big data - challenges and risks · big data embodies new data characteristics created by today’s...

Beispielpräsentation Tamedia, Datum, Autor

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Big Data - Challenges and risks

Dr. Marcel Blattner Chief Data Scientist @Tamedia:Digital

The Tamedia Digital Analytics Team

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Thomas Gresch Julian NordtMarcel Blattner Nicolas Perony Yannick Koechlin

Digital CTO Chief Data Scientist Business Analyst Data Scientist Data Engineer

▪Three years of business experience as a management consultant

▪Technology background with a masters degree in bioinformatics from ETH Zurich

▪Holds a Phd from ETH Zurich, Chair of Systems Design

▪Strong experience with agent based systems on social graphs

▪Experienced full stackengineer with 8+ years of experience

▪Former CTO of Rayneer

▪Leading the digital transformation of Tamedia from a technical perspective

▪Head of the Tamedia Data Analytics Team and other potentially transversal platforms

▪Physicist with a strong Entrepreneurial background

▪Leading the developmentof predictive analytics

▪Physicist (Phd) with a strong background in quantitative analysis and machine learning

Big Data - what else

“Knowing the name of something does not mean to know something”

- Richard P. Feynman

Big Data - The big promise

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The analysis of a vast amount of data

may lead to new insights.

Big Data - The big promise

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Big Data - why is everybody talking about it?

Big Data - why is everybody talking about it?

Data accessibility

Big Data - Characteristics

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Big data embodies new data characteristics created by today’s digital marketplace

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

Information management

Solid information

foundation

Standardised data

management practices

Insights accessible and

available

I

Analytics Skills and Tools

Skills developed as

a core discipline

Enabled by a robust set of

tools and solutions

Develops action-oriented

insights

Act on the data

Fact-driven leadership

Analytics used as

a strategic asset

Strategy and operations

guided by insights

Data Experts Data architecture, management,

governance, policy

Tool Developers Mask complexity and

analytics to lower skills

boundaries

Visualization

Expertise Interpret data sets,

determine correlations and

present in meaningful ways

Decision Making

Executive and

Management Apply information to solve

business issues

Industry Vertical

Domain Expertise Develop hypothesis, identify

relevant business issues,

ask the right questions

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

1/4

have major skill gaps

in mobile, business

analytics, and security

40%

report a skill shortage in the

ability to manage information

We face a big skill shortage.

This will continue

for the next years.

Source: IBM Tech Report 2014

Big Data - Project cycle

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▪ Portfolio companies and TDA

generate use case ideas

▪ Ideas are noted (light weight)

▪ Use case ideas are discussed

between TDA and portfolio

companies

▪ Promising ideas are formulated

in an initial brief and decision

on use case exploration is taken

▪ Iterative and collaborative data discovery

together with portfolio companies (2 – 3

weeks)

▪ Workshop to present findings to wider portfolio

company management, present approximate

business case and get principal decision on

next steps

▪ Formalise business case and define goals, costs,

timelines and commitments and project team

▪ Formal sign off of business case

▪ 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

▪ Operationalization of use case

▪ Training and knowledge

transfer to portfolio companies

Acquisition Exploration Implementation Operationalization

Initial discussion

Initialbrief

Datadiscovery

IdeasOperationalize

service

Iterative development

Design service

Develop & deliver service

FormalizeBusiness

case

Iterative

Gate 1: Principal checks

Gate 2: Business case sign off

Gate 3:

Implementation sign off

Businesscase

Workshop

Foundationalagreement on

use case

Analytic canvas

Deliverables

Big Data Analytics Iterative & Exploratory Data is the structure

IT Team Delivers Data On Flexible

Platform

Business Users

Explore and Ask Any Question

Traditional Analytics Structured & Repeatable

Structure built to store data

Business Users

Determine Questions

IT Team Builds System

To Answer Known Questions

Big Data - Whats the difference to traditional analysis?


IT Team Delivers Data On Flexible

Platform

Business Users

Explore and Ask Any Question



Available Information

AnalyzedInformation

Capacity constrained down sampling of available information



Analyze ALL Available Information

Whole population analytics connects the dots




AnalyzedInformation









AnalyzedInformation


Carefully cleanse a small information before any analysis

AnalyzedInformation








AnalyzedInformation


Carefully cleanse a small information before any analysis

AnalyzedInformation

Analyze information as is & cleanse as needed & existing repeatable

AnalyzedInformation





?AnalyzedInformation

Question

DataAnswer

Hypothesis

Start with hypothesis Test against selected data






Question

DataAnswer

Hypothesis


Data leads the way Explore all data, identify correlations

Data

Correlation

All Information

Exploration

Actionable Insight






Question

DataAnswer

Hypothesis



Data

Correlation

All Information

Exploration

Actionable Insight

Analyze after landing…






Question

DataAnswer

Hypothesis



Data

Correlation

All Information

Exploration

Actionable Insight

Analyze after landing… 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.



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.





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

big data - challenges and risks · big data embodies new data characteristics created by today’s...

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