Ch 11 Behind the Scenes: databases & info systems, Microcomputer Applications for Business
Ch 11 Behind the Scenes: databases & info systems, Microcomputer Applications for Business CGS2100
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This 3 page Class Notes was uploaded by Gianna Molinare on Friday July 22, 2016. The Class Notes belongs to CGS2100 at Florida State University taught by Jack Winston in Summer 2016. Since its upload, it has received 9 views.
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Date Created: 07/22/16
Ch 11 Behind the Scenes: databases & information systems Database building blocks o A database is a collection of related data, which can be: Stored Sorted Organized Queried o Databases make data more meaningful & useful o Databases turn data into info o Advantages of using databases: Databases can manage large amounts of data efficiently Databases make info sharing possible Databases promote data integrity o Disadvantages of using databases Can be more time-consuming & expensive to set up & administer Care is needed to insure they function as intended Administrator is responsible for designing, constructing, and maintaining databases o Planning & creating database How data tables are created: Step 1: input unique field names Step 2: define data type Step 3: set a maximum field size Step 4: set a default value if necessary Repeat for each field in the table Database types o 3 major types of databases: Relational Object-oriented Multidimensional Databases that are set up to facilitate drilling down to retrieving data Relational databases are the most commonly used Organizes data in table format Logically groups similar data into a relation Common field in 1 table (primary key) linked to common field (foreign key) in 2nd table Need to keep data in related tables synchronized Where you need to avoid redundancy Object-oriented databases Store data in objects rather than tables Contain methods for processing or manipulating data Can store more types of data than relational databases Can access data faster Multidimensional databases Database functions Functions Populate database by creating records Allow users to extract subsets of data from database Output data in meaningful & presentable format How businesses use databases Database warehousing & storage Large-scale collection of data Contains & organizes data in 1 place Data comes from multiple databases Consolidates info from various systems Presents enterprise-wide view of operations Business intelligence systems Software-based solutions to gather & analyze info Deliver up-to-the-minute data Integral bc they store functional info All perform similar functions Office support systems What an office support system accomplishes: Office support system (OSS) is designed to improve Transaction-processing systems Batch processing Data is accumulated, then several transactions are processed at once Appropriate for activities that aren't time-sensitive Often more efficient Management info system (MIS) Provides timely & accurate info Enables managers to make critical decisions Directs outgrowth of TPS Powerful if organized & outputted in useful form Often included as feature of TPS Can generate various reports Decision support system (DSS) Another type of business intelligence system Designed to help managers develop solutions for specific problems Uses info from databases & data warehouses Users can add own insights & experiences to solution ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Ethics & data o Before social media, database managers were challenged to collect & collate customer data into coherent format & provide managers w/ data-driven decision options o Early methods included collecting business cards & transcribe data o Rise of social media provided treasure trove of customer & consumer data Knowledge Discovery & Data Mining o 1 factor that drives data mining= evolution of affordable high- performance computers that are able to slice & dice petabyte size databases o There are highly complex methods of data mining Major classes of data mining o Anomaly detection- looks for data out of defined range o Association rule learning- dependency modeling o Clustering- histogram analytics o Classification- sorting data by known characteristics o Regression- modeling w/ least errors (Chi) o Summarizing- data compaction w/ analysis, display conclusions in brief manner Data analytics o Science of examining raw data w/ purpose of concluding info o Distinguished from data mining by scope, purpose, & focus of analysis o Focuses on inference, process of deriving conclusion based solely on what is already known by researcher o Exploratory data analysis- where new features in data are discovered o Confirmatory data analysis- where existing hypotheses are proven true or false o "analytics" has been used by many business intelligence software vendors as buzzword Data for sale o Harvested data are processed & stored in data warehouses o Metadata are used to facilitate search & retrieval of data of interest o Predictive model markup lang= example of methods used for data analysis Ethical fallout from data mining