SlideShare a Scribd company logo
CollabDays New England
2023
Preflight Briefing
• All on my blog: www.wardpeter.com
• Get ready to ask questions
THANK YOU SPONSORS
www.wardpeter.com
About Me
{
background : “Founder- former CTO ",
employee : “SoHo Dragon",
skill : “SharePoint, O365",
books : “Co author of 4 SharePoint books”
writing : “Leadership in a zoom economy with Microsoft Teams”
Co organizer : “SharePoint Saturday New York, Meetup”
1St Sharepoint : Version SharePoint Portal 2003
hobbies : “Yoga, cooking vegetarian food",
}
NEW TEAMS BOOK
Leadership in a Zoom Economy with Microsoft Teams: Applying Leadership to a Remote Workforce
Thank you to the
attendees
7
Sample Footer Text
8
10
Good Data
Data Analysis
Right
Environment
How to make better
decisions
Process matters more than
analysis
Right Environment
Data Analysis
Good Data
Insights
Fact
gathering
and
analysis
formal or
informal
12
Right Environment
Data Analysis
Good Data
13
Data can be wrong
Good Data
Data Analysis
Be very careful of
who gave you
the reports
Good Data
Tuesday, February 2, 20XX
Sample Footer Text
17
Don’t give middle managers insights
Data Analysis
Data Analysis
Information
Doesn’t Equal
Power
Good Data
Medical
knowledge is
doubling every
80 days
22
Some things happen for the
wrong reason
Data Analysis
Flawed Belief
Data Analysis
Experience vs
Expertise
Data Analysis
26
Good Data
Evaluate Decisions
Right Environment
Rewarding Bad Decisions
Good Data
Journalling
Decisions
Good Data
Immediate
Reactions
Right Environment
Cognitive
Flexibility
Good Data
Pre-mortem
Right Environment
Expressing
Problems
33
Right Environment
No General Rule
Right Environment
Good leaders query the team
Right Environment
Meeting to Make a decision
Right Environment
Zone of indifference
Data Analysis
38
Good Data
Data Analysis
Right
Environment
How to make better
decisions
Process matters more than
analysis
Right Environment
Data Analysis
Good Data
Thank You
Teams Tuesday
Monthly remote Meetup
Power BI - Analysis or Process - CollabDays New England 2023
Power BI - Analysis or Process - CollabDays New England 2023

More Related Content

Similar to Power BI - Analysis or Process - CollabDays New England 2023

Building Competitive Moats With Data
Building Competitive Moats With DataBuilding Competitive Moats With Data
Building Competitive Moats With Data
Peter Skomoroch
 
How to Use Data for Product Success with Jet.com Data Manager
How to Use Data for Product Success with Jet.com Data ManagerHow to Use Data for Product Success with Jet.com Data Manager
How to Use Data for Product Success with Jet.com Data Manager
Product School
 
Tips and Tricks to be an Effective Data Scientist
Tips and Tricks to be an Effective Data ScientistTips and Tricks to be an Effective Data Scientist
Tips and Tricks to be an Effective Data Scientist
Lisa Cohen
 
Big data analytics
Big data analyticsBig data analytics
Big data analytics
Isłém Jendoubi
 
Data science and ethics in fundraising
Data science and ethics in fundraisingData science and ethics in fundraising
Data science and ethics in fundraising
James Orton
 
Propose a Human Resource Management strategy and specific organiza.docx
Propose a Human Resource Management strategy and specific organiza.docxPropose a Human Resource Management strategy and specific organiza.docx
Propose a Human Resource Management strategy and specific organiza.docx
briancrawford30935
 
Pro bono OR webinar - Making sense of data
Pro bono OR webinar - Making sense of data Pro bono OR webinar - Making sense of data
Pro bono OR webinar - Making sense of data
Improvement Skills Consulting Ltd.
 
