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Big Data - Challenges and risks
Dr. Marcel Blattner
Chief Data Scientist @Tamedia:Digital
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Beispielpräsentation Tamedia, Datum, Autor
The Tamedia Digital Analytics Team
Thomas Gresch
Marcel Blattner
Julian Nordt
Nicolas Perony
Yannick Koechlin
Digital CTO
Chief Data Scientist
Business Analyst
Data Scientist
Data Engineer
▪Leading the digital
transformation of Tamedia
from a technical
perspective
▪Leading the development
of predictive analytics
▪Three years of business
experience as a
management consultant
▪Holds a Phd from ETH
Zurich, Chair of Systems
Design
▪Experienced full stack
engineer with 8+ years
of experience
▪Technology background
with a masters degree in
bioinformatics from ETH
Zurich
▪Strong experience with
agent based systems on
social graphs
▪Former CTO of Rayneer
▪Head of the Tamedia Data
Analytics Team and other
potentially transversal
platforms
▪Physicist (Phd) with a
strong background in
quantitative analysis and
machine learning
▪Physicist with a strong
Entrepreneurial
background
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Big Data - what else
“Knowing the name of something
does not mean to know something”
- Richard P. Feynman
Big Data - The big promise
ZH Filmfestival
The analysis of a vast amount of data
may lead to new insights.
Big Data - The big promise
ZH Filmfestival
Big Data - why is everybody talking about it?
Big Data - why is everybody talking about it?
Data accessibility
Big Data - Characteristics
Big data embodies new data characteristics created by today’s digital marketplace
ZH Filmfestival
Big data means to analyze a vast amount of data from different sources and formats in
a very short time. The aim is to generate new insights leading to
competitive advantages
Big Data - core skills
Industry Vertical
Domain Expertise
Decision Making
Executive and
Management
Develop hypothesis, identify
relevant business issues,
ask the right questions
Apply information to solve
business issues
Analytics Skills and Tools
Information management
Act on the data
Skills developed as
a core discipline
Solid information
foundation
Fact-driven leadership
Enabled by a robust set of
tools and solutions
Standardised data
management practices
Analytics used as
a strategic asset
Develops action-oriented
insights
Insights accessible and
available
Strategy and operations
guided by insights
Tool Developers
Visualization
Expertise
Mask complexity and
analytics to lower skills
boundaries
Interpret data sets,
determine correlations and
present in meaningful ways
I
Data Experts
Data architecture, management,
governance, policy
Big Data - skills and job trends
Big Data - skill landscape for data analytics team
Big Data - skill shortage
Among organisations worldwide today:
1 in 10
has all the skills it needs
to be successful applying advanced technology
for business benefit
40%
report a skill shortage in the
ability to manage information
1/4
have major skill gaps
in mobile, business
analytics, and security
Source: IBM Tech Report 2014
We face a big skill shortage.
This will continue
for the next years.
Big Data - Project cycle
Acquisition
Exploration
Implementation
Iterative development
Iterative
Ideas
Initial discussion
Formalize
Business
case
Data
discovery
Initial
brief
▪ Portfolio companies and TDA
generate use case ideas
▪ Ideas are noted (light weight)
Foundational
agreement on
use case
Design service
▪ Use case ideas are discussed
between TDA and portfolio
companies
▪ Workshop to present findings to wider portfolio
company management, present approximate
business case and get principal decision on
next steps
▪ Promising ideas are formulated
in an initial brief and decision
on use case exploration is taken
▪ Formalise business case and define goals, costs,
timelines and commitments and project team
▪ Formal sign off of business case
Deliverables
Gate 2: Business case sign off
▪ Iterative and collaborative data discovery
together with portfolio companies (2 – 3
weeks)
Operationalize
service
Develop &
deliver service
Business
case
Workshop
Analytic
canvas
Gate 1: Principal checks
Operationalization
Gate 3:
Implementation sign off
▪ Iterative design and implementation of use
case
▪ Strong collaboration with portfolio
companies
▪ Build up of prototypes and testing of use
case
▪ Delivery of business case goals
ZH Filmfestival
▪ Operationalization of use case
▪ Training and knowledge
transfer to portfolio companies
Big Data - Whats the difference to traditional analysis?
Traditional Analytics
Big Data Analytics
Structured & Repeatable
Structure built to store data
Iterative & Exploratory
Data is the structure
Business
Users
Determine
Questions
IT Team
Builds System
To Answer
Known Questions
IT Team
Delivers Data
On Flexible
Platform
Business
Users
Explore and
Ask Any Question
Big Data - Whats the difference to traditional analysis?
