MIS 327 - Exam Two - Dr. Arthur

Types of decisions

unstructured

structured

semi-structured

Unstructured Decisions (Senior Management)

decision maker must provide judgment to solve problem. Novel, important, non routine.

Ex. Decided entrance or exit from markets.

Structured (Operational Management)

Repetitive and Routine. Involve definite procedure for handling them so do not have to be treated as new.

Ex. Restock Inventory

Semi-Structured (Middle Management)

Only part of problem has clear-cut answer provided by accepted procedure.

Ex. design a marketing plan. Develop a departmental budget.

Steps in the decision making process

Intelligence

Design

Choice

Implementation

Intelligence

Discovering, identifying, and understanding the problems occuring in the organization-why is there a problem, where, what effects it is having on the firm. (problem discovery - what is the problem?)

Ex. Deciding you want to start dating.

Design

Identifying and Exploring Various Solutions. (Solution Discovery - What are the possible solutions)

Ex. Deciding what characteristic or type of person you like.

Choice

Choosing among solution alternatives.

(Choosing solutions - What is the best solution)

Ex. Choosing which girl is the right choice for you.

Implementation

Making chosen alternative work and monitoring how well solution is working.

(Solution Testing - is the solution working? Can we make it work better)

Ex. Asking the girl out.

Automated Decision-Making

Humans eliminated

Decisions-making process capture by computer algorithms

Predefined range of acceptable solutions

Decisions made faster than managers can monitor and control.

Ex. Trading Programs at electronic stock exchange.

Quality Dimensions of Decisions

accuracy

comprehensiveness

fairness

speed

coherence

due process

Accuracy

Reflect reality

Comprehensiveness

Reflects full consideration of the facts and circumstances.

Fairness

reflects the interests of affected parties

Speed

efficient with respect to time and other resources.

Coherence

reflects rational process that can be made understandable

Due Process

is the result of a known process and can be appealed to a higher authority.

Business Intelligence

Infrastructure for managing data from business environment.

- Warehousing

- Integrating

- Reporting

- Analyzing

Elements of Business Intelligence

Data

Infrastructure

Analytics and Methods

User Interface

Platform

Data (element of business intelligence)

Collection of current and historical data.

Both structured and unstructured

Many sources.

Infrastructure (element of business intelligence)

Transactional databases

Enterprise-wide data warehouse

Analytics and Methods (element of business intelligence)

What-If Analysis

Multidimensional Analysis

Predictive Analytics and Big Data Analytics

Operational Intelligence and Analytics

Location Analytics and GIS

What-if analysis

various inputs are changed to see the effect on output

- goal seek, solver, scenarios, data table, forecasts, sensitivity analysis.

Multidimensional Analysis

Display two or more dimensions of the data

Parameterized reports such as pivot tables.

Predictive Analytics and Big Data Analytics

Extracts information from data to model future events and behaviors.

Can use the big data generated from social media, transactions, sensor and machine output, etc., and combine with the customer data.

Includes Data Mining.

Operational Intelligence and Analytics

Day-to-day monitoring of business decisions and activity.

Real-time monitoring

The internet of things provides huge streams of data from connected sensors and devices.

Location Analytics

Support decisions that require knowledge about the geographic distribution of people or other resources.

Geographic Information System (GIS)

help decision makers visualize problems by analyzing and displaying data in the form of digital maps.

User Interface (element of business intelligence)

Data Visualization

Data Visualization

present data in graphical form help users see patterns and relationships in large quantities of data.

Platform (element of business intelligence)

Middle Management/DSS

Senior Management/ESS

Middle Management/DSS

typically perform What-if Analysis or OLAP (multidimensional analysis)

- Statistical models such as sensitivity analysis, goal seek, and pivot tables.

Senior Managment/ESS

Bring together data from different sources, often through a portal: Balanced Scorecard, Dashboard. Include drill down capability, which enables an executive to zoom in on details or zoom out for a broader view.

Balanced Scorecard

uses Key Performance indicators

Dashboard

gives a picture of the overall performance of an organization.

Artificial Intelligence

Computer-based systems that attempt to emulate human behavior and thought patterns.

Many techniques for enhancing decision making are based on artificial intelligence.

