a critical asset. It is designed to secure the country’s future place in the world. In parallel, the monetisation of data will generate gigantic revenues that only a few players will control sufficiently to maximise their gains.
In some sectors, only the monetisation of data can, at least in a transitory phase, make capital-intensive business models viable.
For all these reasons, we consider it essential to take an interest in these topics and why not to take inspiration from certain initiatives in China.
1 The latest study is that of the Chambre de Commerce et de l’Industrie France Chine (CCIFC – the France–China chamber of commerce and industry), conducted from 2 to 14 September 2022 with 303 French companies: 79% consider a deterioration in China’s image; 62% see an impact on their profits, 58% are revising their investment strategies in China; 43% do not plan to increase their presence in the next three years; and 16% are considering reducing their presence in China.
2 GMV: Gross Merchandise Value
3 Jack Ma speech from China Internet+ Conference (中国互联网+峰会) on 16 June 2016
4 2021 data, sources: Forbes, Bloomberg
Philosopher, Partner at Wemean
‘Is Data conscious?’ This question, asked in relation to a character from the series Star Trek, is taken up by the philosopher David Chalmers in his latest book Reality +.1 Data is the name of an android. In the episode of the series titled ‘The Measure of a Man’, a trial takes place to determine whether Data is an intelligent and conscious being.
There is no doubt about the intelligence of the humanoid robot: Data has the capacity to learn, to understand and to manage new situations. However, the question of whether Data is conscious remains unanswered. Does Data have an inner life with perceptions, emotions and conscious thoughts? Or is Data what philosophers call a ‘zombie’? In philosophy, a zombie is a system that, on the outside, behaves like a conscious being but, on the inside, has no conscious experience. It behaves intelligently, but has no inner life or reflexivity about its actions.
Chalmers star ts with this story to question whether a digital system can be conscious or whether only humans and animals are gifted with consciousness. For this astounding Australian philosopher, a system perfectly simulating the functions of a brain could be conscious in the same way as a biological brain. This leads him to dizzying speculations: in that case, mirroring that logic, isn’t our actual consciousness just the effect of a simulation? Don’t we already live in a metaverse, and isn’t our god a computer?
If we make the story of the rather aptly named Data an allegory, we can use it to raise a simple ethical question when we exploit data. What type of data are we dealing with: Zombie Data or Conscious Data? In the first case, we harvest data that seem to behave intelligently, but ultimately their content is empty and without interest. We have all had the experience of trawling through masses of data for sometimes very little reward, or even absurd results! We can add that the data transform us, too, into zombies… Because here we are, reduced to aggregates of outer behaviours (purchases made, keywords typed into search engines, conversations held on social media, etc.) supposed to encapsulate our inner desires – which remain a little more subtle, nevertheless. Zombie Data make Zombie People!
As for Conscious Data, we can be certain that Big Data do not have the system consciousness that Chalmers deems entirely plausible in the future. The only thing left to do then is for human outer consciousness to give meaning to data, to humanise them. This is just like Data the android, who needs a human friend to evolve, a role fulfilled by Captain Picard in Star Trek. Conscious People make Conscious Data!
1 David J. Chalmers, “Reality +. Virtual worlds and the problems of philosophy”, Penguin Books/Allen Lane, 2022
Professor at ESSEC Business School, Chair of Information Strategy and Governance
Valuing our wealth of data, a challenge no company can escape
Evoking the value of data in 2022, when the media is overflowing with examples of companies suffering damage linked to data, may seem counter-intuitive. Yet, it is well known that data has value, and it is the very reason why the attacks targeting data are not just simple cyberattacks. More and more, they aim to seize the informational wealth of the target organisations.
Data security can be broken down into three areas: availability, confidentiality and integrity. Attacking an information system compromises its availability, thereby endangering the process that the system underpins. This is what we were able to observe at the Corbeil-Essonnes hospital a few months ago. Due to a lack of available data linked to the patient, the diagnosis and care process is made longer and more expensive. It can even have an impact on patient health by delaying a course of treatment. During these attacks, we also fear a confidentiality breach of highly sensitive data.
And, if by chance the computer hackers modify these data, they could compromise their integrity. Thus, all three parts of data security are affected, with extensive damage: first, the health of the patient, but also the reputation of the hospital and the cost linked to the restoration of the information systems and all the affected processes. Limiting ourselves to the security of the data is a reductive defensive approach, even if we cannot rule it out. Determining the value of data is a significant issue for most companies. The press publishes daily success stories of start-ups where a good idea for sharing or pooling highly operational information leads to new, unsuspected value. Thus, in 2021, the market capitalisation of Facebook reached around $1 trillion, but the net value of the company based on its assets and liabilities was only $138 billion.1
The difference in terms of value can be explained by the data that Facebook collects from its users and uses in turn to feed its advertising algorithms. For economists, data represent unrivalled assets (in that they can be consumed by various users without diminishing), which do not necessarily depreciate as we use them; on the contrary, they can generate new information, for example when combined with others. For some, however, the value does depreciate, and very quickly at that. All these characteristics mean that data fall into a highly specific class of asset that resembles no other intangible asset, brand, software, patent, etc.
