The Technology Fallacy: How People Are the Real Key to Digital Transformation

Curated on Posted on by Stefaan Verhulst

Book by Gerald C. Kane, Anh Nguyen Phillips, Jonathan R. Copulsky and Garth R. Andrus: “Digital technologies are disrupting organizations of every size and shape, leaving managers scrambling to find a technology fix that will help their organizations compete. This book offers managers and business leaders a guide for surviving digital disruptions—but it is not a book about technology. It is about the organizational changes required to harness the power of technology. The authors argue that digital disruption is primarily about people and that effective digital transformation involves changes to organizational dynamics and how work gets done. A focus only on selecting and implementing the right digital technologies is not likely to lead to success. The best way to respond to digital disruption is by changing the company culture to be more agile, risk tolerant, and experimental.

The authors draw on four years of research, conducted in partnership with MIT Sloan Management Review and Deloitte, surveying more than 16,000 people and conducting interviews with managers at such companies as Walmart, Google, and Salesforce. They introduce the concept of digital maturity—the ability to take advantage of opportunities offered by the new technology—and address the specifics of digital transformation, including cultivating a digital environment, enabling intentional collaboration, and fostering an experimental mindset. Every organization needs to understand its “digital DNA” in order to stop “doing digital” and start “being digital.”

Digital disruption won’t end anytime soon; the average worker will probably experience numerous waves of disruption during the course of a career. The insights offered by The Technology Fallacy will hold true through them all….(More)”.

Credit denial in the age of AI

Curated on Posted on by Stefaan Verhulst

Paper by Aaron Klein: “Banks have been in the business of deciding who is eligible for credit for centuries. But in the age of artificial intelligence (AI), machine learning (ML), and big data, digital technologies have the potential to transform credit allocation in positive as well as negative directions. Given the mix of possible societal ramifications, policymakers must consider what practices are and are not permissible and what legal and regulatory structures are necessary to protect consumers against unfair or discriminatory lending practices.

In this paper, I review the history of credit and the risks of discriminatory practices. I discuss how AI alters the dynamics of credit denials and what policymakers and banking officials can do to safeguard consumer lending. AI has the potential to alter credit practices in transformative ways and it is important to ensure that this happens in a safe and prudent manner….(More)”.

Statistics Estonia to coordinate data governance

Curated on Posted on by Stefaan Verhulst

Article by Miriam van der Sangen at CBS: “In 2018, Statistics Estonia launched a new strategy for the period 2018-2022. This strategy addresses the organisation’s aim to produce statistics more quickly while minimising the response burden on both businesses and citizens. Another element in the strategy is addressing the high expectations in Estonian society regarding the use of data. ‘We aim to transform Statistics Estonia into a national data agency,’ says Director General Mägi. ‘This means our role as a producer of official statistics will be enlarged by data governance responsibilities in the public sector. Taking on such responsibilities requires a clear vision of the whole public data ecosystem and also agreement to establish data stewards in most public sector institutions.’…

the Estonian Parliament passed new legislation that effectively expanded the number of official tasks for Statistics Estonia. Mägi elaborates: ‘Most importantly, we shall be responsible for coordinating data governance. The detailed requirements and conditions of data governance will be specified further in the coming period.’ Under the new Act, Statistics Estonia will also have more possibilities to share data with other parties….

Statistics Estonia is fully committed to producing statistics which are based on big data. Mägi explains: ‘At the moment, we are actively working on two big data projects. One project involves the use of smart electricity meters. In this project, we are looking into ways to visualise business and household electricity consumption information. The second project involves web scraping of prices and enterprise characteristics. This project is still in an initial phase, but we can already see that the use of web scraping can improve the efficiency of our production process.’ We are aiming to extend the web scraping project by also identifying e-commerce and innovation activities of enterprises.’

Yet another ambitious goal for Statistics Estonia lies in the field of data science. ‘Similarly to Statistics Netherlands, we established experimental statistics and data mining activities years ago. Last year, we developed a so-called think-tank service, providing insights from data into all aspects of our lives. Think of birth, education, employment, et cetera. Our key clients are the various ministries, municipalities and the private sector. The main aim in the coming years is to speed up service time thanks to visualisations and data lake solutions.’ …(More)”.

Unblocking the Bottlenecks and Making the Global Supply Chain Transparent: How Blockchain Technology Can Update Global Trade

Curated on Posted on by Stefaan Verhulst

Paper by Hanna C Norberg: “Blockchain technology is still in its infancy, but already it has begun to revolutionize global trade. Its lure is irresistible because of the simplicity with which it can replace the standard methods of documentation, smooth out logistics, increase transparency, speed up transactions, and ameliorate the planning and tracking of trade.

