business models - introduction to data science

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Introduction to Data Science Frank Kienle Business Challenge /Models

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Page 1: Business Models -  Introduction to Data Science

Introduction to Data Science

Frank Kienle

Business Challenge /Models

Page 2: Business Models -  Introduction to Data Science

classic false move in an immature data culture is “working on the problem where they have convenient data, without really thinking about the problem” lessons from my experience

the link to the business and delivering value continuously is the biggest challenge for data scientists/companies

Business challenge

30.09.17 Frank Kienle p. 2

Page 3: Business Models -  Introduction to Data Science

Understanding business models is key to understand value generation

30.09.17 Frank Kienle p. 3

Page 4: Business Models -  Introduction to Data Science

Common Themes Among Successful Data-Driven Startups, Max Levchin (https://www.youtube.com/watch?v=ylPY7EGrsEE)

30.09.17 Frank Kienle p. 4

data brokers

e.g. visualize it, rank it

Share things, Uber à cars … à charwomen --- à daily life equipment

Lower costs for personal services by data, Finance, insurance, contracts, Construction,

Predict it, operate towards the future

Model uncertain upside

Page 5: Business Models -  Introduction to Data Science

Impact on existing business models

30.09.17 Frank Kienle p. 5

Everything-as-a-Service

Page 6: Business Models -  Introduction to Data Science

On-Premises

On-Premisis vs. Cloud

30.09.17 Frank Kienle p. 6

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

You

man

age

Page 7: Business Models -  Introduction to Data Science

On-Premises

Different types of cloud services

30.09.17 Frank Kienle p. 7

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

You

man

age

Infrastructure As a Service

Platform As a Service

Software As a Service

Page 8: Business Models -  Introduction to Data Science

On-Premises

Different types of cloud services

30.09.17 Frank Kienle p. 8

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

You

man

age

Infrastructure As a Service

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

You

man

age

Oth

er M

anag

e

Platform As a Service

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

You

man

age

Oth

er M

anag

e

Software As a Service

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

Oth

er M

anag

e

Page 9: Business Models -  Introduction to Data Science

Customization, higher costs, slower time to value

Customization vs. Standardization

30.09.17 Frank Kienle p. 9

Standardization, lower costs, faster time to value

Page 10: Business Models -  Introduction to Data Science

Value as a Service

30.09.17 Frank Kienle p. 10

§  shift from product-based to software-as-a service based business models using cloud computing as the delivery medium.

§  Sooner or later most of the business models will be subscription based, then the main focus will be on the value of the service to your stakeholders.

§  Over time, the move to SaaS has a commoditization element to it, and the ability to measure customer value and desired business outcomes will be true differentiation. (source: Value-as-a-Service @Rob Bernshteyn)

Page 11: Business Models -  Introduction to Data Science

Challenge for Data Science/AI in value as a service

30.09.17 Frank Kienle p. 11

Standardization, lower costs, faster time to value

§  The shift to software or value as a service requires standardization

§  Standardization requires a repetitive problem to be solved

§  Data science problems are often linked to business specific dependencies

§  A business advantage is defined by a unique value proposition

§  Every data science/AI service which can be commoditized will be sooner or later commoditized and offered as a service

§  Individualized business services will be build on top of platform as a service or supportive software as a service offerings

Page 12: Business Models -  Introduction to Data Science

Many many companies for different sectors: economy, stocks, weather, global calendar/event, …. Example: social media www.gnip.com Example: Oracle (https://www.oracle.com/marketingcloud/partners.html)

Data as a Service Provider

30.09.17 12 Frank Kienle

Page 13: Business Models -  Introduction to Data Science

Overview of data sources •  http://www.knuggets.com/datasets/index.html Machine learning data •  UCI Machine Learning Repository: archive.ics.uci.edu Data Shop: the world’s largest repository of learning interaction data •  https://pslcdatashop.web.cmu.edu

For data science: getting Data is not the problem - Very large flavor of Data Sources

30.09.17 Frank Kienle 13

However, many data are already cleaned for a special focus

Page 14: Business Models -  Introduction to Data Science

World wide service platforms: AWS

30.09.17 Frank Kienle 14

AWS

offe

rs fu

ll st

ack

incl

udin

g ap

plic

atio

n ce

ntric

ser

vice

s

Example customers

Page 15: Business Models -  Introduction to Data Science

World wide service platforms: Microsoft Azur

30.09.17 Frank Kienle 15

Page 16: Business Models -  Introduction to Data Science

World wide service platforms: Google Cloud Platform

30.09.17 Frank Kienle 16

Page 17: Business Models -  Introduction to Data Science

The Dell Imperium (On-Premises and cloud services)

30.09.17 Frank Kienle p. 17

DEL

off

ers

full

stac

k in

clud

ing

appl

icat

ion

cons

ultin

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vota

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Making Sense of Dell – EMC - VMware https://a16z.com/2015/10/26/dell-emc-vmware/

Page 18: Business Models -  Introduction to Data Science

Business models (SaaS) on machine learning

30.09.17 18

§  www.kaggle.com platform for predictive modeling competitions Focus on learn, work, play §  A great ressource for

Frank Kienle

Page 19: Business Models -  Introduction to Data Science

http://www.skytree.net:

Machine Learning Companies (attention strongly personal/external opinion)

30.09.17 Frank Kienle p. 19

The claim to have generalized machine learning models for different use cases is questionable, the link to business understanding not given in the examples Please remember: 80% of your t ime wi l l be spent in understanding/cleaning the data and the link to a business case/business embedding

Page 20: Business Models -  Introduction to Data Science

New services to disrupt existing business https://fleximize.com/paypal-mafia/

30.09.17 Frank Kienle p. 20

Page 21: Business Models -  Introduction to Data Science

New Business models on existing platforms

30.09.17 21 Frank Kienle

www.uber.com Platform cars Technology View: https://eng.uber.com/tech-stack-part-two/

Page 22: Business Models -  Introduction to Data Science

Blue Yonder: Value-as-a-Service by delivery decisions

30.09.17 Frank Kienle p. 22

Source: www.blue-yonder.com

Page 23: Business Models -  Introduction to Data Science

Every products get digitized: àsoftware is eating the world Examples: •  Fastest growing automotive company: Tesla (run by software engineers) •  Today’s fastest growing telecom company is Skype •  LinkedIn is today’s fastest growing recruiting company •  Amazon Buys Whole Foods (software company buys a retailer) •  General Electric: ‘Bytes will eat machines’ (Forum with Marc Andreessen)

Moores law is way more than just doubling transistor density:

every single day it becomes easier for someone else to compete with your product

Software is eating the world! https://a16z.com/2016/08/20/why-software-is-eating-the-world/

23 30.09.17 Frank Kienle

Page 24: Business Models -  Introduction to Data Science

Impact on existing business models!

it is all about the digital transformation

…‘Digitalization is the use of digital technologies to change a business model and provide new revenue and value-producing opportunities; it is the process

of moving to a digital business...

30.09.17 Frank Kienle p. 24

Page 25: Business Models -  Introduction to Data Science

Big data to transform business models

30.09.17 Frank Kienle p. 25

Source: Big Data and the Creative Destruction of Today's Business Models (http://www.atkearney.de/)

Page 26: Business Models -  Introduction to Data Science

General Electric: The power of 1%

30.09.17 Frank Kienle p. 26

Bytes eats machines