How to Build a Healthcare Analytics Team and Solve Strategic ProblemsHealth Catalyst
Health systems have vast amounts of data, but frequently struggle to use that data to solve strategic problems in a timely fashion. A healthcare analytics team, made up of the right people with the right tools and skillsets, can help address these challenges. This article walks through the steps organizations need to take to put an effective analytics team in place. These include the following:
Recognizing the need for change.
Demonstrating the value of an analytics team.
Conducting a current state assessment.
Identifying solutions.
Implementing a phased approach.
Building a roadmap.
Making the pitch.
Putting the roadmap into action.
The article also includes the foundation skills to look for when putting together the team and tips on how best to organize.
Do-It-Yourself (DIY) Data Governance FrameworkDATAVERSITY
A worthwhile Data Governance framework includes the core component of a successful program as viewed by the different levels of the organization. Each of the components is addressed at each of the levels, providing insight into key ideas and terminology used to attract participation across the organization. A framework plays a key role in setting up and sustaining a Data Governance program.
In this RWDG webinar, Bob Seiner will share two frameworks. The first is a basic cross-reference of components and levels, while the second can be used to compare and contrast different approaches to implementing Data Governance. When this webinar is finished, you will be able to customize the frameworks to outline the most appropriate manner for you to improve your likelihood of DG success.
In this webinar, Bob will discuss and share:
- Customizing a framework to match organizational requirements
- The core components and levels of an industry framework
- How to complete a Data Governance framework
- Using the framework to enable DG program success
- Measuring value through the DIY DG framework
Using Jobs to be Done to Create a Sustainable Growth StrategyAlan Klement
In 2018 at the Elevate conference in Toronto, Alan Klement discusses how he and others use Jobs to be Done (JTBD) theory to create sustainable growth strategy
How to Build a Healthcare Analytics Team and Solve Strategic ProblemsHealth Catalyst
Health systems have vast amounts of data, but frequently struggle to use that data to solve strategic problems in a timely fashion. A healthcare analytics team, made up of the right people with the right tools and skillsets, can help address these challenges. This article walks through the steps organizations need to take to put an effective analytics team in place. These include the following:
Recognizing the need for change.
Demonstrating the value of an analytics team.
Conducting a current state assessment.
Identifying solutions.
Implementing a phased approach.
Building a roadmap.
Making the pitch.
Putting the roadmap into action.
The article also includes the foundation skills to look for when putting together the team and tips on how best to organize.
Do-It-Yourself (DIY) Data Governance FrameworkDATAVERSITY
A worthwhile Data Governance framework includes the core component of a successful program as viewed by the different levels of the organization. Each of the components is addressed at each of the levels, providing insight into key ideas and terminology used to attract participation across the organization. A framework plays a key role in setting up and sustaining a Data Governance program.
In this RWDG webinar, Bob Seiner will share two frameworks. The first is a basic cross-reference of components and levels, while the second can be used to compare and contrast different approaches to implementing Data Governance. When this webinar is finished, you will be able to customize the frameworks to outline the most appropriate manner for you to improve your likelihood of DG success.
In this webinar, Bob will discuss and share:
- Customizing a framework to match organizational requirements
- The core components and levels of an industry framework
- How to complete a Data Governance framework
- Using the framework to enable DG program success
- Measuring value through the DIY DG framework
Using Jobs to be Done to Create a Sustainable Growth StrategyAlan Klement
In 2018 at the Elevate conference in Toronto, Alan Klement discusses how he and others use Jobs to be Done (JTBD) theory to create sustainable growth strategy
Have you ever searched for a flight online? Do you wonder when and where you can get the best price for your travel plans? And, why are there different flight prices? Now, do you want to know why it is hard to do a meta-search engine for travel, and especially for flights?
Presentation provided by SkyScanner, a leading travel search site offering unbiased, comprehensive and free flight, hotel and car hire search services, used by over 40 million unique visitors every month. Skyscanner opened its office in Sofia in October 2014 and is quickly growing its team here to help solve complex travel problems and continually improve their product.
引用"哈佛最受歡迎的行銷課 "中提到的案例,如何不競爭,真正在定位上做出差異化?
