Business Intelligence in Banking Kevin – 1501147113 Steven EkaPutranto – 1501148362 RendyWinarta– 1501149226 Gladys Natalia – 1501165476 Stefani Trifosa - 1501158893
Topics • BI Benefit in Banking • BI Implementation problem • Storage needed for BI implementation • BI Architecture • Usage Data warehouse in BI • BI Applications • User of BI • Example of BI in screen shoot and explanation
Considering and analyzing the total client relationships is vital for successful bank operations in the conditions of growing competition. • Most software solutions in the business intelligence domain are focused on market segmentation, defining a clear picture of the clients and their relationships with banks, defining a clear picture of the market potential and the bank’s ability to use this potential
Segmentation : a customer segment is a group of client composed based on specific shared characteristics • Customer profitability : profitability analysis is the analysis of clients in accordance with the expected impact on the bank’s profit, and thus the total return on equity (ROE)
Cross-selling and up-selling : these types analysis enable assessing clients in terms of the ability to use several products and services simultaneously (loans, deposits, cards, e-banking, etc.) • Channel effectiveness : enables the identification and analysis of various channels for communication with clients and delivery of products through these channels
Campaign management : the main objective is to analyze and compare the effects of marketing campaign on the increase in clients numbers, increase in the numbers and level sold products, earnings, etc.
Cross-Organizational Collaboration To succeed at BI, an enterprise must nurture a cross-organizational collaborative culture in which everyone grasps and works toward the strategic vision. • Business Sponsor Strong business sponsors truly believe in the value of the BI project. Business sponsors establish proper objectives for the BI applications, ensuring that they support the strategic vision. Sponsors also approve the business-case assessment and help set the project scope
Dedicated Business Representation More often than not, the primary focus of BI projects is technical rather than business-oriented. The reason for this shortcoming: most BI project share run by IT project managers with minimal business knowledge. These managers tend not to involve business communities. • Availability of Skilled Team Members the business and technical skills required to implement a BI application are quite different than other operational online transaction processing (OLTP) projects. Skilled Team Members taking important role to help define the work of BI in an organization.
Business Analysis and Data Standardization • Some of these issues are : • Identifying Information Needs Indentify the business issue, address it well after issues are identified , can provide better business analysts. • Data Merge and Standardization The biggest challenge faced by every BI project is its team’s ability to understand the scope, effort and importance of making the required data available for knowledge workers. Therefore, datamerge and standardization activitiesmust be planned and started at thebeginningofthe BI project.
In Business Intelligent system, there are two main types of storage system that could provide historical current and predictive views of business operations. They are: • Data Warehouse Data warehouse are responsible to tore all the data, and also facilitate reporting and analysis needed for business intelligence. • Data Mart Data mart I a subset of an organizational data store oriented to specific purpose or major data subject that may be distributed to support business needs.
The architecture of a bank’s business intelligence system is very heterogeneous and comprises several layers: • Operational database and external data • The data integration and transformation layers • The data warehouse layer • The data access layer (applications, OLAP, data mining, etc.) • The front end (layer for access to information).
The data warehouse is the significant component of business intelligence. It is subject oriented, integrated. The data warehouse supports the physical propagation of data by handling the numerous enterprise records for integration, cleansing, aggregation and query tasks It can also contain the operational data which can be defined as an updateable set of integrated data used for enterprise wide tactical decision-making of a particular subject area. It contains live data, not snapshots, and retains minimal history
The following are some examples of BI applications: • A company that provides natural gas to homes created a dashboard that supports operational performance metric management and allows real time decision making. In one application of the dashboard, the number of repeat repair calls was reduced, resulting in a saving of $1.3 million. • At a large member-owned distributor to hardware stores, use of a dashboard reduced theamount of inventory that must be liquidated or sold as a loss leader from $60 million to$10 million. Their BI system also allowed their member stores to see their ownperformance relative to similar stores.
IT User They are those who use BI for development purposes, report generation, presentation and delivery. • Power User Professional analysts who are experienced in using complex tools, and are the individuals who often use BI tools to manipulate data to help decision-making. • Business User The managers who review the analyses presented by the power users and create their own reports and presentations.
Casual User Decision makers. They usually use BI to help with their presentation and delivery of information. • Extra-Enterprise User including those external parties, customers, regulators, external business analysts, partners, suppliers, or anyone with a need for reported information for tactical decision-making.
OLTP (on-line transaction processing) is a major task of traditional relational DBMS. Day to day operations such as purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc. are done in OLTP. OLTP also aims at reliable and efficient processing of a large number of transactions and ensuring data consistency. • OLAP (on-line analytical processing) is a major task of data warehouse system, data analysis and decision making, aims at efficient multidimensional processing of large data volumes (fast, interactive answer to large aggregate queries.