Optimising Your Content for Findability
Optimising Your Content for FindabilityOptimising Your Content for Findability
Optimising Your Content for Findability
Findwise
 
Semantic mark-up with schema.org: helping search engines understand the Web
Semantic mark-up with schema.org: helping search engines understand the WebSemantic mark-up with schema.org: helping search engines understand the Web
Semantic mark-up with schema.org: helping search engines understand the Web
Peter Mika
 
Foundational Strategies for Trust in Big Data Part 2: Understanding Your Data
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataFoundational Strategies for Trust in Big Data Part 2: Understanding Your Data
Foundational Strategies for Trust in Big Data Part 2: Understanding Your Data
Precisely
 
Big Data Presentation at SCQAA-SF on June 12 2013
Big Data Presentation at SCQAA-SF on June 12 2013Big Data Presentation at SCQAA-SF on June 12 2013
Big Data Presentation at SCQAA-SF on June 12 2013
Sujit Ghosh
 
Frank Bien Opening Keynote - Join 2016
Frank Bien Opening Keynote - Join 2016Frank Bien Opening Keynote - Join 2016
Frank Bien Opening Keynote - Join 2016
Looker
 
Frank Bien Opening Keynote - Join 2016
Frank Bien Opening Keynote - Join 2016Frank Bien Opening Keynote - Join 2016
Frank Bien Opening Keynote - Join 2016
Looker
 
RDAP13 Elizabeth Moss: The impact of data reuse
RDAP13 Elizabeth Moss: The impact of data reuseRDAP13 Elizabeth Moss: The impact of data reuse
RDAP13 Elizabeth Moss: The impact of data reuse
ASIS&T
 
Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Ana...
Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Ana...Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Ana...
Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Ana...
Brian Alpert
 
Humans, AI and Decisions Making - 3 - What are the editorial questions AI can...
Humans, AI and Decisions Making - 3 - What are the editorial questions AI can...Humans, AI and Decisions Making - 3 - What are the editorial questions AI can...
Humans, AI and Decisions Making - 3 - What are the editorial questions AI can...
phillbjones
 
Introduction to Business and Data Analysis Undergraduate.pdf
Introduction to Business and Data Analysis Undergraduate.pdfIntroduction to Business and Data Analysis Undergraduate.pdf
Introduction to Business and Data Analysis Undergraduate.pdf
AbdulrahimShaibuIssa
 
How to unlock new data-driven potential for your organization
How to unlock new data-driven potential for your organizationHow to unlock new data-driven potential for your organization
How to unlock new data-driven potential for your organization
Michal Hodinka
 
Introduction to SciVal
Introduction to SciValIntroduction to SciVal
Introduction to SciVal
Genevieve Musasa
 
Will Bigger and Better Data Help Deliver More Major Donors?
Will Bigger and Better Data Help Deliver More Major Donors?Will Bigger and Better Data Help Deliver More Major Donors?
Will Bigger and Better Data Help Deliver More Major Donors?
Azadi Sheridan
 

Similar to Power BI - Analysis or Process - CollabDays New England 2023 (20)

Building Competitive Moats With Data
Building Competitive Moats With DataBuilding Competitive Moats With Data
Building Competitive Moats With Data
 
How to Use Data for Product Success with Jet.com Data Manager
How to Use Data for Product Success with Jet.com Data ManagerHow to Use Data for Product Success with Jet.com Data Manager
How to Use Data for Product Success with Jet.com Data Manager
 
Tips and Tricks to be an Effective Data Scientist
Tips and Tricks to be an Effective Data ScientistTips and Tricks to be an Effective Data Scientist
Tips and Tricks to be an Effective Data Scientist
 
Big data analytics
Big data analyticsBig data analytics
Big data analytics
 
Data science and ethics in fundraising
Data science and ethics in fundraisingData science and ethics in fundraising
Data science and ethics in fundraising
 
Propose a Human Resource Management strategy and specific organiza.docx
Propose a Human Resource Management strategy and specific organiza.docxPropose a Human Resource Management strategy and specific organiza.docx
Propose a Human Resource Management strategy and specific organiza.docx
 
Pro bono OR webinar - Making sense of data
Pro bono OR webinar - Making sense of data Pro bono OR webinar - Making sense of data
Pro bono OR webinar - Making sense of data
 