a. 逆向操作品牌:大家提供什麼,我就偏不提供
b. 跨界演出的品牌:這不是_____!這其實是______
c. 敵意挑釁的品牌:我們就是________(有這些缺點)!這就是我們的態度,我們就是只歡迎_____(某一種客戶)
亞洲 Hadoop 產品與解決方案引領者 Etu,於年度 Etu Solution Day (ESD) 活動中發表「2014 年台灣 Big Data 市場 5 大趨勢預測」。Etu 也首度發表兩岸的 10 大行業、21 種 Hadoop Big Data 已經被驗證的應用,如電信業的經營分析與客服查詢、電子商務的精準推薦、數位媒體的內容推薦、零售行業的使用者行為分析、高科技製造的資料倉儲工作分流卸載與製程良率分析、政府與地產的輿情分析、電力的能源管理、保險的巨量小圖檔管理等。預期 2014 年的台灣 Big Data 市場將更為成熟,經過驗證階段後,進入最後導入階段的企業也可望有倍數的成長。
Etu 負責人蔣居裕表示:「UDN 的採用,說明了台灣企業導入 Big Data 應用的需求在特定產業力道明顯上揚,『2014 年台灣 Big Data 市場的 5 大趨勢預測』也呼應了這樣的看法。」蔣居裕說:「一、首先過河的人,要開始挑戰資料價值的海洋,越早期投入者,越用越深,越深越廣;二、Total Data BI 帶動企業採用多結構化資料倉儲。客戶行為分析、精準行銷、客戶體驗是應用目標;三、從新舊系統整合到 End-to-End 解決方案,大部分企業期待廠商能夠完整交付 Big Data 應用與專業技術顧問。『容易』(Ease) 是 Big Data 產品進入企業的關鍵字;四、資料探索工具當道,力助 Business User 比 IT 人員更能挖掘 Big Data 的價值。『探索』(Discovery) 是 Big Data 分析的神髓所在 —— 探索關聯、探索意圖、探索缺少什麼;五、Big Data 教育訓練課程,從以處理技術為主者,快速擴展到資料分析。但均會被含括在『資料科學』大傘下。資料科學家萬中選一,強調專業分工的資料科學團隊,才是實踐資料價值希望之所在。」
ESD 2013 另外還展現了藉由 Etu Appliance 所架構起來的 Etu Ecosystem,展示了由 Etu 以及 ISV 夥伴們所開發的 End-to-End 解決方案:Etu Recommender,除了原有的個人化精準推薦,現在還可與第三方工具整合,進行資料視覺化探索,建置使用者行為分析資料倉儲;合作夥伴堂朝數位整合的雲端電子刊物加值平台、PilotTV 前線媒體的收視量測系統、樺鼎商業資訊的視覺化分析工具、以及衛信科技的 SDN 網路管理完整解決方案,則分別透過 Etu Appliance 來做巨量、可擴展的檔案格式轉換運算、臉部辨識資料及時處理與分析、多結構化資料倉儲、網路資料封包預處理等工作。這些方案的共同點,就是它們都是基於不斷獲得各種產品創新獎項的 Etu Appliance 所開發或整合的應用。
Summary of Insights Learned from the Data Science Program Team TrainingFred Chiang
Who really has the skills and talents to leverage the most value out of data? The Data Science Program (DSP) was co-founded by Code for Tomorrow and Etu. We believe that building and deploying a data science team consisting of members who possess and have the ability to utilize their different skill sets from a variety of industries is more practical and realistic. Versus hoping to find an individual data scientist who is an expert in a wide variety of technical fields ranging from math, statistics, and visualizations, as well as a solid background in other fields such as business, communication, and etc. The Data Science Program has identified four pertinent categories to place our members into. These four categories are Campaigner, Data Analyst, Data Hygienist, and Designer. Each team will have these four categories filled. During the training every team learns how to do data processing, data analysis, and visualization together with the sole purpose to use these skills to solve a common problem. After four weeks of intensive study, each team comes up with enterprise-grade team projects demonstrating the innovation of data-driven businesses.
After two rounds of DSP Team Training, DSP has accumulated 10 team projects and has graduated more than 60 alumni who are passionate about data science. During this journey of developing and deploying teams trained in data science, the most valuable aspects we walked away with was the witnessing of members growing in confidence from the learning and experience, the building of team work, and the overall growth of each individual. At the end of the day, our hope of as members of DSP, including myself is to instill and motivate more people to devote themselves to the exploration of data science. Now think about how you can do the same.
Have you ever searched for a flight online? Do you wonder when and where you can get the best price for your travel plans? And, why are there different flight prices? Now, do you want to know why it is hard to do a meta-search engine for travel, and especially for flights?
Presentation provided by SkyScanner, a leading travel search site offering unbiased, comprehensive and free flight, hotel and car hire search services, used by over 40 million unique visitors every month. Skyscanner opened its office in Sofia in October 2014 and is quickly growing its team here to help solve complex travel problems and continually improve their product.
