data management mis 503 management information systems mba program

Data Management

MIS 503Management Information Systems

MBA Program

Definitions

• Database: A DB is an organized collection of logically related data

• Data: stored representations of meaningful objects and events– Structured: numbers, text, dates– Unstructured: images, video, documents

• Information: data processed to increase knowledge in the person using the data

• Metadata: data that describes the properties and context of user data

Data Entities, Attributes, and Keys

• An entity is a generalized class of people, places, or things for which data is collected, stored, and maintained.

• A attribute is a characteristic of an entity.

• Data item - a value of an attribute can be found in the fields of the record describing an entity.

Key Fields• Keys are special fields that serve two main purposes:

– Primary keys are unique identifiers of the relation in question. Examples include employee numbers, social security numbers, etc. This is how we can guarantee that all rows are unique

– Foreign keys are identifiers that enable a dependent relation (on the many side of a relationship) to refer to its parent relation (on the one side of the relationship)

– A secondary key is a field in a record that does not uniquely identify the record but which is used to look up fields (e.g., Last Name) in, for example, and index

– Candidate Key – an attribute that could be a key…satisfies the requirements for being a key

• Keys can be simple (a single field) or composite (more than one field)

Data Model

• A data model is a diagram of entities and their relationships.

• Data modeling involves understanding a specific business problem and analyzing the data and information needed to deliver a solution.

• The data model will be optimized to balance storage efficiency and query efficiency

E-R Diagrams

• Entity-relationship (ER) diagrams use graphical diagrams to demonstrate the organization of and relationships between entities.

• Relationships include:– one-to-many (1:N)– one-to-one (1:1)– many-to-many (N:M)

ER Modeling

Inside symbol:minimum cardinality

CourseNoCrsDescCrsUnits

Course

OfferNoOffLocationOffTime

Offering

Has

Single line: onecardinality

Outside symbol:maximum cardinality

Circle: zerocardinality

Crow's foot: manycardinality

ER Modeling

• Relational Model - the relational model describes data using a standard tabular format– All data elements are placed in two-dimensional tables,

called relations– A relation contains rows (tuples, records) and columns

(attributes, fields) with each intersecting cell containing an item of data

• Each attribute has a domain, which is the structure or constraints on the type of data an attribute can hold

Relational Database Model

Customer Table Order Table

Field Name Description Order Number Primary Key

Customer Name Self Explanatory Order Item Self Explanatory

Customer Address Self Explanatory Number of Items Ordered Self Explanatory

Customer ID Primary Key-----> Customer ID Secondary Key

Order Number Secondary Key

Data Management Issues

• Data redundancy – Un-needed duplication of data

• Data integrity – Can we rely on the data?– Is it accurate?– Is it secure?

• Program-data dependence - programs and data that are developed and organized so that the data is linked to the application program and the data is incompatible with other programs or data management tools

The Database Approach to Data Management

• The database approach – Central repository of shared data– Data is managed by a controlling agent– Stored in a standardized, convenient

formTo implement a database one must have a Database Management System (DBMS)

Database Management Systems (DBMS)

• A database management system is a group of programs used as an interface between a database and application programs or a database and the user.– DBMSs are classified by the type of

database model they support.– Modern information systems are usually

built on data housed in one or more DBMS

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TECHNICAL ASPECTS OF MANAGING THE DATA RESOURCE

• A DBMS helps manage data by providing seven functions:1. Data storage, retrieval, update2. Backup3. Recovery4. Integrity control5. Security control6. Concurrency control7. Transaction control

Tools for Managing Data

Advantages of DBMSs

• Reduced data redundancy.• Improved data integrity.• Faster program development.• Easier modification and updating.• Data and program independence.• Standardization of data access.• A framework for program development.• Better overall protection of the data.• Shared data and information resources.

Data Normalization

• A common problem with data organization is that data are often not well organized– Anomalies are problems and irregularities in data.– Data anomalies often result in giving users

incorrect information, causing them to be misinformed about actual business conditions.

• If data are not well organized, the table may need to be normalized…

Well-Structured Relations

• A relation that contains minimal data redundancy and allows users to insert, delete, and update rows without causing data inconsistencies

• Goal is to avoid anomalies– Insertion Anomaly – adding new rows forces user to

create duplicate data– Deletion Anomaly – deleting rows may cause a loss of

data that would be needed for other future rows– Modification Anomaly – changing data in a row forces

changes to other rows because of duplication

General rule of thumb: a table should not pertain to more than one entity type

What’s wrong with this table?

