SlideShare une entreprise Scribd logo
1  sur  45
Télécharger pour lire hors ligne
All rights reserved 1
Managing Your
Project Manager:
A Survival Guide for Data
Professionals
Karen López
www.infoadvisors.com
All rights reserved
About me
Karen López is a principal
consultant at InfoAdvisors. She
specializes in the practical
application of data management
principles.
blog.infoadvisors.com
All rights reserved 3
Agenda
 Warning Signs
 Getting it Right
 Survival Tips
All rights reserved 4
About this Presentation
 We will be using an interactive
format – your opinion counts
 Opinions are good
All rights reserved 5
Why this Topic?
Because Project Management is
HARD
Many IT professionals do not
understand modeling
Your project manager will, in part or
in whole, define the success of the
project
All rights reserved 6
Karen’s Contribution
Contributor to Roundtable on
Project Management, James
Bullock (Editor), Gerald M.
Weinberg (Editor), Marie Benesh
(Editor), 2001, Dorset House,
093263348X
All rights reserved
POLLS…
• Who are you?
• Model Much?
• Manage
Much?
• …
7
All rights reserved 8
Warning Signs
All rights reserved 9
Warning Signs
Your Project Manager is:
 Above pitching in
 Unwilling to remove
obstacles
 Compensated solely on
Project Plan dates
All rights reserved 10
Warning Signs
The project plan looks
great, but…
 Dates are hard and fast
 Level of effort isn’t
 The process is cutting
edge, but the project plan
isn’t
All rights reserved 11
Warning Signs
Issue / Defect Lists
 Rudy’s Rutabaga Rule
 Perception of ‘No changes’
 Lies, Damn Lies, and Statistics
 Analysis issues (every issue)
portrayed as ‘Data Model Issues’
All rights reserved 12
Warning Signs
You have a Methodology, but…
 The deliverables are more
important than the work they
deliver
 You are expected to spend more
time preparing the documents
than doing the work
 More money is spent on
publishing the deliverables than
the tool used to prepare them
All rights reserved 13
Warning Signs
Who will have update access to the
models?
 Just the Modelers?
 The Modelers and the DBAs
 The Modelers, the DBAs & the
Developers
 The Modelers, the DBAs the
Developers, the Project
Managers, the Business Users,
the Project Sponsors, and …
All rights reserved 14
Warning Signs
Done For Now (DFN)
 Milestones are met, for now
 Make up work never completed
 Great for an iterative effort, not
for a waterfall effort
 No scheduled task times for
iteration
 White Bread Warning
 The cost of fixing DFN grows
rapidly the further you go….
All rights reserved 15
Warning Signs
Finish-to-Start mindsets
 Nothing, absolutely nothing can
start until the data models are
done
 No tasks can start or be done in
parallel
 Outstanding Issues are always
called Data Model Issues
 No one understands that it is the
modeling that is important, not
the models.
All rights reserved 16
Warning Signs
YOU think your job is only data
modeling…
 You think everyone will see the
value of the data modeling
 You think everyone knows what a
data model is
 You think everyone agrees what a
good data model is
 You think everyone knows your
jargon
 You don’t understand it’s the
modeling, not the models.
All rights reserved 17
Your Contribution
 What warning signs have you
experienced?
 What warning signs have you
been responsible for?
 What are “Encouraging Signs”
you have observed?
All rights reserved 18
Can you answer…
Who is your Project Manager?
All rights reserved 19
Just Who is your PM?
 One?
 One from each project
partner?
 One and your Manager?
 One from each partner, the
official one, your manager,
her manager and your
spouse?
All rights reserved 20
Getting it Right
Some things to think about…
All rights reserved 21
Getting it Right: How long
will it take?
 1 hour per attribute….
 5 days per subject area…
 More than you think….
 From now until the end of the
project….
 What if Quality is Job Null?
All rights reserved 22
Getting it Right: How long
will it take?
Questions to ask:
 What kind of project is this?
 Strategic, Enterprise, long term
value
 Tactical, Enterprise, medium term
value
 Chaotic, agile, extreme, desperate,
someone will is probably going to
go to jail if we don’t get this done on
time….
All rights reserved 23
Getting it Right: How long
will it take?
Questions to ask:
 Where will the work be done
 Just on the other side of the
cube
 In a few corporate locations