Optimising Your Content for Findability
Optimising Your Content for FindabilityOptimising Your Content for Findability
Optimising Your Content for Findability
 
Semantic mark-up with schema.org: helping search engines understand the Web
Semantic mark-up with schema.org: helping search engines understand the WebSemantic mark-up with schema.org: helping search engines understand the Web
Semantic mark-up with schema.org: helping search engines understand the Web
 
Foundational Strategies for Trust in Big Data Part 2: Understanding Your Data
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataFoundational Strategies for Trust in Big Data Part 2: Understanding Your Data
Foundational Strategies for Trust in Big Data Part 2: Understanding Your Data
 
Big Data Presentation at SCQAA-SF on June 12 2013
Big Data Presentation at SCQAA-SF on June 12 2013Big Data Presentation at SCQAA-SF on June 12 2013
Big Data Presentation at SCQAA-SF on June 12 2013
 
Frank Bien Opening Keynote - Join 2016
Frank Bien Opening Keynote - Join 2016Frank Bien Opening Keynote - Join 2016
Frank Bien Opening Keynote - Join 2016
 
Frank Bien Opening Keynote - Join 2016
Frank Bien Opening Keynote - Join 2016Frank Bien Opening Keynote - Join 2016
Frank Bien Opening Keynote - Join 2016
 
RDAP13 Elizabeth Moss: The impact of data reuse
RDAP13 Elizabeth Moss: The impact of data reuseRDAP13 Elizabeth Moss: The impact of data reuse
RDAP13 Elizabeth Moss: The impact of data reuse
 
Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Ana...
Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Ana...Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Ana...
Cut Through the Web Analytics Fog: Using GA Data Grabber to Act on Google Ana...
 
Humans, AI and Decisions Making - 3 - What are the editorial questions AI can...
Humans, AI and Decisions Making - 3 - What are the editorial questions AI can...Humans, AI and Decisions Making - 3 - What are the editorial questions AI can...
Humans, AI and Decisions Making - 3 - What are the editorial questions AI can...
 
Introduction to Business and Data Analysis Undergraduate.pdf
Introduction to Business and Data Analysis Undergraduate.pdfIntroduction to Business and Data Analysis Undergraduate.pdf
Introduction to Business and Data Analysis Undergraduate.pdf
 
How to unlock new data-driven potential for your organization
How to unlock new data-driven potential for your organizationHow to unlock new data-driven potential for your organization
How to unlock new data-driven potential for your organization
 
Introduction to SciVal
Introduction to SciValIntroduction to SciVal
Introduction to SciVal
 
Will Bigger and Better Data Help Deliver More Major Donors?
Will Bigger and Better Data Help Deliver More Major Donors?Will Bigger and Better Data Help Deliver More Major Donors?
Will Bigger and Better Data Help Deliver More Major Donors?
 

Recently uploaded

原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
mkkikqvo
 
社内勉強会資料_Hallucination of LLMs               .
社内勉強会資料_Hallucination of LLMs               .社内勉強会資料_Hallucination of LLMs               .
社内勉強会資料_Hallucination of LLMs               .
NABLAS株式会社
 
Namma-Kalvi-11th-Physics-Study-Material-Unit-1-EM-221086.pdf
Namma-Kalvi-11th-Physics-Study-Material-Unit-1-EM-221086.pdfNamma-Kalvi-11th-Physics-Study-Material-Unit-1-EM-221086.pdf
Namma-Kalvi-11th-Physics-Study-Material-Unit-1-EM-221086.pdf
22ad0301
 
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
z6osjkqvd
 
Telemetry Solution for Gaming (AWS Summit'24)
Telemetry Solution for Gaming (AWS Summit'24)Telemetry Solution for Gaming (AWS Summit'24)
Telemetry Solution for Gaming (AWS Summit'24)
GeorgiiSteshenko
 
Drownings spike from May to August in children
Drownings spike from May to August in childrenDrownings spike from May to August in children
Drownings spike from May to August in children
Bisnar Chase Personal Injury Attorneys
 
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
uevausa
 
一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理
一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理
一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理
nyvan3
 
