Digital Transformation Lab - Best of Practitioner Research - Jun 2021 - Barry Magee I'm an experienced senior business leader focused on how data-driven transformation creates organisational value with deep experience in sales, marketing, strategy, operations, and change management. I’m a recognized industry-leading specialist and academic on effective and systemic innovation using data and analytics to build competitive advantage and tangible results. https://www.linkedin.com/in/barrymagee/

1. DATA-LED TRANSFORMATIONS
What happens if you try to make sales and marketing organisations more data-
driven?
1. What is the experiment about?
2. How did we approach it
3. What happened
4. Why do we think it happened that
way?
5. What simple rules can be
proposed?
4. What questions for research?
What happens if you try to make sales and marketing organisations more data-
driven?
70%
failure rate

2. THE PRESENTING PROBLEM
What happens if you try to make organisations more data-driven?
Industry baseline 3-
4%

3. WHERE TO START?
What happens if you try to make organisations more data-driven?

4. Feedback System
A
Single Use-Case ‘Solve for X’ Models
3
B
C
D
BI Integration of Data
Single Version of Truth
Integration of Data
2
Mix of Data Sources
1
E
F
Integrated ‘Systems’ Model
4
Drives Actor Behaviour
Integrated Workflow
5
6
technical ‘stack’
HOW DID WE APPROACH IT?
use case “jobs to be done”
5
Lo-Fi
(No Ferraris!)
Workflow
5 Years – 14 Iterations

5. WHAT HAPPENED - BUSINESS IMPACT
What happens if you try to make organisations more data-driven?
customer
conversion
rate
Industry benchmark – forester – max
4%
artefact performance over time
trendline
21%
Average
4%
Baseline
Lo-Fi - No Ferraris!

6. WHY THE RESISTANCE?
What happens if you try to make organisations more data-driven?

7. DISCONNECTED SYSTEMS
What happens if you try to make organisations more data-driven?

8. 2 ROUTES – SPECIFIC V WHOLISTIC
What happens if you try to make organisations more data-driven?
system
issue

9. 2 ROUTES – SPECIFIC V WHOLISTIC
What happens if you try to make organisations more data-driven?
What stakeholders and users ask for… What organisation needs….

10. NOT SO SIMPLE PROBLEM
What happens if you try to make organisations more data-driven?
Lack of Coherence
2
Lots of Mental Models
1
Problems present Symptomatically
3
Not solving Systematically
4

11. SIMPLE RULES
What happens if you try to make organisations more data-driven?
Visualize the System
1
Pivot from Lo-Fidelity
2
ID needs not just wants
3
Join the dots for stakeholders
4
Beware self-serve BI!
5
Hire Transformers and Tech
6

12. DISTRIBUTED DECISION MAKING
What happens if you try to make organisations more data-driven?
What we
SHOULD
be doing
What we
THINK
we’re
doing
What we’re
ACTUALLY
doing
• Gartner: culture is the biggest challenge
to adoption of data & analytics initiatives.

13. RESEARCH OBJECTIVES
What happens if you try to make organisations more data-driven?
How, if at all, can data and analytics
artefacts be used in a diagnostic
manner to surface, observe and
diagnose cultural phenomena?
1
Are there specific artefacts, methodologies
or practices that address issues with the
cultural challenges of organisational
transformations?
2

14. DISTRIBUTED DECISION MAKING
What happens if you try to make organisations more data-driven?
• Our evidence suggests data-led
transformation challenged by actors with
belief systems on what ‘good looks like’.
• D&A initiatives seem to surface the
‘hidden’ conflicts between mental
models on how an organisation behaves.
• Where decision making is distributed,
do data-driven transformations create a
‘challenge’ to current mental models
and beliefs?

15. AN ITERATIVE JOURNEY
What happens if you try to make organisations more data-driven?
2bn
datapoints
900
users
30k
B2B
interactions
Renewals
Business
Services
Business
Business
Development
Digital Sales
Field Sales
Marketing
Resource
Planning
Customer
Success
5 Years – 14 Iterations
iterate – iterate -
iterate

16. THE HYPOTHESIS
What happens if you try to make organisations more data-driven?
1. Data & Analytics used to solve presenting not causal
problems
2. Problems described symptomatically by ‘suffering’
stakeholders
3. Taking self-diagnosis at face value is ill advised
4. We should use data more as a ‘diagnostic’ tool
5. Better to solve the system and not the symptom
6. Is there a way to surface and identify existing mental
models?
7. Can data strategy create coherence around shared
mental model?

17. THE HYPOTHESIS
What happens if you try to make organisations more data-driven?
Data and Analytics are only a means to an end. They are catalysts through which we can:
create transparency in
the organisation to
identify performance
enhancement
opportunities (technical)
use those insights to
spark work to enhance
process and workflow in
the organisation (process)
leverage new capabilities
to drive new work
practices and enhance
how we prioritise work
effort and goals
(people)

18. ROCKS, PEBBLES AND SAND
What happens if you try to make organisations more data-driven?
people & culture
I don’t trust that
data!
I don’t trust that
analysis!
Who are you to tell
me this?
Org strategy isn’t
operationalized
Siloed
organisations
Lack of ‘Joined-Up’
Thinking
Cognitive
Dissonance
Lack of awareness of
‘Reality’ on the floor
Lack of
Understanding of
Complexity
Execution
Bias across
organisatio
n
Mis-aligned
goals
Culture Eats
Strategy
“I don’t recognize
that data”
I’m paid to deliver
these KPIs
Simplificatio
n,
confirmation
,
generalizatio
n, anchoring,
and action
biases
operational & process
Is anyone using the data &
analytics outputs?
Can we tell if it
worked or not?
Can we tell why it
worked or not?
How to create a process to observe
behaviour
How to I manage my
customers?
How do we change
pipeline dynamics?
How do we
accelerate pipeline?
How do we expand
pipeline?
How do we improve operational
efficiency
How do we
optimize
product and
customer
mix? technical
Expert vs ML
approaches?
Which data
model used?
Single source of
data issues
Data accuracy
issues
Technical skills
gaps
Taxonomical
issues
Taxonomical
issues
Product
development
Data and Analytics
Project Failure

19. A WICKED PROBLEM
What happens if you try to make organisations more data-driven?