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54. 5% technology 95%
culture & change management
Open
Close
Comms
Values
Production
R&D
Business models
Product
Marketing
Sales
Customer care
Experience
Network
NEXT
58. Tuotekehitys
&
innovaaUot
TyökulOuuri,
verkostot
ja
ekosysteemit
Tuotanto
&
jakelunhallinta
MarkkinoinU
&
myynU
Asiakaspalvelu
Analogiset
Sähköiset
Digitaaliset
-‐prosessit
Mittarit - Digiprosentti
1.
Yrityksen
nykyiset
Sähköiset
ja
digitaaliset
Toimintamallit
2.
Benchmark
yli
Toimialojen
=
Mitä
käytössä
vs.
Parhaat.
60. Arvoketju
• Missä kohtaa arvoketjua olemme parhaita?
• Mitkä toimijat uhkaavat tekemistämme?
• Mitä mahdollisuuksia digitalisaatio tuo
• Miten varmistamme kyvymme ajatella
liiketoimintamme asiakkaan/ulkopuolisen
silmin?
61. Digitaalinen liiketoimintaympäristö
Markkinat
Yleisö
Ekosysteemit
Asiakkaat
Ratkaisu
Arvo
1. Kevytyrittäjät, henkilöbrändit =
Artistit, kirjailijat, asiantuntijat, yrittäjät
2. Yritysten omat kanavat & Kauppapaikat
3. Yritysten yhteisöt esim. Novita, Pentik, Varusteleka
4. Kauppapaikat esim. Oikotie, autotalli, Amazon, jne.
5. Ekosysteemit esim. Tori.fi, Digitalist, jne.
Digitalist
62. 300
000
sivulatausta
12
000
seuraajaa
75
00
FB
fania
5000
Osallistujaa
4000
FB
ryhmässä
10
kumppania
”Digitalisoidaan Suomi
pikseli kerrallaan”
TBWA Digitalist Marketing Forum
Dingle Digitalist Social Business Forum
Ixonos Digitalist Customer Experience Forum
Technopolis Digitalist pop-up
MTV Digitalist 5M
Meltwater Digitalist Communications Forum
IBM Digitalist Leadership Forum
Salesforce Digitalist Growth Forum
Sonera Digitalist IOT Forum
Solita Digitalist Thinkers Forum
Digitalist Network 2016
67. 3 November 2016
Confidential and proprietary: Any use of this material without specific
permission of McKinsey & Company is strictly prohibited
Machine learning
in the digital age
68. 68McKinsey & Company
Mindfulness – “The quality or state of being
conscious or aware of something”
Take a big breath and relax,
with your eyes open and looking
at the hourglass
Bring your awareness to the
sensations of breathing
You may softly count your breaths,
one to ten and then start over
Once your mind settles down
during the first few minutes,
get more absorbed in the breath
Be aware of whatever is moving
through the mind
1
2
3
4
5
76. 76McKinsey & Company
Tesla announces driverless car with
ability to drive "across U.S." for mass
production S-model in 2017
2016
Autonomous
driving
82. 82McKinsey & Company
Connectivity and processing power
Billions of people connected on the go, unprecedented
processing power, storage, and knowledge access
New stakeholders vs incumbents
Value created by entrants that provide value from data
Incumbents threatened
New points of view
New way of looking at decisions and events across
physical, digital & biological worlds
New business models
Emergence of new disruptive business models
reshaping production, consumption and delivery
models
Incentives redefined
Intermediate players in value chain must enable
data, promote transparency
Why does machine learning matter
83. 83McKinsey & Company
End-to-end analytics transformation driven
by cultural and organisational change
Motorsports
CONTEXT
RESULTS
40%
JOURNEY
20%
• Formula 1 is the largest
racing series in the world
• Continuous in-season
engineering
improvements are key to
winning on the track
• Spend on testing is
heavily limited by
regulations
• Innovative use of
communication data to
find the most effective
R&D operating model
• Analytics-empowered
teams now able to focus
on optimising parts with
highest predicted
potential
Improvement in
investment yield
Earlier warning on
project performance
What is possible? CLIENT EXAMPLE
84. 84McKinsey & Company
Boosting
traditional
P&L levers
Delivering
the digital
model
Developing
new areas of
growth
Strategic
priorities
Gather and analyse
real-time data to fully
realise digital and
seamless multi-
channel experience
Explore new operating
and business models
Generate revenue and
improve margin, optimise
efficiency, control and
manage risks
What are the opportunities for you?