 Here, there, and everywhere
 Days away
All rights reserved 24
Getting it Right: How long
will it take?
Questions to ask:
 What tools will we be using?
 Our favorite data modeling tool
 A data modeling tool
 A modeling tool
 Tool? You need a special
tool? How much are they?
All rights reserved 25
Getting it Right: How long
will it take?
Questions to ask:
 What process will we be using?
 CMM-5 / Best Practice / ?
 Our usual methodology
 Process? What do you mean by
“process”.
 Here’s the project plan – that’s our
process.
All rights reserved 26
Your Contribution
 How do you estimate modeling
efforts?
 Where/how have your estimates
been the most inaccurate?
 Shouldn’t we just double all our
estimates?
All rights reserved 27
Getting it Right: How much
will it cost?
 .5 FTE per average project …
 2 FTEs if you wait too late in the
project
 More than you think
 It depends…how much money
do you have?
 The Orange Juice Test
All rights reserved 28
Getting it Right: Who
should be involved?
 Data Modeler
 Logical
 Physical
 Enterprise Architect
 DBA
 Process Modeler
 Requirements Analysts
 Business Analysts
 Subject Matter Experts
 ???
All rights reserved 29
Getting it Right: When?
 Right from the start
 Defining Scope
 Recruiting existing modeling
resources
 Preparing modeling environment
 Tailoring standards
 Recruiting People
 Training
All rights reserved 30
Timing is Everything
 When’s a good time to start
modeling?
 Just before the code is delivered “Can
you fix this”?
 When they need a database “Please
Hurry!”
 When the other modeling is happening
“Our tool can’t generate a working
database…can yours?”
 From the beginning “How can I help
you?”
All rights reserved 31
Survival Tips
Some things to think harder
about…
All rights reserved 32
Survival Tips
Be collaborative (even if
they don’t deserve it )
• Intranet
• PDFs
• Spare copies of Diagrams
• I think I can…
• Feel their pain
All rights reserved 33
Survival Tips
Say Baseline (or whatever
word you chose), not Done
• Baselines: Versions with
expected changes
• Done: No expected changes
• Baseline everything, not just the
data models
All rights reserved 34
Survival Tips
Don’t be the Data Gang
/ Clique / Opposing
Team
• Do Lunch
• Share Knowledge
• Sit with the team
• Talk the team up, not down
All rights reserved 35
Survival Tips
Know the Project Plan
• A plan is not just a Gantt
Chart
• Current and future tasks
• Provide insight to the PM on
Risks and Contingencies
• Identify problems when you
have proposed solutions
All rights reserved 36
Survival Tips
Know the Issues List
• Be more ready with stats
than anyone else
• Know the complexity of
Issues
• Make sure that Issues are
really Issues
All rights reserved 37
Survival Tips
Win the Confidence of the PM
• Admit to mistakes
• Accurately reiterate others’
viewpoints, even if you don’t agree
• Feel the pain
• Know the political goals as well as
the project goals
• Get to the point where the PM asks
you first
All rights reserved 38
Survival Tips
Acknowledge
philosophical differences
• The earlier in the debate, the
better
• Once the decision is made,
don’t waste everyone else’s time
revisiting it
• Know how to express the pros
and cons of all sides
All rights reserved 39
Survival Tips
Be a Professional
• Track your errors
• Track the causes
• Build tools to overcome
these errors
• Document your procedures
• Leverage your tools
All rights reserved 40
Survival Tips
Be a Professional
• Know more about the tool
than anyone else on the team
• Be literate in other modeling
techniques and tool
• Know the lingo of other
approaches
All rights reserved 41
Survival Tips
Be a Professional
• Know the limits of your tool
• Leverage its features
• Manage the Sizzle
• Produce readable diagrams
• Produce really readable diagrams 
• Never present a diagram without the
model…
All rights reserved 42
Your Contribution
 What are your best survival
tips?
 How did you learn them?
 Do you record formal post-
project reviews?
All rights reserved 43
Finally…
 You will fail if you think your
only job is to produce a high
quality data model
 Carry your ammo, but don’t be
trigger happy
 Be able to address every
problem / challenge / issue with
Cost, Benefit, and Risk
All rights reserved 44
Recommended Book
Secrets of Consulting,
Gerald Weinberg, 1986,
Dorset House, 0932633013
All rights reserved
Thank YOU!
blog.infoadvisors.com