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdfreading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
perranet1
 
一比一原版斯威本理工大学毕业证(swinburne毕业证)如何办理
一比一原版斯威本理工大学毕业证(swinburne毕业证)如何办理一比一原版斯威本理工大学毕业证(swinburne毕业证)如何办理
一比一原版斯威本理工大学毕业证(swinburne毕业证)如何办理
actyx
 
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
Marlon Dumas
 
一比一原版悉尼大学毕业证如何办理
一比一原版悉尼大学毕业证如何办理一比一原版悉尼大学毕业证如何办理
一比一原版悉尼大学毕业证如何办理
keesa2
 
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
Timothy Spann
 
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
agdhot
 
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative ClassifiersML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
MastanaihnaiduYasam
 
一比一原版南昆士兰大学毕业证如何办理
一比一原版南昆士兰大学毕业证如何办理一比一原版南昆士兰大学毕业证如何办理
一比一原版南昆士兰大学毕业证如何办理
ugydym
 
06-18-2024-Princeton Meetup-Introduction to Milvus
06-18-2024-Princeton Meetup-Introduction to Milvus06-18-2024-Princeton Meetup-Introduction to Milvus
06-18-2024-Princeton Meetup-Introduction to Milvus
Timothy Spann
 
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
Vietnam Cotton & Spinning Association
 
Build applications with generative AI on Google Cloud
Build applications with generative AI on Google CloudBuild applications with generative AI on Google Cloud
Build applications with generative AI on Google Cloud
Márton Kodok
 
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
lzdvtmy8
 

Recently uploaded (20)

原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
 
社内勉強会資料_Hallucination of LLMs               .
社内勉強会資料_Hallucination of LLMs               .社内勉強会資料_Hallucination of LLMs               .
社内勉強会資料_Hallucination of LLMs               .
 
Namma-Kalvi-11th-Physics-Study-Material-Unit-1-EM-221086.pdf
Namma-Kalvi-11th-Physics-Study-Material-Unit-1-EM-221086.pdfNamma-Kalvi-11th-Physics-Study-Material-Unit-1-EM-221086.pdf
Namma-Kalvi-11th-Physics-Study-Material-Unit-1-EM-221086.pdf
 
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
 
Telemetry Solution for Gaming (AWS Summit'24)
Telemetry Solution for Gaming (AWS Summit'24)Telemetry Solution for Gaming (AWS Summit'24)
Telemetry Solution for Gaming (AWS Summit'24)
 
Drownings spike from May to August in children
Drownings spike from May to August in childrenDrownings spike from May to August in children
Drownings spike from May to August in children
 
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
一比一原版加拿大渥太华大学毕业证(uottawa毕业证书)如何办理
 
一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理
一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理
一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理
 
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdfreading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
reading_sample_sap_press_operational_data_provisioning_with_sap_bw4hana (1).pdf
 
一比一原版斯威本理工大学毕业证(swinburne毕业证)如何办理
一比一原版斯威本理工大学毕业证(swinburne毕业证)如何办理一比一原版斯威本理工大学毕业证(swinburne毕业证)如何办理
一比一原版斯威本理工大学毕业证(swinburne毕业证)如何办理
 
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
 
一比一原版悉尼大学毕业证如何办理
一比一原版悉尼大学毕业证如何办理一比一原版悉尼大学毕业证如何办理
一比一原版悉尼大学毕业证如何办理
 
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
 
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
一比一原版加拿大麦吉尔大学毕业证(mcgill毕业证书)如何办理
 
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative ClassifiersML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
 
一比一原版南昆士兰大学毕业证如何办理
一比一原版南昆士兰大学毕业证如何办理一比一原版南昆士兰大学毕业证如何办理
一比一原版南昆士兰大学毕业证如何办理
 
06-18-2024-Princeton Meetup-Introduction to Milvus
06-18-2024-Princeton Meetup-Introduction to Milvus06-18-2024-Princeton Meetup-Introduction to Milvus
06-18-2024-Princeton Meetup-Introduction to Milvus
 
