The document focuses on big data analytics as a transformative opportunity for organizations, stressing its importance in decision-making and the creation of data-driven business models. It outlines the key components of big data, including its characteristics, value chains, and the necessary steps to develop analytics capabilities within organizations. Additionally, it highlights challenges in adopting big data analytics and the need for a cultural shift towards data-driven decision-making.

1. Big Data Analytics: A New Business
Opportunity
Dr. Edward Curry
Insight Centre for Data Analytics
National University of Ireland, Galway (NUIG)
edward.curry@insight-centre.org
GIS Ireland 2016, Ballsbridge Hotel, Dublin • Monday17th October 2016

2. About Me
Vice President

3. Agenda
n What is Big Data Analytics and how does it
deliver Value?
n How to use Data to Make Decisions
n Transformative Data Value Chains
n Developing a Big Data Analytics Capability
n Towards a Data Economy

4. What is Big Data Analytics?
4

5. Organiza(ons and Big Data
“Analytics is much more than a new technology trend. It represents a paradigm
shift, upending the way people think, plan and act and that includes those leading
public service agencies. Because of its potential, government analytics puts a new
and pressing responsibility squarely on the shoulders of public officials.
- Moneyball Under the Dome - Government Analytics for Public Officials, Accenture, 2014

6. Definitions of Big Data

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The “V’s” of Big Data
Volume Velocity Veracity Variety Value
Data at Rest
Terabytes to
exabytes of exis(ng
data to process
Data in
Mo(on
Streaming data,
requiring mseconds to
respond
Data in Many
Forms
Structured,
unstructured, text,
mul(media,…
Data in Doubt
Uncertainty due to
data inconsistency &
incompleteness,
ambigui(es, latency,
decep(on
€
€
€
€
€
€ €
€
Data into
Money
Business models can
be associated to the
data
Adapted by a post of Michael Walker on 28 November 2012

8. Mega Trends – Availability of Data
Datafication
Video, Images, Audio, Text/Numbers
Open Data
(over 519 open data catalogues and
portal now available)

9. Mega Trends – People and Things
Social Media
Engagement, Coordination,
Communication, Contributions,…
Internet of Things
(50 billion devices by 2020 - OECD)

10. Big Data is transforming Business models

11. The Value Disciplines of Big Data
Value
Discipline
Strategic Focus Key Business Capabilities
Operational
Excellence
• Product and service reliability
• Competitive pricing
• Customer convenience
• Cost reduction
• Responsiveness improvement
• Productivity improvement
• Order processing and fulfillment
• Customer service
• Supply chain
• Inventory management
• Merchandising
• Financial management
Customer
Intimacy
• Enhanced Customer experience
• Customer loyalty
• Customer lifetime value
• Increasing Customer
willingness to pay
• Micro-segmentation
• Personalisation
• Customer relationship
management
• Advertising and marketing
• Campaign management
Product
Leadership /
Business
Model
Innovation
• Product and service
innovation
• Creativity
• Leveraging internal
and external knowledge
• Product and service development
• Rapid commercialization of
promising products and services
• Quality assurance
• Customer support
Adapted from M. Treacy and F. Wiersema, “Customer Intimacy and other Value
Disciplines,” Harvard Business Review, January-February 1993, pp. 84-93.

12. How to use Data to Make Decisions
12

13. Decision
Making
Data
AcquisiEon
Big Data
Data
Analysis
Knowledge
Base
Value Add
OperaEonal Excellence
Customer InEmacy
Product Leadership
Business Model
InnovaEon
Adapted: OECD. (2014). Data-driven Innovation for growth and well-being.
Structured data
Unstructured data
Events
Real-Eme Data streams
MulEmodality
SemanEc analysis
Machine learning
InformaEon extracEon
Linked Data
Data discovery
Community analysis
Decision support
PredicEon
In-use analyEcs
Modelling & SimulaEon
ExploraEon & VisualisaEon
The Big Data Value Cycle

14. Analy(cs: From Descrip(ve ↦ Prescrip(ve
Raw
Data
Standard
Reports OLAP
Structured
Data
What happened?
Descrip(ve
Analy(cs
Predic(ve
Modelling
Prescrip(ve
Analy(cs
What might
Happen next?
What should I
do about it?
Why did it happen?
(Correla(on Analy(cs)

15. When to Listen to your Data…
15
n Is there a clear signal in your data?
n You need to balance the signal-to-noise ratio with
the risk associated with a wrong decision

16. Blending Analytics and Intuition
16
Source: Sloan Management Review (2016) Beyond the hype: the hard work behind analytics success

17. Transformative Data Value Chains

18. Connected care and health informatics
PreventionHealthy living Diagnosis Treatment Home care
Connected personalized care

19. Aggregating different
data silos
Healthcare
providers
Health
Tech
sector
Payers Pharma
sector
Hospitals, GPs,
Health Systems
Consumer
Integrated data
Insights

