This document summarizes the challenges and process of migrating spatial utility network data from multiple sources into a standardized Oracle database (SpatialNet). Key challenges include dealing with unstructured, inconsistent data from different sources in various formats. The migration process involves preparing the data by documenting, profiling, and correcting issues; developing a neutral data model; loading the data into Oracle staging; promoting it to SpatialNet while applying standard rules; and validating the results. Tools like FME and Oracle capabilities are leveraged. Planning, automation, and training users are emphasized to avoid pitfalls and shortcuts that could compromise the migration.
The document discusses challenges related to migrating data from legacy systems to new applications and systems. It notes there are typically many source systems in various formats with incomplete or unknown information. Effective data migration requires understanding source systems, data mapping, quality analysis, and design of the migration process. It also stresses the importance of data governance and quality to ensure migrated data can be effectively used.
Moving the Elephant in the Room: Data Migration at ScaleTyrone Hinderson
This presentation demystifies the problem of moving a major portion of a company’s technological infrastructure from an unsustainable legacy system to a newer, more scalable solution. The goal is to show that with the proper approach, such a transition can be undertaken successfully and without assuming unmanageable risk. To this end, we will share anecdotes from Conductor’s own endeavor to replace an immense relational datastore and a corresponding data ETL process which, together, form a crucial component of our application that nonetheless cannot be supported indefinitely. The presentation will cover the process of deciding to redesign a large component of your tech stack, along with best practices for perceiving and mitigating risk and ultimately securing performance and cost improvements.
The document discusses Eskan Bank's data migration process within its core banking system migration project. It aims to analyze Eskan Bank's experience with data migration and compare it to established methods and best practices. A survey of Eskan Bank IT personnel was conducted to evaluate the data migration phase and identify opportunities for improvement. The dissertation aims to formulate an enhanced data migration model for Eskan Bank based on the analysis.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
The document discusses various techniques for data warehousing and online analytical processing (OLAP), including constructing data warehouses, star schemas, materialized views, data cubes, and data mining. Specifically, it describes how a data warehouse can be used to integrate data from multiple sources and support complex OLAP queries run against historical data. It provides examples of star schemas, materialized views, data cubes, and market basket analysis to find frequent itemsets.
This document proposes a redesign of the Cubes analytical workspace to make it more pluggable and flexible. Key points of the redesign include:
1. Splitting backends into separate objects for browsers, stores, and model providers for more modular composition.
2. Allowing browsers and stores to work with different data sources and schemas within a single workspace.
3. Using an external workspace object to provide the appropriate browser and manage configuration, replacing the previous single backend concept.
A Reference Process Model for Master Data ManagementBoris Otto
This document presents an overview of a reference process model for master data management. It includes an introduction discussing business requirements for master data and challenges in managing master data quality. It also describes the research methodology used to develop an iterative reference process model. The results section provides an overview of the reference process model and discusses its evaluation through three case studies. The conclusion recognizes the model's contribution in explicating the design process for master data management organizations.
This document summarizes the challenges and process of migrating spatial utility network data from multiple sources into a standardized Oracle database (SpatialNet). Key challenges include dealing with unstructured, inconsistent data from different sources in various formats. The migration process involves preparing the data by documenting, profiling, and correcting issues; developing a neutral data model; loading the data into Oracle staging; promoting it to SpatialNet while applying standard rules; and validating the results. Tools like FME and Oracle capabilities are leveraged. Planning, automation, and training users are emphasized to avoid pitfalls and shortcuts that could compromise the migration.
The document discusses challenges related to migrating data from legacy systems to new applications and systems. It notes there are typically many source systems in various formats with incomplete or unknown information. Effective data migration requires understanding source systems, data mapping, quality analysis, and design of the migration process. It also stresses the importance of data governance and quality to ensure migrated data can be effectively used.
Moving the Elephant in the Room: Data Migration at ScaleTyrone Hinderson
This presentation demystifies the problem of moving a major portion of a company’s technological infrastructure from an unsustainable legacy system to a newer, more scalable solution. The goal is to show that with the proper approach, such a transition can be undertaken successfully and without assuming unmanageable risk. To this end, we will share anecdotes from Conductor’s own endeavor to replace an immense relational datastore and a corresponding data ETL process which, together, form a crucial component of our application that nonetheless cannot be supported indefinitely. The presentation will cover the process of deciding to redesign a large component of your tech stack, along with best practices for perceiving and mitigating risk and ultimately securing performance and cost improvements.