Contenu connexe

Tendances

Lean innovation games_v1.0
Lean innovation games_v1.0Lean innovation games_v1.0
Lean innovation games_v1.0Namrata Datta
 
138755986 problem-solving-methods-ppt
138755986 problem-solving-methods-ppt138755986 problem-solving-methods-ppt
138755986 problem-solving-methods-pptANn Villanueva
 
How to Get Stuff Delivered
How to Get Stuff DeliveredHow to Get Stuff Delivered
How to Get Stuff DeliveredBarry Hodge
 
Alice Phieu - Mini Design Sprint (Ideation Workshop)
Alice Phieu - Mini Design Sprint (Ideation Workshop)Alice Phieu - Mini Design Sprint (Ideation Workshop)
Alice Phieu - Mini Design Sprint (Ideation Workshop)Alice Phieu
 
How to Make a Good Idea Great
How to Make a Good Idea GreatHow to Make a Good Idea Great
How to Make a Good Idea GreatBarry Hodge
 
Problem solving skills
Problem solving skillsProblem solving skills
Problem solving skillsBinay Roy
 
One, No One, One Hundred Thousand Projects (Uno, Nessuno, Centomila Progetti)
One, No One, One Hundred Thousand Projects (Uno, Nessuno, Centomila Progetti)One, No One, One Hundred Thousand Projects (Uno, Nessuno, Centomila Progetti)
One, No One, One Hundred Thousand Projects (Uno, Nessuno, Centomila Progetti)Gaetano Mazzanti
 
Bit by Bit: A Framework for Building Technological Competence as a Lawyer
Bit by Bit: A Framework for Building Technological Competence as a LawyerBit by Bit: A Framework for Building Technological Competence as a Lawyer
Bit by Bit: A Framework for Building Technological Competence as a LawyerJack Pringle
 

Tendances (10)

Lean innovation games_v1.0
Lean innovation games_v1.0Lean innovation games_v1.0
Lean innovation games_v1.0
 
138755986 problem-solving-methods-ppt
138755986 problem-solving-methods-ppt138755986 problem-solving-methods-ppt
138755986 problem-solving-methods-ppt
 
Mental Models2
Mental Models2Mental Models2
Mental Models2
 
How to Get Stuff Delivered
How to Get Stuff DeliveredHow to Get Stuff Delivered
How to Get Stuff Delivered
 
Alice Phieu - Mini Design Sprint (Ideation Workshop)
Alice Phieu - Mini Design Sprint (Ideation Workshop)Alice Phieu - Mini Design Sprint (Ideation Workshop)
Alice Phieu - Mini Design Sprint (Ideation Workshop)
 
How to Make a Good Idea Great
How to Make a Good Idea GreatHow to Make a Good Idea Great
How to Make a Good Idea Great
 
2015 10-28-guild council
2015 10-28-guild council2015 10-28-guild council
2015 10-28-guild council
 
Problem solving skills
Problem solving skillsProblem solving skills
Problem solving skills
 
