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Articles

Some Fundamentals – Process, Data and Models

Designing the Organization for Program Success Part 2

Designing the Organization for Program Success Part 2

  

The Hip, the Hype, the Fears, the Intrigue and the Reality 

Designing the Organization for Program Success Part 2

Designing the Organization for Program Success Part 2

Designing the Organization for Program Success Part 2

Organization Structure and Design for AI and Analytics Success

  

 Organization Structure and Design 

Designing the Organization for Program Success Part 1

Designing the Organization for Program Success Part 2

Designing the Organization for Program Success Part 1

Designing the Organization for Program Success Part 1: Establish Clear Business Outcomes

  

  

Establish Clear Business Outcomes

Justifying a Data Driven Organization

Using Regulations and Mandates as a Catalyst for Innovation

Designing the Organization for Program Success Part 1

  

Don’t Confuse Organization Gaps for Project Gaps!

8 Common Problems in AI an Analytics Success

Using Regulations and Mandates as a Catalyst for Innovation

Using Regulations and Mandates as a Catalyst for Innovation

Characterizing Common Problems in AI an Analytics Success

 

Characterizing Common Problems in AI an Analytics Success

Using Regulations and Mandates as a Catalyst for Innovation

Using Regulations and Mandates as a Catalyst for Innovation

Using Regulations and Mandates as a Catalyst for Innovation

Source data relevant for predicting asthma with APIs that also support CMS FHIR mandate.

.. meaningful use requirements or you are  responsible implementing the CMS (Centers for Medicare & Medicaid  Services) payer mandate, you might ..

Has Major League Baseball (MLB) ever had a 400 hitter?

Bad data scientist = You can’t think like a statistician

Bad data scientist = You can’t think like a statistician

Statistical inference is hard for most people - “Was TY Cobb a 400 hitter?”

Statistical inference is hard for most people.....

Bad data scientist = You can’t think like a statistician

Bad data scientist = You can’t think like a statistician

Bad data scientist = You can’t think like a statistician

Business intelligence, visualization, statistics, machine learning.

 Most things work ‘better together’..... 

Not just AI and Machine Learning; we need Statistics

Bad data scientist = You can’t think like a statistician

Not just AI and Machine Learning; we need Statistics

Image by Alexander Sinn.  There is no doubt that people are doing amazing things with AI and ML.

 Five Statistical Disciplines that will not be replaced with AI 

No AI and machine learning without statistics

5 Critical Needs in a Data Science and AI Architecture!

Not just AI and Machine Learning; we need Statistics

Image by Gerd Altmann. Everything in Statistics builds upon 4 basic elements.

 There is talk about ‘all you need is AI’ or ‘machine learning is better than statistics’.....

COVID-19 – The Issue is Not the Modeling !

5 Critical Needs in a Data Science and AI Architecture!

5 Critical Needs in a Data Science and AI Architecture!

Data is THE issue for the COVID-19 models!

It is a difficult time. I have been trying to limit the amount of news... 

5 Critical Needs in a Data Science and AI Architecture!

5 Critical Needs in a Data Science and AI Architecture!

5 Critical Needs in a Data Science and AI Architecture!

If you are looking for the critical needs in data science and AI architecture, look here!

 We are at full employment. Now more than ever companies need to utilize all their human resources. 

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