Blockchain essentially provides the supply chain with an unalterable ledger of verified transactions, and thus enables trust every step of the way through the trade process. Every stakeholder involved in that process – from producer to warehouse worker to shipper to financial institution to recipient at the final destination – can trust that the information contained in that indelible ledger is accurate. Fraud will no longer be an issue, middlemen can be eliminated, shipments tracked, quality control maintained to highest standards and consumers can make decisions based on more than the price. Blockchain dramatically reduces the amount of paperwork involved, along with the myriad of agents typically involved in the process, all of this resulting in soaring efficiencies. Making the most of this new technology, however, requires solid policy. Most people have only a vague idea of what blockchain is. There needs to be a basic understanding of what blockchain can and can’t do, and how it works in the economy and in trade. Once they become familiar with the technology, policy-makers must move on to thinking about what technological issues could be mitigated, solved or improved.

Governments need to explore blockchain’s potential through its use in public-sector projects that demonstrate its workings, its potential and its inevitable limitations. Although blockchain is not nearly as evolved now as the internet was in 2005, co-operation among all stakeholders on issues like taxonomy or policy guides on basic principles is crucial. Those stakeholders include government, industry, academia and civil society. All this must be done while keeping in mind the global nature of blockchain and that blockchain regulations need to be made in synch with regulations on other issues are adjacent to the technology, such as electronic signatures. However, work can be done in the global arena through international initiatives and organizations such as the ISO….(More)”.

Opening Internet Monopolies to Competition with Data Sharing Mandates

Curated on Posted on by Stefaan Verhulst

Policy Brief by Claudia Biancotti (PIIE) and Paolo Ciocca (Consob): “Over the past few years, it has become apparent that a small number of technology companies have assembled detailed datasets on the characteristics, preferences, and behavior of billions of individuals. This concentration of data is at the root of a worrying power imbalance between dominant internet firms and the rest of society, reflecting negatively on collective security, consumer rights, and competition. Introducing data sharing mandates, or requirements for market leaders to share user data with other firms and academia, would have a positive effect on competition. As data are a key input for artificial intelligence (AI), more widely available information would help spread the benefits of AI through the economy. On the other hand, data sharing could worsen existing risks to consumer privacy and collective security. Policymakers intending to implement a data sharing mandate should carefully evaluate this tradeoff….(More).

The Data Gaze: Capitalism, Power and Perception

Curated on Posted on by Stefaan Verhulst

Book by David Beer: “A significant new way of understanding contemporary capitalism is to understand the intensification and spread of data analytics. This text is about the powerful promises and visions that have led to the expansion of data analytics and data-led forms of social ordering. 

 It is centrally concerned with examining the types of knowledge associated with data analytics and shows that how these analytics are envisioned is central to the emergence and prominence of data at various scales of social life.  This text aims to understand the powerful role of the data analytics industry and how this industry facilitates the spread and intensification of data-led processes. As such, The Data Gaze is concerned with understanding how data-led, data-driven and data-reliant forms of capitalism pervade organisational and everyday life. 

Using a clear theoretical approach derived from Foucault and critical data studies the text develops the concept of the data gaze and shows how powerful and persuasive it is. It’s an essential and subversive guide to data analytics and data capitalism. …(More)”.

A compendium of innovation methods

Curated on Posted on by Stefaan Verhulst

Report by Geoff Mulgan and Kirsten Bound: “Featured in this compendium are just some of the innovation methods we have explored over the last decade. Some, like seed accelerator programmes, we have invested in and studied. Others, like challenge prizes, standards of evidence or public sector labs, we have developed and helped to spread around the world.

Each section gives a simple introduction to the method and describes Nesta’s work in relation to it. In each case, we have also provided links to further relevant resources and inspiration on our website and beyond.

The 13 methods featured are:

  1. Accelerator programmes
  2. Anticipatory regulation
  3. Challenge prizes
  4. Crowdfunding
  5. Experimentation
  6. Futures
  7. Impact investment
  8. Innovation mapping
  9. People Powered Results: the 100 day challenge
  10. Prototyping
  11. Public and social innovation labs
  12. Scaling grants for social innovations
  13. Standards of Evidence…(More)”.

Know-how: Big Data, AI and the peculiar dignity of tacit knowledge

Curated on Posted on by Stefaan Verhulst

Essay by Tim Rogan: “Machine learning – a kind of sub-field of artificial intelligence (AI) – is a means of training algorithms to discern empirical relationships within immense reams of data. Run a purpose-built algorithm by a pile of images of moles that might or might not be cancerous. Then show it images of diagnosed melanoma. Using analytical protocols modelled on the neurons of the human brain, in an iterative process of trial and error, the algorithm figures out how to discriminate between cancers and freckles. It can approximate its answers with a specified and steadily increasing degree of certainty, reaching levels of accuracy that surpass human specialists. Similar processes that refine algorithms to recognise or discover patterns in reams of data are now running right across the global economy: medicine, law, tax collection, marketing and research science are among the domains affected. Welcome to the future, say the economist Erik Brynjolfsson and the computer scientist Tom Mitchell: machine learning is about to transform our lives in something like the way that steam engines and then electricity did in the 19th and 20th centuries. 