One, No One, One Hundred Thousand Projects (Uno, Nessuno, Centomila Progetti)
One, No One, One Hundred Thousand Projects (Uno, Nessuno, Centomila Progetti)One, No One, One Hundred Thousand Projects (Uno, Nessuno, Centomila Progetti)
One, No One, One Hundred Thousand Projects (Uno, Nessuno, Centomila Progetti)
 
Bit by Bit: A Framework for Building Technological Competence as a Lawyer
Bit by Bit: A Framework for Building Technological Competence as a LawyerBit by Bit: A Framework for Building Technological Competence as a Lawyer
Bit by Bit: A Framework for Building Technological Competence as a Lawyer
 

Similaire à Big Challenges in Data Modeling - Data Modelers and Project Managers

50 Shades of Fail
50 Shades of Fail 50 Shades of Fail
50 Shades of Fail SmartBear
 
501 Talks Tech: Design Thinking Workshop by Dupla Studios
501 Talks Tech: Design Thinking Workshop by Dupla Studios501 Talks Tech: Design Thinking Workshop by Dupla Studios
501 Talks Tech: Design Thinking Workshop by Dupla Studios501 Commons
 
Whittle Modeling Wizards 2012
Whittle Modeling Wizards 2012Whittle Modeling Wizards 2012
Whittle Modeling Wizards 2012jonathw
 
Competion-Pack 2023.pdf
Competion-Pack 2023.pdfCompetion-Pack 2023.pdf
Competion-Pack 2023.pdfAimeMoh
 
Competion-Pack 2023.pdf
Competion-Pack 2023.pdfCompetion-Pack 2023.pdf
Competion-Pack 2023.pdfAimeMoh
 
Software development management slides by George Berkowski (Hailo)
Software development management slides by George Berkowski (Hailo)Software development management slides by George Berkowski (Hailo)
Software development management slides by George Berkowski (Hailo)MiniBar
 
Resource and technology design process
Resource and technology  design processResource and technology  design process
Resource and technology design processRobby Jackson
 
UI For Alien Cowboys
UI For Alien CowboysUI For Alien Cowboys
UI For Alien CowboysMatt Jones
 
A Design Thinking Workshop for the MSIS CoreCarl M. Briggs Ph..docx
A Design Thinking Workshop for the MSIS CoreCarl M. Briggs Ph..docxA Design Thinking Workshop for the MSIS CoreCarl M. Briggs Ph..docx
A Design Thinking Workshop for the MSIS CoreCarl M. Briggs Ph..docxblondellchancy
 
Webinar - Design thinking 101 - 2018-07-24
Webinar - Design thinking 101 - 2018-07-24Webinar - Design thinking 101 - 2018-07-24
Webinar - Design thinking 101 - 2018-07-24TechSoup
 
Why do usability problems go unfixed?
Why do usability problems go unfixed?Why do usability problems go unfixed?
Why do usability problems go unfixed?Caroline Jarrett
 
Selecting Engineering Project
Selecting Engineering ProjectSelecting Engineering Project
Selecting Engineering ProjectA B Shinde
 
Nerd, Geek, and Gear Herding: Technical Management Techniques for Managers v 2.0
Nerd, Geek, and Gear Herding: Technical Management Techniques for Managers v 2.0Nerd, Geek, and Gear Herding: Technical Management Techniques for Managers v 2.0
Nerd, Geek, and Gear Herding: Technical Management Techniques for Managers v 2.0NTEN
 
A Deep Dive into A3 Thinking
A Deep Dive into A3 ThinkingA Deep Dive into A3 Thinking
A Deep Dive into A3 ThinkingKaiNexus
 
How to be a good developer
How to be a good developerHow to be a good developer
How to be a good developerAshley Davis
 
Understand your customer in developing countries
Understand your customer in developing countriesUnderstand your customer in developing countries
Understand your customer in developing countriesElaine Chen
 
Agile?! Are You Crazy???
Agile?! Are You Crazy???Agile?! Are You Crazy???
Agile?! Are You Crazy???lazygolfer
 