An Example of Normalization

• Click here to view a step-by-step example of normalization

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TECHNICAL ASPECTS OF MANAGING THE DATA RESOURCEDatabase Architecture

Database – shared collection of logically related data, organized to meet needs of an organization

Database Architecture – way in which the data are structured and stored in the database

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Figure 5.3 The Data Pyramid

The Three-level DB Schema

• Schema - a general description of the entire database that shows all of the record types and their relationships.– A user view (external schema) is the portion

of the database a user can access.– The conceptual view is the logical design of

the database (how should the database be organized regardless of physical constraints)

– The internal view (physical view) is the physical storage structure for the database

• A subschema shows only some of the records and their relationships in the database.

Database Schemas

SQL

• Structured Query Language - a query language is a specialized type of data manipulation language.

• Query languages make retrieving information and manipulating a database easy and fast.

• SQL - structured query language.

Structured Query Language (SQL)

• Basic structure of a SQL expression– The select clause lists the attributes desired in

answer to a query– The from clause is a list of relations or tables

that the query language processor should consult in filling the request

– The where clause describes the attributes desired in the answer

Emerging Database Trends

Distributed Databases

• Distributed Processing - involves placing processing units at different locations and typing them together with data communications equipment and systems.– A distributed database is a database in

which the actual data may be spread across several small databases connected via telecommunication devices.

Storage Area Networks (SAN)

• High-speed, special purpose network or subnet that interconnects different kinds of storage devices with associated data servers to benefit a larger network of users.

• Part of an overall network for computing resources.

• Usually physically located near larger computing resources such as a mainframe or server.

Network-Attached Storage (NAS)

• Hard disk storage with its own network address rather than being attached to a server or workstation.

• Includes:– Multi-disk Redundant Arrays of

Integrated Disks (RAID) systems– Software to configure and map file

locations– Designed to handle a variety of

network protocols

The Data Warehouse

• Businesses collect a tremendous amount of transactions data from routine operations

• These data can be analyzed to understand the business better– Requires multidimensional analysis called

Online Analytical Processing (OLAP)– Helps create a learning organization that is

better able to understand its markets, customers and itself

Definition• Data WarehouseData Warehouse:

– A subject-oriented, integrated, time-variant, non-updatable collection of data used in support of management decision-making processes

– Subject-oriented: e.g. customers, patients, students, products

– Integrated: Consistent naming conventions, formats, encoding structures; from multiple data sources

– Time-variant: Can study trends and changes– Nonupdatable: Read-only, periodically refreshed

• Data MartData Mart:– A data warehouse that is limited in scope

Components of a star schemastar schema

Fact tables contain factual or quantitative data

Dimension tables contain descriptions about the subjects of the business

1:N relationship between dimension tables and fact tables

Dimension tables are denormalized to maximize performance

Data Mining

• Discovers interesting structure in large amounts of data

• This structure consists of– Patterns

– Statistical or predictive models of the data

– Relationships between the data

• Applied extensively to customer data– Allows firms to determine for instance which

products sell together

Object-Oriented Databases

• Traditionally relational databases supported a limited number of data types– Alphabet, numeric, dates, and time

• Modern organizations use a variety of data– Graphics objects, audio clips, videos,

subscripted arrays, and complex data for data mining

• RDBMS vendors have extended their packages to handle such data objects

Threats to Data Security• Accidental losses attributable to:

– Human error– Software failure– Hardware failure

• Theft and fraud.• Improper data access:

– Loss of privacy (personal data)– Loss of confidentiality (corporate data)

• Loss of data integrity• Loss of availability (through, e.g. sabotage)

Data Management Security Techniques/Procedures

• Views or subschemas• Integrity controls• Authorization rules• User-defined procedures• Encryption• Authentication schemes• Backup, journalizing, and checkpointing

• The need to manage data is permanent• Data can exist at several levels• Application software should be separate from the database• Application software can be classified by how they treat

data1. Data capture2. Data transfer3. Data analysis and presentation

MANAGERIAL ISSUES IN MANAGING DATA

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Principles in Managing Data

• Application software should be considered disposable

• Data should be captured once• There should be strict data

standards

MANAGERIAL ISSUES IN MANAGING DATA

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Principles in Managing Data

MANAGERIAL ISSUES IN MANAGING DATA

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The Data Management Process

Figure 5.6 Asset Management Functions

MANAGERIAL ISSUES IN MANAGING DATA

• Organizations should have policies regarding:– Data ownership – Data administration