Tackling the value of data also requires us to agree on the vocabulary to be used: data versus information. Without reopening the debate on the difference between data and information, we can consider them identical in an initial approach to the topic. Some, however, will wish to distinguish data – the input of the system, unmodifiable, a result of measuring a phenomenon – from information – the output of the system after cleaning, restatement, refinement, aggregation, transformation, etc.
The value of information has been studied in line with accounting practices notably by Moody and Walsh.2 They first endeavoured to demonstrate that information can be considered as an asset: it offers a service and an economic advantage, it is controlled by the organisation and it is the result of past transactions. They then proposed three appraisal methods to value information.
The first is based on costs – of acquisition, processing, conservation, etc. It is the easiest to put in place because these elements are more or less present in the financial controller’s dashboard. However, these costs do not reflect all aspects of data, for example the development of their value over time. The second appraisal method is based on the market and consists of determining the value that can be obtained by selling the data.
Here, we talk about an exchange value. This approach requires a considerable effort. Moreover, it is not always possible to obtain a reliable measure of the value of the data. Finally, the third method is based on utility.
This means appraising the use value of the data by estimating the economic value that it can generate as a product or a catalyst. But this value is difficult to anticipate, and estimating the share of its catalytic effect is also highly complex.
Thus, it seems that the various approaches to determine the value of data are partial but complementary: some are based on the use value or exchange value of data; others assume rational corporate behaviour and assess data at the level of investment made to acquire it and manage it throughout its life cycle; still others are based on risk. The risk approaches see data as the target of threats to the company or organisation. Such risks may be operational; thus, the missing or damaged data may cause certain processes to function poorly.
But there are also legal or regulatory risks, as more and more texts stipulate obligations for data; the General Data Protection Regulation is just one example, albeit the most well known, no doubt. The risks can also be strategic when they concern the reputation of the company or lead the company to make bad decisions.
Finally, some authors have taken an approach based on externalities for open data, which are available to all but which, by making the most of them, can bring a benefit to society at large.
The concept of data value is linked to the objective of proper data governance: maximising data value by minimising the associated risks and costs.3 By adopting this three-pronged approach (value, risk and cost), we can better obtain a holistic view of data value and improve its valuation.
These three aspects are complementary, but we must not exclude context.
Indeed, the same information does not have the same value depending on the temporal, geographic, economic or political context in which the valuation process is conducted. The question of why the valuation is needed must be answered in order to characterise the relevant contextual elements: political, economic, social, technological, ecological and legal (PESTEL) in particular.
The object of the valuation must itself be identified. One of the difficulties in estimating the value of data is choosing the appropriate level of detail: are we talking about the entirety of an information system (e.g. the client information system) or
a set of data (e.g. the client database) or indeed a key piece of information (e.g. the launch price of a competing product)? It is clear that the value of an information system is not the simple sum of the value of its components.
Few approaches for the valuation of data are sufficiently holistic and general to enable their application to any type of data in any context. Recommendations can be made, for example, the recommendation to choose between a top-down approach and a bottom-up approach. But the holistic approach can only be holistic by combining these two value paths.
It is because the company is still unable to measure the real and potential value of data that it does not make sufficient investment in data governance and information sharing.
It is a vicious circle that ultimately leads to the company being unable to realise the full value of data.
A virtuous circle can be built by starting with the most critical data, for example (but not necessarily) client data, and gradually getting on board all data actors – producers, transformers, sellers, distributors, consumers of these data. They have the different points of view necessary for a holistic approach.
1 J. Akoka, I. Comyn-Wattiau, Evaluation de la valeur des données – Modèle et méthode, Record of the 40th Congress of INFORSID (INFormatique des ORganisations et Systèmes d’Information et de Décision), Dijon, 2022
2 D. Moody, P. Walsh, Measuring the Value of Information – an Asset Valuation Approach, Record of the European Conference on Information Systems (ECIS), 1999
3 World Economic Forum, Articulating Value from Data, White Paper, 2021
Senior Economic Advisor, Accuracy
The twin green and digital transitions: resolve for investment and perspicacity for macroeconomic management
The world economy is facing numerous challenges. In the short term, we have an unusual rhythm in prices and a deterioration of growth prospects, taking place in complicated political environments internally in many countries and in a worrying environment internationally (actions of Russia in Ukraine, China around Taiwan and Iran with its Arab neighbours). In the long term, ageing populations are of concern in a number of regions around the globe, economic ‘regulation’ seems to be moving away from the neoliberal corpus towards a more Keynesian approach and the twin green and digital transitions are under way.
