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 n Introduction Managing Data ă The Three-Level Database Model ă Four Data Models ă Getting Corporate Data into Shape Todays Lecture n Managing Information ă Four Types of Information ă Data Warehouses ă Document ă Content Management Management Introduction n “Managing information resources” initially meant managing data, first in files, then in corporate databases which were: ă Well structured ă Carefully defined, and ă Controlled by IS department Introduction n Data vs Information vs Knowledge ă Data: n facts devoid of meaning or intent ă Information: data in context ă Knowledge: information with direction or intent 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 Introduction ă PCs users used alone n Needed to share files n Version control, back-up etc ă Web sites / content Introduction n Initially created their own n Need for recovery, version control n Corporate consistency ă IS to the ‘rescue’ n Management procedures n Discipline Introduction n Corporate databases are still a major IS department responsibility ă Sometimes housed in a variety of database models ă Production databases transaction ă Data warehouses ă CRM Customer Relationship Management 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: ă Different ă Same names for same data, and name for different data etc Getting Corporate Data into Shape: The Problem: Inconsistent Data Definitions cont n n n n n n Account Number AcctNum AcctNumb Acct# A/CNum Note: people (in the majority of cases) werent stupid ă They never dreamt their files / databases etc would be used in this manner ă 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 ă 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: ă To determine what data is being used outside the organizational unit that creates it ă 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: Clean up the data definitions Control shared data Manage data distribution, and 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: To redevelop operational and financial transaction systems using SAP To develop a knowledge-management architecture, including data warehousing, and 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 ă They ă With have created a distributed SAP architecture 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 cont n To convert SAP data to knowledge, Monsanto uses data warehouses ă The sole source of master data is the ERD, but the data can be distributed wherever they are needed 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. ă 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 ă 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 cont n Getting the data in shape was a huge undertaking, but it has made the company more flexible n Monsanto is already reaping bottom-line benefits from better integration and greater flexibility Summary n Introduction n Managing Data ă The Three-Level Database Model ă The Three-Level Database Model: ă Four Data Models Advantages Summary n n We have covered today Getting Corporate Data into Shape ă The Problem: Inconsistent Data Definitions ă The n Role of Data Administration ă ERP (Enterprise Resource Planning) Monsanto Case Example: Managing Corporate Data / ERP .. .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. .. Today’s Lecture n n Introduction Managing Data ă The Three-Level Database Model ă Four Data Models ¨ Getting Corporate Data into Shape Today’s Lecture n Managing Information ă Four Types of Information. .. “intellectual assets” n Information resources need to be well managed as information becomes an important strategic resource Managing Data n Database management systems are the main tool for managing computerized