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Representative image of a person authenticating their biometric data. Photo: Reuters.

The following is an excerpt from Lives of Data: Essays on Computational Cultures from India, edited by Sandeep Mertia, foreword by Ravi Sundaram, Institute of Network Cultures (Amsterdam, 2020).

This book is published under a CC BY-NC-SA 4.0 license and is available here.

It is not difficult to see what is wrong with official statistics in India. There is gap between theory and practice. There is gap between the means and the end in the absence of any clearly perceived purpose.

~ P. C. Mahalanobis, Statistics as Key Technology, 1965

Data is its own means. It is an unlimited non-rivalrous resource. Yet, it isnt shared freely. What began as a differentiator is now the model itself.

~ Nandan Nilekani, Why India needs to be a Data Democracy, 2017

Data shadows our situation. Many believe it can determine our situation. There were enthusiastic claims that Big Data would lead to a fourth industrial revolution and the end of Theory, and that it will transform how we live, work, and think. Arguably, much of the early 2010s hype around the big data revolution has already been replugged into popular narratives of artificial intelligence (AI). The media infrastructures that enliven digital data and the fast-moving claims of data revolution are now evidently more globalised and capitalised than ever before. If we look a little under the hood, techniques such as data mining have moved from the margins of techno-scientific practice to normative centres of global computing in less than two decades. How did data become so powerful, pervasive, and relatable in the first place? To understand the global momentum of the data revolution, it is crucial to inquire into the many lineages, affinities, and relations of data in context-sensitive ways.

Data Revolution(s) in Context

The contrast between the two epigraphs above is a good place to begin tracking lives of data. The first epigraph is from a lecture in 1965 at the 125th Annual Meeting of the American Statistical Association by P. C. Mahalanobis, founder of the Indian Statistical Institute (ISI) and a member of the Planning Commission, a powerful body at that time. In this lecture, he emphasised the need to establish a purposive view of statistics as a fully developed technology of a multi-discipline character. This was especially so in the underdeveloped countries where the principle of authority of the government reigned supreme over independent statistical analysis and interpretation. Mahalanobis made these observations at a time when the ISI and Indias official statistics and economic planning system were receiving global recognition for pioneering work in research, training, sample-survey methods, and economic planning. He clearly placed statistical knowledge production in the service of postcolonial nation-building. The desire to perceive a clearly defined purpose when the ISI was already at the cutting edge of large-scale data collection and processing stands in puzzling contrast to contemporary modes of data-driven governance which claim data is its own means.

The second epigraph is from an opinion piece by Nandan Nilekani, co-founder of Infosys and founding Chairman of Unique Identification Authority of India (UIDAI), the government body responsible for the worlds largest biometric database, Aadhaar. In this article he argues for the value of big data and artificial intelligence for disrupting existing patterns of information management, and cautions against data colonisation by state and global platforms.

It is important to note that what we now know as Aadhaar actually began in 1999 as an identity card project for citizens living in border states. The Rangarajan Commission, set up in January 2000 to look into the growing concern regarding the quality of data in the entire statistical system, recommended the creation of a centralised database of citizens (population register) in which every citizen would have a unique identification number. Within a few years of the UIDAI being set up in 2009, Aadhaar became a primary key linking databases of bank accounts, mobile phones, income tax returns, payment apps, email IDs, and so on, even if such a linking is not mandated by the law.

Aadhaar has afforded development of application programming interfaces (APIs), and web and mobile applications with payment interfaces demanding Aadhaar verification for government and private services across domains. Perhaps nobody in 2009 could have imagined connecting biometric data to mobile phone SIM cards. Anumeha Yadav (Chapter 7) draws on her detailed field reports to show how the project grew from select pilot implementation in 2011 to a national legal and policy imperative by 2017. She notes a growing public alertness to the importance of enrolling with Aadhaar to ensure the ratification of rights, irrespective of the unclear legal status and the widespread technological glitches in the everyday functioning of the project.

The story of Aadhaar raises questions about what counts as data, who can design its purposes, and how its means and ends are discovered. It is a story that is at once expansionist and contingent: in India, the evolution of Aadhaar indicates that we need to reflect on computational culture without prefiguring the object of computation and its potential relationship to taxonomies of social control.

To understand the shift that has taken place between the data in the mid-20th-century statistical regime of economic planning and big data aggregation and prediction in the contemporary, we need to re-examine the history of computing in India, which has been largely tethered to the IT revolution. We examine different techniques and affordances of computation in different media ecologies consisting of human computers and mass-media such as telecom in the decades before the emergence of the internet.