AI Techniques

Capture Knowledge

Discover patterns and behaviors in large amounts of data

Perform some human-like action

Generate solutions to problems too complex for humans to solve alone.

Types of AI Systems

Expert Systems

Neutral Networks

Generic Algorithms

Fuzzy Logic

Intelligent Agents

Machine Learning

Natural Language Processing

Computer Vision Systems

Robotics

Expert Systems

Capture human expertise in a limited domain of knowledge

- Knowledge Base

- Inference Engine

Knowledge Base

model human knowledge as a set of rules collectively.

Inference Engine

Searches the rules and fires those rules that are triggered by facts entered

Useful for dealing with problems of classification in which there are relatively few alternative outcomes and in which these possible outcomes are all known in advance.

Neural Networks

Find patterns and relationships in massive amounts of data. "Learn" patterns by searching for relationships, building models, and correcting over and over again the models own mistakes. useful for solving complex, poorly understood problems for which large amounts of data have been collected.

Genetic Algorithms

Find the optimal solution for a specific problem by examining very large number of alternatives solutions for that problem.

Used to solve complex problems that are very complex, involving hundreds or thousands of variables or formulas.

Can evaluate many solution alternatives quickly to find the best one.

Based on machine learning techniques inspired by evolutionary biology.

Fuzzy Logic

Rule-based technology that represents imprecision in categories (e.g., "cold" versus "cool") by creating rules that use approximate or subjective values.

Describes a particular phenomenon or process linguistically and then represents that description in a small number of flexible rules.

Provides solutions to problems requiring expertise that is difficult to represent in the form of crisp If-then rules.

Intelligence Agents

Programs that work in the background without direct human intervention to carry out specific, repetitive, and predictable tasks.

Agent uses limited knowledge base to accomplish tasks or make decisions on the user's behalf.

Shopping bots.

Machine Learning

computers improving performance by using algorithms to learn patterns from data and examples

Natural Language Processing

Software that can process voice or text commands using natural human language.

Computer Vision Systems

Emulate human visual system to view and extract information from real-world images.

Robotics

Design and use of movable machines that can substitute for humans.

Knowledge Management

set of business processes developed for creating, storing, transferring, and applying knowledge.

Increases the ability of organization to learn from environment and to incorporate knowledge into business processes and decision making.

Structured Knowledge

Explicit knowledge that exists in formal documents (reports, presentations). Also found informal organization rules.

Semistructured Knowledge

email, voice mail, digital pictures, bulletin-board postings.

Unstructured or Tacit Knowledge

Knowledge residing in heads of employees, rarely written down.

Enterprise-Wide Knowledge Management Systems (KMS)

deal with all three types of knowledge. General purpose, firm-wide systems that collect, store, distribute, and apply digital content and knowledge.

Include capabilities for searching for information storing both structured and unstructured data and locating expertise within the firm.

Include technologies such as portals, search engines, collaboration tools, learning management systems

Database

collection of related files containing records on people, places, or things. In access, consists of one or more tables.

Entity

Categories of data about which we maintain information (such as Customer, Supplier, Order). In access equate to a table.

Table

Set of related records. Every record has fields in the same order.

Attributes

specific characteristics of each entity (such as Supplier name, customer email, part description). In Access, equates to a field.

Field

Individual data element. In Access, equates to a column.

Record

Set of related fields. Set of attributes for a single instance of an entity.

Relational Database

organize data into two-dimensional tables (relations) with columns and rows. Fields store data representing an attribute. rows store data for separate records, or tuples. Key field uniquely identifies each record. Primary Key: one field in each table, cannot be duplicated, provides unique and non-null identifier for all information in any row.

Entity Relationship (e-r) Diagram

used to clarify table relationships in a relational database. One-to-One relationship, One-to-many relationship, Many-to-Many relationship.

Normalization

Streamlining complex groupings of data to minimize redundant data elements and awkward many-to-many relationships

Referential Integrity Rules

used by relational databases to ensure that relationships between coupled tables remain consistent. Ex. when one table has a foreign key that points to another table, you may not add a record to the table with foreign key unless there is a corresponding record in the linked table.

DBMS (Database Management System)

Specific type of software for creating, storing, organizing and accessing data from a database; Separates the logical and physical views of the data.

