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Managing Information Technology

CHAPTER 5THE DATA RESOURCE

Building Blocks of InformationTechnology

Hardware

Software

Network

Data

WHY MANAGE DATA?

•Organizations could not function long withoutcritical business data

•Cost to replace data would be very high

•Time to reconcile inconsistent data may be toolong

•Data often needs to be accessed quickly

WHY MANAGE DATA?

•Data should be:

–Cataloged

–Named in standard ways

–Protected

–Accessible to those with a need to know

–Maintained with high quality

•There are technical and managerial issues tomanaging data

TECHNICAL ASPECTS OF DM

•Data model is an overall map for business data

•Data modeling involves:

–Methodology, or steps followed to identify anddescribe data entities

–Notation, or a way to illustrate data entitiesgraphically

The Data Model

TECHNICAL ASPECTS OF DM

•Development process for data management systeminvolves six basic steps

Requirements Analysis

Conceptual Design

Logical Design

Physical Design

Implementation

Maintenance

The Data Model: Methodology

TECHNICAL ASPECTS OF DM

•User requirements usually gathered in text formatthrough personal interviews with users

•Data modeled in conceptual design phase as entity-relationship diagram (ERD)

•Data modeled in logical design phase as a set ofrelations (tables)

The Data Model: Methodology

TECHNICAL ASPECTS OF DM

•Entity-relationship diagram (ERD)

–Most common method for representing a datamodel and organizational data needs

–Entities: things about which data are collected

–Attributes: actual elements of data that are to becollected

–Relationships: relevant associations betweenorganizational entities

The Data Model: Notation

TECHNICAL ASPECTS OF DM

•ERD example:

–Entities are Customer, Order, and Product

–Relationships are Submits and Includes

The Data Model: Notation

TECHNICAL ASPECTS OF DM

•ERD example:

The Data Model: Notation

TECHNICAL ASPECTS OF DM

•Relations (tables)

–Structure consisting of rows and columns

–Each row represents a single entity

–Each column represents an attribute

•ERDs are converted into sets of relations

The Data Model: Notation

TECHNICAL ASPECTS OF DM

•ERD example:

The Data Model: Notation

TECHNICAL ASPECTS OF DM

•Convert ERD to relations:

The Data Model: Notation

TECHNICAL ASPECTS OF DM

•Data about data

•Needed to unambiguously describe data forthe enterprise

•Documents the meaning of all the businessrules that govern data

•Cannot have quality data without high-qualitymetadata

Metadata

TECHNICAL ASPECTS OF DM

•Enterprise modeling

–Top-down approach

–Describes organization and data requirements athigh level, independent of reports, screens, ordetailed specifications

–Not biased by how business operates today

Data Modeling

TECHNICAL ASPECTS OF DM

•Enterprise modelingsteps:

–Divide work into majorfunctions

–Divide each function intoprocesses

–Divide processes intoactivities

–List data entities assignedto each activity

–Identify relationshipsbetween entities

Data Modeling

TECHNICAL ASPECTS OF DM

•View integration

–Bottom-up approach

–Each report, screen, form, and documentproduced from databases (called user views)identified first

Data Modeling

TECHNICAL ASPECTS OF DM

•View integration steps:

–Create user views

–Identify data elements in each user view and put intoa structure called a normal form

–Normalize user views

–Integrate set of entities from normalization into onedescription

•Normalization: process of creating simple datastructures from more complex ones

Data Modeling

TECHNICAL ASPECTS OF DM

•Prepackaged data models – an alternative toenterprise data modeling

•Advantages:

–Developed using proven, up-to-date components

–Require less time and money

–Easier to evolve data model

–Greater application compatibility

–Easier to share data across organizations

Data Modeling

TECHNICAL ASPECTS OF DM

Data Modeling Guidelines

Objective

Modeling effort must be justified bysome overriding need

Scope

Coverage for a data model must becarefully considered

Outcome

The more uncertain the outcome, thelower the chances for success

Timing

Start with high-level model and fill indetails as major systems projectsundertaken

Data Modeling

TECHNICAL ASPECTS OF DM

1.Database processing activity can be specifiedwith a procedural language (3GL) or

2.Special-purpose language

–Structured query language (e.g., SQL)

–Data exchange language (e.g., XML)

Example SQL Query

SELECT ORDER_ID, CUSTOMER_ID, CUST-NAME, ORDER_DATE

FROM CUSTOMER, ORDER

WHERE ORDER_DATE > ‘04/12/08’ AND

CUSTOMER.CUSTOMER_ID = ORDER.CUSTOMERID;

Data Programming

MANAGERIAL ISSUES OF DM

•Data values may change, but a company willalways have customers, products, employees, etc.about which it needs to keep current data

•Business processes will change, but only theprograms will need to be rewritten

The need to manage data is permanent

Principles in Managing Data

MANAGERIAL ISSUES OF DM

•Most new data are captured in operationaldatabases

•Managerial and strategic databases typicallysubsets, summaries, or aggregates of operationaldatabases

•If managerial databases are constructed fromexternal sources, there may be problems withdata consistency