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
 
Build applications with generative AI on Google Cloud
Build applications with generative AI on Google CloudBuild applications with generative AI on Google Cloud
Build applications with generative AI on Google Cloud
 
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
 

Power BI - Analysis or Process - CollabDays New England 2023

Editor's Notes

  1. You are the best-looking audience I’ve seen. In fact you are better looking than the people in todays keynote
  2. The best place to start if we’re trying to improve the quality of our decisions is to look at how organizations make decisions. . When it comes to decisions, organizations default to gathering data and rarely analyzing decisions. People aren’t taught to make decisions, they are taught coding, project management Countdown approach
  3. All non technical
  4. Odds are three things went into that decision: It probably relied on the insights of a few key executives; (2) it involved some sort of fact gathering and analysis; and (3) it was likely enveloped in some sort of decision process—whether formal or informal—that translated the analysis into a decision. Now how would you rate the quality of your organization’s strategic decisions? If you’re like most workers, the answer wouldn’t be positive:
  5. This session is asking: What is really going on? THIS DOESN’T MAKE SENSE Most business decisions were not made on “gut calls” but rather rigorous analysis.  And yet they were poor decisions. In short, most people did the all the legwork we think we’re supposed to do: they delivered large quantities of detailed analysis. Yet this wasn’t enough. “research indicates that, contrary to what one might assume, good analysis in the hands of managers who have good judgment won’t naturally yield good decisions.” People aren’t taught how to make decisions.
  6. Projections are put together by people who are interested in a particular outcome, have a subconscious bias, and their apparent precision makes it fallacious. Sales people As an executive, I need accuracy and truth.
  7. The young and the old lady.
  8. To make better decisions, we can sort of reduce errors or have better insight or preferably both. And yet, these often seem in conflict with one another. I thought a good place to start this would be, what sparks insight and what prevents us from putting our insights into use? Is it because they often contradict the beliefs we hold? Are they giving you all the data
  9. To be a good manager, you want things to run smoothly. And insights are not ways of running smoothly. Insights are disorganizing and disruptive. And so, that’s a major reason that organizations, without even intending to, block the insights that come their way. Give them reports to what they control More data doesn’t mean better outcomes But insights are disorganizing, as you point out. Insights make you change the way you think, and make you change all kinds of things. And they may not be right. And so, most organizations actually inhibit insights.  Insights- Leaders Middle Managers – Operational Reports Is that because the organizations are mostly focused on the error-reducing side versus the gaining insight side, and that’s the tension between these things? They don’t like variants. They don’t like things that are outside of the norm.
  10. Emotional and logistic lenses
  11. How do you valid the data Content is important Who’s correct The BI developer should ask- What decision will you be making from this report
  12. Are we experimenting? Or are we set on the process
  13. A second type of insight is contradiction insight, where something happens that doesn’t make sense. And there you do have to change what you believe or wonder what’s going on.  And the example I use there is a story I heard from a police officer. He was driving around with a partner who was in his first year, and they were stuck in traffic. There’s a red light. And the partner, this young guy looks at the car ahead, which is a new BMW. And he sees the driver take a deep drag on a cigarette and then flick the ashes. And he says, “Who flicks the ashes in a brand new BMW? That doesn’t make sense. Something is off here.” So they light them up, and pull the car over. Sure enough, it was a stolen car. So that’s the second type of insight, which is a contradiction insight, where something happens that you didn’t expect. Now, this didn’t force anybody to revise their mental model of their thinking. It just allowed them to investigate further.
  14. a misconception resulting from incorrect information.  So in terms of insight, in terms of changing our beliefs, that’s the secret sauce for this kind of pathway, where even practically for any of the pathways, is to become curious about things that don’t make sense.
  15. People with lots of experience. And I found that experience is essential for coming up with insights. However, people with experience have lots of scar tissue, from things that got tried before and failed. . People with lots of experience. And I found that experience is essential for coming up with insights. I’ll ask them, “Tell me the last mistake you made. Let’s talk about that.” Journyman are not experts Understand experience - 20 years is not 20 years of learning Experience, and they probably worked in a job very similar to the one that you’re working in now. The problem is, their experience in that role was 20 years ago or 15 years ago. And I would imagine that the environment has changed a lot, whereas the experience of your manager or your team leader, or whatever, might be more recent, and they would have a more accurate view to what the lay of the land is right now.