Similaire à Big Challenges in Data Modeling - Data Modelers and Project Managers (20)

Unleashing the Creative Potential of Your Teams
Unleashing the Creative Potential of Your TeamsUnleashing the Creative Potential of Your Teams
Unleashing the Creative Potential of Your Teams
 
50 Shades of Fail
50 Shades of Fail 50 Shades of Fail
50 Shades of Fail
 
501 Talks Tech: Design Thinking Workshop by Dupla Studios
501 Talks Tech: Design Thinking Workshop by Dupla Studios501 Talks Tech: Design Thinking Workshop by Dupla Studios
501 Talks Tech: Design Thinking Workshop by Dupla Studios
 
The no authority CAD Manager
The no authority CAD ManagerThe no authority CAD Manager
The no authority CAD Manager
 
Whittle Modeling Wizards 2012
Whittle Modeling Wizards 2012Whittle Modeling Wizards 2012
Whittle Modeling Wizards 2012
 
Competion-Pack 2023.pdf
Competion-Pack 2023.pdfCompetion-Pack 2023.pdf
Competion-Pack 2023.pdf
 
Competion-Pack 2023.pdf
Competion-Pack 2023.pdfCompetion-Pack 2023.pdf
Competion-Pack 2023.pdf
 
Software development management slides by George Berkowski (Hailo)
Software development management slides by George Berkowski (Hailo)Software development management slides by George Berkowski (Hailo)
Software development management slides by George Berkowski (Hailo)
 
Resource and technology design process
Resource and technology  design processResource and technology  design process
Resource and technology design process
 
UI For Alien Cowboys
UI For Alien CowboysUI For Alien Cowboys
UI For Alien Cowboys
 
A Design Thinking Workshop for the MSIS CoreCarl M. Briggs Ph..docx
A Design Thinking Workshop for the MSIS CoreCarl M. Briggs Ph..docxA Design Thinking Workshop for the MSIS CoreCarl M. Briggs Ph..docx
A Design Thinking Workshop for the MSIS CoreCarl M. Briggs Ph..docx
 
Webinar - Design thinking 101 - 2018-07-24
Webinar - Design thinking 101 - 2018-07-24Webinar - Design thinking 101 - 2018-07-24
Webinar - Design thinking 101 - 2018-07-24
 
Why do usability problems go unfixed?
Why do usability problems go unfixed?Why do usability problems go unfixed?
Why do usability problems go unfixed?
 
Selecting Engineering Project
Selecting Engineering ProjectSelecting Engineering Project
Selecting Engineering Project
 
Nerd, Geek, and Gear Herding: Technical Management Techniques for Managers v 2.0
Nerd, Geek, and Gear Herding: Technical Management Techniques for Managers v 2.0Nerd, Geek, and Gear Herding: Technical Management Techniques for Managers v 2.0
Nerd, Geek, and Gear Herding: Technical Management Techniques for Managers v 2.0
 
A Deep Dive into A3 Thinking
A Deep Dive into A3 ThinkingA Deep Dive into A3 Thinking
A Deep Dive into A3 Thinking
 
Guide decisions
Guide decisions Guide decisions
Guide decisions
 
How to be a good developer
How to be a good developerHow to be a good developer
How to be a good developer
 
Understand your customer in developing countries
Understand your customer in developing countriesUnderstand your customer in developing countries
Understand your customer in developing countries
 
Agile?! Are You Crazy???
Agile?! Are You Crazy???Agile?! Are You Crazy???
Agile?! Are You Crazy???
 