In Chapter 1, I explore the role of the first computers of Indiaboth human and electronicfrom the 1930s to 1960s in generating official statistics. In Chapter 2, Karl Mendonca analyses the role of computerization in the 1980s at a major advertising company involved in the cinema business, and how the company later repurposed its cinema distribution network into a courier company. In different ways, both chapters challenge the notion of a clear and stable rationale for the evolution of computers and big data.

It was not until the early 2000s that database practitioners began to seriously look at data mining as a mode of knowledge production. New concepts of scale and computational processing power emerged and developed through trade-offs and reconfigurations of statistical accuracy, localised data storage and retrievability, hardware and software load balancing, and electricity consumption.

Of particular importance was the shift from relational (structured design) to non-relational (distributed design) database management systems. Here, we must not forget the co-production of affordances, users, and publics. After all, a computer database is only one specific instance of a wider set of relationalities made durable by the thoroughly material and well-constructed craft of software engineeringeven if it is widely imagined to be abstract and mystical.

In the Indian context, while the IT industry has become symbolic of a new middle-class imaginary of technology and social mobility, the epistemic cultures of software engineering and their relations with global developments are yet to be adequately unpacked. We do not know how Indias political and infrastructural conditions affect Aadhaars database design or the development of high energy-consuming data centers for data sovereignty, to name but two examples.

In a post-colony like India, any critical engagement with data-driven knowledge production has to consider the persistent role of colonial biopolitics. It is well established that statisticsformerly termed political arithmetichave played a key role in the production of people, identity, and nation-states. From the construction of enlightenment ideas such as the individual, national populations in Europe, and the citizen in the USA, the intended and unintended consequences of counting and categorising people run far and wide.

European colonies became sites for exotic and imperious enumerative and classificatory systems framed by orientalist pedagogies that displaced and serialised existing social orders. From the inventions of fingerprinting and the enumeration of complex traditions of faith and social difference into the fixities of religious identity and objectification of caste, such a biopolitics sought to make populations knowable and governable.

Post-independence India saw an expansion of bureaucracy, official statistics, and planning. Subsequently, government and transnational businesses used data modelling of the economy and populations to understand citizenship entitlements and consumer profiles. The intersections of state and market interests after economic liberalisation in 1991 transformed the national political economy as well as the everyday cultural conditions of governance.

In particular, the entry of private digital technology vendors and consultants in state and international development projects afforded new means and incentives for collecting and analysing data. Supporters of the Aadhaar project often claim that the state is a much more benign collector of data than companies such as Google and Facebook. Putting questions of veracity aside, the implications of this distinction are suggestive. The purported commensurability between data imaginaries and practices of Indias welfare state and those of big technology companies widens the scope of inquiry into the politics of data-driven governance and bureaucracy.

From state-owned biometrics to state-promoted transnational mobile apps, the contemporary (surveillance-friendly) road between the ideology of the state and that of popular digital media is punctuated by diverse and distributed data-driven pathways.

At one level, the shift from colonial fingerprinting to contemporary biometric technologies shows some continuity in terms of tactics of governance and subjectification of bodies. If we look closely though, the machinic-readability of fingerprints opens new analytical challenges for theorising governmentality. The contemporary modes of data-driven subjectification are deeply entangled with proliferation of digital technologies of identification in governance, finance, media, and consumer products across developmental and business models. How can we map this expansion and proliferation in sociotechnically specific ways?

From navigating the nudge marketing of discount codes on mobile payment apps to facing new determinations of citizenship and identity through myriad paper-based and digital documents, among other things, the emergent mutations of power, subjectivity, and data demand a closer look into the design and material form of media. This is particularly challenging in conditions of fragmented digital infrastructures, where diverse intermedial forms emerge and coalesce in everyday practices for bypassing the lack of end-to-end connectivity and formal access.

Sandeep Mertia is a PhD Candidate at the Department of Media, Culture, and Communication, and Urban Doctoral Fellow at New York University. He is an ICT engineer by training, and former Research Associate at The Sarai Programme, Centre for the Study of Developing Societies.

Lives of Data emerged from research projects and workshops at the Sarai programme, Centre for the Study of Developing Societies. It seeks to better understand the status of data objects, relationalities, and difference in computational cultures. A critical focus on India necessitates pluralistic vantage points for examining the contemporary global discourse of data revolution in relation to the enduring legacies of colonialism and 20th-century modernisation programs. From state-supported technological boosterism of its digital superpower status to everyday lives of over a billion people in one of the most diverse and unequal societies in the world, Indias sociotechnical conditions assemble deeply contrasting lives of data.

This collection of essays features a diverse group of interdisciplinary scholars and practitioners, engaging the emergence, limits, potentialities, politics, practices, and consequences of data-driven knowledge production and circulation. Encompassing history, anthropology, science and technology studies (STS), media studies, civic technology, data science, digital humanities, and journalism, the essays open up possibilities for a truly situated global and sociotechnically specific understanding of data, computing, and society.

