About Ta-Ming Chang, Richard Biography Public - 2020/4/6
張大明執行長,育睿科技(2012)創辦人與摩方人力資本科技(2019)共同創辦人,資訊教育及教育科技專家,國際副價值工程專家AVS,曾任PMI-TW國際專案管理學會台灣分會秘書長,巨匠電腦數位學習部門內容專家,台灣首位4C/ID模式實證研究發表人,自2010年取得淡江大學教育科技研究所教育科技碩士學位後,持續在教育科技領域研究﹑設計、科技與服務的創新,實證後應用於教學設計、訓練設計、培育服務發展等,積極在培育產業推廣、發展教育學習即服務(ELaaS)的創新商業模式,2016提出歐倍特六動力(ALBITER Power 6,思翱倍力大數據人才培育服務)的設計發展原則:目標、方案、問題、引導、數據、時習,2018年獲得淡大教科系推薦傑出系友代表。2019年與夥伴共同創辦「摩方人力資本科技」,整合教育科技、績效科技、資料技術、金融科技、人力資源科技等創新工具與方法,推出摩方人資本銀行的創新整合服務平台。期望以人力資本的貨幣化數據帳戶,促使組織內外專案成員協同合作更緊密有效率,藉由個人或組織的人力資本儲存與交易,使數位世代的職涯發展更成功與成熟,加值優化個人與組織的智財人力資本。
About Ta-Ming Chang, Richard Biography Public - 2020/4/6
張大明執行長,育睿科技(2012)創辦人與摩方人力資本科技(2019)共同創辦人,資訊教育及教育科技專家,國際副價值工程專家AVS,曾任PMI-TW國際專案管理學會台灣分會秘書長,巨匠電腦數位學習部門內容專家,台灣首位4C/ID模式實證研究發表人,自2010年取得淡江大學教育科技研究所教育科技碩士學位後,持續在教育科技領域研究﹑設計、科技與服務的創新,實證後應用於教學設計、訓練設計、培育服務發展等,積極在培育產業推廣、發展教育學習即服務(ELaaS)的創新商業模式,2016提出歐倍特六動力(ALBITER Power 6,思翱倍力大數據人才培育服務)的設計發展原則:目標、方案、問題、引導、數據、時習,2018年獲得淡大教科系推薦傑出系友代表。2019年與夥伴共同創辦「摩方人力資本科技」,整合教育科技、績效科技、資料技術、金融科技、人力資源科技等創新工具與方法,推出摩方人資本銀行的創新整合服務平台。期望以人力資本的貨幣化數據帳戶,促使組織內外專案成員協同合作更緊密有效率,藉由個人或組織的人力資本儲存與交易,使數位世代的職涯發展更成功與成熟,加值優化個人與組織的智財人力資本。
Decision Transformer: Reinforcement Learning via Sequence Modeling,” transforms the reinforcement learning (RL) landscape by treating RL as a conditional sequence modeling problem.
Presentation at Data Innovation Summit 2021. Trusted, well managed data is key to AI and machine learning success. Data citizens need data insights and data scientists need to spend more time building models. Everyone wants to spend less time finding, discovering, and munging data and ensuring the data quality to deliver business results. However, traditional data approaches lock data away and slow AI implementation leaves much of this work on the data practitioner’s shoulders. This session will cover how AI is also helping solve these problems. New data tools that combine automation with human expertise are enabling data and knowledge sharing (including new data classes like IOT data), data democratization, and cloud migration. AI-driven data enablement ensures everyone can find the right data and make intelligent use of it. Join us for a lively discussion on the most critical resource for AI: your data.
Enterprise Data Governance Framework With Change ManagementSlideTeam
“You can download this product from SlideTeam.net”
Presenting this set of slides with name Enterprise Data Governance Framework With Change Management. The topics discussed in these slides are Strategy, Organization, Management. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience. https://bit.ly/3b4VcEH
Henry Peyret Presentation - Data Governance 2.0.
Based on the analysis of Digital Transformation and Values Transformation, Forrester gives its insight and orientations in terms of Data Governance 2.0 and Data Citizenship.
Battery Ventures State of the OpenCloud Report 2022Battery Ventures
Battery Ventures' 2022 State of the OpenCloud report, compiled by General Partner Dharmesh Thakker and his team Danel Dayan, Jason Mendel and Patrick Hsu. The report analyzes the macro technology and economic trends impacting the cloud market, and provides advice for cloud-native entrepreneurs who are navigating these trends to build large, enduring businesses.
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<p>Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality Management effectively in support of business strategy, which in turn allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.</p>
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<p>Learning Objectives:</p>
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<ul><li>Understand foundational Data Quality concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization</li><li>Recognize how chronic business challenges for organizations are often rooted in poor Data Quality</li><li>Share case studies illustrating the hallmarks and benefits of Data Quality success</li></ul>
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Decision Transformer: Reinforcement Learning via Sequence Modeling,” transforms the reinforcement learning (RL) landscape by treating RL as a conditional sequence modeling problem.
Presentation at Data Innovation Summit 2021. Trusted, well managed data is key to AI and machine learning success. Data citizens need data insights and data scientists need to spend more time building models. Everyone wants to spend less time finding, discovering, and munging data and ensuring the data quality to deliver business results. However, traditional data approaches lock data away and slow AI implementation leaves much of this work on the data practitioner’s shoulders. This session will cover how AI is also helping solve these problems. New data tools that combine automation with human expertise are enabling data and knowledge sharing (including new data classes like IOT data), data democratization, and cloud migration. AI-driven data enablement ensures everyone can find the right data and make intelligent use of it. Join us for a lively discussion on the most critical resource for AI: your data.
Enterprise Data Governance Framework With Change ManagementSlideTeam
“You can download this product from SlideTeam.net”
Presenting this set of slides with name Enterprise Data Governance Framework With Change Management. The topics discussed in these slides are Strategy, Organization, Management. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience. https://bit.ly/3b4VcEH
Henry Peyret Presentation - Data Governance 2.0.
Based on the analysis of Digital Transformation and Values Transformation, Forrester gives its insight and orientations in terms of Data Governance 2.0 and Data Citizenship.
Battery Ventures State of the OpenCloud Report 2022Battery Ventures
Battery Ventures' 2022 State of the OpenCloud report, compiled by General Partner Dharmesh Thakker and his team Danel Dayan, Jason Mendel and Patrick Hsu. The report analyzes the macro technology and economic trends impacting the cloud market, and provides advice for cloud-native entrepreneurs who are navigating these trends to build large, enduring businesses.
<!-- wp:paragraph -->
<p>Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality Management effectively in support of business strategy, which in turn allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.</p>
<!-- /wp:paragraph -->
<!-- wp:paragraph -->
<p>Learning Objectives:</p>
<!-- /wp:paragraph -->
<!-- wp:list -->
<ul><li>Understand foundational Data Quality concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization</li><li>Recognize how chronic business challenges for organizations are often rooted in poor Data Quality</li><li>Share case studies illustrating the hallmarks and benefits of Data Quality success</li></ul>
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