This document discusses using graphical models and machine learning techniques to improve management processes for 21st century businesses. It argues that current management practices have not evolved significantly and are poorly integrated with digital systems. The document proposes designing management tools and business models based on principles of continuous learning and integration between human and machine systems. It presents examples like the machine learning canvas and Wardley mapping to help conceptualize business problems and solutions in a way that facilitates machine learning. The goal is to develop tools that allow businesses to constantly adapt and improve using data and predictive analytics.

1. Towards the use ofGraphical
Models in business modelling and
technologystrategy
A P Moore
@latticecut
TENSORFLOW LONDON
#TENSORFLOWLDN

2. 19TH CENTURY MANAGEMENT
Right now, your company has
21st-century
[Internet]-enabled business
processes, mid-20th century
management processes, all built
atop 19th-century
management principles.
- Gary Hamel

3. Business Models for
21st Centurybusinesses
A P Moore
@latticecut
TENSORFLOW LONDON
#TENSORFLOWLDN

4. MOTIVATIONS
— Management ideas / tools have not progressed significantly
– usually poorly integrated into business process systems
— Software 2.0 - https://medium.com/@karpathy/software-2-0-a64152b37c35
Designing for constant evolution
— Continuous improvement/integration human and machine systems

5. PROGRAM SPACE
Source: https://medium.com/@karpathy/software-2-0-a64152b37c35

6. DROP OUT
SYSTEMS DESIGN
Source: Neural Networks and Deep Learning, Aurelien Geron, 2018

7. TUNE IN AND DROP OUT
Would a company perform better if its
employees were told to toss a coin every
morning to decide whether or not to go
to work? Well, who knows; perhaps it
would! The company would obviously be
forced to adapt its organization; -
Aurelien Geron, 2017

8. PROGRESS

9. BUSINESS MODELS

10. WARDLEY MAPPING

11. SITUATIONAL AWARENESS

12. BUSINESS DESIGN PATTERNS

13. SATALIA

14. YOUR FIRST MAP

15. USER CENTRICITY

16. DEPENDENCY GRAPH

17. METRIC SPACE

18. MEASURE/ESTIMATE EVOLUTION

19. MANAGEMENT TOOL

22. Operators of
large
Data centres
Existing software
license model with
highly personalized
service
Existing channel
model and value add
resellers.
Enable our customers (operators of data
centres) to more efficiently consumer power in
their data centers
Existing system
modified to be
provided on a hosted
license basis (to
prevent conflict with
existing channels)
Development of
internal cloud based
skills
Support existing sales
Hardware & cloud vendors providing
infrastructure for new service.
Existing distribution channel
for sales
High quality sensor
Software setup
Relatively low cost
of software
New marketing
capability and more
digitally focused
sales team
Existing license model
maintained for both
cloud and on premise
(hybrid model).

23. Operators of
large
Data centres
Existing software
license model with
highly personalized
service
Existing channel
model and value add
resellers.
Enable our customers (operators of data
centres) to more efficiently consumer power in
their data centres
Existing system
modified to be
provided on a hosted
license basis (to
prevent conflict with
existing channels)
Development of
internal cloud based
skills
Support existing sales
Hardware & cloud vendors providing
infrastructure for new service.
Existing distribution channel
for sales
High quality sensor
Software setup
Relatively low cost
of software
New marketing
capability and more
digitally focused
sales team
Existing license model
maintained for both
cloud and on premise
(hybrid model).

26. The Machine Learning Canvas (v0.4)​ ​Designed for: ​ ​ Designed by: ​ ​ Date: ​ ​ Iteration: ​ .
Decisions
How are predictions used to
make decisions that provide
the proposed value to the end-user?
ML task
Input, output to predict,
type of problem.
Value
Propositions
What are we trying to do for the
end-user(s) of the predictive system?
What objectives are we serving?
Data Sources
Which raw data sources can
we use (internal and
external)?
Collecting Data
How do we get new data to
learn from (inputs and
outputs)?
Making
Predictions
When do we make predictions on new
inputs? How long do we have to
featurize a new input and make a
prediction?
Offline
Evaluation
Methods and metrics to evaluate the
system before deployment.
Features
Input representations
extracted from raw data
sources.
Building Models
When do we create/update
models with new training
data? How long do we have to
featurize training inputs and create a
model?
Live Evaluation and
Monitoring
Methods and metrics to evaluate the
system after deployment, and to
quantify value creation.
machinelearningcanvas.com​ by Louis Dorard, Ph.D. ​Licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

29. LEARNING LOOPS

31. GRAPHICAL MODELS
MANAGEMENT TOOLS FOR 21ST C?

32. LEGAL ANALYTICS
SEQUENCE FORECASTING

33. ACKNOWLEDGEMENTS
Dr Louis Dorard
AdjunctTeaching Fellow, UCL School of Management (UCL CS PhD)
l.dorard@ucl.ac.uk
@louisdorard
Dr Dave Chapman
Deputy Director, UCL School of Management
d.chapman@ucl.ac.uk
NikhilVadgama
Deputy Director, UCL Centre for Blockchain Technology
nikhil.vadgama@ucl.ac.uk
Simon Wardley
Leading Edge Forum
@swardley