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Data Management Policies

MANAGERIAL ISSUES IN MANAGING DATA

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Data Ownership

Corporate information policy – foundation for managing the ownership of data

Page 149 Figure 5.8 Example Data Access Policy

Data Administration

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Key functions of the data administration group:• Promote and control data sharing• Analyze the impact of changes to application systems when data definitions

change• Maintain the data dictionary• Reduce redundant data and processing• Reduce system maintenance costs and improve system development

productivity• Improve quality and security of data• Insure data integrity

MANAGERIAL ISSUES IN MANAGING DATA

Data Administration

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Key functions of the database administrator (DBA):• Tuning database management systems.• Selection and evaluation of and training on database technology.• Physical database design.• Design of methods to recover from damage to databases.• Physical placement of databases on specific computers and storage devices.• The interface of databases with telecommunications and other technologies.

MANAGERIAL ISSUES IN MANAGING DATA

CRM

• Customer relationship management (CRM): A customer service approach that focuses on building long-term and sustainable customer relationships that add value both for the customer and the company

CRM

• Classification of CRM programs– Loyalty program– Prospecting– Save or win back– Cross-sell/up-sell

• eCRM: Customer relationship management conducted electronically

CRM

• Extent of service1. Customer acquisition (prepurchase

support)

2. Customer support during purchase

3. Customer fulfillment (purchase dispatch)

4. Customer continuance support (postpurchase)

CRM

• Benefits of CRM– Provides:

• choices of products and services• fast problem resolution and response• easy and quick access to information

• Limitations of CRM– Requires integration with a company’s

other information systems which is costly– Difficult to support mobile employees

CRM

• Five factors required to implement a CRM program effectively:

1. Customer-centric strategy

2. Commitments from people

3. Improved or redesigned processes

4. Software technology

5. Infrastructure

CRM Applications and Tools

• Classifications of CRM applications– Customer-facing applications– Customer-touching applications– Customer-centric intelligence

applications– Online networking and other

applications

CRM Applications and Tools

• Customer-facing applicationsCustomer interaction center (CIC): A comprehensive service entity in which EC vendors address customer service issues communicated through various contact channels

• Intelligent agents in customer service and call centers

CRM Applications and Tools

CRM Applications and Tools (cont.)

• Customer-touching applications– Personalized Web Pages– E-Commerce Applications– Campaign Management

CRM Applications and Tools (cont.)

– Web Self-ServiceActivities conducted by users on the Web to provide answers to their questions (e.g., tracking) or for product configuration

• Self-tracking• Self-configuration and customization

CRM Applications and Tools

• Customer-centric applications– Data reports– Data warehouse

A single, server-based data repository that allows centralized analysis, security, and control over the data

CRM Applications and Tools

• Data analysis and mining– Analytic applications automate the

processing and analysis of CRM datacan be used to analyze the performance, efficiency, and effectiveness of an operation’s CRM applications

– Data mining involves sifting through an immense amount of data to discover previously unknown patterns

CRM Applications and Tools

• Online networking and other applications– Forums– Chat rooms– Usenet groups– E-mail newsletters– Discussion lists

CRM Applications and Tools

• Mobile CRMthe delivery of CRM applications to any user, whenever and wherever needed

• Voice communicationpeople are more comfortable talking with a person, even a virtual one, than they are interacting with machines. The smile and the clear pronunciation of the agent’s voice increases shoppers’ confidence and trust

CRM Applications and Tools

• Role of knowledge management and intelligent agents in CRM– Automating inquiry routing and answering

queries requires knowledge– Generated from historical data and from

human expertise and stored in knowledge bases for use whenever needed

– Intelligent agents support the mechanics of inquiry routing, autoresponders, and so on

7 Habits of Highly Effective Data Modelers*

• Immerse– Immerse yourself in the task environment to find out

what the client wants• Challenge

– Challenge existing assumptions; dig out the exceptions and test the boundaries of the model

• Generalize– Reduce the number of entities whenever possible;

simpler is easier to understand• Test

– Read it to yourself and to others to see if it makes sense and is relevant to the problem

*adapted from R. Watson (1999)

7 Habits of Highly Effective Data Modelers

• Limit– Set reasonable limits to the time and scope of the data

modeling activities. Identify the core entities and attributes that will solve the problem and stick to those

• Integrate– Identify how your project’s model fits with the organization’s

information architecture. Can it be integrated with the corporate data model? Look at the big picture.

• Complete– Don’t leave the data model ill-defined. Define entities,

attributes, and relationships carefully.

Questions?