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Big Data Analytics: A New Business
Opportunity
Dr. Edward Curry
Insight Centre for Data Analytics
National University of I...
About	Me	
Vice	President
Agenda
n  What is Big Data Analytics and how does it
deliver Value?
n  How to use Data to Make Decisions
n  Transformative...
What is Big Data Analytics?
4
Organiza(ons	and	Big	Data	
“Analytics is much more than a new technology trend. It represents a paradigm
shift, upending t...
Definitions of Big Data
21/10/16 7www.bdva.eu
The “V’s” of Big Data
Volume	 Velocity	 Veracity	Variety	 Value	
Data	at	Rest	
Terabytes	to		
exabyt...
Mega Trends – Availability of Data
Datafication
Video, Images, Audio, Text/Numbers
Open Data
(over 519 open data catalogue...
Mega Trends – People and Things
Social Media
Engagement, Coordination,
Communication, Contributions,…
Internet of Things
(...
Big	Data	is	transforming	Business	models
The Value Disciplines of Big Data
Value
Discipline
Strategic Focus Key Business Capabilities
Operational
Excellence
•  Pro...
How to use Data to Make Decisions
12
Decision	
Making	
Data	
AcquisiEon	
Big	Data	
Data	
Analysis	
Knowledge	
Base	
Value	Add	
OperaEonal	Excellence	
Customer	...
Analy(cs:	From	Descrip(ve	↦	Prescrip(ve	
Raw
Data	
Standard		
Reports	 OLAP	
Structured	
Data	
What	happened?	
Descrip(ve	...
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 ...
Blending Analytics and Intuition
16
Source: Sloan Management Review (2016) Beyond the hype: the hard work behind analytics...
Transformative Data Value Chains
Connected care and health informatics
PreventionHealthy living Diagnosis Treatment Home care
Connected personalized care
Aggregating different
data silos
Healthcare
providers
Health
Tech
sector
Payers Pharma
sector
Hospitals,	GPs,		
Health	Sys...
Developing a Big Data Analytics
Capability
22
23 BIG 318062
BIG
Big Data Public Private Forum
THE DATA VALUE CHAIN
Data
Acquisition
Data
Analysis
Data
Curation
Data
Sto...
21/10/16 24www.bdva.eu
21/10/16 25www.bdva.eu
21/10/16 26www.bdva.eu
21/10/16 27www.bdva.eu
4 Key Steps to an Analytics Capability
1.  Understand your business objectives
¨  What are you trying to achieve for the b...
4 Key Steps to an Analytics Capability
3  Encourage a data-driven culture with creative
involvement and innovation from em...
Data Science and
Data Skills
21/10/16 31www.bdva.eu21/10/16 31www.bdva.eu
CHALLENGES TO SCALE THE
DATA ECONOMY
21/10/16 32www.bdva.eu
The main BDV cPPP Elements are:
Innovation Spaces: Cross-sector
interdisciplinary Data Innovation h...
21/10/16 33www.bdva.eu
Key Challenges to Data Economy
Barrier: Europe is behind other regions
in the adoption of Big Data
...
21/10/16 34www.bdva.eu
  Demonstrate Relative Advantage: Demonstrate increase of
productivity/competitiveness of the targe...
21/10/16 35www.bdva.eu
Promote Secure Data Sharing for
Innovation
Hubs to bring together…
Data Owners
+
Data Innovators
.....
Summary
n  Need for big data data analytics in organisations
will continue as they need to act more smartly in
the way the...
New	Horizons	for	a	Data-Driven	Economy	
A	Roadmap	for	Usage	and	Exploita(on	of	Big	Data	in	Europe	
Jose	Maria	Cavanillas	(...
Resources on Big-Data
Big Data Analytics: A New Business Opportunity
Big Data Analytics: A New Business Opportunity
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Big Data Analytics: A New Business Opportunity

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This talk introduces Big Data analytics and how they can be used to deliver value within organisations. The talk will cover the transformational potential of creating data value chains between different sectors. Developing a Big Data analytics capability will be discussed in addition to the challenges facing the emerging data economy.

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Big Data Analytics: A New Business Opportunity

  1. 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. 2. About Me Vice President
  3. 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. 4. What is Big Data Analytics? 4
  5. 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. 6. Definitions of Big Data
  7. 7. 21/10/16 7www.bdva.eu 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. 8. Mega Trends – Availability of Data Datafication Video, Images, Audio, Text/Numbers Open Data (over 519 open data catalogues and portal now available)
  9. 9. Mega Trends – People and Things Social Media Engagement, Coordination, Communication, Contributions,… Internet of Things (50 billion devices by 2020 - OECD)
  10. 10. Big Data is transforming Business models
  11. 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. 12. How to use Data to Make Decisions 12
  13. 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. 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. 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. 16. Blending Analytics and Intuition 16 Source: Sloan Management Review (2016) Beyond the hype: the hard work behind analytics success
  17. 17. Transformative Data Value Chains
  18. 18. Connected care and health informatics PreventionHealthy living Diagnosis Treatment Home care Connected personalized care
  19. 19. Aggregating different data silos Healthcare providers Health Tech sector Payers Pharma sector Hospitals, GPs, Health Systems Consumer Integrated data Insights
  20. 20. Developing a Big Data Analytics Capability 22
  21. 21. 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.
  22. 22. 21/10/16 24www.bdva.eu
  23. 23. 21/10/16 25www.bdva.eu
  24. 24. 21/10/16 26www.bdva.eu
  25. 25. 21/10/16 27www.bdva.eu
  26. 26. 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
  27. 27. 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
  28. 28. Data Science and Data Skills
  29. 29. 21/10/16 31www.bdva.eu21/10/16 31www.bdva.eu CHALLENGES TO SCALE THE DATA ECONOMY
  30. 30. 21/10/16 32www.bdva.eu 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
  31. 31. 21/10/16 33www.bdva.eu 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
  32. 32. 21/10/16 34www.bdva.eu   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
  33. 33. 21/10/16 35www.bdva.eu 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”
  34. 34. 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
  35. 35. 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
  36. 36. Resources on Big-Data

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