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5/22/2017 1 Professor Lili Saghafi MANAGING INFORMATION TECHNOLOGY • Lecture 4 THE DATA RESOURCE • By : Prof. Lili Saghafi 1-2 PART 1: IT BUILDING BLOCKS Building Blocks of Information Technology Hardware Software Network Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall Data 4-3 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 do 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? HIPAA is the federal Health Insurance Portability and Accountabi lity Act of 1996. 4-4 the challenges related to supporting the communication needs • • • • • Network availability and reliability Data security Ease of use (with zero onsite support) Inexpensive to deploy and operate Network throughput (data rate) and latency 5 Networking Requirement Status and Challenges Network Availability and Reliability Major challenge: dependable and reliable wireless coverage Easy to use and zero-support networking equipment Depends upon the technology knowledge/experience of the user Inexpensive Dependent on the networking equipment and technology available and its position on the technology maturity curve IT capital costs compete with medical equipment costs, and typically the latter take precedent unless there is a regulatory requirement and the Network throughput (data rate) and latency Technology continues to evolve: 4G wireless technology is now being offered, but the 3G/4G definitions are somewhat blurred Security Not a technology challenge, but an increasing administrative challenge due to increased security rules and state-level oversight Easy acquisition and deployment of new networking technologies A key challenge here will be testing the new technologies under different MMC conditions 6 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 4-7 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 4-8 Data Relationships and EntityRelationship Diagrams • Entity-relationship diagrams (ERDs) – An ERD is a graphical model that shows relationships among system entities Data Relationships and EntityRelationship Diagrams • Entity-relationship diagrams (ERDs) – An ERD is a graphical model that shows relationships among system entities – Each entity is a rectangle, labeled with a noun – Each relationship is a diamond, labeled with a verb – Types of relationships • One-to-one (1:1) • One-to-many (1:M) • Many-to-many (M:N) – A full ERD shows all system relationships Data Relationships and EntityRelationship Diagrams • One-to-one (1:1) relationship – Exists when exactly one of the second entity occurs for each instance of the first entity – Examples • • • • One office manager heads one office One vehicle ID number is assigned to one vehicle One driver drives one delivery truck One faculty member is chairperson of one department Data Relationships and EntityRelationship Diagrams • One-to-many (1:M) relationship – Exists when one occurrence of the first entity can be related to many occurrences of the second entity, but each occurrence of the second entity can be associated with only one occurrence of the first entity – Examples • • • • One individual owns many automobiles One customer places many orders One department employs many employees One faculty advisor advises many students Data Relationships and EntityRelationship Diagrams • Many-to-many (M:N) relationship – Exists when one instance of the first entity can be related to many instances of the second entity, and one instance of the second entity can be related to many instances of the first – Examples • A student enrolls in one or more classes, and each class has one or more students registered • A passenger buys tickets for one or more flights, and each flight has one or more passengers • An order lists one or more products, and each product is listed on one or more orders Data Relationships and EntityRelationship Diagrams • A full ERD shows all system relationships – Examples • A sales rep serves one or more customers, but each customer has only one sales rep • A customer places one or more orders, but each order has only one customer • An order lists one or more products, and each product can be listed in one or more orders • A warehouse stores one or more products, and each product can be stored in one or more warehouses Data Relationships and EntityRelationship Diagrams • Cardinality – Describes how instances of one entity relate to another – Mandatory vs. optional relationships – Crow’s foot notation is one method of showing cardinality Data Relationships and EntityRelationship Diagrams • Cardinality – Describes how instances of one entity relate to another – Mandatory vs. optional relationships – Crow’s foot notation is one method of showing cardinality – Most CASE products support the drawing of ERDs Data Relationships and EntityRelationship Diagrams • Creating an ERD 1. Consider the nature of business 2. Identify the entities 2. Determine all significant events or activities for two or more entities 3. Analyze the nature of the interaction 4. Draw the ERD Relationships within Relational Database • Relationship classifications – 1:1 – 1:M – M:N • E-R Model – ERD Maps E-R model – Chen – Crow’s Feet 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 4-28 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 4-29 30 31 32 33 34 35 DATA MODELING LOGICAL DESIGN NOTATION ERD Example: to relations: Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall Convert ERD 4-36 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 4-37 ENTERPRISE MODELING Future-oriented Corporate Data Model 1. Divide work into major functions 2. Divide each function into processes 3. Divide processes into activities (e.g., forecast sales for next quarter) 4. List data entities assigned to each activity 5. Check for consistent names Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 4-38 TECHNICAL ASPECTS: DATA MODELING VIEW INTEGRATION – Bottom-up approach – Each report, screen, form, and document produced from databases (called user views) is identified 1. Create user views 2. Identify data element in each user view and put into a structure called a normal form 3. Normalize user views 4. Combine user views 5. Reconcile any differences with enterprise model Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 4-39 TECHNICAL ASPECTS: DATA MODELING NORMALIZATION • The process of creating simple data structures from more complex ones using a set of rules that yields a stable structure. Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall Source: Kenneth C. Laudon and Jane P. Laudon 4-40 TECHNICAL ASPECTS: DATA MODELING PACKAGED (UNIVERSAL) DATA MODELS • Advantages: - Developed using proven components - Requires less time and money - Easier to evolve - Will easily work with other applications from the same vendor - Provides a starting point for requirements - Promotes holistic and flexible views - Easier to share data across organizations in same industry Copyright © 2011 Pearson Education, 4-41 Inc. publishing as Prentice Hall TECHNICAL ASPECTS: DATA MODELING DATA MODELING GUIDELINES Objective Some overriding need Scope Coverage for a data model Outcome The more uncertain the outcome, the lower the chances for success Start with high-level model and fill in details as major systems projects undertaken Timing Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall 4-42 TECHNICAL ASPECTS: DATA MODELING DATABASE PROGRAMMING Database processing activity can be specified with a: - Procedural language (3GL) - One or more special purpose languages (4GL) Structured query language (SQL) Data exchange language (XML) Example: SQL Query SELECT OrderID, CustomerID, CustomerName, OrderDate FROM Customer, Order Copyright 2011 Pearson Education, AND WHERE ©OrderDate > ‘04/12/11’ Inc. publishing as Prentice Customer.CustomerID = Hall Order.CustomerID 4-43 MANAGERIAL ISSUES 1. 2. 3. 4. 5. 6. 7. PRINCIPLES IN MANAGING DATA 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 can be classified by how it treats data. Application software should be considered disposable. Data should be captured once. There should be strict data standards. 4-44 45 46 Albert Einstein defined insanity as “doing the same thing over and over again and expecting different results.” 47 Perhaps throwing appliances at a network to make IT security headaches go away fits this definition. 48 MANAGERIAL ISSUES PRINCIPLES 4-49 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 4-50 PRINCIPLES IN MANAGING DATA 2. Data can exist at several levels within an organization • Most new data are captured in operational databases • Managerial and strategic databases typically subsets, summaries, or aggregates of operational databases • If managerial databases are constructed from external sources, there may be problems with data consistency 4-51 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 of the organization when authorized • Meaning and structure of data not hidden from other applications 4-52 PRINCIPLES IN MANAGING DATA 4-53 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 persons 4-54 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 stored in them 4-55 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 4-56 PRINCIPLES IN MANAGING DATA 7. 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 4-57 PRINCIPLES IN MANAGING DATA 5 TYPES OF DATA STANDARDS 4-58 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 4-59 DATA MANAGEMENT PROCESS 4-60 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 4-61 DATA MANAGEMENT PROCESS Data Warehouse a large data storage facility containing data on major aspects of the enterprise 4-62 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 4-63 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 4-64 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 4-65 MANAGERIAL ISSUES Example: Corporate Information Policy for Data Access 4-66 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 4-67 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 4-68 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 4-69 Thank you Any Question? 5/22/2017 70 Professor Lili Saghafi 5/22/2017 Professor Lili Saghafi