Have you ever get overwhelmed with the buzzwords like Business Intelligence, Big Data, Business Analytics, Data Warehouse, Data Mining, Data Visualization, Decision Support Systems, and Expert Systems? This presentation gives you a brief enlightening introduction and outlines how these technologies can help organizations earn competitive advantage.
Furthermore, the presentation explains the some important BI tools' adoption and return on investment considerations.
9. For questions such as
• How are my sales?
• How much will I sell next year?
• Are my customer satisfied with my services?
• Which other products are my customers
interested in?
• Which parts of the business are not profitable?
19. What is Decision Support System?
• Interactive computer-based information
system that supports decision-making
activities.
• It is a an application of BI
20. What is Expert Systems?
• A computer program that simulates the judgment
and behavior of a human or an organization that has
expert knowledge and experience in a particular field
21. Data Visualization?
Goal is to communicate information clearly and
efficiently to users via the information graphics
selected, such as tables and charts
25. Big Data
“Big data can be
defined as datasets
that are beyond the
ability of common
databases softwares
to store, manage,
capture and analyze”
Ref: Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H., 2011
31. Big Data creates value in several ways
• Making data more readily accessible to relevant stakeholders at the
right time creates enormous value of data
34. • Segment the population in order to customize products and services
to meet the segment needs
35. • Innovate new business models, products and services to not only
satisfy customers but to capture new opportunities and create new
markets.
36. Use of Big data as competitive advantage
• Find hidden patterns
• Identify new growth opportunities
• Precise prediction of consumer behavior
• Increases productivity
• New sources of value
• Catalyst for trend shifting in industries
37. Crunch of Big Data
“We’ve never had greater, better analyzed, more
pervasive, or increasingly connected computing power
and information at a cheaper price in the history of the
world”
Ref: McKinsey & Company
69. “Managers are making bad decisions because of
bad data”
Professor Nour El-Kadri
Telfer School of Management
Is BI and Big Data Analytics are the Solution for Bad Decision Making?
78. Why BI May not Enhance Decision Making?
High Concentration
of Analytics Skills
Shifting BI from
Conventional Uses
to More Critical
Applications
Low Strategic
Priority of Data
Accessibility Issues
83. References
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• Assuncao, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., & Buyya, R. (2013). Big Data Computing and Clouds: Challenges, Solutions, and Future Directions. Journal of Parallel and Distributed Computing. Retrieved from
http://arxiv.org/abs/1312.4722
• Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.
• Carneiro, H. A., & Mylonakis, E. (2009). Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases, 49(10), 1557–1564.
• Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188.
• City of Boston. (2014). Street Bump Mobile Application. Retrieved November 17, 2014, from http://www.cityofboston.gov/DoIT/apps/streetbump.asp
• Elliott, T. (2011, March 9). Business Analytics vs Business Intelligence? Retrieved from http://timoelliott.com/blog/2011/03/business-analytics-vs-business-intelligence.html
• Ferrando-Llopis, R., Lopez-Berzosa, D., & Mulligan, C. (2013). Advancing value creation and value capture in data-intensive contexts. In Big Data, 2013 IEEE International Conference on (pp. 5–9). IEEE. Retrieved from
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6691685
• Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2008). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014.
• Harford, T. (2014, March 28). Big data: are we making a big mistake? FT Magazine. Retrieved from http://on.ft.com/1qgq8al
• Hill, K. (2012, February 16). How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did. Forbes Magazine. Retrieved from http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-
was-pregnant-before-her-father-did/
• Liautaud, B. (2000). E-Business Intelligence: Turning Information into Knowledge into Profit. (M. Hammond, Ed.). New York, NY, USA: McGraw-Hill, Inc.
• Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. Mckinsey Global Institute. Retrieved from
http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation
• Nusca, A. (2013, May 14). McLaren CIO: How we’re working with big data. Retrieved from http://www.zdnet.com/mclaren-cio-how-were-working-with-big-data-7000015383/
• Panorama Software. (2011). Business Intelligence 3.0: Revolutionizing Organizational Data. Retrieved from http://www.reply.eu/Documents/11174_img_Business_Intelligence_3_0_Whitepaper.pdf
• Ponniah, P. (2011). Data Warehousing Fundamentals for IT Professionals. John Wiley & Sons.
• Shah, S., Horne, A., & Capellá, J. (2012). Good data won’t guarantee good decisions. Harvard Business Review, 90(4), 23–25.
• Simon, P. (2014a, March 11). Big Data Lessons From Netflix. Retrieved from http://www.wired.com/2014/03/big-data-lessons-netflix/
• Simon, P. (2014b, March 25). Potholes and Big Data: Crowdsourcing Our Way to Better Government. Retrieved November 17, 2014, from http://www.wired.com/2014/03/potholes-big-data-crowdsourcing-way-better-
government/
• Turban, E. (2007). Decision Support and Business Intelligence Systems (8 edition.). Prentice Hall.
• Vesset, D., Nadkarni, A., Olofson, C., & Schubmehl, D. (2012). Worldwide Big Data Technology and Services 2012-2016 Forecast. International Data Corporation (IDC). Retrieved from http://bit.ly/1bHgypq
Editor's Notes
Big data can be characterized in the following ways:
Volume: The quantity of the data that determines it’s potential.
Variety: The diversification of the data which increase its accuracy and help decision making.
Velocity: The speed at which data is being gathered and processed. The faster it is, the more timely manner it can be used
Value: The worth derived from analyzing the big data.
Veracity: The degree to which data is reliable and trustworthy which quantifies its quality.
There are five ways in which big data creates value for organizations:
Creating transparency: Making data more readily accessible to relevant stakeholders at the right time creates enormous value of data. No matter how sophisticated data is, if it is not accessible when needed then it is of no value.
Enabling experimentation: As more transactional data is created and stored, it gives organization liberty to experiment the data in order to discover needs, expose variability and thus improve performance.
Segmentation: Big data allows organizations to segment the population in order to customize products and services to meet the segment needs
Supporting human decision making: Advance and competent analytics tools allow data to be interpreted in a way to substantially improve decision making, reduces risk, and find hidden patterns that would be otherwise go unnoticed.
Innovation: Big data allows companies to innovate new business models, products and services to not only satisfy customers but to capture new opportunities and create new markets.