The document discusses Eskan Bank's data migration process within its core banking system migration project. It aims to analyze Eskan Bank's experience with data migration and compare it to established methods and best practices. A survey of Eskan Bank IT personnel was conducted to evaluate the data migration phase and identify opportunities for improvement. The dissertation aims to formulate an enhanced data migration model for Eskan Bank based on the analysis.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
The document discusses various techniques for data warehousing and online analytical processing (OLAP), including constructing data warehouses, star schemas, materialized views, data cubes, and data mining. Specifically, it describes how a data warehouse can be used to integrate data from multiple sources and support complex OLAP queries run against historical data. It provides examples of star schemas, materialized views, data cubes, and market basket analysis to find frequent itemsets.
This document proposes a redesign of the Cubes analytical workspace to make it more pluggable and flexible. Key points of the redesign include:
1. Splitting backends into separate objects for browsers, stores, and model providers for more modular composition.
2. Allowing browsers and stores to work with different data sources and schemas within a single workspace.
3. Using an external workspace object to provide the appropriate browser and manage configuration, replacing the previous single backend concept.
A Reference Process Model for Master Data ManagementBoris Otto
This document presents an overview of a reference process model for master data management. It includes an introduction discussing business requirements for master data and challenges in managing master data quality. It also describes the research methodology used to develop an iterative reference process model. The results section provides an overview of the reference process model and discusses its evaluation through three case studies. The conclusion recognizes the model's contribution in explicating the design process for master data management organizations.
CRMUG UK November 2015 - Data Migration Without Tears by Mike FeingoldWesleyan
The document discusses various considerations and options for migrating data from an old CRM system to Microsoft Dynamics CRM 2015. It recommends planning the migration early in the project, choosing a migration tool like Scribe or SSIS, and addressing issues like activity migration, CRM restrictions, and plugins/workflows. Directly updating the CRM database may be necessary for some fields. Speed can be increased by disabling plugins and optimizing CRM performance. Careful design of the migration process is important to avoid duplicating data.
The document discusses data migration strategies and methodologies for migrating data from one ERP system to another. It outlines four basic rules for data migration: 1) data migration is a business issue, 2) the business knows best, 3) if you can't count it, it doesn't count, and 4) no one needs perfect data. It also describes the ETLV (extract, transform, load, validate) strategy and methodology for planning, migrating, and validating the data between systems.
Adopting a Process-Driven Approach to Master Data ManagementSoftware AG
What is a lasting solution to the sea of errors, headaches, and losses caused by inconsistent and inaccurate master data such as customer and product records? This is the data that your business counts on to operate business processes and make decisions. But this data is often incomplete or in conflict because it resides in multiple IT systems. Master Data Management (MDM)'s programs are the solution to this problem, but these programs can fail without the investment and involvement of business managers.
Listen to Rob Karel, Forrester analyst, and Jignesh Shah from Software AG to learn about a new, process-driven approach to MDM and why it is a win-win for both business and IT managers.
Visit us at http://www.softwareag.com Become part of our growing community: Facebook: http://www.facebook.com/softwareag Twitter: http://www.twitter.com/softwareag LinkedIn: http://www.linkedin.com/company/software-ag YouTube: http://www.youtube.com/softwareag
This presentation elaborates on design decisions and design options when it comes to designing the master data architecture.
The presentation was given at the 16th Americas Conference on Information Systems (AMCIS 2010) in Lima, Peru.
Many significant business initiatives and large IT projects depend upon a successful data migration. Your goal is to minimize as much risk as possible through effective planning and scoping. This paper will provide insight into what issues are unique to data migration projects and offer advice on how to best approach them.
From Relational Database Management to Big Data: Solutions for Data Migration...Cognizant
Big data migration testing for transferring relational database management files is a very time-consuming, high-compute task; we offer a hands-on, detailed framework for data validation in an open source (Hadoop) environment incorporating Amazon Web Services (AWS) for cloud capacity, S3 (Simple Storage Service) and EMR (Elastic MapReduce), Hive tables, Sqoop tools, PIG scripting and Jenkins Slave Machines.
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Building a strong Data Management capability with TOGAF and ArchiMateBas van Gils
This is the deck that I used for my presentation at the EAM conference in 2013. It gives a high-level overview of the need for a solid data management capability before giving and overview of how enterprise architecture methods can be used to build this capability.
This document discusses data governance and data architecture. It introduces data governance as the processes for managing data, including deciding data rights, making data decisions, and implementing those decisions. It describes how data architecture relates to data governance by providing patterns and structures for governing data. The document presents some common data architecture patterns, including a publish/subscribe pattern where a publisher pushes data to a hub and subscribers pull data from the hub. It also discusses how data architecture can support data governance goals through approaches like a subject area data model.
