Lead researcher information
The Research Program:
Data Driven Decision Support System is one of the emerging approaches for organizational leaders to help make critical decisions that could make or break their competitiveness. Creative risk takers, and visionary leaders use this approach as the next generation smart management strategy to make their company big data - enabled organization. McAfee & Brynjolfsson (2012) pointed out that companies in the top third of their industry in the use of data driven decision making were , on the average, 5% more productive and 6% more profitable than their competitors. About 2.5 Exabyte of data are created every day and this number doubles every 40 months and now more data cross the internet every second than were stored in the entire internet two decades ago. Financial institutions are being continuously challenged by shrinking revenues and need to improve operational cost efficiency. Many of these institutions sit on large customer transaction data. In order to address the growing demand the need to create newer business models or frameworks that use various technologies in data science is critical.
The title of the research program is “The role of Big Data, Business Intelligence, and Predictive Analytics in the Financial Sector”. As a program, it includes several projects which may, in turn, have their own specific research topics. Projects that constitute the program are listed here under. Possible research topics are indicated under each project, and researchers are encouraged to come up with their own topics that fall under a specific project.
Justification for the program
Understanding how the financial institutions can better derive value from their data is the main topic of this collaboration effort. The use of data science supported by human intelligence changes traditional decision making process by providing knowledge to predict and prescribe business outcomes. Big data, business intelligence , and predictive analytics supported by data security and protection made financial institutions smarter, flexible, and competitive in local and global markets.
The following are high level summary of the dynamic and complex nature of managing data in the financial sector
• The volume of data gathered in the financial sector is extremely large making it difficult to analyze, predict patterns, and find correlations between variables
• The need to have a real-time predictive analytics for making informed decision
• The need to have a balanced approach to risk and compliance
• The need to transform volumes of unstructured data collected from ATMs and servers, social data from the social networks, click stream data from content management systems, voice loges from call centers, and communication data from emails
How to Collaborate
Interested researchers should send their proposal to the lead research fellow, Getaneh Bitew-Fenta, PhD, Data Scientist, Center for International Business Valuation, firstname.lastname@example.org . The center works with various government and private organizations as well as higher education institutions to solicit and allocate research fund. If your organization is interested to join the center, please send your letter of intent to Dereje Tessema, PhD, PMP, CEA, managing director of the center (email@example.com).
Possible Research Topics:
a. Big Data in the Financial Sector
b. Business Intelligence
c. Data Analytics and Forecasting
d. Database design for financial institutions
e. Data Security and Data Protection