"big data" and business analytics: key requirements for high business value realization

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‘Big Data’ and Business Analytics:

Key Requirements for High

Business Value Realization

Samuel Fosso Wamba

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mardi 27 mai 2014 2

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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mardi 27 mai 2014 3

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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mardi 27 mai 2014 9

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



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



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



Special issue on big data and BA



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



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



Questions?

Samuel Fosso Wamba

CompTIA RFID-Certified Professional

Founder of e-m-RFID.biz

Co-Founder of RFID Academia

Associate Professor

www.samuelfossowamba.com

http://www.samuelfossowamba.com/

"big data" and business analytics: key requirements for high business value realization

Presentations & Public Speaking