Enterprise Data World Webinar: How to Get Your MDM Program Up & RunningDATAVERSITY
How to get your MDM program up & running”
This session will deliver a Master Data Management primer to introduce:
Master vs Reference data
Multi vs Single domain MDM solutions
A MDM reference architecture and
MDM implementation architectures
This will be illustrated with a real world example from describing how to identify & justify the appropriate data subjects areas that are right for mastering and how to align an MDM initiative with in-flight business initiatives and make the business case.
Enterprise data serves both running business operations and managing the business. Building a successful data architecture is challenging due to data complexity, competing stakeholder interests, data proliferation, and inaccuracies. A robust data architecture must address key components like data repositories, capture and ingestion, definition and design, integration, access and distribution, and analysis.
Third Nature - Open Source Data Warehousingmark madsen
An introductory presentation on open source for data warehousing and business intelligence. Covers some history of open source, projects in different areas, and some information on adoption.
You can download this and demo.case study PDFs at
http://thirdnature.net/tdwi_osbi_material.html
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Curran Kelleher is a data visualization and analytics consultant with a Ph.D. in Computer Science. He has extensive experience developing interactive data visualizations using D3.js and designing visualization dashboards. He has worked as a consultant, software engineer, researcher, and teacher.
This document summarizes a presentation on the data visualization library D3.js. It introduces D3, noting that it was created by Mike Bostock and Jeff Heer as the next generation of Protovis. It provides details on the speaker, Curran Kelleher, and his background. The document outlines that D3 has a great academic paper, tons of examples and libraries based on it, and a vibrant community. It concludes by stating the presentation will demonstrate how to make a basic bar chart with D3.
Slides from a talk on the Open Source data visualization project called Chiasm. This talk was given at the Houston Data Visualization meetup on August 10, 2015.
This document discusses the architecture and strategy for plugins in Chiasm, a data visualization tool. It proposes moving from using Bower and RequireJS to a system using JSPM and SystemJS for managing plugins. Key points include having plugins stored in separate repositories, configuring Chiasm to use plugins, and automating the process of creating, using, testing and bundling plugins.
1) The document describes a technique called Fractal Perspective for visualizing semantic network data through a nested, zoomable interface similar to maps.
2) It reviews related work in tree map and graph visualization approaches.
3) Fractal Perspective uses a "perspective projection" algorithm to lay out semantic graph nodes in a fractal, zoomable layout that aims to improve on node-link diagrams and other approaches.
CodeHub is a software development tool for JavaScript and HTML which provides a browser-based code editor which allows you to save and run your code, the ability to save and publish of all versions of your code, and support for dependency management and deployment.
Visualizing the Bureau of Labor Statistics Employment DatasetCurran Kelleher
This document discusses visualizing employment data from the Bureau of Labor Statistics using Tableau. It outlines preprocessing steps like downloading raw FTP data, parsing it, and loading it into a SQL database. It addresses issues like Tableau not supporting hierarchical data cubes, county data being too large, and difficulties with hierarchical time dimensions. Visualizations were created but issues arose around overlapping labels and probing with many dimensions.
CRMUG UK November 2015 - Data Migration Without Tears by Mike FeingoldWesleyan
The document discusses various considerations and options for migrating data from an old CRM system to Microsoft Dynamics CRM 2015. It recommends planning the migration early in the project, choosing a migration tool like Scribe or SSIS, and addressing issues like activity migration, CRM restrictions, and plugins/workflows. Directly updating the CRM database may be necessary for some fields. Speed can be increased by disabling plugins and optimizing CRM performance. Careful design of the migration process is important to avoid duplicating data.
The document discusses data migration strategies and methodologies for migrating data from one ERP system to another. It outlines four basic rules for data migration: 1) data migration is a business issue, 2) the business knows best, 3) if you can't count it, it doesn't count, and 4) no one needs perfect data. It also describes the ETLV (extract, transform, load, validate) strategy and methodology for planning, migrating, and validating the data between systems.
Adopting a Process-Driven Approach to Master Data ManagementSoftware AG
What is a lasting solution to the sea of errors, headaches, and losses caused by inconsistent and inaccurate master data such as customer and product records? This is the data that your business counts on to operate business processes and make decisions. But this data is often incomplete or in conflict because it resides in multiple IT systems. Master Data Management (MDM)'s programs are the solution to this problem, but these programs can fail without the investment and involvement of business managers.
Listen to Rob Karel, Forrester analyst, and Jignesh Shah from Software AG to learn about a new, process-driven approach to MDM and why it is a win-win for both business and IT managers.
Visit us at http://www.softwareag.com Become part of our growing community: Facebook: http://www.facebook.com/softwareag Twitter: http://www.twitter.com/softwareag LinkedIn: http://www.linkedin.com/company/software-ag YouTube: http://www.youtube.com/softwareag
This presentation elaborates on design decisions and design options when it comes to designing the master data architecture.
