MIS Exam #1 Chapter 8

data artist

a business analytics specialist who uses visual tools to help people understand complex data

big data

a collection of large, complex data sets, including structured and unstructured data, which cannot be analyzed using traditional database methods and tools

recommendation engine

a data-mining algorithm that analyzes a customer's purchases and actions on a website and then uses the data to recommend complementary products

prediction

a statement about what will happen or might happen in the future

regression model

a statistical process for estimating the relationships among variables. include many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variable

optimization model

a statistical process that finds the way to make a design, system, or decision as effective as possible, for example, finding the values of controllable variables that determine maximal productivity or minimal waste.

cluster analysis

a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible

behavioral analysis, correlation analysis, exploratory data analysis, pattern recognition analysis, social media analysis, speech analysis, text analysis, web analysis

advanced data analytics

data understanding

analysis of all current data along with identifying any data quality issues

evaluation

analyze the trends and patterns to assess the potential for solving the business problem

social media analysis

analyzes text flowing across the Internet, including unstructured text from blogs and messages

web analysis

analyzes unstructured data associated with websites to identify consumer behavior and website navigation

text analysis

analyzes unstructured data to find trends and patterns in words and sentences

data modeling

apply mathematical techniques to identify trends and patterns in the data

outlier

data value that is numerically distant from most of the other data points in a set of data

deployment

deploy the discoveries to the organization for work in everyday business

correlation analysis

determines a statistical relationship between variables, often for the purpose of identifying predictive factors among the variables

estimation analysis

determines values for an unknown continuous variable behavior or estimated future value

variety

different forms of structured and unstructured data

market basket analysis

evaluates such items as websites and checkout scanner information to detect customers' buying behavior and predict future behavior by identifying affinities among customers' choices of products and services

data scientist

extracts knowledge from data by performing statistical analysis, data mining, and advanced analytics on big data to identify trends, market changes, and other relevant information

variety, veracity, volume, velocity

four common characteristics of big data

business understanding

gain a clear understanding of the business problem that must be solved and how it impacts the company

data preparation

gather and organize the data in the correct formats and structures for analysis

exploratory data analysis

identifies patterns in data, including outliers, uncovering the underlying structure to understand relationships between the variables

distributed computing

processes and manages algorithms across many machines in a computing environment

affinity grouping analysis

reveals the relationship between variables along with the nature and frequency of the relationships

sensor data, weblog data, financial data, click-stream data, point of sale data, accounting data

structured data

velocity

the analysis of streaming data as it travels around the internet

fast data

the application of big data analytics to smaller data sets in near-real or real-time in order to solve a problem or create business value

pattern recognition analysis

the classification or labeling of an identified pattern in the machine learning process

data mining

the process of analyzing data to extract information not offered by the raw data alone

speech analysis

the process of analyzing recorded calls to gather information; brings structure to customer interactions and exposes information buried in customer contact center interactions with an enterprise

data profiling

the process of collecting statistics and information about data in an existing source

anomaly detection

the process of identifying rare or unexpected items or events in a data set that do not conform to other items in the data set

classification analysis

the process of organizing data into categories or groups for its most effective and efficient use

data replication

the process of sharing information to ensure consistency between multiple data sources

volume

the scale of data

analytics

the science of fact-based decision making

veracity

the uncertainty of data, including biases, noise, an dabnormalities

data (foundation for data-directed decision making), discovery (process of identifying new patterns, trends, and insights), deployment (process of implementing discoveries to drive success)

three elements of data mining

forecasting model

time-series information is time-stamped information collected at a particular frequency. Forecasts are predictions based on time-series information allowing users to manipulate the time series for forecasting activities.

business intelligence dashboards

track corporate metrics such as critical success factors and key performance indicators and include advanced capabilities such as interactive controls, allowing users to manipulate data for analysis

satellite images, photographic data, video data, social media data, text messages, voice mail data

unstructured data

behavioral analysis

using data about people's behaviors to understand intent and predict future actions

optimization model, forecasting model, and regression model

data mining model for techniques

estimation analysis, affinity grouping analysis, cluster analysis, and classification analysis

data mining techniques

cube

common term for the representation of multidimensional information

virtualization

creation of a virtual version of computing resources, such as an operating system, a server, a storage device, or network resource

algorithms

mathematical formulas placed in software that performs an analysis on a data set

data visualization tools

move beyond excel graphs and charts into sophisticated analysis techniques such as pie charts, controls, instruments, maps, time series graphs, and more

analysis paralysis

occurs when the user goes into an emotional state of over-analysis (or over-thinking) a situation so that a decision or action is never taken, in effect paralyzing the outcome

infographics (information graphics)

present the results of data analysis, displaying the patterns, relationships, and trends in a graphical format