Let us pause on this last point. The green transition is essential. It is essential for the preservation of the planet and of all the species that live on it. We must ‘decarbonise’ industry and transport, succeed in the energy renovation of buildings and develop renewable energy on a large scale. The digital transition is also indispensable. It represents the continued process of enabling companies, administrations and households to incorporate new technologies (for example, the cloud, the internet of things or artificial intelligence) in many aspects of their activities. It is worth bearing in mind that the necessary transformations are not purely technological issues; there is a very significant human aspect, with cultural and behavioural adaptations to be made.
The amount of investment in play is impressive. For the eurozone alone, considering an annual envelope of €500 billion a year, for multiple years (certainly no less than 10), does not seem unreasonable. At least that is the order of magnitude determined when summarising some authoritative work on the subject. That represents more than four points of GDP!
The sums committed are so vast that questioning their macroeconomic implications would not be a futile exercise. Let us propose a simple forecast to 2032. The starting point is this resolve for investment linked to the twin transitions: the €500 billion a year, which, when changing from current currency to constant currency (the currency used when measuring the economic growth – that of GDP), becomes €440 billion. The other elements of demand, including investment spending outside of the twin transitions, remain on the same trajectory as observed over the past few years with one exception: extra investment is reflected by more imports and therefore by a reduction in external trade surplus. For this exercise, we assume that there will be no shock from prices or economic policy over the period.
THE TABLE ABOVE HIGHLIGHTS THE MAIN IMPLICATIONS TO CONSIDER.
THREE ARE PARTICULARLY NOTEWORTHY:
• GDP growth would reach 1.5% a year. Though this forecast exercise appears reasonable, we must admit that potential for growth is estimated at 1% a year. Of course, we could consider that the additional investment effort will contribute to more growth. But we could also defend the idea that, at least in part, this new accumulation of capital would replace the destruction of fixed assets that have become obsolete.
We must not forget demographic developments either, which send a rather negative message about the active population (effect to be offset perhaps by a return to a situation with close to full employment).
In any case, one suspicion remains: is the quantification based on these assumptions too optimistic?
• The share of household consumption in GDP would fall by 2.5 points over the period to reach 49.5%. The current level is already not particularly high: 52% against an average of 55% between 1995 and 2010 (and a high of 59% in 1980), a period that was therefore followed by a gradual decline. With the change in macroeconomic ‘regulation’ that we are starting to see, one that emphasises more inclusive growth, is this really credible?
• If the investment / GDP ratio must progress by almost 4.5 points by 2032, then savings must follow; this is how macroeconomic balances work! Where could this come from? In part from lower savings in Europe heading towards the rest of the world. We have spoken about a fall in external trade after all…
For the remainder, it will be necessary to choose between greater efforts by households to save, an increase in corporate profits and/or a decrease in the public deficit.
NONE OF THESE OPTIONS IS SELF-EVIDENT.
The first brings us back to the question of a reduction of household consumption in GDP; we just saw it.
The second suggests a further distortion of wealth created in favour of business. But that might go against current sentiment (new ‘regulation’, including the development of ESG – environment, social and governance – criteria)…
The third seems reasonable, of course, but making the choice between bringing current spending down and increasing tax income is no easy task (public investment would most likely be protected).
If this scenario is not quite unacceptable, but still seems a bit ‘messed up’, then we need to try to imagine what would be reasonable to expect under the two constraints of succeeding in the twin transitions and not deluding ourselves on future economic growth.
In fact, the adjustment can only be made in two areas: either (i) on savings placed in the rest of the world (the counterpart of the external trade balance), with the possibility that flows would invert and that the eurozone would need to ‘import’ foreign savings, or (ii) on a slowdown in consumption spending (whether household or government).
The first solution would weaken Europe on the international stage.
In terms of macroeconomics, Europe would appear less solid, which would reinforce the impression already given by the microeconomy (lower profitability of companies in the Old World compared with those in the New World and smaller presence in the sectors of tomorrow) and by po li t ics (unreso lved issues of integration and its geopolitical role). The resulting financial balances will be more uncertain, whether in terms of interest rates or exchange rates; it would be impossible to think otherwise.
The second idea, which is obviously akin to frugality, seems difficult to put in place in a more Keynesian environment that is stamped by an ambition to share wealth more in favour of households. That is, of course, unless public authorities find the winning formula to incentivise households to save more.
We understand it; the ambition to drive investment, for innumerable sound reasons, has destabilising macroeconomic effects. We must anticipate them and prepare ourselves; after all, prevention is better than cure…