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How Data in India Went From Being a Tool of Planning to a Tool of Control - The Wire Science

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This report provides a regional analysis of the market. It focuses on manufacturers, suppliers, segmentation, application, major players and more. Changing market dynamics such as market drivers, market restraints and market threats are provided in this research report. It also includes recent industry trends and developments with the competitive landscape. This report studies different attributes of business such as recent developments, technological platforms, tools and techniques that help in understanding the existing market. In addition, this report discusses the key drivers influencing the market growth, opportunities, challenges and the risks faced by key players and the market as a whole.

This research study assesses the dynamics and movement of the global market across various geographies. The study on the global market includes qualitative factors such as value chain analysis, drivers and restraints. Actual market sizes and forecasts have been provided for all the above-mentioned segments. The study includes the profiles of key players in the market with a significant global and regional presence.

Burglar alarm marketis expected to reachUSD 5.53 billion by 2026from USD 3.58 billion in 2018 and is projected to grow at a CAGR of 5.6% in the forecast period of 2019 to 2026. The upcoming market report contains data for historic years 2017, the base year of calculation is 2018 and the forecast period is 2019 to 2026.

Get Exclusive Sample Report + All Related Graphs & ChartsHere@https://www.databridgemarketresearch.com/request-a-sample?dbmr=global-burglar-alarm-market&AM

Major Industry Competitors:Burglar Alarm Market

The renowned players in burglar alarm market are Inovonics Wireless Corporation, Siemens AG, AEON SYSTEMS, INC., ASSA ABLOY, Johnson Controls, Napco Security Technologies, Inc., Securitas, Collins Aerospace, Hangzhou Hikvision Digital Technology Co.,Ltd., Honeywell International Inc., ADT, Banham Group and RISCO Group.

Revealing the Competitive scenario

In todays competitive world you need to think one step ahead to chase your competitors, our research offers reviews about key players, major collaborations, merger & acquisitions along with trending innovation and business policies to present better insights to drive the business into right direction

Key Segmentation: Burglar Alarm Market

By System & Hardware (Central monitoring receiver, Remote terminal unit and Alarm sensors), End-users (Small and Medium Enterprises, Residential)

Regional Outlook

North America(US, Canada, Mexico)

South America(Brazil, Argentina, rest of south America)

Asia and Pacific region(Japan, china, India, New Zealand, Vietnam, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, etc)

Middle east and Africa(UAE, Saudi Arabia, Oman, etc)

Europe(Germany, Italy, U.K, France, Spain, Netherlands, Belgium, Switzerland, Russia, etc)

Rapid Business Growth Factors

In addition, the market is growing at a fast pace and the report shows us that there are a couple of key factors behind that. The most important factor thats helping the market grow faster than usual is the tough competition.

What are the major market growth drivers?

Increasing demand for detecting unwarranted intrusion of personnel and prevent theft

Growing demand in telecommunication network and PSTN

Research strategies and tools used of Burglar Alarm Market:

This Burglar Alarm market research report helps the readers to know about the overall market scenario, strategy to further decide on this market project. It utilizes SWOT analysis, Porters Five Forces Analysis and PEST analysis.

Key Points of this Report:

The depth industry chain include analysis value chain analysis, porter five forces model analysis and cost structure analysis

The report covers North America and country-wise market of Burglar Alarm

It describes present situation, historical background and future forecast

Comprehensive data showing Burglar Alarm capacities, production, consumption, trade statistics, and prices in the recent years are provided

The report indicates a wealth of information on Burglar Alarm manufacturer

Burglar Alarm market forecast for next five years, including market volumes and prices is also provided

Raw Material Supply and Downstream Consumer Information is also included

Any other users requirements which is feasible for us

Key Developments in the Market:

In 2017, Google Nest launched a smart burglar alarm, which is beneficial in providing facial recognition and other tools. It also offered built in video camera and speakers with new alarm system software.

In 2018, Rings launched a home security system with help of burglar smart alarm that offers cameras and professional monitoring service to detect the atmosphere of the home.

Some extract from Table of Contents

Overview of Global Burglar Alarm Market

Burglar Alarm Size (Sales Volume) Comparison by Type

Burglar Alarm Size (Consumption) and Market Share Comparison by Application

Burglar Alarm Size (Value) Comparison by Region

Burglar Alarm Sales, Revenue and Growth Rate

Burglar Alarm Competitive Situation and Trends

Strategic proposal for estimating availability of core business segments

Players/Suppliers, Sales Area

Analyze competitors, including all important parameters of Burglar Alarm

Global Burglar Alarm Manufacturing Cost Analysis

The most recent innovative headway and supply chain pattern mapping

Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe, MEA or Asia Pacific.

