ISDS- Chapter 8: Understanding Big Data and Its Impact on Business

variety

-different forms of structured and unstructured data -data from spreadsheets and databases as well as from email, videos, photos, and PDFs, all of which must be analyzed

velocity

-the analysis of streaming data as it travels around the internet -analysis necessary of social media messages spreading globally

volume

-the scale of data -includes enormous volumes of data generated daily -massive volume created by machines and networks -big data tools necessary to analyze zettabytes and brontobytes

veracity

-the uncertainty of data, including biases, noise, and abnormalities -uncertainty or untrustworthiness of data -data must be meaningful to the problem being analyzed -must keep data clean and implement processes to keep dirty data from accumulating in systems

big data includes the four common characteristics

-variety -veracity -volume -velocity

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.

regression model

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 variables.

databases

___________ contain information in a series of two-dimensional tables

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 can not be analyzed using traditional database methods and tools

dimension

a particular attribute of information

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

-optimization -forecast -regression

data mining prediction analysis methods

-classification -estimations -affinity grouping -clustering

data mining techniques

data visualization

describes technologies that allow users to see or visualize data to transform information into a business perspective

estimation analysis

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

analysis paralysis

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

forecasting model

predictions based on time-series information allowing users to manipulate the time series for forecasting activities.

infographics

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

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

cube

the common term for the representation of multidimensional info

virtualization

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

data mining

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

classification analysis

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

business intelligence dashboards

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

data mining tools

use a variety of techniques to find patterns and relationships in large volumes of information