85. 85McKinsey & Company
Driving change across your company
Improve
Margin
Material cost reduction by reduced complexity
of components
5-10%
Inventory buffer reduction through improved
forecasting
>50%
Improved R&D productivity driving reduced time
to market
20%Revenue uplifts through improved sales
force effectiveness at same cost base
15%
Component
complexity
management
Supply chain
forecasting and
inventory
management
Digital
procurement and
R&D
Sales force cost
efficiency and
effectiveness
Yield
management
Yield improvement through improved planning
2-4%
Generate
Revenue
Improve MROI by
modelling marketing
spend effectiveness
300% 20%
Increase sales through
better Next Best Action
suggestions
Increase conversion by
tailoring commercial
solutions to customers
75%
Increase potential customer by identifying new high
potential customers
8-10%
Reduce churn rate
through improved
customer profiling
20-25%
Marketing
and new customer
onboarding
Product sales:
cross and up-
selling (NBA)
Value-added
services and
solution tailoring
Customer base
acquisition
Customer
retention
86. 86McKinsey & Company
Machine Learning Based Study
Largest 500,000 companies
350 TB unstructured business data
10 Million business relationships
100 Million people behavioural data
15 Billion page views
Classified companies into
levels of AI Maturity…
87. 87McKinsey & Company
Even where AI capability exists, maturity is low
Companies employing AI at scale
967
494
87Strategic direction for business
Building applications
Lab projects/proof of concept
88. 88McKinsey & Company
Application of AI concentrated in digital and
data based business
60%
in digital and
data based
businesses
Companies investing in AI by industry
Analyzed by spiderbook
8.78%
Internet
4.19%
Telecommunications
3.37%
Research
2.66%
Retail
2.55%
Marketing and advertising
32%
Software
information
technology
services
2.35%
Financial service
2.15%
Automotive
2.04%
Government administration
1.33%
Internet
1.33%
Telecommunications
1.33%
Research
1.53%
Retail
1.63%
Marketing and advertising
1.63%
Financial service
1.74%
Banking
1.84%
Management consulting
1.94%
Semiconductors
1.33%
Internet
0.92%
Internet
90. 90McKinsey & Company
The Fortune 1000 company churn rate
1973 1983 1993 2003 2013
35% 45% 60% 70%
Companies new
to the Fortune
1000
2023
over
80%
Companies
expected to
fall
94. McKinsey & Company 94
# 1 Vision:
Unreasonable
aspirations
Vision Processes
Business
demand
Location
Sourcing
& partner
mgmt.
People
Organization
Architecture
Governance
95. McKinsey & Company 95
What does this mean?
What does this not mean?