Table Of Contents Is AvailableHere@https://www.databridgemarketresearch.com/toc?dbmr=global-burglar-alarm-market&AM

Why Is Data TriangulationImportantIn Qualitative Research?

This involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, other data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Top to Bottom Analysis and Vendor Share Analysis. Triangulation is one method used while reviewing, synthesizing and interpreting field data. Data triangulation has been advocated as a methodological technique not only to enhance the validity of the research findings but also to achieve completeness and confirmation of data using multiple methods

About Data Bridge Market Research:

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Burglar alarm market Emerging Opportunities, Revenue Analysis and Growth Projections - KSU | The Sentinel Newspaper

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The following is an excerpt from Lives of Data: Essays on Computational Cultures from India, edited by Sandeep Mertia, foreword by Ravi Sundaram, Institute of Network Cultures (Amsterdam, 2020).

This book is published under the Creative Commons Attribution-NonCommercial- NoDerrivatives 4.0 International (CC BY-NC-SA 4.0) license and is available here.

It is not difficult to see what is wrong with official statistics in India. There is gap between theory and practice. There is gap between the means and the end in the absence of any clearly perceived purpose.

~ P. C. Mahalanobis, Statistics as Key Technology, 1965

Data is its own means. It is an unlimited non-rivalrous resource. Yet, it isnt shared freely. What began as a differentiator is now the model itself.

~ Nandan Nilekani, Why India needs to be a Data Democracy, 2017

Data shadows our situation. Many believe it can determine our situation. There were enthusiastic claims that Big Data would lead to a fourth industrial revolution and the end of Theory, and that it will transform how we live, work, and think. Arguably, much of the early 2010s hype around the big data revolution has already been replugged into popular narratives of artificial intelligence (AI). The media infrastructures that enliven digital data and the fast-moving claims of data revolution are now evidently more globalized and capitalized than ever before. If we look a little under the hood, techniques such as data mining have moved from the margins of techno-scientific practice to normative centers of global computing in less than two decades. How did data become so powerful, pervasive, and relatable in the first place? To understand the global momentum of the data revolution, it is crucial to inquire into the many lineages, affinities, and relations of data in context-sensitive ways.

Lives of Data: Essays on Computational Cultures from India Ed. Sandeep MertiaInstitute of Network Cultures (Amsterdam, 2020)

Data Revolution(s) in Context

The contrast between the two epigraphs above is a good place to begin tracking lives of data. The first epigraph is from a lecture in 1965 at the 125th Annual Meeting of the American Statistical Association by P. C. Mahalanobis, founder of the Indian Statistical Institute (ISI) and a member of the Planning Commission, a powerful body at that time. In this lecture, he emphasized the need to establish a purposive view of statistics as a fully developed technology of a multi-discipline character. This was especially so in the underdeveloped countries where the principle of authority of the government reigned supreme over independent statistical analysis and interpretation. Mahalanobis made these observations at a time when the ISI and Indias official statistics and economic planning system were receiving global recognition for pioneering work in research, training, sample-survey methods, and economic planning. He clearly placed statistical knowledge production in the service of postcolonial nation-building. The desire to perceive a clearly defined purpose when the ISI was already at the cutting edge of large-scale data collection and processing stands in puzzling contrast to contemporary modes of data-driven governance which claim data is its own means.

The second epigraph is from an opinion piece by Nandan Nilekani, co-founder of Infosys and founding Chairman of Unique Identification Authority of India (UIDAI), the government body responsible for the worlds largest biometric database, Aadhaar. In this article he argues for the value of big data and artificial intelligence for disrupting existing patterns of information management, and cautions against data colonization by state and global platforms. It is important to note that what we now know as Aadhaar actually began in 1999 as an identity card project for citizens living in border states. The Rangarajan Commission, set up in January 2000 to look into the growing concern regarding the quality of data in the entire statistical system, recommended the creation of a centralized database of citizens (population register) in which every citizen would have a unique identification number. Within a few years of the UIDAI being set up in 2009, Aadhaar became a primary key linking databases of bank accounts, mobile phones, income tax returns, payment apps, email IDs, and so on, even if such a linking is not mandated by the law. Aadhaar has afforded development of application programming interfaces (APIs), and web and mobile applications with payment interfaces demanding Aadhaar verification for government and private services across domains. Perhaps nobody in 2009 could have imagined connecting biometric data to mobile phone SIM cards. Anumeha Yadav (Chapter 7) draws on her detailed field reports to show how the project grew from select pilot implementation in 2011 to a national legal and policy imperative by 2017. She notes a growing public alertness to the importance of enrolling with Aadhaar to ensure the ratification of rights, irrespective of the unclear legal status and the widespread technological glitches in the everyday functioning of the project. The story of Aadhaar raises questions about what counts as data, who can design its purposes, and how its means and ends are discovered. It is a story that is at once expansionist and contingent: in India, the evolution of Aadhaar indicates that we need to reflect on computational culture without prefiguring the object of computation and its potential relationship to taxonomies of social control.