Plus de DATAVERSITY

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
 
Data at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceData at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
 
Exploring Levels of Data Literacy
Exploring Levels of Data LiteracyExploring Levels of Data Literacy
Exploring Levels of Data LiteracyDATAVERSITY
 
Building a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsBuilding a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
 
Make Data Work for You
Make Data Work for YouMake Data Work for You
Make Data Work for YouDATAVERSITY
 
Data Catalogs Are the Answer – What is the Question?
Data Catalogs Are the Answer – What is the Question?Data Catalogs Are the Answer – What is the Question?
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
 
Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
 
Data Modeling Fundamentals
Data Modeling FundamentalsData Modeling Fundamentals
Data Modeling FundamentalsDATAVERSITY
 
Showing ROI for Your Analytic Project
Showing ROI for Your Analytic ProjectShowing ROI for Your Analytic Project
Showing ROI for Your Analytic ProjectDATAVERSITY
 
How a Semantic Layer Makes Data Mesh Work at Scale
How a Semantic Layer Makes  Data Mesh Work at ScaleHow a Semantic Layer Makes  Data Mesh Work at Scale
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
 
Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?DATAVERSITY
 
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
 
Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
 
Data Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and ForwardsData Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
 
Data Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement TodayData Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
 
2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics2023 Trends in Enterprise Analytics
2023 Trends in Enterprise AnalyticsDATAVERSITY
 
Data Strategy Best Practices
Data Strategy Best PracticesData Strategy Best Practices
Data Strategy Best PracticesDATAVERSITY
 
Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?DATAVERSITY
 
Data Management Best Practices
Data Management Best PracticesData Management Best Practices
Data Management Best PracticesDATAVERSITY
 
MLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive AdvantageMLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
 

Plus de DATAVERSITY (20)

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
 
Data at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceData at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and Governance
 
Exploring Levels of Data Literacy
Exploring Levels of Data LiteracyExploring Levels of Data Literacy
Exploring Levels of Data Literacy
 
Building a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsBuilding a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business Goals
 
Make Data Work for You
Make Data Work for YouMake Data Work for You
Make Data Work for You
 
Data Catalogs Are the Answer – What is the Question?
Data Catalogs Are the Answer – What is the Question?Data Catalogs Are the Answer – What is the Question?
Data Catalogs Are the Answer – What is the Question?
 
Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?
 
Data Modeling Fundamentals
Data Modeling FundamentalsData Modeling Fundamentals
Data Modeling Fundamentals
 
Showing ROI for Your Analytic Project
Showing ROI for Your Analytic ProjectShowing ROI for Your Analytic Project
Showing ROI for Your Analytic Project
 
How a Semantic Layer Makes Data Mesh Work at Scale
How a Semantic Layer Makes  Data Mesh Work at ScaleHow a Semantic Layer Makes  Data Mesh Work at Scale
How a Semantic Layer Makes Data Mesh Work at Scale
 
Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?
 
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
 
Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?
 
Data Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and ForwardsData Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and Forwards
 
Data Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement TodayData Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement Today
 
2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics
 
Data Strategy Best Practices
Data Strategy Best PracticesData Strategy Best Practices
Data Strategy Best Practices
 
Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?
 
Data Management Best Practices
Data Management Best PracticesData Management Best Practices
Data Management Best Practices
 
MLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive AdvantageMLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive Advantage
 

Dernier

Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsNathaniel Shimoni
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPathCommunity
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...Wes McKinney
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS:  6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS:  6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integrationmarketing932765
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI AgeCprime
 
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Mark Goldstein
 
QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesBernd Ruecker
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesThousandEyes
 
Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Farhan Tariq
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfNeo4j
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch TuesdayIvanti
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxLoriGlavin3
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesMuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesManik S Magar
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfpanagenda
 

Dernier (20)

Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directions
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to Hero
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS:  6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS:  6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI Age
 
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
 
QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architectures
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
 
Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdf
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch Tuesday
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesMuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
 

