seizing the machine-learning opportunity how to define and implement your …ja... ·...

© Dain Studios 2016© Dain Studios 2016

Seizing the Machine-Learning Opportunity –How to define and implement your AI strategyTulevaisuuden tuotekehitys

Helsinki, May 24, 2018

Ulla Kruhse-LehtonenCEO, Co-founderTel. +358 45 639 [email protected]

© Dain Studios 2016

2

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

One of the 32 coolest and most influential women in Nordic tech by Business Insider Nordic, 2018

Ulla Kruhse-Lehtonen


20+ Clients14 Industries5 Countries

3 StudiosHelsinkiBerlin

Munich

Data + AI+ INsight

From AI Strategy to Execution

2 years old

Team of 1810 Data Scientists

/ Engineers8 PhD

Own AI productsTravel AI

Smart Recruit

DAIN


4

Data Scientist.The Sexiest Job

at Sanoma.

Source: Sanoma Strategy 2013


Growth provides challenges and opportunities for all market players


Going beyond the hype...


Machine-learning applications across industries


Leverage data for existing business optimization as well as for new business

Internal DataExternal Data

New Business

Current Business

Data Partnerships

Business OptimizationBusiness Optimization

Data as a Business

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/or analytical capabilities to enable them to grow and improve their business (e.g. Data Market Platform)

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


Data, Analytics, and AI play a significant role in the development of intelligent products and services

Source: Eric Rice, 2011


Data and AI change the way how you develop productsExample self-driving cars

Tesla way of developmentMore than 200.000 cars deliver data

BMW way of developmentClose to <50 cars deliver data

EXAMPLES


Unleash operational inefficienciesEXAMPLES


Business models are changing

From:

To:

Source: Siemens Mindsphere

EXAMPLES


Foundation of Smart Cities, Factories, EcosystemsEXAMPLES


It all starts with a vision

What are the business goals want to achieve?

Where do I want to go with my business?

What are my prioritized use cases to get there?

It starts with a vision


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


Identify, define, and prioritize the AI opportunitiesEXAMPLE

Customer Journey / KPIs / Use Case Mapping AI & Data Opportunities

To

uch

po

ints

Ob

jecti

ves

KP

Is

Efficient in sales and marketing, continuously collecting valuable customer insights for product design & our partners

Optimize

conversion

Capture user

profiles

Best ownership

experience

Upgrade, resell

Appeal Guide Reassure Serve Support Enable Ensure longevity

Discover Consider Prefer Get started Use Extend use Upgrade

Always on, always serving our customers, and continuously engaging the market with the Nokia brand and products.

Best shopping

experience.

No hassle

onboarding

Efficient

marketing

Brand advocacy

# Reach (organic, paid)

#uniques (own sites)

# Engagement

# Likes

# newsletter registrations

Retention, upsell

Most lucrative

brand

# activation

# NPS

# conversion*

# active devices

# usage index* *

# NPS

# conversion*

# accounts (Care)

# number of care cases

# sentiments

Best customer

care

Understand and

engage users

Consistent omni-

channel experience

Desired

products

Dimensions: Overall, Country, touch point, Device model

# conversion

# basket value

# accounts (eCom)

# NPS store

Data Potential + User Volume Total Data Value

Data Potential is driven by the following:• Relevance of data for energy-

service provisioning• High business impact potential• Utilization in many use cases• The quality of the data is high

User Volume is driven by the following:• Number of accessible users

and/or devices (out of total customer base)

• Number of user/device records

The Total Data Value describes the data value opportunity assuming that value creation is realized via

analytics and automation, and the use cases are successfully

integrated into relevant business processes

Data Integration Effort*

Da

ta P

ote

nti

al

low high

low

high

10

6

11

17

16

15

12

18

19

Quick wins


Assess your company’s analytics maturity level and set a target state

Source: Davenport & Harris, 2007

EXAMPLE


Embedding analytics into business processes

Service Roll-Out

Spearheads

Analytics/AI Modeling

It is 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).


Leadership, governance, and a right incentive scheme drive the change toward a data-driven organization

Leadership

Governance Incentive

Data is a strategic asset for future

business

• Set the vision and drive direction –ensure continuation

• Thought leadership

• Drive and steer implementation of vision and strategy across the company

• Resolve conflict of interest or trade-offs

• Provide motivation for whole organization to head towards common direction

• Measure progress along defined KPIs• Incentivize data-driven innovation


Data success is not only about data, analytics, and technology: How to drive an analytics culture?

Break down silosLead from the top AND

the middleSharpen the business

strategyExecute effectively

• Silos are culture killers• Ensure data capabilities are

used in the most beneficial areas for the company

• Most impactful analytics requires cross functional collaboration

• Requires full leadership attention

• Build passion for data-driven decision making

• Upskill workforce in analytics

• Derive AI strategy from business strategy

• Define common metrics and incentives for whole organization

• No opt-out allowed for strategic targets

• Utilize defined KPIs for systematic implementation

• Take action - learn and iterate

• BUT understand that analytics is a journey


22

Company Personas


We have identified six company personas(A highly unscientific presentation)

Black Box Optimist Details, Details Pessimist

No Rush Covering our Backs Smart


The Black Box Optimist

Company rationale:

“We are behind and need to do something quickly…”

”Machine learning 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


Details, Details

Company rationale:

“First, we need to have the business strategy, digital strategy, technology strategy, and marketing strategy in place…”

“We need detailed business case calculations for the next 5 years in place before we can start with execution…”

“All roles and the organizational structure need to be defined and approved…”

Challenges:

• Drowning in complexity

• Stagnate and lose time by answering questions you only will be able to address when you get going


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


No Rush

Company rationale:

“At some point, we will probably need to ramp up our data capabilities, but our business is going well so let’s not rush anywhere…”

“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


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 own domain knowledge

• Not learning as an organization


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 solid customer understanding…”

“We are not afraid of change; we embrace the journey…”

Challenges:

• Take the whole organization onto the journey

• Satisfy the stock market


Key takeaways

1

2

3

Don’t outsource thinking – you need to define your Business Opportunities according to business priorities.

AI is a journey – you need to build a analytics culture across the whole company led by the leadership to make a difference.

People first – you don’t need to know exactly where to start but get a couple of good people and place them wisely to start specifying things


31

Recruiting Data Scientists (and other data people)


AI and Big Data mean new roles for a company

Analytics Strategist Data ScientistConsumer Data Privacy and Protection Officer

Data Steward/ Custodian

Solution Architect Data Architect Big Data Engineer Database Developer

Business / Data Science

Legal

Technology


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 AI Execution - Summary


Ulla Kruhse-LehtonenCEO DAIN Studios Finland, Co-founder

[email protected]: +358 45 639 3125

Helsinki – Berlin - Munich

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