(ex: MS Access, MS SQL Server, MySQL, Oracle Database)

Single Human Resources Database

provides many different views of data, depending on the information requirements of the user.

Data Definition Capabilities

specify structure of content of database. Uses a data dictionary.

Data Dictionary

stores definitions of data elements and their characteristics.

Data Manipulation Capabilities

query and reporting: Structured Query Language (SQL), Query-by-example (QBE), Report Generation.

Querying SQL

[Select Part] Part_Number, [Part] Part_Name, [Supplier] Supplier_Number, [Supplier] Supplier_Name,

[From Part], [Supplier]

[Where Part]. Supplier_Number = [Supplier]. Supplier Number and Part_Number = 137 OR Part_Number = 150

query by example (QBE)

database language that enables the user to fill out a grid (form) to construct a sample or description of the data wanted

The Challenge of Big Data

Massive quantities of unstructured and semi-structured data. Requires new technologies and tools.

Business Intelligence Infrastructure

Array of tools for obtaining useful information from vast quantities of data: Data Warehouses and Data Marts, Hadoop, Analytic Platforms.

Data Warehouse

databases that stores current and historical data that may be of interest to decision makers. Consolidates and standardizes data from many systems, operation and transactional databases, internal and external. Data can be accessed but not altered.

Data Mart

Subset of data warehouses that is highly focused and isolated for a specific population of users.

Hadoop

open-source software framework for big data. Breaks data task into sub-problems and distributes the processing to many inexpensive computer processing nodes. Combines result into smaller data set that is easier to analyze.

Analytic Platforms

preconfigured hardware-software systems, designed for query processing and analytics. Use both relational and non-relational technology to analyze large data sets. Includes queries and reports, OLAP, data mining.

Analytical tools

tools required after the data is gathered for consolidating, analyzing, and provide access to large amounts of data to find insights that can improve decision making.

- software for database reporting and querying

- Tools for multidimensional data analysis (online analytical processing or OLAP).

- Data Mining

Online Analytical Processing (OLAP)

supports multidimensional data analysis, enabling users to view the same data in different ways using multiple dimensions.

- each aspect of information - product, pricing, region, or time period - represents a different dimension.

- enables users to obtain online answers to ad hoc question such as these in fairly rapid amount of time.

Multidimensional data model

typically used for the design of corporate data warehouses and departmental data marts in a shape of a cube.

Data Mining

Finds hidden patterns and relationships in large databases and infers rules from them to predict future behavior. Analyzes patterns in customer data for one-to-one marketing campaigns or for identifying profitable customers.

associations, sequences, classifications, clustering, forecasting

Types of information obtainable from data mining

Predictive Analysis

uses data mining techniques, historical data, and assumptions about future conditions to predict outcomes of events, such as the probability a customer will respond to an offer or purchase a specific product.

Text Mining

unstructured data accounts for 80 percent of an organization's useful information. allows businesses to extract key elements from, discover patterns in, and summarize large unstructured data sets.

Sentiment Analysis

mnes outline text comments online or in email to measure customer sentiment.

Web Mining

discovery and analysis of patterns and information from the web in order to understand customer behavior, evaluate website, or quantify success of marketing.

Content Mining

mines content of websites.

structure mining

mines website structural elements, such as links.

Usage Mining

mines user interaction data gathered by Web servers

available

Firms use the web to make information from their internal databases __________________________ to customers and partners. Middleware and software make this possible.

Web interfaces

provide familiarity to users and savings over redesigning legacy systems.

Information Policy

States organization's rules for organizing, managing, storing, sharing information

Data Administration

responsible for specific policies and procedures through which data can be managed as a resource.

Database Administration

Database design and management group responsible for defining and organizing the structure and content of the database, and maintaining the database.

Poor Data Quality

major obstacle to successful customer relationship management

Data Quality Problems

caused by redundant and inconsistent data produced by multiple systems and data input errors

Data Quality Audit

structured survey of the accuracy and completeness of data.

Data Cleansing

detects and corrects incorrect, incomplete, improperly formated, and redundant data.

Data Validation

set of constraints that help control the type and accuracy of data entered into a field.

Access Techniques

-Required field

-Default values

-Validation rule (and text)

-Lookup list

-Input mask

-Key field

-Referential integrity