Information Technology for Management
Chapter 5:
Per OxyChem’s Cloud Computing Formula for Success:
Problem
Loss of IT infrastructure from divestiture
Limited time frame and resources
Inadequate in-house IT staff
Solutions
Managed cloud infrastructure
On-demand, scalable services
-Illustrates use of cloud computing to improve effectiveness and control costs.
Infrastructure Components:
IT infrastructure:
Platform for supporting all information systems in the business
-Computer hardware, Computer software, Data management technology, Networking and telecommunications technology, and Technology services
Types of Computers:
Personal computers and mobile devices, Workstations, Servers, Mainframes, Supercomputers, and Grid computing
Client/Server Computing:
Form of distributed computing
Splits processing between “clients and servers”
Two-tiered client/server architecture
Multi-tiered client/server architecture (N-tier): Web servers & Application servers
Storage, Input and Output Technology:
-Primary secondary storage technologies: Magnetic disk (SSD’s), Optical disks, Magnetic tape, & Storage Networking: SANs
-Input devices (i.e. Keyboard) and Output devices (i.e.Monitor)
Contemporary Hardware Trends:
-The mobile digital platform (i.e. Tablet computers/netbooks)
-Consumerization of IT and BYOD
-Nanotechnology and quantum computing
-Virtualization (i.e. Software-defined storage (SDS))
Cloud Computing:
-Computing resources obtained over the Internet
Infrastructure as a service (IaaS)
Software as a service (SaaS)
Platform as a Service (PaaS)
-Public vs Private clouds
-Utility computing, on-demand computing
-Hybrid Cloud
-Data storage security is in hands of the provider
Green Computing:
Green IT
Practices and technologies for minimizing impact on environment
High-performance and power-saving processors:
Multicore processors
Reduced power consumption
Operating System Software:
Software that controls computer activities
GUIs
Multitouch
PC operating systems (Windows, Mac, UNIX, Linux (Open Source))
Mobile Operating systems (Chrome, Android, iOS)
Application Software and Desktop Productivity Tools
Software packages and desktop productivity tools:
Word processing software, Spreadsheet software, Data management software, Presentation graphics, Software suites and Web browsers
HTML and HTML5:
Hypertext markup language (HTML):
Page description language for specifying how elements is placed on a web page and for creating links to other pages and objects
HTML5:
Next evolution of HTML
Enables multimedia embedding without 3rd party plugins like Flash
Web Services:
Software components that exchange information with one another using universal web communication standards and languages
XML (eXtensible Markup Language): foundation of webservices
Service oriented architecture (SOA): collection of services used to build an organization’s software systems
Software Trends:
-Open-source software: (Linux, Apache)
-Cloud-based software and tools: (SaaS (software as a service) & Google Docs)
-Mashups: (Zip Realty uses Google Maps and Zillow.com)
-Apps: (Mobile Apps)
Capacity Planning and Scalability:
-Capacity Planning
Predicting when hardware system becomes saturated
Ensuring computing power for current and future needs
Factors include:
Maximum number of users
Impact of current, future software
Performance measures
-Scalability
Ability of system to expand to serve large number of users without breaking down.
Total Cost of Ownership (TCO) model:
-Analyzing direct and indirect costs to determine the actual cost of owning a specific technology
Direct costs: hardware, software purchase costs
Indirect costs: ongoing administration costs, upgrades, maintenance, etc.
Hidden costs: support staff, downtime, etc.
-TCO can be reduced through increased centralization, standardization of hardware and software resources.
Using Technology Service Providers:
-Outsourcing
Using external provider to run computer center and networks
Web hosting service
Offshore software outsourcing
Service level agreements (SLAs)
-Using cloud services
Appealing to businesses with smaller IT budgets
Pricing is per hour, per-use
Switching costs
Managing Mobile Platforms:
Mobile devices provide productivity gains
Expenses of equipping employees with devices
Network configuration
Software
Device security
Stolen or compromised devices
Mobile device management (MDM) software
Managing Software Localiztion for Global Business:
-Software localization
Local language interfaces
Complex software interfaces
-Differences in local cultures
-Differences in business processes
-These factors add to TCO of using technology service providers
Chapter 6
What is a database?
-Database:Collection of related filescontaining records on people,places or things.
-Entity:Generalized categoryrepresenting person,place or thing (i.e. SUPPLIER, PART)
-Attributes:
Specific characteristics of each entity:
SUPPLIER name, address
PART description, unit price, supplier
Relational Databases:
-Organize data into two-dimensional tables (relations) with columns and rows.
-One table for each entity:
i.e. (CUSTOMER, SUPPLIER, PART, SALES)
Fields (columns) store data representing an attribute
Rows store data for separate records, or tuples.
-Key field: uniquely identifies each record.
-Primary key
Establishing Relationships:
-Entity-relationship diagram: Used to clarify table relationships in a relational database.
-Relational database tables may have:
One-to-one relationship
One-to-many relationship
Many-to-many relationship: (Requires “join table” or intersection relation that links the two tables to join information).
-Normalization
Streamlining complex groups of data
Minimizes redundant data elements
Minimizes awkward many-to-many relationships
Increases stability and flexibility
-Referential integrity rules
Ensure that relationships between coupled tables remain consistent
Database Management Systems (DBMS):
-Software for creating, storing, organizing, and accessing data from a database.