Big Challenges in Data Modeling - Data Modelers and Project Managers

  • 1. All rights reserved 1 Managing Your Project Manager: A Survival Guide for Data Professionals Karen López www.infoadvisors.com
  • 2. All rights reserved About me Karen López is a principal consultant at InfoAdvisors. She specializes in the practical application of data management principles. blog.infoadvisors.com
  • 3. All rights reserved 3 Agenda  Warning Signs  Getting it Right  Survival Tips
  • 4. All rights reserved 4 About this Presentation  We will be using an interactive format – your opinion counts  Opinions are good
  • 5. All rights reserved 5 Why this Topic? Because Project Management is HARD Many IT professionals do not understand modeling Your project manager will, in part or in whole, define the success of the project
  • 6. All rights reserved 6 Karen’s Contribution Contributor to Roundtable on Project Management, James Bullock (Editor), Gerald M. Weinberg (Editor), Marie Benesh (Editor), 2001, Dorset House, 093263348X
  • 7. All rights reserved POLLS… • Who are you? • Model Much? • Manage Much? • … 7
  • 9. All rights reserved 9 Warning Signs Your Project Manager is:  Above pitching in  Unwilling to remove obstacles  Compensated solely on Project Plan dates
  • 10. All rights reserved 10 Warning Signs The project plan looks great, but…  Dates are hard and fast  Level of effort isn’t  The process is cutting edge, but the project plan isn’t
  • 11. All rights reserved 11 Warning Signs Issue / Defect Lists  Rudy’s Rutabaga Rule  Perception of ‘No changes’  Lies, Damn Lies, and Statistics  Analysis issues (every issue) portrayed as ‘Data Model Issues’
  • 12. All rights reserved 12 Warning Signs You have a Methodology, but…  The deliverables are more important than the work they deliver  You are expected to spend more time preparing the documents than doing the work  More money is spent on publishing the deliverables than the tool used to prepare them
  • 13. All rights reserved 13 Warning Signs Who will have update access to the models?  Just the Modelers?  The Modelers and the DBAs  The Modelers, the DBAs & the Developers  The Modelers, the DBAs the Developers, the Project Managers, the Business Users, the Project Sponsors, and …
  • 14. All rights reserved 14 Warning Signs Done For Now (DFN)  Milestones are met, for now  Make up work never completed  Great for an iterative effort, not for a waterfall effort  No scheduled task times for iteration  White Bread Warning  The cost of fixing DFN grows rapidly the further you go….
  • 15. All rights reserved 15 Warning Signs Finish-to-Start mindsets  Nothing, absolutely nothing can start until the data models are done  No tasks can start or be done in parallel  Outstanding Issues are always called Data Model Issues  No one understands that it is the modeling that is important, not the models.
  • 16. All rights reserved 16 Warning Signs YOU think your job is only data modeling…  You think everyone will see the value of the data modeling  You think everyone knows what a data model is  You think everyone agrees what a good data model is  You think everyone knows your jargon  You don’t understand it’s the modeling, not the models.
  • 17. All rights reserved 17 Your Contribution  What warning signs have you experienced?  What warning signs have you been responsible for?  What are “Encouraging Signs” you have observed?
  • 18. All rights reserved 18 Can you answer… Who is your Project Manager?
  • 19. All rights reserved 19 Just Who is your PM?  One?  One from each project partner?  One and your Manager?  One from each partner, the official one, your manager, her manager and your spouse?
  • 20. All rights reserved 20 Getting it Right Some things to think about…
  • 21. All rights reserved 21 Getting it Right: How long will it take?  1 hour per attribute….  5 days per subject area…  More than you think….  From now until the end of the project….  What if Quality is Job Null?
  • 22. All rights reserved 22 Getting it Right: How long will it take? Questions to ask:  What kind of project is this?  Strategic, Enterprise, long term value  Tactical, Enterprise, medium term value  Chaotic, agile, extreme, desperate, someone will is probably going to go to jail if we don’t get this done on time….
  • 23. All rights reserved 23 Getting it Right: How long will it take? Questions to ask:  Where will the work be done  Just on the other side of the cube  In a few corporate locations  Here, there, and everywhere  Days away
