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Glossary of ISOM Quiz III Chpt 9

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Created by ddeetz

The general activity of updating (i.e. inserting, modifying, and deleting), querying (i.e. retrieving), and presenting text and number data from databases for operational purposes
Online transaction processing (OLTP)
The general activity of querying (i.e. retrieving) and presenting text and number data from data warehouses (and data marts) for analytical purposes
Online analytical processing (OLAP)
Model information to support managers and business professionals during the decision-making process
Include On-line Analytical Processing (OLAP) Tools
Excel can be a DSS

Decision Support Systems (DSS)
A system containing enterprise-wide information presented via simple and easy to use interfaces for quick insight and analysis by executives
Can be OLAP-tool based
E.g. digital dashboards

Executive Information Systems (EIS)
Three quantitative models typically used by DSS
-Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model (SOLVER)

-What-if analysis – checks the impact of a change in an assumption on the proposed solution (IF FUNCTION, SCENARIO MANAGER)

-Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of output (GOAL SEEK)





designed to access DW data and they are more capable of true analysis than standard reporting tools typically used to access relational operational data.
BI tools (a.k.a. OLAP Tools)
Most BI (OLAP) tools offer the following capabilities
-Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information (PIVOT TABLES)

-Drill-down – enables users to get details, and details of details, of information

-Slice-and-dice – looks at information from different perspective



integrates information from multiple components and presents it in a unified display
Digital Dashboard
-Finding “interesting” patterns in large amounts of data

-The patterns should be:
accurate
meaningful
understandable
actionable

-Intersection of database management, machine learning (artificial intelligence), and statistics







Data Mining
-Also known as Market Basket Analysis or Association Rule Mining

-determines which objects or features appear together
finds correlations among variables

-E.g. Beer and diapers tend to be bought together




Affinity Grouping
Association-rule mining discovers correlations among items within transactions
Association Rule Mining A.K.A. Market Basket Analysis
the fraction of transactions containing items X, which also contain items Y.

_______ (XY) = Count (transactions containing X and Y) / Count (transactions containing X)

Confidence
the fraction of transactions that contain both X and Y items
_______ (XY) = Count (transactions containing X and Y) / Count (all transactions)
Support
The support measures the ______ of the rule, so we are interested in rules with relatively high support
significance
The confidence measures the ______ of the correlation, so rules with low confidence are not meaningful, even if their support is high
strength
-gives companies ability to discover and utilize information they already own, and turn it into the knowledge that directly impacts corporate performance

-__ incorporates database management, data warehousing and data mining

-The term__ is a



Business Intelligence
-Software that enables business users to see and use (analyze) large amounts of complex data.
-Query and Reporting Tools
for retrieving data from databases
-BI (OLAP) Tools
for retrieving data from data warehouses and/or data marts
-Data Mi




Business Intelligence Tools

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