isds 351 exam 1

Information system (IS)

A combination of hardware and software components, along with data, processes, and people, that function collaboratively to complete a task

Five Component Model

Hardware, software, data, procedures/processes, and people. Hardware + software = technology side; people + processes = business side; data links the two

Five Component Model: direction of automation

Information systems move work away from people and into technology

Five Component Model: difficulty of change

Change gets harder moving toward the business side (people and processes)

Five things organizations use IS to do

Process fundamental transactions; communicate with employees, customers, and partners; analyze large data to detect trends; track costs and schedule progress on projects; monitor results and recommend actions

Cost leadership

Competitive advantage by providing the same value as competitors but at a lower price

Differentiation

Competitive advantage by charging higher prices for products the customer perceives as better

Focus (strategy)

Competitive advantage by understanding and serving a target market better than anyone else

Process

A structured set of related activities that takes input, adds value, and creates an output

Procedure

Defines the steps to follow to achieve a specific end result

What good procedures describe

How to achieve the desired end result, who does what and when, and what to do if something goes wrong

Organizational structure

Defines relationships between organization members, including roles, responsibilities, and lines of authority; roles often change when a new IS is introduced

Value chain

A series of activities that transform inputs into outputs, increasing the value of the input

Supply chain

A key value chain whose primary processes are inbound logistics, operations, outbound logistics, marketing and sales, and service; gets the right product to the right customer in the right quantity, at the right time and cost

Three benefits of strategic planning

Provides a framework and clear direction for decision making; focuses resources on agreed-on key priorities; lets the organization be proactive rather than reactive

Strategic planning pyramid: changes rarely (base)

Values, vision, and mission

Strategic planning pyramid: changes carefully (middle)

Objectives, key results, and strategies

Strategic planning pyramid: changes constantly (top)

Initiatives, programs, and projects

Porter's Five Forces Model

Most used model for assessing industry competition: bargaining power of suppliers, bargaining power of buyers, threat of new entrants, threat of substitute products, and rivalry among existing competitors

Purpose of Porter's Five Forces

Determines the level of competition and long-term profitability of an industry

Strategy

Describes how an organization will achieve its vision, mission, objectives, and key results; common themes are improvements in revenue, customers, and efficiency

Drivers of IS organizational strategy

Corporate strategy and business unit strategies drive IT organizational strategy, which involves innovative thinking and IT investments (technologies, vendors, competencies, people, systems, projects)

Three roles of an IS organization (inner to outer)

Cost center/service provider (core infrastructure); business partner/peer (business enablement); game changer/business innovator (business innovation). The roles are additive

Tangible benefits

Benefits that can be measured directly and assigned a monetary value

Intangible benefits

Benefits that cannot be directly measured or easily quantified in monetary terms

Total cost of ownership (TCO)

All expenses for creating, implementing, and using an item over its entire useful life

Innovation

The application of new ideas to a firm's products, processes, and activities, leading to increased value

Sustaining innovation

Innovation that results in enhancements to existing products, services, and ways of operating

Disruptive innovation

An innovation that initially provides a lower level of performance than the marketplace has grown to accept

Reengineering (business process reengineering)

The radical redesign of business processes, organizational structures, information systems, and values of the organization

Continuous improvement

A form of innovation that constantly seeks ways to improve business processes and add value to products and services

Digital transformation (DTX)

Significantly modifying existing services, products, and procedures through information systems to bring new value and efficiency to the customer experience

Technology acceptance model (TAM)

Model stating that perceived usefulness (U) and perceived ease of use (E) strongly influence whether someone will use an information system

Diffusion of Innovation groups (in order)

Innovators 2.5%, early adopters 13.5%, early majority 34%, late majority 34%, laggards 16%

Four things business data helps leaders do

Understand what has taken place, identify operating issues, examine causes of issues, and identify business opportunities

Benefits of quality data

Improves decision making (removes guesswork), increases customer satisfaction (personalization), and increases sales (accurate targeting)

Data management

An integrated set of functions defining how data is obtained, certified fit for use, stored, secured, and processed; ensures accessibility, reliability, and timeliness

Data governance

Defines the roles, responsibilities, and processes for ensuring data can be trusted and used by the organization; requires business leadership

Database administrator (DBA)

Skilled IS professional who defines users' data needs, builds databases, tests and evaluates them, monitors performance, and keeps data secure. DBA can also mean database architect

Data steward

Typically a non-IS employee responsible for managing critical data entities or attributes, including sourcing data, maintaining definitions, analyzing quality, and reconciling issues

Data lifecycle management (DLM)

A policy-based approach to managing the flow of an enterprise's data from acquisition or creation through storage until it is outdated and deleted

Database approach

Data management approach where multiple information systems share a pool of related data

Database management system (DBMS)

Software used to access and manage a database and provide an interface between the database and its users and programs; a single point of control over data

Entity

A person, place, or thing for which data is collected, stored, and maintained (e.g., employee, invoice, product)

Attribute

A characteristic of an entity (e.g., employee number, last name, hire date)

Data modeling

A tool used to design a database, at the organizational level or the specific business application level

