Quiz 3 study notes PDF

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Quiz 3 Notes

Database: - Organized collection of data meant to facilitate our or more purpose - Technology that allows you to transform data into information - Organized so the info can be easily accessed, manage and updated Why does Data Management matter? Any data that is incomplete or out of context cannot be trusted What is the goal of Data Management? To provide the tools to transform raw data into useable information of the highest quality Why is data management expensive and difficult? - Volume of data is increasing exponentially - Data is scattered throughout the organization - Data is created offline without going through quality control checks Scope of data Management Master Data Management (MDM) - process to integrate data from various sources and enterprise apps in order to create a unified view of data Document Management System (DMS) - hardware and software to manage, archive, and purge file and electronic documents (edocuments) Enterprise Content Management (ECM) - web content, collab tools, scanning, search optimization - ECM aims at managing the lifecycle information from publication all the way through archival Database Management Systems (DBMS) - facilitates acmes to unrealized data - Allows users to manage data - Provides application programs access to the stored data in a consistent manner

Problems With the Tradtional File Environment - Data Redundancy: - Presence of duplicate data in multiple files - Data inconsistency: - Same attribute has different values - Program - data dependence: - When changes in program requires change to data accessed by program - Lack of flexibility - Poor security - Lack of data sharing and availability. Data Management System (DBMS) - Interface between applications and physical data files - Separates logical and physical data files - Solves problems of traditional file environment - Controls redundancy - Eliminates inconsistency - Uncouples programs and data - Enables organization to centrally manage data and data security Data Filtering and Profiling: - inspecting the data for errors, inconsistencies, redundancies and incomplete information Data Quality: - correcting, standardizing, and verifying and integrity of the data Data Synchronization: - integrating, matching, or linking data from disparate sources Data Enrichment: - Enhancing data using information from internal and external sources Data Maintenance: - checking and controlling data integrity overtime Relational DBMS Concepts Relational DBMS - represent data as two-dimensional tables called relations or files - Each table contains data entity and attribute

Physical vs.Logical Views Physical view; How data is organized, specified and stored Logical View: How data appears n entity to the end-users Entity: - a person , place, thing, transactions, or event about which information is stored - The rows in each table contain the entities EntityClass (table):

- a collection of similar entities ! - Customer, order, borderline, distributors, and product entity class ! Attributes (fields,columns) - the columns in each table contain the attributes: ! - Customer ID! - Customer Name ! - Contact Name! - Phone!

Primary Key: A field (or group of fields) that uniquely identifies a given entity in a table ! Foreign Key: A primary key of one table that appears as an attribute in another and acts to provide a logical relationship between the two tables ! Metadata is data about data: - the structure of the stored files ! - Includes location, format, field name, data description ! The base operations used to develop useful sets of data 1. Select: create a subset of data ! 2. Join: combines relational tables ! 3. Project: creates subset of columns in table ! Data management Practices; - Include all staged of the data life cycle ! - Enterprise-wide scope ! - Span various types of data and information ! - Provide different levels of granularity ! Data Governance: - managing info across an entire organization ! - Policies that are designed to ensure that data are handled in a certain, well defined fashion! - Follows ambiguous rules for creating, collecting, handling, and protecting information! - Objective to make info available, transparent, and useful for the people who are authorized to access it from the moment it enters unit it is outdated. Ex. Master data management !

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Master data vs Transaction data Master Data: consistent and uniform set of identities and extended attributes that describe the care entities o the enterprise such as customer, product, employee, vendor, geographic location ! Transaction Data: Generated and captured by operational systems, describe the business activities or transactions ! Advantages of Master data management: - improves search effectiveness ! - Increases accuracy ! - Streamlines new product entry into the data base management system ! - Facilitates processing of transactions ! Ensuring Data Quality - faculty input ! - Before new data base in place, need to:! - Identify and correct faculty data ! Corporate Initiatives to Improve Data Quality Data Quality Audit: - structures survey of the accuracy and level of completeness of th data in an information system ! - Survey samples from data files ! - Survey end users for perceptions of quality ! Data Cleansing: - software to detect and correct data that are incorrect, incomplete, improperly formatted, or redundant ! - Enforces consistency among different sets of data from separate information systems ! Stored Data Formats - Atomic Data: data in units that cannot be subdivided ! - First name, price, etc. ! - Binary large objects (BLOB): ! - Digitized image and multimedia (audio video) ! - Large size ! - Stored as a single entity (can’t be handled in smaller chunks) ! Typical Database Design Phases 1. Requirements gathering: ! 1. Customer interviews and business requirement ! 2. Report Design: ! 1. Output required (another method for requirements gathering)! 3. Data design:! 1. Data items inventory, metadata, and primary keys for data ! 4. Table design: ! 1. Entity-relationship diagrams (ERD) and normalization ! 5. Form design: !

1. Input interface design ! Conceptual Design: - abstract model of database from a business perspective ! Physical Design: - detailed description of business information needs ! Entity-Relationship Diagram (ERD): - methodology for documenting databases illustrating relationships between data base entities ! Normalization: - Process of creating small stable data structures from complex groups of data ! ERD Notation:

Query - a set of instructions used for working with data ! - Creating a quest is like asking the database a question ! - Running a query performs these instructions and provides the answers! - The results from a query are referred to as the recordset ! 3 basic query operations to develop useful sets of data 1. Select ! 2. Join ! 3. Project ! Structured Query Language (SQL) - the most popular query language used for interacting with a database! - Allows people to perform complicated searching by using relatively simple statements or key ! DBMS Products Fall Into Two Broad Categories: - enterprise DBMS and Personal/Desktop DBMS ! Enterprise DBMS - these products process large operational and analytical database ! - Support thousands of users at a time, along with multiple database applications ! - IBM’s DB2, Microsoft SQL server ! Personal/Desktop DBMS: - these products are designed for smaller, simpler database applications !

- Such products are sued for persona or small workgroup applications that involve fewer than 100 users, and normally fewer than 15!

- Single user ! - e.g MS Access, filemaker, airtable !

Data Warehouse, Data Marts and Data mining: Defintion: Process, product !...