-Separates the logical and physical views of the data.
Logical view: how end users view data
Physical view: how data are structured and organized.
-Examples: Microsoft Access, DB2, Oracle Database, Microsoft SQL Server, MySQL
Operations of a Relational DBMS:
-Select:Creates a subset of all records meeting stated criteria
-Join:Combines relational tables to present the server with more information than is available from individual tables
-Project:Creates a subset consisting of columns in a table & permits user to create new tablescontaining only desired information
Capabilities of Database Management Systems:
-Data definition capabilities:Specify structure of content of database
-Data Dictionary:Automated or manual file storing definitions of data elements and their characteristics
-Querying and reporting:Data manipulation language
-Structured query language (SQL)
– Microsoft Access query-building tools
Report generation, i.e. Crystal Reports
Non-relational databases:
– “NoSQL”
-Handle large data sets of data that are not easily organized into tables, columns, and rows
-Use more flexible data model: (Don’t require extensive structuring)
-Can manage unstructured data, such as social media and graphics
-i.e. Amazon’s Simple DB, MetLife’s Mongo DB
Cloud Databases and Distributed Databases:
-Relational database engines provided by cloud computing services
Pricing based on usage
Appeal to small or medium-sized businesses
-Amazon Relational Database Service
Offers MySQL, Microsoft SQL Server, Oracle Database engines
-Distributed databases
Stored in multiple physical locations
Google’s Spanner cloud service
The Challenge of Big Data:
-Massive quantities of unstructured and semi-structured data from Internet and more
3Vs: Volume, variety, velocity
Petabytes and exabytes
-Big datasets offer more patterns and insights than smaller datasets (i.e. Customer behavior, weather patterns)
-Requires new technologies and tools
Business Intelligence Infrastructure:
-Array of tools for obtaining useful information from internal and external systems and big data
Data Warehouses
Data Marts
Hadoop
In-memory computing
Analytical platforms
Data Warehouses:
-Data warehouse:Database that stores current and historical data that may be of interest to decision makers.
Consolidates and standardizes data from many systems, operational and transactional databases
Data can be accessed but not altered
-Data mart:Subset of data warehouses that is highly focused and isolated for a specific population of users.
-Hadoop:Open-source software framework for big data
Breaks data task into sub-problems and distributes the processing to many inexpensive computer processing nodes.
Combines result into smaller data set that is easier to analyze.
Key Services: Hadoop Distributed File System (HDFS)
-In-Memory Computing:
Relies on computer’s main memory (RAM) for data storage
Eliminates bottlenecks in retrieving and reading data
Dramatically shortens query response times
Enabled by high-speed processors, multicore processing
Lowers processing costs
Analytic Platforms:
Preconfigured hardware-software systems
Designed for query processing and analytics
Use both relational and non-relational technology to analyze large data sets
Include in-memory systems, NoSQL DBMS
i.e. IBM Pure Data System for Analytics (integrated database, server, storage components).
Data lakes
Analytical Tools: Relationships, Patterns, Trends
Once data is gathered, tools are required for consolidating, analyzing, and to use insights to improve decision making.
Software for database querying and reporting
Multidimensional data analysis (OLAP)
Data mining
Online Analytical Processing (OLAP)
-Supports multidimensional data analysis, enabling users to view the same data in different ways using multiple dimensions.
Each aspect of information—product, pricing, cost, region, or time period—represents a different dimension
i.e. comparing sales in East in June versus May and July
-Enables users to obtain online answers to ad hoc questions like these quickly.
Data Mining:
-Finds hidden patterns and relationships in large databases and infers rules from them to predict future behavior.
-Types of information obtainable from data mining:
Associations: occurrences linked to a single event
Sequences: events linked over time
Classifications: patterns describing a group an item belongs to
Clustering: discovering yet unclassified groupings
Forecasting: uses series of values to forecast future values
Text Mining:
Unstructured data (mostly text files) accounts for 80 percent of an organization’s useful information.
Text mining allows businesses to extract key elements from, discover patterns in and summarize large unstructured data sets.
Sentiment analysis: mines online text comments online or in email to measure customer sentiment.
Web Mining:
-Discovery and analysis of useful patterns and information from the web.
i.e. to understand customer behavior, evaluate website, quantify success of marketing.
-Content mining: mines content of websites
-Structure mining: mines website structural elements such as links
-Usage mining: mines user interaction data gathered by web servers
Databases and the Web:
-Firms use the web to make information from their internal databases available to customers and partners.
-Middleware and other software make this possible
Web server
Application servers or CGI
Database server
-Web interfaces provide familiarity to users and savings over redesigning legacy systems.
Establishing an Information Policy:
-Information policy:States organization’s rules for organizing, managing,storing and sharing information
-Data administration:Responsible for specific policies and procedures through which data can be managed as a resource.
-Database administration:Database design and management group responsible for defining and organizing the structure and the content of the database andmaintaining the database.
Ensuring Data Quality:
-Poor data quality: major obstacle to successful customer relationship management.
-Data quality problems caused by:
Redundant and inconsistent data produced by multiple systems.
Data input errors
-Data quality audit
-Data cleansing