Lecture Business management information system - Lecture 16: Managing information resources

Lecture Business management information system - Lecture 16: Managing information resources. This chapter presents the following content: Managing data, the three-level database model, four data models, getting corporate data into shape, managing information, four types of information, data warehouses, document management, content management.

Managing Information

Resources Lecture 16

Managing Information Resources

n Managing Information Resources Lectures explores the management of data information, and knowledge

n It begins by identifying some problems in managing data, and then surveys the evolution of database management systems, including the next-generation systems

n It explores the various types of information that

companies need to manage as they treat information as

an organizational resource

Managing Information

Resources

n It concludes by discussing one of the most important

issues facing companies today: how to manage

knowledge

n Case examples include Monsanto, Owens & Minor,

HICSS Personal Proceedings, Tapiola Insurance Group, Tennessee Valley Authority, Eastman Chemical

Company and Groove Networks

Today’s Lecture

n Introduction

n Managing Data

¨ The Three-Level Database Model

¨ Four Data Models

¨ Getting Corporate Data into Shape

n “Managing information resources” initially meant

managing data, first in files, then in corporate databases which were:

¨ Well structured

¨ Carefully defined, and

¨ Controlled by IS department

n Data vs Information vs Knowledge

¨ Data: facts devoid of meaning or intent

¨ Information: data in context

¨ Knowledge: information with direction or intent

n As the breadth of the kinds of information resources has expanded, so has the job of managing them The job

may not start in the IS department but it invariably ends

up there

¨ PCs users used ‘alone’

n Needed to share files

n Version control, back-up etc

¨ Web sites / content

n Initially created their own

n Need for recovery, version control

n Corporate consistency

¨ IS to the ‘rescue’

n Management procedures

n Discipline

Introduction

n Corporate databases are still a major IS department responsibility

¨ Sometimes housed in a variety of database models

¨ Production databases – transaction

¨ Data warehouses

¨ CRM –Customer Relationship Management

n Information in the form of documents (electronic or

paper) and Web content has exploded the size of

databases organizations now manage

n Knowledge management is becoming a key to exploiting

“intellectual assets”

n Information resources need to be well managed as

information becomes an important strategic resource

Introduction

Managing Data

n Database management systems are the main tool for managing computerized corporate data

n They have been around since the 1960s and are based

on two major principles:

¨ A three- level conceptual model and

¨ Several alternative ‘data models’ for organizing the data

DBMS EXAMPLE

Managing Data:

The Three-Level Database Model

n Level 1 – The external, conceptual, or local level,

containing the various “user views” of the corporate data that each application program uses

¨ Not concerned with how the data will be physically stored or what data is used by other applications

Managing Data:

The Three-Level Database Model

n Level 2 - The logical or “enterprise data” level

¨ ‘Technical’ (human) view of the database = under control of the DBAs

n Level 3 - The physical or storage level, specifying the way the data is physically stored

¨ End user not concerned with all these ‘pointers and flags’ (how the data is physically organized) = they are for use by the DBMS

The Three-Level Database Model:

Advantages

n Level 2 absorbs changes made at Level 3 such

as using a new physical storage device

¨ Individual application programs in Level 1 do not need

to be changed when the physical layer changes

n Data only needs to be stored once in Level 2,

and different programs can draw on it and vary

the relationships among the data

Managing Data:

Four Data Models

The second major concept in database management is alternate ways to define relationships among data

1. Hierarchical model: structures data so that each

element is subordinate to another in a strict

hierarchical manner

‒ Parent, child etc.

2. Network model: allows each data item to have more

than one parent,

‒ Relationships stated by pointers stored with the data

Managing Data:

Four Data Models cont.

3. Relational model:

where the data is stored in tables

– Eight relational operations can be performed on this

data

Select, Project, Join, Product, Intersection,

Difference, Union, Division

4. Entity-Relationship model:

Managing Data:

Four Data Models cont.

n Microsoft Access

¨ Relational systems are not as efficient as hierarchical or

network database systems, but because relational systems allow people to create relationships among data on the fly.

More flexible

Microsoft Access DBMS Example

Managing Data:

Four Data Models cont.

n First used to handle end user queries – they are now

widely used in high-volume transaction systems with huge files

n Hence, they have become the database technology of

choice in today’s systems

¨ Also = largely due to decrease in costs of

technology: processing and disk storage

Managing Data:

Four Data Models cont

4 Object model: can be used to store any type of data, whether a:

– Traditional name or address,

Managing Data:

Four Data Models cont

n The tenets of objects have become increasingly

important in the world of computing

– E.g Web Services because the XML modules utilize

object principles

Managing Data:

Four Data Models cont

n Typical, yet complex database applications that may

require objects:

– CAD for a large office building

– Large retail chains record every product code

scanned

– Insurance policy files e.g claim forms, images,

video etc

Managing Data:

Four Data Models cont.

n Object models retain traditional DBMS features including:

¨ End user tools

¨ High level Query languages

¨ Concurrency control

¨ Recovery

¨ Ability to handle huge amounts of data

Managing Data:

Four Data Models cont.

n Include two other major concepts

Managing Data:

Four Data Models cont.

n Finally, security is of major importance in today’s DBMSs

¨ Problem = compounded by distributed,

heterogeneous Internet-linked databases

n Companies may want to permit access to some portions

of their databases whilst restricting other portions

Managing Data:

Four Data Models cont.