To understand the shift that has taken place between the data in the mid-20th-century statistical regime of economic planning and big data aggregation and prediction in the contemporary, we need to re-examine the history of computing in India, which has been largely tethered to the IT revolution. We examine different techniques and affordances of computation in different media ecologies consisting of human computers and mass-media such as telecom in the decades before the emergence of the internet. In Chapter 1, I explore the role of the first computers of Indiaboth human and electronicfrom the 1930s to 1960s in generating official statistics. In Chapter 2, Karl Mendonca analyses the role of computerization in the 1980s at a major advertising company involved in the cinema business, and how the company later repurposed its cinema distribution network into a courier company. In different ways, both chapters challenge the notion of a clear and stable rationale for the evolution of computers and big data.

It was not until the early 2000s that database practitioners began to seriously look at data mining as a mode of knowledge production. New concepts of scale and computational processing power emerged and developed through trade-offs and reconfigurations of statistical accuracy, localized data storage and retrievability, hardware and software load balancing, and electricity consumption. Of particular importance was the shift from relational (structured design) to non-relational (distributed design) database management systems. Here, we must not forget the co-production of affordances, users, and publics. After all, a computer database is only one specific instance of a wider set of relationalities made durable by the thoroughly material and well-constructed craft of software engineeringeven if it is widely imagined to be abstract and mystical. In the Indian context, while the IT industry has become symbolic of a new middle-class imaginary of technology and social mobility, the epistemic cultures of software engineering and their relations with global developments are yet to be adequately unpacked. We do not know how Indias political and infrastructural conditions affect Aadhaars database design or the development of high energy-consuming data centers for data sovereignty, to name but two examples.

In a post-colony like India, any critical engagement with data-driven knowledge production has to consider the persistent role of colonial biopolitics. It is well established that statisticsformerly termed political arithmetichave played a key role in the production of people, identity, and nation-states. From the construction of enlightenment ideas such as the individual, national populations in Europe, and the citizen in the USA, the intended and unintended consequences of counting and categorizing people run far and wide. European colonies became sites for exotic and imperious enumerative and classificatory systems framed by orientalist pedagogies that displaced and serialized existing social orders. From the inventions of fingerprinting and the enumeration of complex traditions of faith and social difference into the fixities of religious identity and objectification of caste, such a biopolitics sought to make populations knowable and governable.

Post-independence India saw an expansion of bureaucracy, official statistics, and planning. Subsequently, government and transnational businesses used data modelling of the economy and populations to understand citizenship entitlements and consumer profiles. The intersections of state and market interests after economic liberalization in 1991 transformed the national political economy as well as the everyday cultural conditions of governance. In particular, the entry of private digital technology vendors and consultants in state and international development projects afforded new means and incentives for collecting and analyzing data. Supporters of the Aadhaar project often claim that the state is a much more benign collector of data than companies such as Google and Facebook. Putting questions of veracity aside, the implications of this distinction are suggestive. The purported commensurability between data imaginaries and practices of Indias welfare state and those of big technology companies widens the scope of inquiry into the politics of data-driven governance and bureaucracy. From state-owned biometrics to state-promoted transnational mobile apps, the contemporary (surveillance-friendly) road between the ideology of the state and that of popular digital media is punctuated by diverse and distributed data-driven pathways.

Representative image of an Aadhaar card. Photo: PTI

At one level, the shift from colonial fingerprinting to contemporary biometric technologies shows some continuity in terms of tactics of governance and subjectification of bodies. If we look closely though, the machinic-readability of fingerprints opens new analytical challenges for theorizing governmentality. The contemporary modes of data-driven subjectification are deeply entangled with proliferation of digital technologies of identification in governance, finance, media, and consumer products across developmental and business models. How can we map this expansion and proliferation in sociotechnically specific ways? From navigating the nudge marketing of discount codes on mobile payment apps to facing new determinations of citizenship and identity through myriad paper-based and digital documents, among other things, the emergent mutations of power, subjectivity, and data demand a closer look into the design and material form of media. This is particularly challenging in conditions of fragmented digital infrastructures, where diverse intermedial forms emerge and coalesce in everyday practices for bypassing the lack of end-to-end connectivity and formal access.

