management information systems chapter 6

6-2 define a database mgmt. system (DBMS), describe how it works, and explain how it benefits orgs

DBMS - specific type of software for creating, storing, organizing, and accessing data from a database relieves end user of programmer of task of understanding where/how data are actually stored by separating logical and physical views of data

6-1 what is a database and how does a relational database organize data?

a database is a group of related files that keeps track of people, places, and things about which orgs maintain info a relational database organizes data in 2D tables w/ rows and columns called relations. each table contains data about an entity and its attributes.

6-1 define a database

a group of related files that keeps track of people, places, and things about which orgs maintain info

6-1 define a relational database and explain how it organizes and stores information

a relational database organizes data in 2D tables w/ rows and columns called relations. each table contains data about an entity and its attributes. each row represents a record and each column represents an attribute/field. key fields identify each record uniquely for retrieval or manipulation

6-1 explain the role of entity-relationship diagrams and normalization in database design

an entity-relationship diagram graphically depicts the relationships b/w tables (entities) in a relational database normalization - process of breaking down complex groupings of data and streamlining them minimize redundancy and awkward many-to-many relationships

6-3 list and describe the components of a contemporary business intelligence infrastructure

array of tools for obtaining useful info from internal and external systems and big data data warehouses - DB that stores current and historical data that may be of interest to decision makers; consolidates/standardizes data from many systems, and operational/transactional DBs data marts - subset of data warehouses that is highly focused and isolated for specific population of users Hadoop - open-source software framework for big data; breaks data task into sub-problems and distributes the processing to many inexpensive computer processing nodes in-memory computing - relies on computer's main memory for data storage; eliminates bottlenecks in retrieving/reading data, shortens query response times; enabled by high-speed processors and multicore processing analytical platforms - preconfigured hardware-software systems; designed for query processing and analytics; uses both relational and non-relational tech to analyze large data sets

6-3 define big data and describe the tech for managing and analyzing big data

big data - data sets w/ volumes so huge they are beyond ability of typical DBMS to capture, store, and analyze; characterized by volume, velocity, and variety offers more patterns and insights than smaller datasets --> customer behavior, weather patterns, etc. stored in petabyte and exabyte range

6-3 what are the principal tools and tech for accessing info from databases to improve business performance and decision making?

contemporary data mgmt. technology has an array of tools for obtaining useful info from all types of data businesses use today, including semi-structured and unstructured big data in very large quantities from many different sources

6-2 name and describe the three major capabilities of a DBMS

data definition - specify structure of content of database data dictionary - automated or manual file sorting definitions of data elements and their characteristics data manipulation language (querying and reporting) - consists of elements used to access and modify data in the table; structured query language (SQL); report generation

6-4 list and describe the most common data quality problems

data input errors: -misspelled names -transposed numbers -incorrect/missing codes redundant and inconsistent data produced by multiple systems *poor data quality - major obstacle to successful customer relationship mgmt.

6-4 list and describe the most important tools and techniques for assuring data quality

data quality audit - structured survey of accuracy and level of completeness of the data in an IS data cleansing (data scrubbing) - activities for detecting and correcting data in DB that are incorrect, incomplete, improperly formatted, or redundant; enforces consistency among different sets of data that originated in separate IS

6-4 why are info policy, data administration, and data quality assurance essential for managing the firm's data resources?

data that are inaccurate, incomplete, or inconsistent create serious operational and financial problems for businesses if they lead to bad decisions about the actions firms should take

6-1 define and explain the significance of entities, attributes, and key fields

entities - generalized categories representing a person, place, or thing on which we store info attributes - specific characteristics of entities key fields - uniquely identifies each record so that record can be retrieved, updated, or sorted; primary key - unique identifier for all info in any row of the table, cannot be duplicated

6-3 define data mining, describe what types of info can be obtained from it, and explain how it differs from OLAP

finds hidden patterns and relationships in large DBs and infers rules from them to predict future behavior -associations - occurrences linked to single event -sequences - events linked over time -classifications - patterns describing a group an item belongs to -clustering - discovering as yet unclassified groupings -forecasting - uses series of values to forecast future values data mining is more discovery-driven than OLAP; performs high-level analyses of patterns or trends, but also provides more detail when needed

6-4 define info policy and data administration and explain how they help orgs manage their data

info policy - states org's rules for organizing, managing, storing, sharing info data administration - responsible for specific policies and procedures through which data can be managed as a resource helps orgs manage data by planning for data, overseeing logical DB design, data dictionary development, and monitoring how info system's specialists and end-user groups use data

6-2 define and compare the logical and physical views of data

logical view - how end users view data physical view - how data are actually structure and organized

6-3 explain how users can access info from a company's internal databases throughout the web

middleware and other software makes this possible, such as web servers, application servers or CGIs, and DB servers DB servers - computer dedicated to DBMS; retrieves SQL requests and provides require data which is transferred from org's internal DB back to web server for delivery on form of web page to user

6-2 define and describe the three operations of a relational DBMS

select - creates subset of all records meeting stated criteria join - combines relational tables to present server w/ more info than is available from individual tables project - creates subset consisting of columns in a table; permits user to create new tables containing only desired info

6-3 describe the capabilities of online analytical processing (OLAP)

supports multidimensional data analysis, enabling user to view same data in different ways using multiple dimensions; enables users to obtain online answers to ad hoc questions in fairly rapid amount of time ad hoc questions - comparing sales in east region in June vs. May and July

6-3 explain how text mining and web mining differ from conventional data mining

text mining - allows business to extract key elements from, discover patterns in, and summarize large unstructured data sets web mining - discovery and analysis of useful patterns and info from web -content mining - mines content of websites -structure mining - mines website structural elements, such as links -usage mining - mines user interaction data gathered by web servers

6-2 define a non-relational DBMS and how it differs from a relational DBMS

uses more flexible data model designed for managing large data sets across many distributed machines and for easily scaling up and down; handles large data sets that are not easily organized into tables, columns, and rows; manages unstructured data such as social media and graphics