¨ This selective accessibility requires reliably

authenticating ‘users’

n Unless security and integrity are strictly enforced, users will not be able to (fully) trust the systems

Managing Data

n Getting Corporate Data into Shape

Getting Corporate Data into Shape

n In the midst of this growing richness of data and

information, companies are still struggling to get their

internal alphanumeric data under control

n The installation of company-wide software packages

such as SAP, enterprise data warehouses, and intranets has once again brought to the fore the problems of “dirty data”

Getting Corporate Data into Shape

¨ Data from different databases that has:

n Different names

n Uses different time frames, or

n That otherwise does not match

n Attempts to get under control go back a long way:

Getting Corporate Data into Shape: The Problem: Inconsistent Data Definitions

n Problem: data definitions incompatible from:

¨ Application to application

¨ Department to department

¨ Site to site, and

¨ Division to division

Getting Corporate Data into Shape: The Problem: Inconsistent Data Definitions

n Reason: to get application systems up and running

quickly, system designers sought data from the cheapest source or politically expedient source

n Result: different files with:

¨ Different names for same data, and

¨ Same name for different data etc

Getting Corporate Data into Shape: The Problem: Inconsistent Data Definitions cont.

n Note: people (in the majority of cases) weren’t stupid

¨ They never dreamt their files / databases etc would

be used in this manner

¨ Historical ‘stand alone’ computing

n Information collation, use, communication etc = never thought possible

Getting Corporate Data into Shape: The

Role of Data Administration

n The use of DBMS - database management software, reduced, to some extent, the problems of inconsistent and redundant data in organizations

¨ However merely installing & running a DBMS is not sufficient to manage data as a corporate resource

n Database administration: concentrates on administering databases and the software that manages them

Getting Corporate Data into Shape: The

Role of Data Administration cont.

n Data administration is broader:

¨ To determine what data is being used outside the

organizational unit that creates it

¨ Whenever data crosses organizational boundaries, its definition and format need to be standardized

n Data dictionaries are the main tools by which

data administrators control standard data

definitions

Getting Corporate Data into Shape:

ERP (Enterprise Resource Planning)

n To bring order to the data mess, data administration

has four main functions:

1. Clean up the data definitions

2. Control shared data

3. Manage data distribution, and

4. Maintain data quality

Getting Corporate Data into Shape:

ERP (Enterprise Resource Planning)

n Interestingly, many companies really did not take these

four jobs seriously until the mid 1990s, when they needed consistent data to install a company-wide ERP package

n ERP provided the means to consolidate data to give

management a corporate-wide view of operations

Monsanto Case Example: Managing Corporate Data / ERP

n Monsanto case study to illustrate one company’s

success in getting its corporate data in shape

n Monsanto is a provider of agricultural products,

pharmaceuticals, food ingredients, and chemicals

50% revenues outside USA, it is decentralized

Monsanto Case Example: Managing Corporate Data / ERP

n Monsanto established three large enterprise wide IT

projects:

1. To redevelop operational and financial transaction

systems using SAP

2. To develop a knowledge-management architecture,

including data warehousing, and

3. To link transaction and decision support systems via

common master data, known as enterprise reference data (ERD)

Monsanto Case Example: Managing Corporate Data / ERP

cont

n Monsanto is too large and complex to operate SAP as a single installation

¨ They have created a distributed SAP architecture

¨ With separate instances of SAP for reference data, finance, and operations in each business unit

§ The master reference data integrates these distributed components

Monsanto Case Example: Managing Corporate Data / ERP

n To get corporate data in shape, Monsanto created a

department called ERD Stewardship to set data

standards and enforce quality—hence its nickname, “the data police.”

¨ Independent of MIS

Monsanto Case Example: Managing Corporate Data / ERP

cont.

n Another newly created function is entity specialists =

managers with the greatest stake in the quality of data

n Also, data managers who now adhere to the new ERD rules

¨ This has led to a cultural change: The idea of

“tweaking” a system to fix a local discrepancy,

formerly common, can now cause a major disruption

in operations or a bad decision based on faulty data

Monsanto Case Example: Managing Corporate Data / ERP

n Introduction

n Managing Data

¨ The Three-Level Database Model

¨ The Three-Level Database Model: Advantages

¨ Four Data Models

n We have covered today

n Getting Corporate Data into Shape

¨ The Problem: Inconsistent Data Definitions

¨ The Role of Data Administration

¨ ERP (Enterprise Resource Planning)

n Monsanto

Case Example: Managing Corporate Data / ERP