Signs of this impending change can still be hard to see. Productivity statistics, for instance, remain worryingly unaffected. This lag is consistent with earlier episodes of the advent of new ‘general purpose technologies’. In past cases, technological innovation took decades to prove transformative. But ideas often move ahead of social and political change. Some of the ways in which machine learning might upend the status quo are already becoming apparent in political economy debates.

The discipline of political economy was created to make sense of a world set spinning by steam-powered and then electric industrialisation. Its central question became how best to regulate economic activity. Centralised control by government or industry, or market freedoms – which optimised outcomes? By the end of the 20th century, the answer seemed, emphatically, to be market-based order. But the advent of machine learning is reopening the state vs market debate. Which between state, firm or market is the better means of coordinating supply and demand? Old answers to that question are coming under new scrutiny. In an eye-catching paper in 2017, the economists Binbin Wang and Xiaoyan Li at Sichuan University in China argued that big data and machine learning give centralised planning a new lease of life. The notion that market coordination of supply and demand encompassed more information than any single intelligence could handle would soon be proved false by 21st-century AI.

How seriously should we take such speculations? Might machine learning bring us full-circle in the history of economic thought, to where measures of economic centralisation and control – condemned long ago as dangerous utopian schemes – return, boasting new levels of efficiency, to constitute a new orthodoxy?

A great deal turns on the status of tacit knowledge….(More)”.

New York City ‘Open Data’ Paves Way for Innovative Technology

Curated on Posted on by Stefaan Verhulst

Leo Gringut at the International Policy Digest: “The philosophy behind “Open Data for All” turns on the idea that easy access to government data offers everyday New Yorkers the chance to grow and innovate: “Data is more than just numbers – it’s information that can create new opportunities and level the playing field for New Yorkers. It’s the illumination that changes frameworks, the insight that turns impenetrable issues into solvable problems.” Fundamentally, the newfound accessibility of City data is revolutionizing NYC business. According to Albert Webber, Program Manager for Open Data, City of New York, a key part of his job is “to engage the civic technology community that we have, which is very strong, very powerful in New York City.”

Fundamentally, Open Data is a game-changer for hundreds of New York companies, from startups to corporate giants, all of whom rely on data for their operations. The effect is set to be particularly profound in New York City’s most important economic sector: real estate. Seeking to transform the real estate and construction market in the City, valued at a record-setting $1 trillion in 2016, companies have been racing to develop tools that will harness the power of Open Data to streamline bureaucracy and management processes.

One such technology is the Citiscape app. Developed by a passionate team of real estate experts with more than 15 years of experience in the field, the app assembles data from the Department of Building and the Environmental Control Board into one easy-to-navigate interface. According to Citiscape Chief Operational Officer Olga Khaykina, the secret is in the app’s simplicity, which puts every aspect of project management at the user’s fingertips. “We made DOB and ECB just one tap away,” said Khaykina. “You’re one tap away from instant and accurate updates and alerts from the DOB that will keep you informed about any changes to ongoing project. One tap away from organized and cloud-saved projects, including accessible and coordinated interaction with all team members through our in-app messenger. And one tap away from uncovering technical information about any building in NYC, just by entering its address.” Gone are the days of continuously refreshing the DOB website in hopes of an update on a minor complaint or a status change regarding your project; Citiscape does the busywork so you can focus on your project.

The Citiscape team emphasized that, without access to Open Data, this project would have been impossible….(More)”.

Big Data in the U.S. Consumer Price Index: Experiences & Plans

Curated on Posted on by Stefaan Verhulst

Paper by Crystal G. Konny, Brendan K. Williams, and David M. Friedman: “The Bureau of Labor Statistics (BLS) has generally relied on its own sample surveys to collect the price and expenditure information necessary to produce the Consumer Price Index (CPI). The burgeoning availability of big data has created a proliferation of information that could lead to methodological improvements and cost savings in the CPI. The BLS has undertaken several pilot projects in an attempt to supplement and/or replace its traditional field collection of price data with alternative sources. In addition to cost reductions, these projects have demonstrated the potential to expand sample size, reduce respondent burden, obtain transaction prices more consistently, and improve price index estimation by incorporating real-time expenditure information—a foundational component of price index theory that has not been practical until now. In CPI, we use the term alternative data to refer to any data not collected through traditional field collection procedures by CPI staff, including third party datasets, corporate data, and data collected through web scraping or retailer API’s. We review how the CPI program is adapting to work with alternative data, followed by discussion of the three main sources of alternative data under consideration by the CPI with a description of research and other steps taken to date for each source. We conclude with some words about future plans… (More)”.