Sandeep Mertia is a PhD Candidate at the Department of Media, Culture, and Communication, and Urban Doctoral Fellow at New York University. He is an ICT engineer by training, and former Research Associate at The Sarai Programme, Centre for the Study of Developing Societies.

Lives of Data emerged from research projects and workshops at the Sarai programme, Centre for the Study of Developing Societies. It seeks to better understand the status of data objects, relationalities, and difference in computational cultures. A critical focus on India necessitates pluralistic vantage points for examining the contemporary global discourse of data revolution in relation to the enduring legacies of colonialism and 20th-century modernisation programs. From state-supported technological boosterism of its digital superpower status to everyday lives of over a billion people in one of the most diverse and unequal societies in the world, Indias sociotechnical conditions assemble deeply contrasting lives of data.

This collection of essays features a diverse group of interdisciplinary scholars and practitioners, engaging the emergence, limits, potentialities, politics, practices, and consequences of data-driven knowledge production and circulation. Encompassing history, anthropology, science and technology studies (STS), media studies, civic technology, data science, digital humanities, and journalism, the essays open up possibilities for a truly situated global and sociotechnically specific understanding of data, computing, and society.

See the original post:

How Data in India Went From Being a Tool of Economic Planning to Big Data Aggregation - The Wire

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HONG KONG and SHANGHAI, Jan. 14, 2021 /PRNewswire/ -- The Ping An Digital Economic Research Center (PADERC), a member of Ping An Insurance (Group) Company of China, Ltd. (HKEx:2318; SSE:601318), has created four new investment strategies for environmental, social and corporate governance (ESG) investing using Ping An's proprietary CN-ESG data for China A-shares, in light of surging demand in China for ESG ratings and data with wider coverage and a better fit for China's market.

Ping An ESG framework aligns with international standards and Chinese regulations

The investment strategies detailed in the report, "Applications of Ping An CN-ESG Data and Framework in Quantitative Investment Strategy", use the proprietary CN-ESG database and scoring framework developed by the Ping An Group. Ping An was the first asset owner in China to sign the United Nations Principles for Responsible Investment. The framework leverages Ping An's expertise in finance and technology and aligns with international standards as well as guidelines from Chinese regulators to incorporate material topics for Chinese companies.

With technologies such as web crawlers, data mining, machine learning, knowledge graphs, natural language processing (NLP) and satellite remote sensing, the CN-ESG system can verify ESG disclosure-based data as well as mine non-disclosure-based data to provide investors with richer multi-dimensional information.

PADERC's report provides an in-depth analysis on the data characteristics, effectiveness, and strategy back-testing results of the CN-ESG database and scoring framework, which covers more than 3,900 listed companies in the China A-share market with five years of historical data (2015-2019). The framework can provide quarterly results that are further adjusted based on news sentiment scores in real-time compared to annual or semi-annual updates from most ESG rating providers.

ESG factors independent of financial factors

PADERC found the Ping An's CN-ESG scores among A-share companies is close to a normal distribution. The factor correlation test results show that scores have notable performance of quality factors. The overall correlation between CN-ESG factors and traditional financial factors is generally low, showing high levels of independence of ESG factors, which indicates these can provide new data and viewpoints for investment decisions.

The results of the factor layered test show that Ping An CN-ESG factors have a relatively strong positive screening effect on the Chinese Securities Index (CSI) 300 and CSI 800 stock pools. The financial window dressing factors constructed by evaluating the quality and authenticity of the company's financial data yielded 11.61% of long-short gains since 2015.

ESG investment strategies that balance excess returns with ESG objectives

Based on CN-ESG data, PADERC constructed four types of ESG investment strategies that use artificial intelligence (AI) to balance excess investment returns and ESG investment targets:

1) Ping An AI-ESG Selected 100 Strategy: This positive screening strategy selects companies with the highest ESG scores. Based on the broader CSI 800 stock pool, it can better leverage additional information from ESG scores. This strategy achieved an annualized excess return of 4.44%. The annual weighted average ESG score quantile of the portfolio is 94.2% among the benchmark stock pool.

2) Ping An AI-ESG Enhancement Strategy: On the basis of ESG scores-based positive screening, PADERC added ESG factors to its Ping An Digital Economic Research Center 500+ No.1 AI Stock Selection Strategy and there was notable excess return. The AI stock selection strategy is based on linear and non-linear algorithms to capture complex market structures to predict the excess return of individual stocks. The Ping An AI-ESG Enhancement Strategy has an annualized excess return of 16.34%, and the annual weighted average ESG score quantile of the portfolio is 78.7% among the benchmark stock pool.

