The educational needs of the 21st century pose a number of serious problems for current educational practices. First and foremost, we see the 21st century as a time that is characterized by constant change. Educational practices that focus on the transfer of static knowledge simply cannot keep up with the rapid rate of change. Practices that focus on adaptation or reaction to change fare better but are still finding themselves outpaced by an environment that requires content to be updated almost as fast as it can be taught. What is required to succeed in education is a theory that is responsive to the context of constant flux, while at the same time is grounded in a theory of learning. Accordingly, understanding the processes of learning which underwrite the practices emerging from participation in digital networks may enable us to design learning environments that harness the power of digital participation for education in the 21st century.

This paper is the latest in Deloitte’s ongoing ‘Business futures’ research programme, which aims to provide insights into business in the future. We are committed not only to examining the potential impact of digital technologies on the labour market, on occupations and on different sectors of the economy, but also to helping our own workforce and the community adapt to life in the machine age.

“Performance ecosystem: A decision framework to take performance to the next level” was released by Deloitte University, compiled and translated by Deloitte China Technology, Media & Telecommunication Industry team. In this article, we innovatively defined the ecosystems taxonomy, and provided examples of some firms to illustrate the differences between these types. We hope to provide a much more innovative decision framework for executives, thus help them examine the forward way of enterprises from a higher perspective of ecosystem.

This report provides an overview of the definitions and competency frameworks of data literacy, as well as the assessment tools used to measure it. These are based on the existing literature and current practices around the world. Data literacy, or the ability to derive meaningful information from data, is a relatively new concept. However, it is gaining increasing recognition as a vital skillset in the information age. Existing approaches to measuring data literacy—from self-assessment tools to objective measures, and from individual to organizational assessments—are discussed in this report to inform the development of an assessment tool for data literacy in the Canadian public service.

Will pessimistic predictions of the rise of the robots come true? Will humans be made redundant by artificial intelligence (AI) and robots, unable to find work and left to face a future defined by an absence of jobs? Or will the optimists be right? Will historical norms reassert themselves and technology create more jobs than it destroys, resulting in new occupations that require new skills and knowledge and new ways of working?

L’industrie canadienne des médias numériques est dynamique, diversifiée et créative. Au Canada, on dénombre environ 2 300 (Industrie Canada, 2010) à 3 200 (CRHSC, 2009) entreprises œuvrant dans le secteur des médias numériques (MN), dont 85 % sont entièrement détenues par des intérêts canadiens (CIAIC, 2009). Ces entreprises offriraient de l’emploi à plus de 52 000 personnes (CRHSC, 2009). Leur chiffre d’affaires s’élève à 3,5 milliards de dollars (Industrie Canada, 2010) et elles génèrent des revenus annuels de plus de 5,1 milliards de dollars (CRHSC, 2009). Il s’agit d’une « jeune » industrie, dont les entreprises comptent en moyenne neuf ans d’existence; près de 15 % d’entre elles ont été mises sur pied entre 2006 et 2008 (CIAIC, 2009). Les entreprises œuvrant dans le secteur des médias numériques sont établies partout au Canada, et on en retrouve d’importantes grappes à Vancouver, Toronto, Montréal et Halifax.

Le remplacement de l’homme par la machine est peu à peu devenu une réalité, dans des secteurs aussi va- riés que l’automobile, l’agroalimentaire, la pétrochimie ou l’industrie pharmaceutique. Depuis le début des années 2000, une nouvelle vague de transformation digitale est à l’œuvre, qui a des impacts sur des métiers de plus en plus nom- breux. Et pour la première fois, la production de biens matériels n’est plus la seule concernée. Les services le sont aussi : big data, digitalisation, machine appre- nante, autant de tendances susceptibles de transfor- mer profondément des activités de service, intellec- tuelles, qu’on croyait jusqu’ici protégées de l’automatisation. Certes la digitalisation de l’économie ouvre de nouvelles perspectives de création d’emplois, notam- ment dans les domaines de l’environnement, de la performance des entreprises, de la relation client et – bien sûr – des nouvelles technologies elles-mêmes, qui sous-tendent cette évolution. On sait également que les entreprises digitales connaissent la croissance la plus dynamique1). Mais les emplois créés ne se substitueront pas aux emplois détruits, ni en termes de niveau de compétence requis, ni en termes de po- sition sur la chaîne de valeur, ni en termes de réparti- tion géographique. La digitalisation de l’économie est porteuse d’un risque de déstabilisation des grands équilibres écono- miques, sociaux et géographiques, dont on voit déjà les effets (polarisation des revenus et accroissement des inégalités, polarisation des chaînes de valeur). La France, qui n’a pas su adapter son appareil in- dustriel à la vague d’automatisation qui a marqué le secteur secondaire depuis les années 1990, doit réus- sir la transformation digitale à venir, qui aura des conséquences majeures sur le secteur tertiaire. L’in- suffisant investissement dans l’automatisation dans le secteur manufacturier (visible aujourd’hui à travers le faible taux de robotisation du parc français comparé à celui de ses concurrents), n’a pas permis à la France de protéger son appareil productif et ses emplois in- dustriels, bien au contraire. La perte de compétitivité liée au retard français a détruit beaucoup d’emploi in- dustriel. Il faut éviter que l’histoire ne se répète.

This report argues that computer-intensive automation (CIAutomation) is likely to change the nature of work and manufacturing value creation in the emerging Platform Economy. The industrial and service changes based on low-cost computation, as they become more generalized, may reverse Robert Gordon’s observations about the slowing growth in productivity. However, the increased adoption of CIAutomation also poses profound dilemmas for society that revolve around whether this automation will be used to solely to replace workers or can be integrated into production of goods and services in ways that augment human capacities and intelligence. Finally, we speculate upon the role of the state in in governing and shaping the emergence of the Platform Economy

In April 2011, the UK Commission for Employment and Skills launched Phase 1 of the Employer Investment Fund, marking the start of a new employer-led approach to skills investment. EIF enabled Sector Skills Councils (SSCs) to work closely with employers to develop and co-finance relevant skills and training solutions. This report provides early evidence of the effectiveness of this new approach. Its findings will inform the UK Commission’s decisions for the future management of employer-led skills projects and will be of help to those who bid for future investments that are employer led, innovative and sustainable. In Phase 1 of the EIF programme, around £5 million of investment was awarded to support 14 projects. This included six pre-existing Women and Work projects and eight ‘innovation’ projects to build the capacity of sectors to address identified skill needs. The investment period ran from June 2011 to March 2012. The commissioning model for the EIF Phase 1 innovation projects was based on SSCs bidding competitively for investment. The bid process required employers in each sector to make a substantial commitment (a cash or in-kind contribution) to evidence active support for the proposed skills solution. This model would go on to be used (with some adaptations) in subsequent EIF rounds and in the similar Growth and Innovation Fund (GIF) programme. By the end of 2012, the UK Commission had invested £94 million across 112 EIF and GIF projects, attracting £80 million of employer investment. The qualitative evaluation of EIF Phase 1 provides an early insight into the operation of the UK Commission’s investment model. It followed projects from the later stages of EIF delivery (in January to March 2012) to around 9-12 months beyond the investment phase (with follow-up from December 2012 to March 2013). As such, it provides important insight into the sustainability of UK Commission investments and lessons on how sector bodies and their partners can effectively prepare for the transition beyond EIF or GIF investment. The research was based on four longitudinal case studies with a sample of EIF Phase 1 innovation projects, plus telephone interviews with staff in the other seven SSCs managing EIF Phase 1 projects. It also included programme-level interviews with the UK Commission and the Department for Business, Innovation and Skills (BIS).