MP3 / MD740Strategy & Information Systems

Oct. 13, 2004

Databases & the Data Asset, Types of Information Systems, Artificial Intelligence

Topics Covered

• Data & Information• Data Warehousing• Leveraging Data

– Harrah’s– Business Intelligence

• Types of Information Systems– TPS, MIS, DSS

• Artificial Intelligence– Expert Systems, Neural Networks, Genetic

Algorithms

Data, Information, & Knowledge

• Data - raw facts, figures, and details.

• Information - organized, meaningful, and useful interpretation of data. Has a context, answers a question.

• Knowledge - an awareness and understanding of a set of information and how that information can be put to best use.

• Many firms are data rich and info poor: victims of an old or poorly planned architecture

Examples of Data, Information, & Knowledge

Data: raw, no context 900,000 1,150,000 1,200,000 1,100,000

Information: meaningful, has contextQuarter 1 Quarter 2

Post 900,000 1,150,000 Kellogg's 1,200,000 1,100,000

Post lowered its prices after the first quarter.Price change has caused Post sales to rise at the expense of Kellogg’s

Knowledge: information above & other information creates an awareness of impact

Warehouses & Marts

• Data Warehouse– a database designed to support decision-making in an

organization. It is structured for fast online queries and exploration. Data warehouses may aggregate enormous amounts of data from many different operational systems.

• Data Mart– a database focused on addressing the concerns of a

specific problem or business unit (e.g. Marketing, Engineering). Size doesn’t define data marts, but they tend to be smaller than data warehouses.

Data Warehouses & Data Marts

TPS& other

operational systems

DataWarehouse

Data Mart(Marketing)

Data Mart(Engineering)

3rd party data

= query, OLAP, mining, etc.

= operational clients

Differing System Demands

network traffic & processor

demands

time

network traffic & processor

demands

time

Managerial Systems

Operational Systems

“Let the neighbors lure tourists with knights on horseback, fiery volcanoes, pirate ships, and mini-Manhattans. We’ll just keep refining what we’re already pretty good at: drilling into our data and making sure our regular customers are more than satisfied.”

- Gary Loveman, CEO, Harrah’s

Types/Classifications ofInformation Systems

Transaction Processing Systems (TPS)

• A shared IS that uses a combination of IT and manual procedures to process data and information and to manage transactions.

• Examples: Cash Registers (POS), ATM• Characteristics:

– transactions are similar & repeatable– support multiple users in routine, everyday transactions

(usually tactical systems)– data capture with possible report generation– accuracy is critical, TPS “feed” other IS

Reporting Systems - MIS

• Sometimes called Management Reporting Systems or Management Information Systems

• Characteristics– use data captured and stored from TPS– reports consolidated information rather than

details of transactions– supports reoccurring decisions– provides reports in pre-specified formats (on

screen, printed, or data)

Decision Support Systems (DSS)• Allow users interrogate computers on an ad hoc

basis, analyze information, and predict the impact of decisions before they are made. [key: unstructured, user-led exploration]

• Characteristics– Assists in ad-hoc decision making– Used when requirements, processes, or procedures are

unstructured & aren’t known in advance– Provides info needed to define & solve a problem– Provides information in format determined at time of need

Management Levels & IS

Strategic Planning

Management Control

Operational Control

DSS

MIS

TPS

Expert Systems (ES)

• An artificial intelligence system that uses captured human expertise to evaluate and solve problems

• Characteristics:– diagnosis, configuration, and/or recommend a

course of action– problems are structured and repeatable– application scope is limited to a particular

problem area (domain)

Other Types of Artificial Intelligence (AI)

• Neural Networks– hunt for patterns in historical data– build their own expertise based on prior history– require clean data & consistency between

performance history and future events

• Genetic Algorithms– search for optimal solutions based on natural

selection: (1) propose solution (2) evaluate results against earlier solution (3) mutate & return to step 1

Keane’s Space Truss Design