CIS 9002
Kannan Mohan
Department of CIS
Zicklin School of Business, Baruch College
• Articulate the role of business intelligence in
organizations
• Explain the use of Data warehouses, Data mining, and
Artificial Intelligence in helping business decision
making
• Predicting Flu outbreaks
• What drives the price of Bitcoins?
• Target’s foray into analytics
• Watson and Jeopardy
• Unstructured
• Massive amounts
• Not amenable for easy processing using conventional
databases
• 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
Information
technology
Statistics
Business
knowledge
• 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: 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
• 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
• 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 warehouses
• Data marts
• Data mining
• Artificial Intelligence
(Laudon and Laudon, 2009)
Canned reports
• Provide regular summaries of information in a predetermined format
Ad hoc reporting tools
• Create custom reports on an as-needed basis by selecting fields,
ranges, summary conditions, and other parameters
Dashboards
• Display of critical indicators that allow managers to get a graphical
glance at key performance metrics
Online analytical processing (OLAP)
• 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
• Identifying hidden patterns in large datasets
• Areas of application:
• Customer segmentation
• Customer churn
• Marketing and promotion
targeting
• Fraud detection
• Market basket analysis
• Hiring and promotion
• Collaborative filtering
• Financial modeling
Examines data and hunts down and
exposes patterns, in order to build models to exploit findings
Leverages rules or examples to perform a
task in a way that mimics applied human expertise
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
(Laudon and Laudon, 2009)
• 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
• Information Retrieval
• Natural Language Processing
• Machine Learning
• Cognitive Technologies
• Deep Learning
• Data Science
• 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?