The presentation was given at the 16th Americas Conference on Information Systems (AMCIS 2010) in Lima, Peru.
Many significant business initiatives and large IT projects depend upon a successful data migration. Your goal is to minimize as much risk as possible through effective planning and scoping. This paper will provide insight into what issues are unique to data migration projects and offer advice on how to best approach them.
From Relational Database Management to Big Data: Solutions for Data Migration...Cognizant
Big data migration testing for transferring relational database management files is a very time-consuming, high-compute task; we offer a hands-on, detailed framework for data validation in an open source (Hadoop) environment incorporating Amazon Web Services (AWS) for cloud capacity, S3 (Simple Storage Service) and EMR (Elastic MapReduce), Hive tables, Sqoop tools, PIG scripting and Jenkins Slave Machines.
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Building a strong Data Management capability with TOGAF and ArchiMateBas van Gils
This is the deck that I used for my presentation at the EAM conference in 2013. It gives a high-level overview of the need for a solid data management capability before giving and overview of how enterprise architecture methods can be used to build this capability.
This document discusses data governance and data architecture. It introduces data governance as the processes for managing data, including deciding data rights, making data decisions, and implementing those decisions. It describes how data architecture relates to data governance by providing patterns and structures for governing data. The document presents some common data architecture patterns, including a publish/subscribe pattern where a publisher pushes data to a hub and subscribers pull data from the hub. It also discusses how data architecture can support data governance goals through approaches like a subject area data model.
Enterprise Data World Webinar: How to Get Your MDM Program Up & RunningDATAVERSITY
How to get your MDM program up & running”
This session will deliver a Master Data Management primer to introduce:
Master vs Reference data
Multi vs Single domain MDM solutions
A MDM reference architecture and
MDM implementation architectures
This will be illustrated with a real world example from describing how to identify & justify the appropriate data subjects areas that are right for mastering and how to align an MDM initiative with in-flight business initiatives and make the business case.
Enterprise data serves both running business operations and managing the business. Building a successful data architecture is challenging due to data complexity, competing stakeholder interests, data proliferation, and inaccuracies. A robust data architecture must address key components like data repositories, capture and ingestion, definition and design, integration, access and distribution, and analysis.
Third Nature - Open Source Data Warehousingmark madsen
An introductory presentation on open source for data warehousing and business intelligence. Covers some history of open source, projects in different areas, and some information on adoption.
You can download this and demo.case study PDFs at
http://thirdnature.net/tdwi_osbi_material.html
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Curran Kelleher is a data visualization and analytics consultant with a Ph.D. in Computer Science. He has extensive experience developing interactive data visualizations using D3.js and designing visualization dashboards. He has worked as a consultant, software engineer, researcher, and teacher.
This document summarizes a presentation on the data visualization library D3.js. It introduces D3, noting that it was created by Mike Bostock and Jeff Heer as the next generation of Protovis. It provides details on the speaker, Curran Kelleher, and his background. The document outlines that D3 has a great academic paper, tons of examples and libraries based on it, and a vibrant community. It concludes by stating the presentation will demonstrate how to make a basic bar chart with D3.
Slides from a talk on the Open Source data visualization project called Chiasm. This talk was given at the Houston Data Visualization meetup on August 10, 2015.
This document discusses the architecture and strategy for plugins in Chiasm, a data visualization tool. It proposes moving from using Bower and RequireJS to a system using JSPM and SystemJS for managing plugins. Key points include having plugins stored in separate repositories, configuring Chiasm to use plugins, and automating the process of creating, using, testing and bundling plugins.
1) The document describes a technique called Fractal Perspective for visualizing semantic network data through a nested, zoomable interface similar to maps.
2) It reviews related work in tree map and graph visualization approaches.
3) Fractal Perspective uses a "perspective projection" algorithm to lay out semantic graph nodes in a fractal, zoomable layout that aims to improve on node-link diagrams and other approaches.
CodeHub is a software development tool for JavaScript and HTML which provides a browser-based code editor which allows you to save and run your code, the ability to save and publish of all versions of your code, and support for dependency management and deployment.
Visualizing the Bureau of Labor Statistics Employment DatasetCurran Kelleher
This document discusses visualizing employment data from the Bureau of Labor Statistics using Tableau. It outlines preprocessing steps like downloading raw FTP data, parsing it, and loading it into a SQL database. It addresses issues like Tableau not supporting hierarchical data cubes, county data being too large, and difficulties with hierarchical time dimensions. Visualizations were created but issues arose around overlapping labels and probing with many dimensions.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Training: ISO/IEC 27001 Information Security Management System - EN | PECB
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Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.