Jeremy Adamson: Minding the Machines: Building and Leading Analytics Data Science Teams Data Science Online Camp 2021 Website - https://dscamp.org/ Youtube - https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ/featured FB - https://www.facebook.com/Data-Science-Camp-103012708431833

1. Minding the
Machines
Building and Leading Data
Science & Analytics Teams
DSCamp, Dec 2021

2. Jeremy Adamson
Independent 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. 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

7. 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 - MTM

8. 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 - MTM

9. 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.

10. 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

11. 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

12. Tip #4 – Pursue Parsimony
Continually assess each project and process for signs of bloat. Be mindful of the multiplicative impact of
well-intentioned decisions.

13. 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

14. 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

15. Takeaways
Having a successful data science and analytics function will be essential to organizational success in the
future
Analytics must be aligned with corporate strategy, and all activities in support of
overarching organizational goals.
Establishing consistent and parsimonious processes encourages adoption.
Data science is a creative practice and end-to-end ownership is critical to embedding
value into deliverables.
Technical capability is secondary to human factors.

16. Jeremy Adamson
www.rjeremyadamson.com
@r2b7e
rjeremyadamson
Thank You!