▪ Board level "owner"
▪ Stretching and coherent vision
▪ Value-oriented targets
▪ Adding “analytics” to existing responsibilities
▪ Uncoordinated, one-off initiatives
▪ Slot time-to-market
# 1 Vision:
Unreasonable
aspirations
96. Decision
as the focal point
End-to-end
connection from data to
decision (people, IT,
processes)
Step-by-step approach
to enable organization
Use cases
96McKinsey & Company
# 2 Use cases:
Driving change
97. 97McKinsey & Company
# 2 Use cases: accelerator –what it means for an organisation
The
strategic question
Identify
similar
use cases
Develop
hypotheses
Embedding
Apply
analytics
Develop
decision
support
Break into
use cases
Design and
build the
data lake
Test and
refine
Insights
Factory
1
2
3
4 5
6
7
8
9
Links to Insights Factory
98. 98McKinsey & Company
# 2 Use cases: Accelerator – how to get it right
§ There is an opportunity to rapidly
capture significant value
§ The current business model has an
existing analytics interlock
§ You plan to use returns on high-
value use cases to finance an
analytics transformation
§ You need to quickly develop and
attract analytics talent
§ Forming silos with sub-scale teams
§ Failing to unlock value through synergies
§ Failing to change the wider organisational culture
§ Missing exploration of new business models due to focus on current problems
When to choose What to watch out for
99. # 3 Foundation
excellence
Flexible big data IT stack
(Lambda architecture)
IT/Infrastructure
Agile and flexible
software development
(e.g., scrum teams,
microservices)
IT/Software
New capabilities (data engineers and
scientists, analytical engineers,
software developers, GUI designers)
Capabilities and talent
Full spectrum of analytics from r
egression to ensembled learning
Analytics
100. 100McKinsey & Company
#3 – Foundation Excellence: what it means for an organisation
Culture Reactive to market
dynamics
Proactively taking advantage of
and defining market dynamics
IT Traditional warehouse
with siloed data
Integrated architecture based
on data lake
Organisation Traditional organisation
with CDO
Clearly defined roles in agile
organisational structure
Processes
Independently-designed
processes for each business
unit
Aligned, data-enabled processes
with organisation-wide
workflows
Recruit talent externally Build talent internallyEmployees
Data used by few to
manage efficiently
Democratisation of dataData
Central closed platform,
capable of basic analytics
Distributed open platform used
for advanced analytics
Analytics
Decisions based on
periodic reporting
Decisions made in real time
Performance
management
From To
101. 101McKinsey & Company
#3 – Foundation Excellence: how to get it right
101McKinsey & Company
When to
choose
What to watch
out for
When to choose What to watch out for
§ There is clarity on specific needs for use cases
§ It is possible to pilot in smaller business units
§ There is strong political will to implement the programme
§ Long term foundation-only projects
§ ‘Build it and they will come’ mentality
§ Uncontrolled data ingestion
103. Personal experience:
Building your digital
& analytics skills
Example:
Pursuing “analytics”
enlightenment
(e.g., through Coursera)
§ Code in a day
§ Data in a day
§ Hacking in a day
§ Tech in a day
§ Innovation in a day
1.
Innovation
Data Skills
Leadership Infrastructure
104. Personal experience:
Visiting the disruptors
Example:
Doing a Board offsite at a
Silicon Valley ‘bootcamp’
2.
Silicon Valley – still the capital of
tech
106. Personal experience:
Establish Analytics
Advisory Council
3.
Serial analytics
entrepreneur
CDO of non-
competing firm
Technology leader
“Wacky” digital
evangelist/futurologist
CEO of a tech
start-up bootcamp
Senior partner
at a tech VC fund
Digitalle/savvy
customer of the company
111. Be an adaptive
leader – not a
technical one.
Beta 13-40 Hz
Alpha 7-13 Hz
Theta 4-7 Hz
Delta 0-4 Hz
112. “A problem
cannot be
solved at the
same level of
consciousness
that created it.
You must learn
to see the
world anew.”
- Einstein
Serve others rather
than yourself.
113. McKinsey & Company
Let go, don’t
be attached.
“It is not because things
are difficult that we do
not dare, it is because we
do not dare that they are
difficult.”– Seneca
184. DATA REFINERY -‐
INTELLIGENT
FAST
DATA
FABRIC
™
FASTERMIND™
Customer Engagement
Intelligence & Automation
• Engagement actions
• Journey Builder
• Next Best Actions
• Recommendations
• Real Time Profiling
Relevant Other DataRelevant Fast Data
CUSTOMER ENGAGEMENT
AUTOMATION
ACTIONS IN THE MOMENTS
MACHINE LEARN. OPTIMIZE .SCALE
Channels