Business Intelligence Evolution of Data Analysis BI Business

Business Intelligence

Evolution of Data Analysis BI

Business Intelligence (BI) • The goals for Business intelligence projects are to provide information used to: – Support internal enterprise users in the assessment, enhancement and optimization of organizational performance & operation. – Deliver critical business information to end-users about value chain constituencies such as customers and supply-chain partners.

Business Intelligence (BI) • Tools for consolidating, analyzing, and providing access to vast amounts of data to help users make better business decisions • E. g. , Harrah’s Entertainment analyzes customers to develop gambling profiles and identify most profitable customers • End-users can utilize the BI tools to "drill-down" and "slice and dice" to gain a better understanding of transactional and operational information

Business Intelligence (BI) • Principle BI tools include: – Software for database query and reporting – Online analytical processing (OLAP) – Data mining

Business Intelligence

Data Mining • More discovery driven than OLAP • Finds hidden patterns, relationships in large databases and infers rules to predict future behavior • E. g. , Finding patterns in customer data for one-to-one marketing campaigns or to identify profitable customers. • Types of information obtainable from data mining – – – Associations Sequences Classification Clustering Forecasting

Other BI tools • Predictive analysis – Uses data mining techniques, historical data, and assumptions about future conditions to predict outcomes of events – E. g. , Probability a customer will respond to an offer or purchase a specific product • Text mining • Extracts key elements from large unstructured data sets (e. g. , stored e-mails)

Other BI tools • Interactive Dashboard – Various report, ie. ad hoc reporting, e – Trend line analysis • Performance Management – Scorecards – Statistics capabilities • etc. • e. g IBM’s Cognos BI Demo

Benefits of BI • Single point of access to information • Timely answers to business questions • Improve operational efficiency • Eliminate report backlog and delays • Improve strategies with better predictive analysis on marketing, sales, production etc. • Better enterprise resource planning • etc.

Other issues • Establishing an information policy • Firm’s rules, procedures, roles for sharing, managing, standardizing data • E. g. , What employees are responsible for updating sensitive employee information • Ensuring data quality • More than 25% of critical data in Fortune 1000 company databases are inaccurate or incomplete • Data quality audit: • Structured survey of the accuracy and level of completeness of the data in an information system • Data cleansing