business models - introduction to data science

Introduction to Data Science

Frank Kienle

Business Challenge /Models

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

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Understanding business models is key to understand value generation


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


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

Impact on existing business models


Everything-as-a-Service

On-Premises

On-Premisis vs. Cloud


Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

You

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On-Premises

Different types of cloud services


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

On-Premises

Different types of cloud services


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

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Platform As a Service

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

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man

age

Oth

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Software As a Service

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

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Customization, higher costs, slower time to value

Customization vs. Standardization


Standardization, lower costs, faster time to value

Value as a Service


§  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)

Challenge for Data Science/AI in value as a service


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

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

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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

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However, many data are already cleaned for a special focus

World wide service platforms: AWS


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Example customers

World wide service platforms: Microsoft Azur


World wide service platforms: Google Cloud Platform


The Dell Imperium (On-Premises and cloud services)


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

Business models (SaaS) on machine learning

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

Frank Kienle

http://www.skytree.net:

Machine Learning Companies (attention strongly personal/external opinion)


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

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


New Business models on existing platforms

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

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


Source: www.blue-yonder.com

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/

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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...


Big data to transform business models


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

General Electric: The power of 1%


Bytes eats machines

business models - introduction to data science

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