  • 24. All rights reserved 24 Getting it Right: How long will it take? Questions to ask:  What tools will we be using?  Our favorite data modeling tool  A data modeling tool  A modeling tool  Tool? You need a special tool? How much are they?
  • 25. All rights reserved 25 Getting it Right: How long will it take? Questions to ask:  What process will we be using?  CMM-5 / Best Practice / ?  Our usual methodology  Process? What do you mean by “process”.  Here’s the project plan – that’s our process.
  • 26. All rights reserved 26 Your Contribution  How do you estimate modeling efforts?  Where/how have your estimates been the most inaccurate?  Shouldn’t we just double all our estimates?
  • 27. All rights reserved 27 Getting it Right: How much will it cost?  .5 FTE per average project …  2 FTEs if you wait too late in the project  More than you think  It depends…how much money do you have?  The Orange Juice Test
  • 28. All rights reserved 28 Getting it Right: Who should be involved?  Data Modeler  Logical  Physical  Enterprise Architect  DBA  Process Modeler  Requirements Analysts  Business Analysts  Subject Matter Experts  ???
  • 29. All rights reserved 29 Getting it Right: When?  Right from the start  Defining Scope  Recruiting existing modeling resources  Preparing modeling environment  Tailoring standards  Recruiting People  Training
  • 30. All rights reserved 30 Timing is Everything  When’s a good time to start modeling?  Just before the code is delivered “Can you fix this”?  When they need a database “Please Hurry!”  When the other modeling is happening “Our tool can’t generate a working database…can yours?”  From the beginning “How can I help you?”
  • 31. All rights reserved 31 Survival Tips Some things to think harder about…
  • 32. All rights reserved 32 Survival Tips Be collaborative (even if they don’t deserve it ) • Intranet • PDFs • Spare copies of Diagrams • I think I can… • Feel their pain
  • 33. All rights reserved 33 Survival Tips Say Baseline (or whatever word you chose), not Done • Baselines: Versions with expected changes • Done: No expected changes • Baseline everything, not just the data models
  • 34. All rights reserved 34 Survival Tips Don’t be the Data Gang / Clique / Opposing Team • Do Lunch • Share Knowledge • Sit with the team • Talk the team up, not down
  • 35. All rights reserved 35 Survival Tips Know the Project Plan • A plan is not just a Gantt Chart • Current and future tasks • Provide insight to the PM on Risks and Contingencies • Identify problems when you have proposed solutions
  • 36. All rights reserved 36 Survival Tips Know the Issues List • Be more ready with stats than anyone else • Know the complexity of Issues • Make sure that Issues are really Issues
  • 37. All rights reserved 37 Survival Tips Win the Confidence of the PM • Admit to mistakes • Accurately reiterate others’ viewpoints, even if you don’t agree • Feel the pain • Know the political goals as well as the project goals • Get to the point where the PM asks you first
  • 38. All rights reserved 38 Survival Tips Acknowledge philosophical differences • The earlier in the debate, the better • Once the decision is made, don’t waste everyone else’s time revisiting it • Know how to express the pros and cons of all sides
  • 39. All rights reserved 39 Survival Tips Be a Professional • Track your errors • Track the causes • Build tools to overcome these errors • Document your procedures • Leverage your tools
  • 40. All rights reserved 40 Survival Tips Be a Professional • Know more about the tool than anyone else on the team • Be literate in other modeling techniques and tool • Know the lingo of other approaches
  • 41. All rights reserved 41 Survival Tips Be a Professional • Know the limits of your tool • Leverage its features • Manage the Sizzle • Produce readable diagrams • Produce really readable diagrams  • Never present a diagram without the model…
  • 42. All rights reserved 42 Your Contribution  What are your best survival tips?  How did you learn them?  Do you record formal post- project reviews?
  • 43. All rights reserved 43 Finally…  You will fail if you think your only job is to produce a high quality data model  Carry your ammo, but don’t be trigger happy  Be able to address every problem / challenge / issue with Cost, Benefit, and Risk
  • 44. All rights reserved 44 Recommended Book Secrets of Consulting, Gerald Weinberg, 1986, Dorset House, 0932633013