CH5 MIS Exam 1 (2,3,4,5)

data mart

A low-cost, scaled-down version of a data warehouse that is designed for the end-user needs in a strategic business unit (SBU) or a department.

knowledge management (KM)

A process that helps organizations identify, select, organize, disseminate, transfer, and apply information and expertise that are part of the organization's memory and that typically reside within the organization in an unstructured manner.

master data management

A process that provides companies with the ability to store, maintain, exchange, and synchronize a consistent, accurate, and timely "single version of the truth" for the company's core master data.

data warehouse

A repository of historical data that are organized by subject to support decision makers in the organization.

Microsoft ________ is a popular example of a relational database. Access Excel PowerPoint Word

Access

data governance

An approach to managing information across an entire organization.

issues with big data

Big Data Can Come from Untrusted Sources. Big Data Is Dirty- inaccurate, incomplete, incorrect, duplicate, or erroneous data. Big Data Changes, Especially in Data Streams.

relational database model

Data model based on the simple concept of tables in order to capitalize on characteristics of rows and columns of data.

unstructured data

Data that do not reside in a traditional relational database.

knowledge management systems (KMSs)

Information technologies used to systematize, enhance, and expedite intra- and interfirm knowledge management.

_______ are a set of core data that span the enterprise information systems. Data governance Master data Master data management Transaction data

Master data

________ are a set of core data that span the enterprise information systems. Data governance Master data Master data management Transaction data

Master data

basic characteristics of data warehouses and data marts

Organized by business dimension or subject. Use online analytical processing Integrated Time variant. Nonvolatile (users cant update/change the data) Multidimensional

multidimensional structure

Storage of data in more than two dimensions; a common representation is the data cube.

tacit knowledge

The cumulative store of subjective or experiential learning, which is highly personal and hard to formalize.

explicit knowledge

The more objective, rational, and technical types of knowledge.

Which of the following is an accurate representation of the data hierarchy from smallest to largest? bit, byte, field, record, file, database bit, byte, field, file, record, database byte, bit, field, record, file, database byte, bit, field, file, record, database

bit, byte, field, record, file, database

A ________ is a group of eight ________. bit; bytes byte; bits field; files file; fields

byte; bits

KMS cycle

create knowledge capture knowledge refine knowledge store knowledge manage knowledge disseminate knowledge

The ________ creates links between two tables. alien foreign primary secondary

foreign

Database systems minimize ________. inconsistency independence integrity security

inconsistency

The ________ key is an identifier field that uniquely identifies a record. alien foreign primary secondary

primary

A ________ generally describes an entity. byte field file record

record

The ________ key has some identifying information but does not identify the record with complete accuracy. alien foreign primary secondary

secondary

The relational database model is based on the concept of ________-dimensional tables. one two three four

two

Big Data three distinct characteristics:

volume, velocity, and variety

Big Data

•Exhibit variety; •Include structured, unstructured, and semi-structured data; •Are generated at high velocity with an uncertain pattern; •Do not fit neatly into traditional, structured, relational databases; and •Can be captured, processed, transformed, and analyzed in a reasonable amount of time only by sophisticated information systems.

Big Data generally consists of the following:

•Traditional enterprise data—examples are customer information from customer relationship management systems, transactional enterprise resource planning data, web store transactions, operations data, and general ledger data. •Machine-generated/sensor data—examples are smart meters; manufacturing sensors; sensors integrated into smartphones, automobiles, airplane engines, and industrial machines; equipment logs; and trading systems data. •Social data—examples are customer feedback comments; microblogging sites such as Twitter; and social media sites such as Facebook, YouTube, and LinkedIn. •Images captured by billions of devices located throughout the world, from digital cameras and camera phones to medical scanners and security cameras.