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Minding the
Machines
Building and Leading Data Science
and Analytics Teams
Jeremy Adamson
DS Camp – January 2023
Jeremy Adamson
Author and Strategy Consultant
Jeremy Adamson is a leader in AI and analytics
strategy, and has a broad range of experience in
aviation, energy, financial services, and public
administration. Jeremy has worked with several
major organizations to help them establish a
leadership position in data science and to unlock real
business value using advanced analytics.
Jeremy holds a Masters in Transportation
Engineering and a Bachelors in Civil Engineering
from the University of New Brunswick, as well as a
Masters in Business Administration from the
University of Calgary. Jeremy is a Professional
Engineer in the province of Alberta.
Learn more at www.rjeremyadamson.com
Minding the Machines
John Wiley & Sons, July 2021
Traditional Analytics
• Descriptive reporting
• Collect, analyze, report
• Ad-hoc analyses
• Back-room function
• Order takers and technical resources
• Objective: Better understanding of
what happened
Value-driven Analytics
• Process driven and parsimonious
• Integrated with the business
• Business Analysts
• Analytical evangelism
• Advisors and facilitators
• Objective: Unlock value opportunities
using advanced analytics and create a
culture of data-driven decision making.
The practice has evolved from its roots in decision support and business intelligence into a core
component of corporate strategy, and an enabler for all areas of the business.
Failure to launch
Organizations, regardless of size and technical maturity, have been overwhelmingly unsuccessful at
realizing value from their investments in this space, despite significant investment.
Half of organizations believe data science will fundamentally change
their organization in the next 1-3 years (Ammanath, Jarvis & Hupfer, 2020)
“85% of analytics projects end in failure” – Gartner, 2017
“8 out of 10 organizations engaged with AI say their projects have stalled” – TechRepublic, 2019
“Only 27% of executives we surveyed described their Big Data initiatives as successful” –
Capgemini, 2014
“most organizations report failures in their AI projects, with a quarter of
them reporting up to a 50% failure rate” – Fast Company, 2020
It is not just about the technology
Process
Portfolio Project
Management
Project
Execution
Strategy
Value Proposition
and Case for
Change
Organizational
Design and Target
Operating Model
Closing
the Gap
People
Team
Conventions
Relationship
Management
True Value Creation
The practice has evolved from its roots in decision support and business intelligence into a core
component of corporate strategy, and an enabler for all areas of the business.
Thought Leaders – Focus on Process
McKinsey: Winning with AI is a State of Mind 2021
New Vantage Partners: Big Data & AI 2021 Survey
Strategy – What do we do? How do we do it?
Well-articulated strategies establish the “how” and the “what”, providing a framework for all
decision making.
Strategy Playbook
• Current state assessment
• Assess readiness
• Capability mapping
• Defining the future state
• Principles, functions services
• Organizational design
• Target operating model
• Closing the gap
Process – Cognitive Fluency
Properly developed and understood processes give analytics teams the ability to scale and focus on
core competencies.
Process Playbook
• Intake and prioritization
• Scoping and
requirements
• Project planning
• Project execution
• Change management
• Closeout and delivery
Illustrative swimlane process diagram
People – Data Scientists are Artisans
Skilled, passionate, and creative practitioners are the bedrock of an analytics function. Data science
teams succeed or fail on the strength of their people.
Artisans need…
• to apprentice with an experienced
technical leader
• to have business leaders that they
respect
• to operate within a guild
• to be stimulated in their work, and to
have real agency
• to feel respected and heard, and to be
able to express themselves
Data scientist hierarchy of needs
Tip #1 – Consider the Last Mile
Effective Data Storytelling – Brent Dykes
Assets and deliverables that cannot be operationalized are value destructive. Each project should
begin with the end in mind.
Tip #2 – Hear Statistics, Feel Stories
John Snow & CF Cheffins, 1854
The numbers do not have value – only the actions that they initiate are important. Building a
compelling narrative is essential to success.
John Graunt, 1662
Tip #3 – The Model is Not the Product
Each project and activity must align to the organizational goals. Begin with the business outcome
and work backwards.
Picture from blog.militarytobusiness.com
Tip #4 – Pursue Parsimony
Continually assess each project and process for signs of bloat. Be mindful of the multiplicative
impact of well-intentioned decisions.
Tip #5 – Value Creation
Every activity, task, and project, must be directed towards value creation for the organization. Each
team convention, KPI, and communication must be designed under this rule.
Shareholders
Employees
Customers
Outcomes not targets
Number of POCs
Number of projects
Number of collaborators
Time saved
Reports generated
Quality of models
Time to market
Tip #6 – Be Intentional About Relationships
Data science and analytics is a tool for helping humans to make better and faster decisions. Adopt a
service mindset to enable greater success.
Picture from Health and Social Care Alliance Scotland
Individual Lead Goal
Terry F. (Finance) Jane P.
Encourage
Tableau use
Judy W. (HR) Peter B.
Peer review
models
Wanda N. (HR) Jane P.
Encourage
Tableau use
Spencer P. (Tax) Peter B.
Migrate ad hoc
scripts
Spencer P. (Tax) Jane P.
Encourage
Tableau use
Summary
• Working parsimoniously
ensures that our efforts
translate into value
creation
• Technology is a service
function and must be
oriented towards corporate
ends
• The core of technology is
people helping people to
make better decisions
Jeremy Adamson
www.rjeremyadamson.com
@r2b7e
rjeremyadamson
Thank You!