引用"哈佛最受歡迎的行銷課 "中提到的案例,如何不競爭,真正在定位上做出差異化?
a. 逆向操作品牌:大家提供什麼,我就偏不提供
b. 跨界演出的品牌:這不是_____!這其實是______
c. 敵意挑釁的品牌:我們就是________(有這些缺點)!這就是我們的態度,我們就是只歡迎_____(某一種客戶)
亞洲 Hadoop 產品與解決方案引領者 Etu,於年度 Etu Solution Day (ESD) 活動中發表「2014 年台灣 Big Data 市場 5 大趨勢預測」。Etu 也首度發表兩岸的 10 大行業、21 種 Hadoop Big Data 已經被驗證的應用,如電信業的經營分析與客服查詢、電子商務的精準推薦、數位媒體的內容推薦、零售行業的使用者行為分析、高科技製造的資料倉儲工作分流卸載與製程良率分析、政府與地產的輿情分析、電力的能源管理、保險的巨量小圖檔管理等。預期 2014 年的台灣 Big Data 市場將更為成熟,經過驗證階段後,進入最後導入階段的企業也可望有倍數的成長。
Etu 負責人蔣居裕表示:「UDN 的採用,說明了台灣企業導入 Big Data 應用的需求在特定產業力道明顯上揚,『2014 年台灣 Big Data 市場的 5 大趨勢預測』也呼應了這樣的看法。」蔣居裕說:「一、首先過河的人,要開始挑戰資料價值的海洋,越早期投入者,越用越深,越深越廣;二、Total Data BI 帶動企業採用多結構化資料倉儲。客戶行為分析、精準行銷、客戶體驗是應用目標;三、從新舊系統整合到 End-to-End 解決方案,大部分企業期待廠商能夠完整交付 Big Data 應用與專業技術顧問。『容易』(Ease) 是 Big Data 產品進入企業的關鍵字;四、資料探索工具當道,力助 Business User 比 IT 人員更能挖掘 Big Data 的價值。『探索』(Discovery) 是 Big Data 分析的神髓所在 —— 探索關聯、探索意圖、探索缺少什麼;五、Big Data 教育訓練課程,從以處理技術為主者,快速擴展到資料分析。但均會被含括在『資料科學』大傘下。資料科學家萬中選一,強調專業分工的資料科學團隊,才是實踐資料價值希望之所在。」
ESD 2013 另外還展現了藉由 Etu Appliance 所架構起來的 Etu Ecosystem,展示了由 Etu 以及 ISV 夥伴們所開發的 End-to-End 解決方案:Etu Recommender,除了原有的個人化精準推薦,現在還可與第三方工具整合,進行資料視覺化探索,建置使用者行為分析資料倉儲;合作夥伴堂朝數位整合的雲端電子刊物加值平台、PilotTV 前線媒體的收視量測系統、樺鼎商業資訊的視覺化分析工具、以及衛信科技的 SDN 網路管理完整解決方案,則分別透過 Etu Appliance 來做巨量、可擴展的檔案格式轉換運算、臉部辨識資料及時處理與分析、多結構化資料倉儲、網路資料封包預處理等工作。這些方案的共同點,就是它們都是基於不斷獲得各種產品創新獎項的 Etu Appliance 所開發或整合的應用。
Summary of Insights Learned from the Data Science Program Team TrainingFred Chiang
Who really has the skills and talents to leverage the most value out of data? The Data Science Program (DSP) was co-founded by Code for Tomorrow and Etu. We believe that building and deploying a data science team consisting of members who possess and have the ability to utilize their different skill sets from a variety of industries is more practical and realistic. Versus hoping to find an individual data scientist who is an expert in a wide variety of technical fields ranging from math, statistics, and visualizations, as well as a solid background in other fields such as business, communication, and etc. The Data Science Program has identified four pertinent categories to place our members into. These four categories are Campaigner, Data Analyst, Data Hygienist, and Designer. Each team will have these four categories filled. During the training every team learns how to do data processing, data analysis, and visualization together with the sole purpose to use these skills to solve a common problem. After four weeks of intensive study, each team comes up with enterprise-grade team projects demonstrating the innovation of data-driven businesses.
After two rounds of DSP Team Training, DSP has accumulated 10 team projects and has graduated more than 60 alumni who are passionate about data science. During this journey of developing and deploying teams trained in data science, the most valuable aspects we walked away with was the witnessing of members growing in confidence from the learning and experience, the building of team work, and the overall growth of each individual. At the end of the day, our hope of as members of DSP, including myself is to instill and motivate more people to devote themselves to the exploration of data science. Now think about how you can do the same.
探戈DJ教探「歌」v.1.5 Tango DJ teaches you how to classify orquesta 士杰 戴
DJ Paul Dai is one of the most rapidly rising DJs in Taipei. He was invited to play at almost every major milonga venue in Taipei and Taichung, such as Tanguísimo, Así and Corazón. He also has been played in Taipei Tango Festivals in two consecutive years (2018-2019)。Besides performing, he spends lot of time dedicating to his own blog and Fans Page, translating and writing articles about tango and sharing tango music with his fans. With his knowledge of sound and music, dancers believe his DJing cast spells, because they can hardly rest their feet.