Traditional Analytics
Big Data Analytics
Structured & Repeatable
Structure built to store data
Iterative & Exploratory
Data is the structure
Analyzed
Information
IT Team
Delivers Data
On Flexible
Platform
Available Information
Capacity constrained down sampling of
available information
Business
Users
Explore and
Ask Any Question
Big Data - Whats the difference to traditional analysis?
Traditional Analytics
Big Data Analytics
Structured & Repeatable
Structure built to store data
Iterative & Exploratory
Data is the structure
Analyzed
Information
Available Information
Capacity constrained down sampling of
available information
Analyze ALL Available Information
Whole population analytics connects
the dots
Big Data - Whats the difference to traditional analysis?
Traditional Analytics
Big Data Analytics
Structured & Repeatable
Structure built to store data
Iterative & Exploratory
Data is the structure
Analyzed
Information
Available Information
Capacity constrained down sampling of
available information
Analyzed
Information
Carefully cleanse a small information
before any analysis
Analyze ALL Available Information
Whole population analytics connects
the dots
Big Data - Whats the difference to traditional analysis?
Traditional Analytics
Big Data Analytics
Structured & Repeatable
Structure built to store data
Iterative & Exploratory
Data is the structure
Analyzed
Information
Analyze ALL Available Information
Available Information
Capacity constrained down sampling of
available information
Analyzed
Information
Carefully cleanse a small information
before any analysis
Whole population analytics connects
the dots
Analyzed
Information
Analyze information as is & cleanse as
needed & existing repeatable
Big Data - Whats the difference to traditional analysis?
Traditional Analytics
Big Data Analytics
Structured & Repeatable
Structure built to store data
Iterative & Exploratory
Data is the structure
Big Data - Whats the difference to traditional analysis?
Traditional Analytics
Big Data Analytics
Structured & Repeatable
Structure built to store data
Iterative & Exploratory
Data is the structure
Hypothesis
Question
?
Analyzed
Information
Answer
Data
Start with hypothesis
Test against selected data
Big Data - Whats the difference to traditional analysis?
Traditional Analytics
Big Data Analytics
Structured & Repeatable
Structure built to store data
Iterative & Exploratory
Data is the structure
Hypothesis
Question
?
Data
Exploration
All Information
Analyzed
Information
Answer
Data
Start with hypothesis
Test against selected data
Actionable Insight
Correlation
Data leads the way Explore all data, identify correlations
Big Data - Whats the difference to traditional analysis?
Traditional Analytics
Big Data Analytics
Structured & Repeatable
Structure built to store data
Iterative & Exploratory
Data is the structure
Hypothesis
Question
?
Data
Exploration
All Information
Analyzed
Information
Answer
Data
Start with hypothesis
Test against selected data
Analyze after landing…
Actionable Insight
Correlation
Data leads the way Explore all data, identify correlations
Big Data - Whats the difference to traditional analysis?
Traditional Analytics
Big Data Analytics
Structured & Repeatable
Structure built to store data
Iterative & Exploratory
Data is the structure
Hypothesis
Question
?
Data
Exploration
All Information
Analyzed
Information
Answer
Data
Start with hypothesis
Test against selected data
Analyze after landing…
Actionable Insight
Correlation
Data leads the way Explore all data, identify correlations
Analyze in motion…
Big Data - science?
Big data is not science (in the traditional sense)
Big Data - What is missing so far
1. A comprehensive approach to using big data.
Big Data - What is missing so far
1. A comprehensive approach to using big data.
2. Getting the right information into the hands of decision makers.
Big Data - What is missing so far
1. A comprehensive approach to using big data.
2. Getting the right information into the hands of decision makers.
3. Effective ways of turning “big data” into “big insights.”
Big Data - What is missing so far
1. A comprehensive approach to using big data.
2. Getting the right information into the hands of decision makers.
3. Effective ways of turning “big data” into “big insights.”
4. Big data skills are in short supply.
Big Data - What is missing so far
1. A comprehensive approach to using big data.
2. Getting the right information into the hands of decision makers.
3. Effective ways of turning “big data” into “big insights.”
4. Big data skills are in short supply.
5. Big data privacy issues.
Big Data - Healthcare
• Personalized medicine
Genome analytics in oncology
• Precision medicine
Computer based analysis in pathology and radiology
• Personalized and cooperative treatment planning
Personalized therapy planning