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Jeremy Adamson: Minding the Machines. Building and Leading Data Science and Analytics Teams

  • 1. Minding the Machines Building and Leading Data Science and Analytics Teams Jeremy Adamson DS Camp – January 2023
  • 2. Jeremy Adamson Author and Strategy Consultant Jeremy Adamson is a leader in AI and analytics strategy, and has a broad range of experience in aviation, energy, financial services, and public administration. Jeremy has worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics. Jeremy holds a Masters in Transportation Engineering and a Bachelors in Civil Engineering from the University of New Brunswick, as well as a Masters in Business Administration from the University of Calgary. Jeremy is a Professional Engineer in the province of Alberta. Learn more at www.rjeremyadamson.com
  • 3. Minding the Machines John Wiley & Sons, July 2021 Traditional Analytics • Descriptive reporting • Collect, analyze, report • Ad-hoc analyses • Back-room function • Order takers and technical resources • Objective: Better understanding of what happened Value-driven Analytics • Process driven and parsimonious • Integrated with the business • Business Analysts • Analytical evangelism • Advisors and facilitators • Objective: Unlock value opportunities using advanced analytics and create a culture of data-driven decision making. The practice has evolved from its roots in decision support and business intelligence into a core component of corporate strategy, and an enabler for all areas of the business.
  • 4. Failure to launch Organizations, regardless of size and technical maturity, have been overwhelmingly unsuccessful at realizing value from their investments in this space, despite significant investment. Half of organizations believe data science will fundamentally change their organization in the next 1-3 years (Ammanath, Jarvis & Hupfer, 2020) “85% of analytics projects end in failure” – Gartner, 2017 “8 out of 10 organizations engaged with AI say their projects have stalled” – TechRepublic, 2019 “Only 27% of executives we surveyed described their Big Data initiatives as successful” – Capgemini, 2014 “most organizations report failures in their AI projects, with a quarter of them reporting up to a 50% failure rate” – Fast Company, 2020
  • 5. It is not just about the technology Process Portfolio Project Management Project Execution Strategy Value Proposition and Case for Change Organizational Design and Target Operating Model Closing the Gap People Team Conventions Relationship Management True Value Creation The practice has evolved from its roots in decision support and business intelligence into a core component of corporate strategy, and an enabler for all areas of the business.
  • 6. Thought Leaders – Focus on Process McKinsey: Winning with AI is a State of Mind 2021 New Vantage Partners: Big Data & AI 2021 Survey
  • 7. Strategy – What do we do? How do we do it? Well-articulated strategies establish the “how” and the “what”, providing a framework for all decision making. Strategy Playbook • Current state assessment • Assess readiness • Capability mapping • Defining the future state • Principles, functions services • Organizational design • Target operating model • Closing the gap
  • 8. Process – Cognitive Fluency Properly developed and understood processes give analytics teams the ability to scale and focus on core competencies. Process Playbook • Intake and prioritization • Scoping and requirements • Project planning • Project execution • Change management • Closeout and delivery Illustrative swimlane process diagram
  • 9. People – Data Scientists are Artisans Skilled, passionate, and creative practitioners are the bedrock of an analytics function. Data science teams succeed or fail on the strength of their people. Artisans need… • to apprentice with an experienced technical leader • to have business leaders that they respect • to operate within a guild • to be stimulated in their work, and to have real agency • to feel respected and heard, and to be able to express themselves Data scientist hierarchy of needs
  • 10. Tip #1 – Consider the Last Mile Effective Data Storytelling – Brent Dykes Assets and deliverables that cannot be operationalized are value destructive. Each project should begin with the end in mind.
  • 11. Tip #2 – Hear Statistics, Feel Stories John Snow & CF Cheffins, 1854 The numbers do not have value – only the actions that they initiate are important. Building a compelling narrative is essential to success. John Graunt, 1662
  • 12. Tip #3 – The Model is Not the Product Each project and activity must align to the organizational goals. Begin with the business outcome and work backwards. Picture from blog.militarytobusiness.com
  • 13. Tip #4 – Pursue Parsimony Continually assess each project and process for signs of bloat. Be mindful of the multiplicative impact of well-intentioned decisions.
  • 14. Tip #5 – Value Creation Every activity, task, and project, must be directed towards value creation for the organization. Each team convention, KPI, and communication must be designed under this rule. Shareholders Employees Customers Outcomes not targets Number of POCs Number of projects Number of collaborators Time saved Reports generated Quality of models Time to market
  • 15. Tip #6 – Be Intentional About Relationships Data science and analytics is a tool for helping humans to make better and faster decisions. Adopt a service mindset to enable greater success. Picture from Health and Social Care Alliance Scotland Individual Lead Goal Terry F. (Finance) Jane P. Encourage Tableau use Judy W. (HR) Peter B. Peer review models Wanda N. (HR) Jane P. Encourage Tableau use Spencer P. (Tax) Peter B. Migrate ad hoc scripts Spencer P. (Tax) Jane P. Encourage Tableau use
  • 16. Summary • Working parsimoniously ensures that our efforts translate into value creation • Technology is a service function and must be oriented towards corporate ends • The core of technology is people helping people to make better decisions