Data can exist at several levels

Principles in Managing Data

MANAGERIAL ISSUES OF DM

Principles in Managing Data

MANAGERIAL ISSUES OF DM

•Application independence: separation or decouplingof data from application systems- Raw data captured and stored- When needed, data are retrieved but not consumed- Data are transferred to other parts of the organization when authorized

•Meaning and structure of data not hidden from otherapplications

Application software should be separate from the database

Principles in Managing Data

MANAGERIAL ISSUES OF DM

Principles in Managing Data

MANAGERIAL ISSUES OF DM

•Data capture: gather data and populate thedatabase

•Data transfer: move data from onedatabase to another or otherwise bring datatogether

•Data analysis and presentation: providedata and information to authorized persons

Application software can be classified by how it treats data

Principles in Managing Data

MANAGERIAL ISSUES OF DM

•Significant result of applicationindependence- Company can replace the capture, transfer, and presentation software modules separately if necessary- Applications and data are not intertwined

•Obsolete systems do not need to be keptalive only to access data

Application software should be considered disposable

Principles in Managing Data

MANAGERIAL ISSUES OF DM

•Too costly to capture data multiple times andreconcile across applications

•Instead, data should be captured once andsynchronized across different databases

•Data architecture should include inventory ofdata and plan to distribute data

Data should be captured once

Principles in Managing Data

MANAGERIAL ISSUES OF DM

•Data must be clearly identified and defined sothat all users know exactly what they aremanipulating

•Only business managers have the knowledgenecessary to set data standards

•Data steward: a business manager responsiblefor the quality of data in a particular subject orprocess area

There should be strict data standards

Principles in Managing Data

MANAGERIAL ISSUES OF DM

•Five types of data standards- Identifier: Unique value for each business entity- Naming: Unique name or label for each type of data- Definition: Unambiguous description for eachtype of data- Integrity rule: Specification of legitimate values for a type of data- Usage rights: Security clearances for a type of data

There should be strict data standards (cont’d)

Principles in Managing Data

MANAGERIAL ISSUES OF DM

•Data standards should be stored in standardsdatabase called a metadata repository or datadictionary/directory (DD/D)

•Master data management (MDM): disciplines,technologies, and methods to ensure thecurrency, meaning, and quality of referencedata within and across subject areas

There should be strict data standards (cont’d)

Principles in Managing Data

MANAGERIAL ISSUES OF DM

The Data Management Process

MANAGERIAL ISSUES OF DM

•Plan: develop a blueprint for data and therelationships among data across businessunits and functions

•Source: identify the timeliest and highest-quality source for each data element

•Acquire and maintain: build data capturesystems to acquire and maintain data

The Data Management Process

MANAGERIAL ISSUES OF DM

•Define/describe and inventory: define each dataentity, element, and relationship that is beingmanaged

•Organize and make accessible: design thedatabase so that data can be retrieved andreported efficiently in the format that businessmanagers require

–One popular method for making data accessible is bycreating a data warehouse

–A data warehouse is a large data storage facilitycontaining data on all (or at least many) aspects of theenterprise

The Data Management Process

MANAGERIAL ISSUES OF DM

The Data Management Process

MANAGERIAL ISSUES OF DM

•Control quality and integrity: controls must bestored as part of data definitions and enforcedduring data capture and maintenance

•Protect and secure: define rights that eachmanager has to access each type of data

•Account for use: cost to capture, maintain, andreport data must be identified and reportedwith an accounting system

The Data Management Process

MANAGERIAL ISSUES OF DM

•Recover/restore and upgrade: establishprocedures for recovering damaged andupgrading obsolete hardware and software

•Determine retention and dispose: decide, onlegal and other grounds, how much datahistory needs to be kept

•Train and consult for effective use: train usersto use data effectively

The Data Management Process

MANAGERIAL ISSUES OF DM

•Data governance:

–Organizational process for establishing strategy,objectives, and policies for organizational data

–Data governance council sets standards aboutmetadata, data ownership and access, and datainfrastructure and architecture

•Two key policy areas for data governance:

–Data ownership

–Data administration

Data Management Policies

MANAGERIAL ISSUES OF DM

•Data sharing requires business managementparticipation

–Commitment to quality data is essential forobtaining the greatest benefits from a dataresource

–Data must also be made accessible to decreasedata processing costs for the enterprise

•Corporate information policy: foundation formanaging the ownership of data

Data Ownership

MANAGERIAL ISSUES OF DM

Data Ownership

MANAGERIAL ISSUES OF DM

•Transborder data flows: electronic flows ofdata that cross a country’s national boundary

•Data are subject to laws of exporting country

•Laws justified by perceived need to:

–Prevent economic and cultural imperialism

–Protect domestic industry

–Protect individual privacy

–Foster international trade

Data Ownership

MANAGERIAL ISSUES OF DM

•Example transborder issue

–U.S. Company Fined by E.U. for Improper Cross-Border Data Transfer

Data Ownership

MANAGERIAL ISSUES OF DM

•Data administration group: leads data managementefforts in an organization

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 metadata

• Reduce redundant data and processing

• Reduce system maintenance costs and improve systems development productivity

• Improve quality and security of data

• Insure data integrity

Data Administration

MANAGERIAL ISSUES OF DM

•Database administrator (DBA): IS role with theresponsibility for managing computer databases

Key Functions of the Database Administrator

• 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

Data Administration