The document discusses research on big data and business analytics. It aims to understand how firms derive business value from big data analytics and identify key requirements. A literature review develops a framework to classify big data articles. A case study analyzes an emergency service using big data. Requirements for effective analytics are identified. A survey examines business value across countries. The research conceptualizes big data, assesses benefits to business units and organizations, and provides recommendations for senior management to maximize value from big data and analytics.

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‘Big Data’ and Business Analytics:
Key Requirements for High
Business Value Realization
Samuel Fosso Wamba

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Joint collaboration
• Business value of IT
• IT/RFID adoption
• Big data and social media
analytics
• SCM
• Ecommerce/m-
commerce
• Big data and marketing
/customer/ social media
analytics.
• Service Systems
Evaluation
• Complex modelling using
PLS-SEM

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Research questions
• How do firms derive business value by putting big
data into analytics?
• What are the key requirements for high level
business value realization?

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What is big data???
But what is big data???

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First study: A literature review and
case study
• Clarify the definition and concepts related to ‘big data’.
• Develop a conceptual framework for the classification of
articles dealing with ‘big data’.
• Use the conceptual framework to classify and summarize
all relevant articles.
• Conduct an in-depth analysis of a longitudinal case study
of an Australian state emergency service which is
currently using ‘big data’ for improved operations
delivery.
• Develop future research directions where the deployment
and use of ‘big data’ is likely to have huge impacts.

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Literature review process
• Comprehensive search from 2006 to 2012 using the
descriptor: “big data”
– ABI/Inform Complete
– Academic Search Complete
– Business Source Complete
– Elsevier (SCOPUS)
– Emerald
– IEEE Xplore
– Science Direct
– Taylor & Francis
– AIS Basket of Journals
• From 1153 articles to 62 articles for classification

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The V’ concept(s)
• Volume: Large volume of data that either consume huge
storage or consist of large number of records (Russom 2011)
• Velocity: Frequency of data generation and/or frequency of
data delivery (Russom 2011).
• Variety: Data generated from greater variety of sources and
formats, and contain multidimensional data fields (Russom 2011).
• Value: The economic value of different data varies
significantly. Typically there is good information hidden
amongst a larger body of non-traditional data; the challenge is
identifying what is valuable and then transforming and
extracting that data for analysis.” (p. 1) (Oracle 2012)
• Veracity: Inherent unpredictability of some data requires
analysis of big data to gain reliable prediction (Beulke 2011)

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So what is big data???
3V's: Volume+ Velocity+ Variety
(Gartner 2012), (Kwon and Sim 2012), (McAfee and Brynjolfsson 2012)
4V's: Volume+ Velocity+ Variety+ Value
(IDC 2012), (Oracle 2012), (Forrester 2012
5V's: Volume+ Velocity+ Variety+ Value+ Veracity
(White 2012)

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10,000 volunteers
250 staff
250 sites
Flood, Storm, Tsunami
Road Crash Rescue
Community Responder
Vertical Rescue
Land Search
Evidence Search
Aircraft Operations
Logistics Support
Primary Industries
Case study: The NSWSES description
Source: Andrew, E. (2012). Guest Speaker, ISIT404, SISAT

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Insights from the case study
• Importance of a robust platform to handle multiple
sources of data for superior emergency service
management
• Implementation project of IT-enabled ‘Big Data’
capabilities: Overcoming challenges related to the
management of volunteers organizations
• Transforming firm capabilities: ‘big data’ as enabler
of improved decision making for enhanced firm
performance
– Real-time resource allocation, coordination, and asset
movement
– Improved emergency command control center
management for better service delivery

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Second study: a survey
• 10 Requirements for Capitalizing on Analytics
3.0 by Thomas H. Davenport
1. Multiple types of data
2. A new set of data management options (e.g., DW, DB and big data appliances)
3. Faster technologies and methods of analysis.
4. Embedded analytics
5. Data discovery
6. Cross-disciplinary data teams
7. Chief analytics officers
8. Prescriptive analytics
9. Analytics on an industrial scale
10. New ways of deciding and managing

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Third study: a survey business value of big
data and BA
• Business Analysts and IT analysts
Number of participants per country
Country Respondents
France 149
USA 153
Total 302

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Special issue on big data and BA

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Contribution to the knowledge
• Conceptualize the nature of big data
– How they can be leveraged to derive business
value
– Synthesizes critical insights
• Assessing benefits
• Individual business units
– Marketing
– Supply chain
– Customer service
• Organization level

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Recommendations for senior management
• Senior decision makers have to embrace
evidence-based decision making
• Full benefits can be reaped
– Proper talent management
– Robust technology
– Data driven company culture
• Challenges
– Training
– Change management
– Business process reengineering
– IT integration

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Questions?
Samuel Fosso Wamba
CompTIA RFID-Certified Professional
Founder of e-m-RFID.biz
Co-Founder of RFID Academia
Associate Professor
www.samuelfossowamba.com