3) CSI 300 ESG Style Index Series:The CSI 300 ESG Growth Index explores the growth value of the CSI 300 stocks, while controlling its tail risks. The CSI 300 ESG Low Volatility Index reinforces the stability features of ESG investment in both the short and long term. The ESG growth index achieved annualized excess returns of 5.67% and the low volatility index achieved 8.61% relative to the benchmark. The annual weighted average ESG score quantile of the portfolios are 75.1% (ESG growth index) and 73.1% (low volatility index) relative to the benchmark stock pool.

Further testing of excess returns shows that the above active management strategies have almost all achieved excess returns in adverse market conditions, including bond crises, annual bear market downturns, Sino-US trade war, and COVID-19, verifying the effectiveness of ESG factors in challenging environments.

4) AI-ESG MAX Strategy: ESG enhancement of mainstream ETFs enables investors to gradually incorporate ESG concepts into their investing process without changing their traditional investing habits. Based on the CSI 300, controlling for sector deviation, this strategy sets tracking errors to 1%, 3% and 5%. Under different tracking error assumptions, the strategy maximizes ESG scores while achieving annualized excess returns of 3.61%, 3.40% and 3.43% respectively against the benchmark. The back-testing results of the strategy over the past five years show good performance, and excess returns were stable. This type of index enhancement strategy based on ESG factors could help drive an increase in the scale of ESG investing.

Building a richer ESG strategy portfolio to meet investors' diverse needs

Ping An's CN-ESG framework will expand to include fixed income ESG data and climate risk-related AI-driven factors. It will enable more diverse investment options, such as ESG fixed income indices and climate risk-focused indices, to meet investors' diverse needs. Ping An also developed a series of AI-ESG products focusing on corporate management, risk monitoring and analytics solutions for ESG and climate risk analysis, including portfolio sustainability footprint analysis, a portfolio adjustment tool, a sustainable funds screening tool, and climate risk asset pricing models to support ESG investment.

PADERC is a professional institution specializing in macroeconomics and policy research, using big data and artificial intelligence to provide insights on macroeconomic trends, including developments in ESG disclosures and ratings.

For the full report, click here.

About Ping An Group

Ping An Insurance (Group) Company of China, Ltd. ("Ping An") is a world-leading technology-powered retail financial services group. With over 210 million retail customers and 560 million Internet users, Ping An is one of the largest financial services companies in the world.

Ping An has two over-arching strategies, "pan financial assets" and "pan health care", which focus on the provision of financial and health care services through our integrated financial services platform and our five ecosystems of financial services, health care, auto services, real estate services and smart city services. Our "finance + technology" and "finance + ecosystem" strategies aim to provide customers and internet users with innovative and simple products and services using technology. As China's first joint stock insurance company, Ping An is committed to upholding the highest standards of corporate reporting and corporate governance. The Group is listed on the stock exchanges in Hong Kong and Shanghai.

In 2020, Ping An ranked 7th in the Forbes Global 2000 list and ranked 21st in the Fortune Global 500 list. Ping An also ranked 38th in the 2020 WPP Kantar Millward Brown BrandZTM Top 100 Most Valuable Global Brands list. For more information, please visit http://www.pingan.cn.

About Ping An Digital Economic Research Center

Ping An Digital Economic Research Center utilizes more than 50 TB high frequency data points, more than 30 years of historical data and more than 1.5 billion data points to drive research on the "AI + Macro Forecast" and provide insights and methods towards precise macroeconomic trends.

SOURCE Ping An Insurance (Group) Company of China, Ltd.

http://www.pingan.cn

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Market Overview

The global deep learning market is growing at a rapid pace. Market growth attributes to the increasing adoption of cloud-based services and large scale generation of unstructured data. Besides, the increasing demand for deep learning technology for natural language processing and voice/speech recognition applications drive the growth of the market. Moreover, increasing applications of deep learning models for image/speech recognition, data mining, and language translations escalate market growth.

In its detailed analysis, Market Research Future (MRFR) asserts that the global deep learning market is poised to register a 30.87% CAGR throughout the forecast period (2017 -2023). Growing developments of humanoid robots, like Sophia, and the rising uses of augmented (AR) and virtual reality (VR) displays for the automotive and 3D gaming sectors boost the market growth. The uses of deep learning technologies in medical image analysis are rising exponentially.

Resultantly, the burgeoning healthcare sectors worldwide are cited as a major contributing factor for the market growth. Additionally, growing developments in deep learning technology and growth in the numbers of chatbots, alongside increasing R&D investments by companies, push the markets growth. The encouraging adoption of digital voice assistants increasing numbers of start-ups focused on augmented and virtual reality technologies influences the markets growth.