22. Developing a Big Data Analytics
Capability
22

23. 23 BIG 318062
BIG
Big Data Public Private Forum
THE DATA VALUE CHAIN
Data
Acquisition
Data
Analysis
Data
Curation
Data
Storage
Data
Usage
• Structured data
• Unstructured
data
• Event
processing
• Sensor
networks
• Protocols
• Real-time
• Data streams
• Multimodality
• Stream mining
• Semantic
analysis
• Machine
learning
• Information
extraction
• Linked Data
• Data discovery
• ‘Whole world’
semantics
• Ecosystems
• Community data
analysis
• Cross-sectorial
data analysis
• Data Quality
• Trust / Provenance
• Annotation
• Data validation
• Human-Data
Interaction
• Top-down/Bottom-
up
• Community /
Crowd
• Human
Computation
• Curation at scale
• Incentivisation
• Automation
• Interoperability
• In-Memory DBs
• NoSQL DBs
• NewSQL DBs
• Cloud storage
• Query Interfaces
• Scalability and
Performance
• Data Models
• Consistency,
Availability,
Partition-tolerance
• Security and
Privacy
• Standardization
• Decision support
• Predictions
• In-use analytics
• Simulation
• Exploration
• Modeling
• Control
• Domain-specific
usage
Big Data Value Chain
Cavanillas, J. M., Curry, E., & Wahlster, W. (Eds.). (2016). New Horizons for a Data-Driven
Economy: A Roadmap for Usage and Exploitation of Big Data in Europe. Springer
International Publishing.

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28. 4 Key Steps to an Analytics Capability
1. Understand your business objectives
¨ What are you trying to achieve for the business?
– Cost efficiencies?
– New business opportunities?
¨ A clearly articulated business vision is critical together
with associated goals and milestones
¨ Identify and prioritise opportunity areas
2. Put data at the heart of business decisions
¨ Use data to drive agile decision-making and keep the
organisation ahead of the competition.
¨ Start with a focus on critical business decisions
¨ Grow to include everyday actions and decision-making
where data can make a difference
28

29. 4 Key Steps to an Analytics Capability
3 Encourage a data-driven culture with creative
involvement and innovation from employees across
the organisation
¨ Senior-level drive, visibility, and communication are critical
for success.
¨ Appoint executive champion for analytics (Chief Data
Officer)
¨ Drive adoption, create awareness and demonstrate practical
relevance of data analytics insights for all areas of the
organisation, not just in IT
4 Make corporate data easier to discover and access
¨ Simplify the process of discovering and accessing data within
the organisation
¨ Encourage business units to make their data available in easy
to use formats and with self-service platforms for use by
others within the organisation
29

30. Data Science and
Data Skills

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CHALLENGES TO SCALE THE
DATA ECONOMY

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The main BDV cPPP Elements are:
Innovation Spaces: Cross-sector
interdisciplinary Data Innovation hubs
Lighthouse projects:
Demonstrate Big Data Value
R & I Projects: addressing technical
priorities defined BDV SRIA
Ecosystem Enablers: Non-technical
including business models, standards,
etc.
Business
Models

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Key Challenges to Data Economy
Barrier: Europe is behind other regions
in the adoption of Big Data
Barrier: Availability of data is paramount, but
data sharing is uncommon

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Demonstrate Relative Advantage: Demonstrate increase of
productivity/competitiveness of the target sector
Provide proof points: Availability of evidence and practice
efficacy for the target sector to justify investment
Risk: Understanding of the level of risk associated with the
implementation and adoption
Develop Ecosystem: Connect key stakeholders within the
sector across the value chain with active participation
(including SMEs).
Sustainability: Enable large scale replication for sectorial
transformation
Big Data Driving Adoption

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Promote Secure Data Sharing for
Innovation
Hubs to bring together…
Data Owners
+
Data Innovators
...in a secure, trusted,
and controlled
environment
From “proof of concept” to
“proof of ROI”

36. Summary
n Need for big data data analytics in organisations
will continue as they need to act more smartly in
the way they do business
n Increasing availability of (open) data, social media,
and deployment of smarter infrastructure,
applicability of analytics is growing
n Developing a capability is a people not a
technology challenge
36

37. New Horizons for a Data-Driven Economy
A Roadmap for Usage and Exploita(on of Big Data in Europe
Jose Maria Cavanillas (Atos)
Prof. Wolfgang Wahlster (DFKI)
Co-Editors:
37
Open Access PDF
hZp://(ny.cc/NewHorizons
• Provides big picture on how to exploit
big data, including technological,
economic, poliEcal and societal issues
• Details complete lifecycle of big data
value chain, ranging from data
acquisiEon, analysis, curaEon and
storage, to data usage and exploitaEon
• Illustrates potenEal of big data value
within different sectors, including
industry, healthcare, finance, energy,
media and public services
• Summarizes more than two years of
research with wide stakeholder
consultaEon
Overview
Many of the slides today
are based on the work of
the chapter authors

38. Resources on Big-Data