MANAGEMENT INFORMATION SYSTEMS CHAPTER THE DATA RESOURCE Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-1 PART 1: IT BUILDING BLOCKS Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-2 WHY MANAGE DATA? - What costs would your company incur if it did not comply with SOX or other financial reporting laws? - What would your company if its critical business data were destroyed? - What costs would your company incur if sensitive data were stolen or you violated HIPAA requirements to protect healthcare data? - How much time does your company spend reconciling inconsistent data? - How difficult is it to determine what data are stored about the part of the business you manage? - Do you know all the contacts a customer has with your organization? Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-3 TECHNICAL ASPECTS OF MANAGING DATA DATA MODELS • An overall “map” for business data • Involves: • A methodology (process) to identify and describe data entities • A notation = a way to describe data entities Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-4 DATA MODEL: CONCEPTUAL DESIGN PHASE ENTITY-RELATIONSHIP DIAGRAM (ERD) - Entities = things about which data are collected (e.g., Customer, Order, Product) - Attributes = actual elements of data to be collected - Relationships = associations between entities (e.g., Submits, Includes) MOST COMMON DATA MODEL FOR CONCEPTUAL DESIGN PHASE Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-5 TECHNICAL ASPECTS METADATA • Data about data • Unambiguous data description • Documents “business rules” that govern data (e.g., type of data such as alphanumeric; whether a name can change; etc • Quality data requires high-quality metadata Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-6 DATA MODEL: LOGICAL DESIGN PHASE NOTATION • ERDs are converted into sets of Relations, or Tables: – Structure consisting of rows and columns – Each row represents a single entity – Each column represents an attribute Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-7 DATA MODELING LOGICAL DESIGN NOTATION ERD Example: Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall Convert ERD to relations: 4-8 TECHNICAL ASPECTS: DATA MODELING ENTERPRISE MODELING - Top-down approach - High-level model - Describes organization and data requirements at high level, independent of reports, screens, or detailed descriptions of data processing requirements Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-9 ENTERPRISE MODELING Future-oriented Corporate Data Model – Divide work into major functions – Divide each function into processes – Divide processes into activities (e.g., forecast sales for next quarter) – List data entities assigned to each activity – Check for consistent names Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-10 PRINCIPLES IN MANAGING DATA Application Software should be considered disposable Due to application independence: - Company can replace the capture, transfer, and presentation software modules separately if necessary - Applications and data are not intertwined - Aging systems not need to be retained because of the need to access the data stored in them Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-23 PRINCIPLES IN MANAGING DATA Data should be captured once • Too costly to capture data multiple times and reconcile across applications • Instead, data should be captured once and synchronized across different databases • Data architecture should include inventory of data and plan to distribute data Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-24 PRINCIPLES IN MANAGING DATA There should be strict data standards • Data must be clearly identified and defined so that all users know exactly what they are manipulating • Only business managers have the knowledge necessary to set data standards • Database contents must be unambiguously described, and stored in a metadata repository or data dictionary/directory (DD/D) Data steward A business manager responsible for the quality of data in a particular subject or process area Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-25 PRINCIPLES IN MANAGING DATA TYPES OF DATA STANDARDS Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-26 MANAGERIAL ISSUES • Master data management (MDM): disciplines, technologies, and methods to ensure the currency, meaning, and quality of reference data within and across subject areas Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-27 DATA MANAGEMENT PROCESS Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-28 DATA MANAGEMENT PROCESS • Plan: develop a blueprint for data and the relationships among data across business units and functions • Source: identify the timeliest and highest-quality source for each data element • Acquire and maintain: build data capture systems to acquire and maintain data • Define/describe and inventory: define each data entity, element, and relationship that is being managed • Organize and make accessible: design the database so that data can be retrieved and reported efficiently in the format that business managers require o One popular method to make data accessible is to create a Data Warehouse Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-29 DATA MANAGEMENT PROCESS Data Warehouse a large data storage facility containing data on major aspects of the enterprise Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-30 DATA MANAGEMENT PROCESS, CONT • Control quality and integrity: controls must be stored as part of data definitions and enforced during data capture and maintenance • Protect and secure: define rights that each manager has to access each type of data • Account for use: cost to capture, maintain, and report data must be identified and reported with an accounting system • Recover/restore and upgrade: establish procedures for recovering damaged and upgrading