FOR MORE DETAILS:http://www.marketwatch.com/press-release/ticket-printers-production-market-2020-global-analysis-by-trends-size-share-growth-opportunities-demand-challenges-and-forecast-to-2023-2021-01-05

Global Market for Deep Learning Segmentation

The report is segmented into four dynamics;

By Component : Hardware (processor, memory, network, others), Software (solution, platform, others), and Services (installation, training, support & maintenance, others).

By Application : Image Recognition, Data Mining, Signal Recognition, and others.

By End-User : Manufacturing, Security, Retail, Media & Entertainment, Automotive, BFSI, Healthcare, Agriculture, and others.

By Regions : Americas, Europe, Asia Pacific, and the Rest-of-the-World.

Global Deep Learning Market Regional Analysis

North America dominates the global Deep Learning market. The largest market share attributes to the growing adoption of deep learning models for application image recognition, voice & image recognition, data mining, signal recognition, and diagnostics purposes. Besides, substantial investments transpired by key players in the development of AI technology platforms positively impact regional market growth.

Increasing R&D investments and rising business digital transformation push the growth of the market demand in the region. Moreover, the rising use of deep learning technologies for image recognition, data mining, and signal recognition substantiates the market growth. Manufacturing and automotive sectors in the US, Canada, and Mexico boost market growth.

The Asia Pacific region stands second in the global deep learning market. The market is driven by the growing development and penetration of deep learning technology in the region. Additionally, the proliferation of digitization and the spurring rise in the image recognition segment drive the regional markets growth. Rising foreign investments and implementing deep learning models for applications, such as voice, image, and signal recognition, favor the regional markets growth.

The European deep learning market is growing rapidly. Factors such as the augmenting uptake of deep learning technology by the European governments for surveillance, fraud detection, and data mining purposes, and healthcare diagnostics purposes boost the regional market growth. Furthermore, the presence of various notable players and large deployments across the industries in the region act as major tailwinds for the markets growth.

Global Deep Learning Market Competitive Analysis

Highly competitive, the deep learning market appears fragmented due to the presence of several well-established players. Mergers & acquisitions, innovation, and brand reinforcement remain prevailing key trends for the leading players as these strategies support their growth and expansion plans. They deliver reliable, leading-edge solutions and services. For which they invest substantially in R&D to develop adept technologies and products.

Major Players:

Players leading the deep learning market include Intel Corporation (USA), Amazon Inc. (USA), Samsung Electronics Co Ltd (South Korea), Sensory Inc. (USA), Micron Technology (USA), Xilinx Inc. (USA), Mellanox Technologies (USA), Google LLC (USA), Adapteva, Inc. (USA), NVIDIA Corporation (USA), Qualcomm Technologies Inc. (USA), Baidu Inc (China), Advanced Micro Devices Inc. (USA), IBM Corporation (USA), Facebook (USA), Microsoft Corporation (USA), and Tenstorrent (Canada), among others.

ALSO READ :https://www.marketwatch.com/press-release/pallet-racking-market-growth-size-demands-swot-analysis-potential-targets-proposal-stratergy-and-industry-outlook2021-2021-01-07?tesla=y

Industry/Innovation/Related News:

November 21, 2020 -Abacus.AI (the US), a leading AI research company, announced raising USD 22 MN in Series B funding. The company has raised over USD 40 MN since the day of its inception.

November 20, 2020 -A team of researchers at Binghamton University, State University of New York, announced using deep-learning techniques to analyze the numbers and suggest improved public safety by re-allocating resources. The team states that DeepER could be tweaked for other large cities or clusters of smaller cities with similar characteristics that would provide enough data to make predictions.

November 03, 2020 -A team of deep learning enthusiasts from IIT Roorkee (The Indian Institutes of Technology, in Roorkee) won the top prize at the recently-concluded tech4heritage hackathon. The team Ancient AI was able to develop restorative outcomes with the help of deep learning techniques to digitally restore damaged murals of Ajanta caves, utilizing a dataset of reference paintings to develop their AI model.

For More Reference:https://www.openpr.com/news/2205028/performance-analytics-market-covid-19-analysis-2016-receives

Company name: Market Research Future

About Market Research Future:

At Market Research Future (MRFR), we enable our customers to unravel the complexity of various industries through our Cooked Research Report (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research & Consulting Services.

MRFR team have supreme objective to provide the optimum quality market research and intelligence services to our clients. Our market research studies by Components, Application, Logistics and market players for global, regional, and country level market segments, enable our clients to see more, know more, and do more, which help to answer all their most important questions.

Contact:Market Research FutureOffice No. 528, Amanora ChambersMagarpatta Road, Hadapsar,Pune 411028Maharashtra, India+1 646 845 9312Email:[emailprotected]

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