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CIS CH7 Business Intelligence and Decision Making

intelligent agents software programs or "bots" that are sent out to conduct a mission and collect data from web pages on behalf of a user
big data adds semi-structured and unstructured information in addition to the structured info found in databases or retrieved by agents
external database examples U.S. Census Bureau.
predictive analytics data mining approaches and statistical techniques used to predict future behavior, especially to unlock the value of business intelligence for strategy.
online analytical processing (OLAP) software that allows users to "slice and dice" or drill down into massive amounts of data stored in data warehouses to reveal significant patterns and trends
market basket analysis statistical technique that reveals customer behavior patterns as they purchase multiple items
text mining technique used to analyze unstructured text that examines keywords, semantic structures, linguistic relationships, emotion-laden words, & other characteristics to extract meaningful business intelligence.
what-if analysis a simulation model, often constructed using Excel, that calculates the relationships btw many variables; users can change some variables to see how others are affected.
goal seeking A decision support tool, often based on an Excel model, in which the user sets a target value for a particular variable, such as profit/ loss, and tells the program which variable to change to try to reach goal.
optimization An extension of goal seeking in which the user can change many variables to reach some maximum or minimum target, as long as the changes stay w/in constraints the user identifies
forecasting A statistical decision support tool used to analyze historical trends & other business intelligence to estimate some variable of interest, such as customer demand.
artificial intelligence (AI) the capability of some machines to mimic aspects of human intelligence, such as learning, reasoning, judging, and drawing conclusions from incomplete information
CAPTCHA A test created by software developers that the visitor must pass before continuing to register or enter the site; designed to thwart software bots.
expert system software that mimics the reasoning & decision making of a human expert, drawing from a base of knowledge about a particular subject area developed w/ the expert's assistance.
web visitor-related metrics Visitors, unique visitors, average time on site, new visitors, depth of visit, languages, traffic sources, service providers
web content-related metrics page views, bounce rate, top landing pages, top exit pages
Twitter metric updates, followers, following, ReTweeted, referenced
e-commerce metric conversion rate, clickthrough rate (CTR), cost per clickthrough (CPC), cost per impression (CPM is cost per thousand impressions), Position on page
web visitor-related metrics: visitors # of visitors to the website (returning visitors counted again)
web visitor-related metrics: unique visitors # of unique visitors (returning visitors not counted again)
web visitor-related metrics: average time on site avg amt of time visitors spend on the site
web visitor-related metrics: new visitors # of new visitors to the site
web visitor-related metrics: depth of visit the # of page views per visit, which shows how extensively visitors interact w/ & navigate around your site
web visitor-related metrics: languages the # or % of visitors based on the language they configured to use on their computer
web visitor-related metrics: traffic sources sources from which visitors arrive at your site, such as a keyword search in a search engine, an ad, or from a link on related sites. Direct traffic is a visit from someone who used a bookmark or typed the URL in the browser.
web visitor-related metrics: service providers the # of visits coming from ppl using different Internet service providers
web content-related metrics: page views the # of visits per page on the site, showing analysts the most popular content
web content-related metrics: bounce rate % of visits in which the user left the site from the same page he or she entered it. This can mean that the page the user landed on was not very relevant.
web content-related metrics: top landing pages the # of entrances to your site for each page
web content-related metrics: top exit pages the # of exits from the site for each page
Twitter metric: updates # of updates the user published w/in the most recent time period
Twitter metric: followers # of followers reported by Twitter at last count
Twitter metric: following # of individuals the user is following at last count
Twitter metric: ReTweeted # of times a user has been retweeted by other people
Twitter metric: referenced # of times a particular user has been referenced or cited by other ppl in their tweets
E-Commerce metric: conversion rate ratio of visitors who complete some activity (such as buying product) divided by the total # of visitors
E-Commerce metric: clickthrough rate (CTR) the ratio of clicks on an ad divided by the # of times the ad was delivered
E-Commerce metric: cost per clickthrough (CPC) the amount an advertiser pays each time a visitor clicks on the ad to navigate to the advertiser's site
E-Commerce metric: cost per impression (CPM) for banner and display ads, the cost the advertiser pays each time the ad loads onto a user's screen from any site on which it appeared. Cost per thousand impressions
E-Commerce metric: position on page the position in which a sponsored link appears on a page in keyword advertising on search engines
clickstream data business intelligence data that includes every click by every visitor on a website, along w/ associated data such as time spent on the page & the visitor's IP address
stickiness measurement of how long visitors linger at a website
dashboard graphical user interface that organizes & summarizes information vital to the user's role and the decisions that user makes
key performance indicators (KPIs) the quantifiable metrics most important to the individual's role & the organization's success.
dashboard features Key performance indicators, data quality, timeliness, density, chart formats, maps & visual displays
Dashboard feature design tip: Key Performance Indicators (KPIs) choosing KPIs that are most important to the organization & the person using the dashboard is the most important success factor
Dashboard feature design tip: Data quality dashboards make data look good, but charts & graphs are only as useful as the quality of the underlying data. Users should ask for warnings when data is stale or not altogether reliable.
Dashboard feature design tip: timeliness update the dashboard as often as needed for user's situation - daily, hourly, or minute by minute, if necessary.
Dashboard feature design tip: density use 7 or fewer graphs, charts, maps, or tables on one dashboard, to avoid information overload.
Dashboard feature design tip: chart formats keep tables small & charts simple, using familiar types. Avoid 3D & unnecessary animations. Be cautious about pie charts, which can be more difficult to interpret.
Dashboard feature design tip: maps & visual displays when relevant, populate actual maps, seating plans, campus layouts, or other visual displays that combine real images w/ data.
portal gateway that provides access to a variety of relevant info from many different sources on one screen; for an enterprise, the portal provides a secure gateway to resources needed by ee's, customers, & suppliers
mashup an approach to aggregating content from multiple internal & external sources on customizable web pages that relies on Web 2.0 technologies
primary source of business intelligence transactional database, or data warehouse, used by the organization itself for operations, or by its suppliers & customers.
additional sources of business intelligence online data from websites, blogs, email, downloadable tables, wikis, & business reports
3 levels of decision making that business intelligence supports operational, tactical, and strategic
most important sources of business intelligence inside the organization transactional databases, data warehouses, internal data sources and external data sources
Created by: kld0519