obsolete hardware and software • Determine retention and dispose: decide, on legal and other grounds, how much data history needs to be kept • Train and consult for effective use: train users to use data effectively Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-31 MANAGERIAL ISSUES DATA MANAGEMENT POLICIES • Two key policy areas for data governance: - Data ownership - Data administration • Data governance - Data governance council sets standards about metadata, data ownership and access, and data infrastructure and architecture - High-level oversight for establishing strategy, objectives, and policies for organizational data Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-32 MANAGERIAL ISSUES DATA OWNERSHIP Rationales for data ownership: - The need to protect personal privacy, trade secrets, etc Data sharing requires business management participation - Commitment to quality data is essential for obtaining the greatest benefits from a data resource - Data must also be made accessible to decrease data processing costs for the enterprise Corporate Information Policy: provides the foundation for managing the ownership of data Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-33 MANAGERIAL ISSUES Example: Corporate Information Policy for Data Access Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-34 MANAGERIAL ISSUES • Transborder data flows: electronic movements of data that cross a country’s national boundary for processing, storage, or data retrieval • Data are subject to laws of exporting country • Laws to control flows are justified by perceived need to: - Prevent economic and cultural imperialism - Protect domestic industry - Protect individual privacy - Foster international trade Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-35 MANAGERIAL ISSUES DATA ADMINISTRATION UNIT • IS unit accountable for data management 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 Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-36 MANAGERIAL ISSUES DATABASE ADMINISTRATOR (DBA) • IS position with the responsibility for managing an organization’s electronic 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 Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-37 [...]... 4-20 PRINCIPLES IN MANAGING DATA Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-21 PRINCIPLES IN MANAGING DATA 4 Application Software can be classified by how it treats data Data capture: gather data and populate the database Data transfer: move data from one database to another or otherwise bring data together Data analysis and presentation: provide data and information to authorized... resource - Data must also be made accessible to decrease data processing costs for the enterprise Corporate Information Policy: provides the foundation for managing the ownership of data Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-33 MANAGERIAL ISSUES Example: Corporate Information Policy for Data Access Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-34... Hall 4-17 PRINCIPLES IN MANAGING DATA 1 The Need to Manage Data is Permanent • Data values may change, but a company will always have customers, products, employees, etc about which it needs to keep current data • Business processes will change, but only the programs will need to be rewritten Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-18 PRINCIPLES IN MANAGING DATA 2 Data can... 2011 Pearson Education, Inc publishing as Prentice Hall 4-22 PRINCIPLES IN MANAGING DATA 5 Application Software should be considered disposable Due to application independence: - Company can replace the capture, transfer, and presentation software modules separately if necessary - Applications and data are not intertwined - Aging systems do not need to be retained because of the need to access the data... PRINCIPLES IN MANAGING DATA 6 Data should be captured once • Too costly to capture data multiple times and reconcile across applications • Instead, data should be captured once and synchronized across different databases • Data architecture should include inventory of data and plan to distribute data Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-24 PRINCIPLES IN MANAGING DATA... may be problems with data consistency Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-19 PRINCIPLES IN MANAGING DATA 3 Application Software should be separate from the database • Application independence = separation or decoupling of data from application systems - Raw data captured and stored - When needed, data are retrieved but not consumed - Data are transferred to other parts... 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 Copyright © 2011 Pearson Education,... (DD/D) Data steward A business manager responsible for the quality of data in a particular subject or process area Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-25 PRINCIPLES IN MANAGING DATA 5 TYPES OF DATA STANDARDS Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-26 MANAGERIAL ISSUES • Master data management (MDM): disciplines, technologies, and methods... for data and the relationships among data across business units and functions • Source: identify the timeliest and highest-quality source for each data element • Acquire and maintain: build data capture systems to acquire and maintain data • Define/describe and inventory: define each data entity, element, and relationship that is being managed • Organize and make accessible: design the database so that... OrderDate FROM Customer, Order WHERE OrderDate > ‘04/12/11’ AND Customer.CustomerID = Order.CustomerID Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-15 MANAGERIAL ISSUES PRINCIPLES IN MANAGING DATA 1 2 3 4 5 6 7 The need to manage data is permanent Data can exist at several levels within the organization Application software should be separate from the database Application software