Gartner predicts 2017 to be the year, when Machine Learning reaches its peak in terms of inflated expectations. This is the right time to not only talk about the sea of opportunity, but also provide a no-nonsense view of the practical obstacles which organizations will encounter on their path to seizing them. In this talk, Ulla cover these topics from a managerial perspective: - Machine Learning use cases from the industry - Factors of success and non-success - Ideation process for data products - Efficient organizational setup - Recruiting & retaining of data scientists

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Seizing the Machine-Learning
Opportunity
Mostly AI Summit, Vienna
September 4, 2017
Ulla Kruhse-Lehtonen
Tel. +358 45 639 3125
ulla.kruhse-lehtonen@dainstudios.com

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CEO, Founding Partner, DAIN Studios
Vice President, Consumer Analytics and Insights, Sanoma
Director, Consumer Analytics, Nokia
Management Consultant, Accenture, XLENT, Nexus
Economist, Labor Institute for Economic Research
PhD, Economics, Helsinki School of Economics
Information Leader of Year 2013, Finland
Ulla Kruhse-Lehtonen

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DAIN Studios Services and Products
DAIN Studios is a Digital and AI consulting and product company that
started operations in March 2016 in Finland and Germany.
Consultancy
Data Strategy
Digital Transformation
Data Science
Artificial Intelligence
Consumer Privacy
Data Management
Business Intelligence
Available Data Products
Recommendation Engine
Intelligent Data Platform
Smart Dashboards
Our clients include companies in
- Telecommunications
- Banks
- Insurance
- Airlines
- Railways
- Consumer goods
- Media
- Cybersecurity
- Travel

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Data Scientist.
The Sexiest Job
at Sanoma.
Source: Sanoma Strategy 2013

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Machine-learning applications across industries

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Companies can create new business with data
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Internal DataExternal Data
Current Business
New Business
Data & Analytics as a Business
Business Optimization Business Optimization
Data Partnerships
Collaborate with external partners.
Exchange data to enable new
offerings or business models which
would not be possible alone
Provide 3rd parties with access to
your data assets, insights, and
analytical capabilities to enable them
to grow and improve their business.
Utilize external data sources to
enhance your own data asset to
enable further optimization of
business processes
Combine internal data to further
optimize existing business and
processes to enable new offerings

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A productized consumer data asset creates value for our
current business and enables the innovation of new
businesses and products
Value of
consumer
data asset
Time
New,
unknown
innovations
Improvements
to current
business
Disbelief,
frustration
Energy and
excitement

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Data and Analytics play a significant role in the
development of intelligent products and services
Source: Eric Rice, 2011

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Embedding analytics into business processes
Service Roll-Out
Business Spearheads
Analytics Modeling
It is much easier to roll out purely technical data products (e.g. recommendation engines) than
products that involve people having to change their way of working (e.g. marketing automation)

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The vision and use cases guide direction for analytics

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Define the ambition level for data
Data used for current business,
product development, and new
business areas
AmbitiousModerate
Use data for the optimization of
your current business
Data seen as an enabler Data seen as a strategic asset
Mainly internal data used Use internal and external data for
differentiation
Focus on core business Own market seen widely
No/limited commercialization of
internal data
APIs enable data as a business and data
partnerships
Example

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We have identified six company personas
(A highly unscientific presentation)
Black Box Optimist Details, Details Pessimist
No Rush Covering our Backs Smart

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The Black Box Optimist
Company rationale:
• We are behind and need to do something quickly
• Data Science is so complicated
• Let’s get a tool where we can dump our data in and get insights
out – it will cost money, but then it’s done
Challenges:
• Overestimate the possibilities of technology
• Underestimate the impact on organization, required
competences, process changes

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Details, Details
Company rationale:
• We need to have the business strategy, digital strategy,
technology strategy, and marketing strategy including
business case calculations for the next 5 years in place
before we can start with execution
Challenges:
• Drowning in complexity
• Stagnate and lose time by answering questions you only will
be able to address when you get going

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The Pessimist
Company rationale:
• We don’t have anything (no people, no data,...)
• These kinds of projects always fail (“the project that shall
not be named”)
• Let’s start with something very small that doesn’t disturb
our core business
Challenges:
• Hard to make a business difference as use cases siloed,
manual, and small
• Company-level transformation not happening

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No Rush
Company rationale:
• At some point, we will probably need to ramp up our data
capabilities, but our business is going well so there is no rush
• Anyway, how would someone from the outside be able to tell
us what to do? We are the experts of our business
Challenges:
• Past success is no guarantee of future results; total
unpreparedness is dangerous
• Individuals fear that data and analytics will challenge their
role / position

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Covering our Backs
Company rationale:
• Let’s hire McKinsey/BCG/IBM/Accenture/… because they know what to
do
• If it fails anyway, we don’t get the blame as we used the big, expensive
consultants
Challenges:
• Trying to outsource the thinking (and execution) instead of getting own
hands dirty and leverage their domain knowledge
• Not learning as an organization

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Smart
Company rationale:
• We want to be leaders in digital transformation
• Our products and services need to be intelligent and adoptive
• Our sales and marketing processes need to be based on
customer understanding
• We are not afraid of change; we embrace the journey
Challenges:
• Take the whole organization onto the journey
• Satisfy the stock market

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Takeaways
1. Don’t outsource thinking
2. Analytics is a journey
3. You don’t need to know exactly where to start but get a couple of good
people to start specing things out

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Recruiting Data Scientists (and other
data people)

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AI and Big Data mean new roles for a company
Analytics Strategist Data Scientist
Consumer Data Privacy
and Protection Officer
Data Steward/
Custodian
Solution Architect Data Architect Big Data Engineer Database Developer
Business / Data Science
Legal
Technology

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ECRUITING DATA SCIENTISTS
Look for team versatility
Be prepared to pay a competitive salary
Consider recruiting from abroad, but don’t outsource
analytics completely unless your organization is very
mature
Do not only hire Data Scientists, you need the rest of
the talents as well
RETAINING TALENT
• Give targets but don’t micromanage
• Offer opportunities for professional growth
• Give people a sense of belonging, that their work makes
a difference
Retaining and Recruiting Data Scientists

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Getting a job as a Data Scientist
• Study quantitative topics; PhD a plus
• Use data and modeling techniques during
your studies
• Learn to code
• Show business-mindedness: Do not join a
company to “do research”; understand the
big picture and business goals
• Show drivenness: Don’t just expect
“requirements”

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DO
• Invest in people: recruit key persons and
improve the analytics skills of business
people
• Build passion for analytics at every level of
the organization
• Find common ways of working together
across organizational silos ensuring end-to-
end delivery of results
• Define common metrics that guide strategic
and operational decisions
DON’T
• Expect immediate ROI. Analytics
transformation is a long-term effort
• Do not believe that technology resolves the
business questions of your company
• Outsource data and analytics to one company
function
• Allow individual departments to opt out of
strategically important implementation
targets
Do’s and Don’ts in Data Execution - Summary

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© Dain Studios 2016