the journey your company needs to go through to make ai

© Dain Studios 2018© Dain Studios 2018

The journey your company needs to go through to make AI for you

AI Monday, Berlin 10.12.2018

Studios Berlin and Munich:Dirk Hofmann+49 176 [email protected]

mailto:[email protected]


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Data and Analytics is the foundation for a successful (digital) business

Becoming a data-driven company is a journey for the whole organisation

(Data) technology is available to many – leadership makes the difference

I want you to take 4 things with you…

4 People first – get the right people on board and let them guide your way

© Dain Studios 2018

About Me

1999 – 2006 Siemens Mobile, Munich1st GSM Music PhoneSmartphones, Tablets, 3G,…Head of Innovation Mgmt

2006 – 2012 Nokia Mobile, EspooHead of Device Activation ServicesConsumer Data and Interaction StrategyProgram lead Consumer Data and Interaction Strategy100+ team, Data & Analytics teams, Privacy, IT

2012 – 2015 Deutsche Telekom Product & Innovation, Berlin tolino – Tablet and SW portfolioWearables and Connected devices (IoT)

2015 - 2016 Digital Transformation Projects for companies such asSiemens, VW, BMW, Lufthansa, Kudelski Group, P7S1

2016 – DAIN Studios, Helsinki, Berlin, MunichCo-Founder


20+ Clients14 Industries5 Countries

3 StudiosHelsinkiBerlin

Munich

Data, AI, INsightsFrom Strategy to

Execution2.5 years old

Team of 2010 Data Scientists

/ Engineers9 PhD

Own AI productsTravel AI

Smart Recruit


We are right in the early wave of exponential growth


Digitalization provides challenges and opportunities for all market players


Data is the ultimate foundation for this growth


Massive data integration

Hyperscale, real-time matching

Data-driven discovery and

innovation

Radical personalization

&Contextuality

Orthogonal data sets

Data and Analytics are fundamental driver of this digital transformation


Machine-learning applications across industries


Going beyond the hype...


Kone

Talking Elevator

• 1.1 million elevators and escalators are monitored and serviced remotely around the globe.

• 24/7 monitoring of around 200 parameters

• Improved equipment availability and reduced downtime.

• Energy savings due to optimized operation of elevators and escalators.

• Enables new smart building use cases

PREDICTIVE MAINTENANCE

“If component A is showing certain vibrations, while the temperature rises 0.5 degrees in component B, then it is likely that component C will break in about 5 to 7 days. Watson analytics provide these types of conclusions, which has allowed KONE to draw sets of rules on how to respond to the IoT data and generate maintenance requests when needed.”—Jaakko Kaivonen, KONE


Rohde & Schwarz

Air passenger security through Deep Learning

• In use on many German airports: Frankfurt, Munich, Cologne – contract with the German Government

• Uses newest radar technology (millimeterwavefrequency); no X-rays (banned in 2011)

• Fully automated, fast detection of suspicious items

• Pattern recognition done by Convolutional Neural Networks, where the algorithm was fed with a huge amount of data. The algorithm is constantly self-learning and improving its accuracy

• Requires enormous computing power, no maintenance needed

The R&S QPS200 Body Scanner

Computer Vision


Georgia Institute of Technology

Predict individual cancer patient responses to drugs

• Patients do not always respond to standard-of-care cancer therapies

• Ideally, the customization of therapies is based on molecular-level tumor understanding

• Unfortunately, the molecular processes underlying most cancers are currently not well understood

• An alternative path to accurate predictions is based simply on observed, significant correlations

• Machine-learning models (SVM) have been found to predict patient responses with >80 percent accuracy

• Treatments can be administered while the underlying molecular causal connections are unknown

Source: Nature, Scientific Reports 8, Article # 16444, 6.11.2018

Predictions with Genomic Data


We have identified six company personas(Based on a highly unscientific assessment)

Black Box Optimist Details, Details Pessimist

No Rush Covering our Backs Smart


So how to start YOUR journey?


The journey towards a data-driven company needs to be driven by the vision and use cases

Architecture, Technology

Human Competences

Organization, Leadership, Governance, Culture

DELIVERY – USE CASES

DECISION - INTELLIGENCE

DATA

DATA VISION & STRATEGY

DN

AA

sset

Co

mp

any

Enab

ler

Stra

tegy


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 the vision

WHAT keeps you sleepless at night?


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

Use Case Categorization:• Sales and Marketing• Customer Experience (incl. Customer

Service)• Product Development• New Data-Driven Business


Identify quick wins and long term opportunities against the implementation effort

EXAMPLE


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


ENRICHEDSmart Meter

ECOSYSTEM

Social Media

Media Partners

RetailPartners

Market Research / Panel

House

Service Partners

Open data

Other Partners

Demographics

Contracts

Commspreferences

ConsentsID

Smart HomeMarketing campaigns

Customer Service

eMobility

CORE

Account

Online Usage

Mobile app usage

Satisfaction

INTERNAL: CORE & ENRICHEDEXTERNAL: ECOSYSTEM

CONCEPTUAL

AI is built on Data: The 360 B2C Customer View of an Electricity Provider


DataData Assets

DeliveryTop 3 Use Cases

DecisionData Analytics

Human CompetencesTechnology

Current / Optimize

1. xx2. Xx3. xx

Data Partnerships

1. Xx2. Xx3. xx

Data as a business

1.Xx2.Xx3.xx

Potential Current State Planned AND budgeted Planned NOT budgeted

Status

Notassessed

Assess the current state of enablers and use cases against potential, plans, and budgets

EXAMPLE


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


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).


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

Source: Eric Rice, 2011


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


10 things I have learned about AI and Leadership

A Company-wide Transformation

Data-driven Product Development

It’s a Tough JourneyC-Suite`s

Top PrioritySystematic & Value

Driven

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Impacts the core fundamentals of the whole organisation

Define clear goals and set KPIs to monitor

progress

Needs data and analysts right at the

start of development

Crucial to give direction – resolve trade offs –

constant follow up

It is a tough ride way out of the comfort zone

of an organisation


10 things I have learned about AI and Leadership

Needs (central) budget

Get over Pilotities Build data expertiseBuild required environment

Business Goal Led

6 7 8 9 10

No budget – no change. Central fund to accelerate start

AI is not a silver bullet but can be key enabler

for future success

Pilots are a good start but need to scale and

build capabilities

Does not happen overnight – needs

constant hard work

Build data expertise and ensure close link to

leadership


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2

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Data and Analytics is the foundation for a successful (digital) business

Becoming a data-driven company is a journey for the whole organisation

(Data) technology is available to many – leadership makes the difference

Back to the start – the Recap

4 People first – get the right people on board and let them guide your way


Dirk HofmannCEO DAIN Studios Germany, Co-founder

[email protected]: +49 176 56717150

Helsinki – Berlin - Munich

the journey your company needs to go through to make ai

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