Intro to Business Information Systems: Ch 9 Flashcards
manager decision making challenges
need to analyze large amounts of information, need to make decisions quickly, and must apply sophisticated analysis techniques for strategic decisions
decision making process
- Problem Identification
- Data Collection
- Solution Generation
- Solution Test
- Solution Selection
- Solution Implementation
operational level decisions
develop, control, and maintain core business activities required for day to day operations
operational decisions
affect how firm is run day to day
structured decisions
arise in situations where established processes offer potential solutions
managerial level decisions
continuously evaluating company operations to hone firms ability to identify, adapt, and leverage change
managerial decisions
concern how the organizations would achieve goals and objectives set by strategy and responsible for mid level management
semistructured decisions
occur in situations in which few established processes help evaluate potential solutions but not enough to lead to recommended decisions
strategic level decisions
develop overall business strategies, goals, and objectives as a part of the company’s strategic plan
strategic decisions
involve higher level issues concerning overall direction of the organization
unstructured decisions
no procedures or rules exist to guide decision makers to the correct choice
model
simplified representation or abstraction of reality
Online Transaction Processing (OLTP)
capturing of transaction or event information using technology to process information according to the defined business rules, store the information, and update existing information to reflect new information
Transaction Processing System (TPS)
basic business system that serves operational level analysts in an organization
source documents
describes original transaction records
Decision Support System (DSS)
models information using OLAP which provides assistance in evaluating and choosing among different courses of action
Online Analytical Processing (OLAP)
manipulation of information to create business intelligence in support of strategic decision making
Executive Information System (EIS)
specialized DSS supports senior level executives within the organization
granularity
refers to the level of detail in the model or decision making process
infographic
representation of information to make data easily understandable at a glance
visualization
produces graphical displays of patterns and complex relations in large amounts of data
infographic types
bar chart, histogram (groups numbers into ranges), pie chart, time series chart, sparkling (small embedded line graph illustrates a single trend with no axes or labels as the context comes from the related content)
digital dashboard
tracks KPIs and CSFs by compiling information from multiple sources and tailoring it to meet user needs
analytical capabilities of the digital dashboard
consolidation (aggregation), drill down (reverse aggregation), slice and dice (view from different perspectives), pivot (rotates data to display alternative presentations of data)
expert systems
computerized advisory programs imitate reasoning processes of experts in solving difficult problems
algorithms
math formulas placed in software that performs analysis on datasets
genetic algorithm
AI system that mimics evolutionary or survival of the fittest process to generate increasingly better solutions to problems
machine learning
a type of AI that enable s computers to understand concepts in the environment and learn
supervised machine learning
training model from input data and corresponding labels
unsupervised machine learning
training model to find patterns in a dataset (typically an unlabeled dataset)
transfer machine learning
transfers information from one machine to another
data augmentation
when adding additional training examples by transforming existing samples
overfitting
when machine learning model matches training data so closely that the model fails to make correct predictions on new data
underfitting
machine learning model that has poor predictive abilities because it did not learn complexity in the training data
affinity bias
tendency to hire those with similar interests, experiences, or background
conformity bias
conforming regardless of personal views
confirmation bias
looking for evidence that supports preconceived notions
name bias
tendency to prefer certain types of names
measurement bias
problem with data collected that skews the data in one direction
prejudice bias
result of training data influenced by culture and stereotypes
sample bias
problem in using incorrect training data to train machines
variance bias
math property of an algorithm
neural networks
category of AI that attempts to emulate the way the human brain works
fuzzy logic
math method for handling imprecise and subjective information
black box algorithms
process that cannot be easily understood or explained
deep learning
employs specialized algorithms to model and study complex data sets or to establish relationships among data or datasets
reinforcement learning
training machine learning models to make sequences of data
virtual reality
computer simulated real or imaginary environment
augmented reality
viewing the physical world with a computer generated layer of information added