Enterprise data modeling

Data modeling at the organizational level: starts with the strategic needs of the organization, then examines data for functional areas and departments

Data hierarchy (smallest to largest)

Bit, byte, field, record, table, database

Field

A single attribute about an entity

Record

A collection of fields about a specific entity

Primary key

A field or set of fields that uniquely identifies a record in a database table

Foreign key

A field in one table that refers to the primary key in another table

Structured Query Language (SQL)

A special-purpose programming language for accessing and manipulating data stored in a database

Schema

Defines the tables, the fields in each table, and the relationships between fields and tables

Data query language (DQL)

Used to select data from a table in the database

Data definition language (DDL)

Instructions and commands used to define and describe data and relationships in a database

Data dictionary

A collection of metadata describing the data, format, structure, and relationships in a database; standards make data sharing easier

Metadata

Data that describes other data

Data manipulation language (DML)

Lets users, administrators, and applications modify database data. Query: SELECT. Change: INSERT, DELETE, UPDATE

Data cleansing

Detecting and then correcting or deleting incomplete, incorrect, inaccurate, or irrelevant records by cross-checking against a validated data set

Data enhancement

Augments data in a database by adding related information

Relational database

Data organized into relations (tables); rows represent entities, columns represent attributes, and rows are uniquely identified by a primary key

Database as a service (DaaS)

Database stored on a provider's servers and accessed over the Internet; the provider handles administration, eliminating in-house installation, maintenance, and monitoring

View of data: business vs. IT/IS

Same data, different view. Business sees customers, orders, suppliers, invoices; IT/IS sees files/tables, rows, fields, bytes

Business intelligence (BI)

Applications, practices, and technologies for extracting, transforming, integrating, analyzing, visualizing, and interpreting data to support better decision making

Goal of BI

To improve decision making by getting the most value from data and presenting results in an easy-to-understand way

Big data

Data collections so enormous and complex that traditional data management software, hardware, and analysis processes cannot handle them

Analytics

The use of data and quantitative analysis to support fact-based decision making

BI process steps

Collect big data, extract-transform-load (ETL), analyze, visualize, make decisions

Extract, transform, load (ETL)

Takes data from many sources, transforms it into the format for analysis, and stores it in a data container such as a data warehouse

Volume (5 V's)

The quantity of data generated and stored. Problem: massive amounts of data

Velocity (5 V's)

The rate at which new data is generated. Problem: overwhelming flood of data

Value (5 V's)

The worth of the data in decision making. Problem: worth to whom, business or customer?

Variety (5 V's)

Data comes in many formats. Problem: inconsistent format

Veracity (5 V's)

A measure of the quality of the data. Problem: is the data trustworthy?

Big data challenges

Storage, processing, and analysis difficulties; risk of failing to comply with regulations or internal controls; privacy concerns; possible liability lawsuits

Data warehouse

A large (logical/virtual) database holding business information from many sources across the enterprise, loaded through ETL

Data mart

A subset of a data warehouse covering a single aspect of the business; used by small/medium businesses and departments

Data lake

Takes a 'store everything' approach, saving all data in raw, unaltered form until users decide how to use it

NoSQL database

Stores data in a non-relational model spread over multiple servers, needs no predefined schema, and offers flexibility, speed, and redundancy

Hadoop

Open-source software that divides large data sets across servers where the processing software also lives; highly redundant, but limited to batch processing

Descriptive analytics

Identifies patterns and answers 'what happened?' using statistics and visualizations to track metrics and KPIs

Metric

A quantifiable measure of performance for business processes and tasks

Three categories of descriptive statistics

Distribution (possible values and their frequency), central tendency (the typical value), and variability (distance of data points from the center)

Diagnostic analytics

Answers 'why did it happen?' using hypotheses, correlation, and scatterplots

Hypothesis

A proposed explanation that serves as a starting point for additional analysis

Correlation

The extent to which variables are related (positive, negative, or none)

Variable

A field in a dataset that is analyzed

Scatterplot

A chart used to visualize the correlation between two variables

Text analysis

Extracting value from large quantities of unstructured text such as comments, social media posts, and reviews

Sentiment analysis

Determines whether the emotional tone of unstructured text is positive, negative, or neutral

Video analysis

Obtaining information or insights from video footage

Predictive analytics

Answers 'what's next?' by combining statistical modeling, data mining, and machine learning (e.g., regression, time-series analysis, decision trees)

Time-series analysis

Statistical methods for analyzing time-series data to extract meaningful statistics and characteristics

Data mining

Exploring large amounts of data to find hidden patterns used to predict future trends and behaviors

Association analysis

Data-mining technique using specialized algorithms to form statistical rules about relationships in data

Neural network computing

Data-mining technique in which computers process data the way the human brain does

Case-based reasoning

Data-mining technique using historical if-then-else cases to recognize patterns

Machine learning

A system is trained on data and learns patterns to make predictions or decisions

Prescriptive analytics

Answers 'what do we do about what's next?' by recommending ways to optimize business processes using decision analysis, optimization, and simulation