Business IntelligenceCIS 9002

Kannan Mohan

Department of CIS

Zicklin School of Business, Baruch College

Learning Objectives

• Articulate the role of business intelligence in organizations

• Explain the use of Data warehouses, Data mining, and Artificial Intelligence in helping business decision making

Examples• Predicting Flu outbreaks

• What drives the price of Bitcoins?

• Target’s foray into analytics

• Watson and Jeopardy

Big Data

• Unstructured

• Massive amounts

• Not amenable for easy processing using conventional databases

Business Intelligence

• Reporting, data exploration, ad-hoc queries, sophisticated data modeling and analysis

• Analytics

• Extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions

Skills for Data Mining

Information

technology

Statistics

Business knowledg

e

Process for Business Intelligence

• Collection: What kind of data? How much data?

• Storage: Structure, access, security

• Analysis: Structure or not? Algorithms, Assumptions

• Interpretation: Correlation vs. Causation, Type I/II errors, Outliers

Data, Information, and Knowledge

• Data: Raw facts and figures

• Information: Data presented in a context so that it can answer a question or support decision making

• Knowledge: Insight derived from experience, expertise, and ability to interpret

Organizing Data• Database: A single table or a collection of related

tables

• Database management systems (DBMS): Software for creating, maintaining, and manipulating data (Eg. MS Access, MS SQL Server, MySQL)

• Structured query language (SQL): A language used to create and manipulate databases

Relational Databases

• How do you organize data?

• How do you connect different pieces of data?

• How do you answer questions that are important for you?

• Tables and relationships

• Avoiding data integrity problems

Data-driven Decision Making

• Data warehouses

• Data marts

• Data mining

• Artificial Intelligence

Components of a Data Warehouse

(Laudon and Laudon, 2009)

Business Intelligence Toolkit

• Provide regular summaries of information in a predetermined format

Canned reports

• Create custom reports on an as-needed basis by selecting fields, ranges, summary conditions, and other parameters

Ad hoc reporting tools

• Display of critical indicators that allow managers to get a graphical glance at key performance metrics

Dashboards

• Takes data from standard relational databases, calculates and summarizes the data, and then stores the data in a special database called a data cube•Data cube: Stores data in OLAP report

Online analytical processing (OLAP)

Data Mining• Identifying hidden patterns in large datasets

• Areas of application:

• Customer churn

• Fraud detection

• Financial modeling

• Hiring and promotion

• Customer segmentation

• Marketing and promotion targeting

• Market basket analysis

• Collaborative filtering

Artificial Intelligence

• Neural network: Examines data and hunts down and exposes patterns, in order to build models to exploit findings

• Expert systems: Leverages rules or examples to perform a task in a way that mimics applied human expertise

• Genetic algorithms: Model building techniques;

• Where computers examine many potential solutions to a problem, iteratively modifying various mathematical models, and comparing the mutated models to search for a best alternative

Business Intelligence

(Laudon and Laudon, 2009)

Challenges of Big Data

• How do you arrive at interpretations?• Role of theory

• Large enough data set to find anything?

• Security and privacy issues - Who has control over the data?

• Analyzing Big Data• Size and speed of analytics

• Distributing over commodity hardware

Relevant Areas• Information Retrieval

• Natural Language Processing

• Machine Learning

• Cognitive Technologies

• Deep Learning

• Data Science

Summary

• What is business intelligence?

• How do we organize data in databases?

• What is the role of data warehousing, data mining, and artificial intelligence in business decision making?