Why do Data and Analytics struggle in large organizations? This presentation explores the structural and causal issues at play through the lens of 'systems thinking' and 'business dynamics'.

1. DATABERGS

2. DATA AND ANALYTICS HYPE

3. FOOLS GOLD

4. HYPE AND FAILURE
Are we leaving money behind?
Respondents to Gartner’s 2018 CDO survey
cited culture as both the No. 1 driver and the
No. 1 inhibitor to making data-driven decisions.
Big, complex interventions often fail to deliver
the culture change and sometimes get ignored.
The wide-ranging and long-term effort needs to
be broken down into manageable increments.
70
%

5. WHAT’S GOING ON?
What happens if you try to make organizations more data-driven?
Knowledge Mgt 101
• Knowledge
Management
• Data, Analytics
& Business
Intelligence
Change Mgt 101
• Change
Management :
Urgency
• Innovation
Management : Fail
Fast
Strategy 101
• Porter - SWOT -
TOWS &
dilemmas
• Strategy to
Execution :
Performance Mgt
• Balanced
Scorecards : KPQs,
KPIs & OKRs

6. WHAT DID WE DO AND SEE?

7. WHACK-A-MOLE

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

9. AN ITERATIVE JOURNEY
What happens if you try to make organisations more data-driven?
2bn
datapoints
1100
users
30k
B2B
interactions
Renewals
Business
Services
Business
Business
Development
Digital Sales
Field Sales
Marketing
Resource
Planning
Customer
Success
7 Years – 20 Projects
iterate – iterate -
iterate

10. 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? Office
365
Robotic
Process
Automation
Interest to
measure
Employee
Experience
Collaboration
solutions
UEM
Compete
Customer
using S4B,
MS Teams
Machine
Learning
SSO
Compete
Customer in
industry
undergoing
disruption
SaaS
Buying
Signals
VAD
Upcoming
Renewal
UEM
upcoming
Renewal
CWS
Upcoming
Renewal
Workspace
Customer
Customer Behaviour
7
5
7
5
5
9
5
9
7
7
5
9
7
7
SD-WAN to optimize SaaS Experience
Measure Employee Experience
Unified Experience with SSO for SaaS, Web, mobile and virtualized Apps
Replace legacy VPN solution with modern access infrastructure
Web Apps unprotected against contemporary security threats
Maximize Security and Control
Control Data Access accross all Data sources
Cloud Transition / VADS to leverage hybrid Multi Cloud Strategy
Consolidate App delivery infrastructure and enable cloud migration
Simplified Desktop Delivery with DaaS
Flexible and secure WAN infrastructure
Workspace Intelligence to drive Employee Experience
1
2
3
4
5
6
7
8
9
10
11
12
Expert Models
Workspace Intelligence to drive Employee Experience
1
A
Single Use-Case ‘Solve for X’ Models
3
B
C
D
E
F

11. 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

12. A WICKED PROBLEM
What happens if you try to make organisations more data-driven?
Can you integrate
Skype?

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

14. ORGANISATIONAL OBSERVATIONS
different perspectives
little direction
return on time
no feedback
1
3
5
6
3%
97%
competing priorities
2
gaps in ‘how to’
4

15. WHACK-A-MOLE

16. PERCEPTION & REALITY

17. BATTLESHIPS

18. ORGANISATIONAL OBSERVATIONS
different perspectives
little direction
Return on Time
no feedback
1
3
5
6
competing priorities
2
gaps in ‘how to’
4
lack of ‘strategic’ coherence

19. SYSTEMS THINKING 101
• We don’t experience reality directly. We
experience it indirectly via ‘mental models’ we
construct in order to interpret world around us.
• We all have different mental models heavily
influenced by our experiences and perspective.
• Our mental models are only approximations of
reality and often with only way to enhance them
via correcting ‘feedback loops’.

20. SYSTEMS THINKING 101
• Our models tend to over-simplify reality.
• What we perceive in often very linear terms
(A>B) is a lot messier with more complex
interactions between factors (circular)
• We need to perceive the world more accurately:
• The whole vs the parts
• In terms of process
• In terms of dynamics between elements
• Find patterns of interaction
• Organizations are Complex Adaptive Systems

21. PERCEPTION VS REALITY
What happens if you try to make organisations more data-driven?
Finance
Fulfilment
Product
Strategy

22. BATTLESHIPS

23. DATA AND ANALYTICS REVISITED

24. COBBLERS

25. 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.

26. DISTRIBUTED DECISION MAKING
What happens if you try to make organisations more data-driven?
A
C
B

27. 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
Mental Model COHERENCE
Mental Model
DISSONANCE
Strategic COHERENCE
Strategic DISSONANCE
70
%

28. DISTRIBUTED DECISION MAKING
What happens if you try to make organisations more data-driven?
• data-led transformation challenged by
actors with competing belief systems
on what ‘good looks like’.
• initiatives seem to surface the ‘hidden’
conflicts between mental models on
how an organisation behaves.
• expert model approach creates strategic
coherence on how an organisation
executes.

29. COBBLERS

30. SO WHAT : SIMPLE RULES

31. 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 disconnects in
approaches and
alignment.
feedback mechanism
use those insights to
spark alignment to
enhance process and
workflow in the org
correct alignment
leverage new capabilities
to enhance how we
prioritise work effort
and goals
operationalize strategy

32. THE HYPOTHESIS

33. SIMPLE RULES
What happens if you try to make organisations more data-driven?
Align Mental Models
2
No Ferraris!
70
%
Rethink Data as Diagnostic
1 Solve Systemically
3

34. COMPLEX SYSTEMS
What happens if you try to make organisations more data-driven?
https://herdingcats.typepad.com/my_weblog/2017/05/book-of-the-month.html