  16. Executives are the best at this
  17. Look at historic data. How do we evaluate someone’s ability to make decisions? What got you to that decision Who are you? New employees wanting to change everything
  18. Did the person get lucky…… is this the only piece of evidence we have? So you can’t just look at the outcome, but you have to look at what the person was thinking about when they made the decision
  19. I think the idea of a decision journal is a great idea. And I hadn’t heard of that before.  I would like to know when the employee is making a decision, what is the decision, what are the goals that the employee wants to achieve. The primary goal, but there may be other goals that the employee is aware of. What are the prime pieces of information the employee is using to make the decision? Who are the other people or teams that are going to be affected by this decision? Those are the things that I’d like to examine. I found that 80% of my cleanup was made from 20% of the decisions
  20. we should discourage people from coming up with immediate reactions, but that’s ridiculous, because that’s not the way we think, and it would cripple us. When do I need to make this decision? Do I have the freedom. So instead, you want us to come up with a quick reaction, but if we’re wrong, there’s going to be an anomaly, and we want to be able to revisit it. That’s the way we break out of fixation, is we notice the anomalies. The way we get stuck in fixation and make fixation errors is we explain away the anomalies, hold on to the original wrong impression until it’s far too late.
  21. Cognitive flexibility theory is the notion of trying to help people achieve expertise by preventing them from locking into routines and standard ways of doing things so that they can become more naturally adaptive. 
  22. Instead of doing a postmortem for projects that fail after they failed, let’s move it to the beginning.” And that’s why it’s called a pre-mortem. And the way it works is if we’re on a team, we take everybody on the team, we’re all sitting around the table. And usually we do it at a kickoff meeting,  Post Mortem. Helps everyone but the patient.
  23. What we find is it surfaces ideas and flaws that people hadn’t considered. But it also creates a culture of candor in the team, where people are starting to get used to expressing problems rather than covering them up. And it creates a sense of trust that I can say something and I’m not going to get criticized for it. Often at the end of a meeting, somebody will say, “All right, we’re just about done with the meeting. Does anybody see any problems?” Nobody wants to identify a problem. We’ve just spent the last hour and a half discussing the plan. Nobody wants to admit that there’s a problem. People aren’t even thinking about problems. They’re all in goal mode. Let’s get started. They’re impatient to start. And there could be consequences of exposing problems. With a pre-mortem, we reversed that dynamic. The way you show your smart in a pre-mortem is the quality of the items that you generate.
  24. So, there’s no general rule for how a team should make a decision. It’s going to depend on the situation and the context. There was a movie with Matt Damon a number of years ago called The Martian. And Matt Damon is part of a team that went to Mars. And something happened and they needed to depart, and they gathered everybody together, but they couldn’t find Matt Damon. 
  25. I don’t like the idea of consensus decisions. And I don’t like the idea of a consensus decision in this kind of dangerous environment. Because as the people in the spaceship went around, there was enormous pressure on everybody to go along with the consensus, which was, “We should go back and rescue him.” And it was public. And they went back and they risked all of their lives in order to save him. US army – We defend democracy, not practice it.
  26. When you go to a meeting to make a decision, have everybody in the room write out the problem on a piece of paper that they think they’re solving with this decision, and then compare how different and how much variance there is in those problem statements. Do we have the right insight into the problem? Have we defined the problem?” And this is where if you have a sole decision maker instead of a group, you can acknowledge that the responsibility of that decision maker may be to listen to other people’s definitions of the problem. 
  27. I love the idea of the zone of indifference. The way the phenomenon works is if I’ve got two choices, a terrible option, and a wonderful option, quick, which one do you pick? Okay, that’s not a hard decision. These are the hardest decisions people ever wrestle with. And the paradox is if the advantages and disadvantages of the two options are almost perfectly balanced, it doesn’t matter which one we pick.