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Stack #3771506
| Question | Answer |
|---|---|
| Before the use of computers and database management systems (DBMS), - | manual file systems were used to maintain an organization's records and files. |
| Using this traditional file system: - | Data and information were stored and processed using a traditional file system (paper, files, and documents). |
| Each file is independent of the other file, which leads to data redundancy, inconsistency, and file management issues. | |
| Database management systems (DBMS) - | help to overcome many of the issues associated with traditional file management systems. Here are some of the issues DBMS help to overcome. |
| Reduction of Data Redundancy - | Data redundancy is the duplication of data. |
| Reduction of Data Redundancy example - | If you are managing the data of a gym where a customer is enrolled in multiple workout classes, the same customer details will be stored twice, taking up storage and causing data redundancy. |
| Data redundancy can lead to higher storage fees and inefficient access times. | |
| Decreased Data Inconsistency - | Data redundancy often leads to inconsistent data. |
| Decreased Data Inconsistency example - | If you are managing the data of a gym, let's say the customer needs to change their address. In a file management system, customer data is stored twice. |
| If all the address data is not changed in each record, data inconsistency will occur. | |
| Increased Data Security - | The use of a DBMS makes it is easier to secure data and information. A DBMS allows for the creation of access constraints so that only authorized users are able to access the data. Users are assigned a different set of access rules; this helps to protect |
| A database management system (DBMS) - | is a computer program that is used to create, process, and administer a database. |
| DBMS software vendors - | Due to the complexities surrounding the DBMS development process, most organizations do not develop their own DBMS. Instead, they use DBMS created by software vendors including Oracle, Microsoft, and IBM. |
| It is important to note the differences between a database and a DBMS. | |
| Database - | A database is a collection of tables, relationships, and metadata. A DBMS helps to organize the data found in a database. |
| DBMS - | A DBMS is a software program designed to organize and administer a database. |
| Data integrity - | means the database is reliable, accurate, and aligned to the goals of the organization. |
| Data centralization is critical in increasing data integrity. | |
| When data is centralized, - | it means it is stored in only one place. |
| When multiple lists and data sources are maintained, information can become inconsistent leading to decreased data integrity. | |
| databases enable - | more efficient data maintenance. |
| Databases are designed to maintain data and information about various types of data objects including: - | Objects (items in stock/inventory) |
| Events (transactions and item returns/exchanges) | |
| People (customers, employees, vendors) | |
| Places (procurement centers and wholesalers) | |
| Database management systems (DBMS) are developed to allow for the creation, reading, updating, and deletion of data in a database. - | DBMS include security features and access controls. |
| also have the ability for managers and database administrators to manipulate, query, and store information in a database. | |
| A relational database management system (RDBMS) - | is a type of database management system (DBMS) with a row and column-based table structure that connects data via relationships. |
| elements include features that maintain data security, accuracy, integrity, and consistency. | |
| Basic RDBMS functions - | Allow the user to create, read, update, and delete data. |
| Are collectively referred to as CRUD. | |
| RDBMS provides. - | data dictionaries and metadata collections to assist in data handling. These helps support well-defined data structures and relationships. |
| An additional feature of these types of databases is data storage. | |
| Relational databases - | organize data into tables based on structured data groupings. |
| Relational databases use links, called relationships, between tables. | |
| Relational databases use links, called relationships, between tables. - | Tables are used to hold information about the objects to be represented in the database. |
| Information in tables is stored in rows called records or objects, and columns called fields. These relationships define how the data in the tables are related. | |
| common field that is included in both tables is used to create the relationship. | |
| Rows among multiple tables can be made related using foreign keys. | |
| Data can be accessed in many different ways without reorganizing the database tables themselves. | |
| . A field - | is a group of related characters in a database table. A field is a column in a table that represents a characteristic of someone or something. Field Names in the Members table include Member ID, First Name, Last Name, Address, City, State, Zip, E-mail, an |
| Design View - | lets you change Field Names and Field Properties. Data Type specifies the data in the field, such as text, numeric, or date/time. Setting Data Types for each field decreases data input mistakes. As you move from one field to the next, the Field Properties |
| A record - | is a collection of related fields in a data file. Records are a collection of characteristics that describe the identity of an entity. A record is also referred to as a row in a table. Select a record by moving the cursor to the left of the record you wan |
| Database Primary Key - | Select SongID and set it as the Primary Key for the Songs table. Create the Primary Key by clicking the Primary Key button. A primary key is a special relational database field designated to uniquely identify all records in a table. A primary key must con |
| Database Foreign Key | |
| The ERD illustrates - | the use of a Foreign Key. In a relational database, the Foreign Key is a common field between tables that is not the primary key. For example, What is the Foreign Key in the Songs Table? AlbumID is the foreign key in the Songs Table. What is the Foreign K |
| Microsoft Access is a relational database. A relational database - | organizes data into various tables based on logical groupings. In Design View, select the Design tab, then select Relationships. A relationship is a link between tables that defines how the data are related. A common field between the two tables is used t |
| Referential integrity - | is the accuracy and consistency of data within a table relationship in a database. In relational databases, two or more tables can be linked using a relationship. |
| The creation of relationships between data is achieved using: - | A primary key value (in the primary or parent table) |
| Foreign keys (in associated tables) | |
| Because of this, we need to ensure that data on both sides of the relationship remain intact. | |
| Referential integrity requires that, - | whenever a foreign key value is used, it must reference a valid existing primary key in the parent table. For example, if you were to delete record 1556 in a primary table, you need to be sure that there is not a foreign key in any related table with a va |
| You should only be able to delete a primary key if - | there are no associated records to that primary key. If you delete this record, you'll end up with an orphaned record. |
| Structured Query Language (SQL), - | Used for human interface and communication with relational databases |
| Considered the standard language | |
| SQL uses - | user-generated lines of code (statements) to answer questions against the database. |
| SQL statements are - | used to perform a variety of database tasks, including the retrieval of data (query) and database updates. |
| Most relational databases use SQL, but - | most also have proprietary extensions that allow for customized interactivity. |
| Standard SQL commands include select, insert, update, delete, create, and drop. | |
| query - | is a question—a request for information from a database. |
| Information is generated using a - | specific query language. |
| Structured Query Language (SQL) is one of the most popular query languages. | |
| Queries can filter based on specific criteria, calculate/summarize data, and automate data management tasks. | |
| Relational database management systems (RDBMS) - | have a variety of tools that can be used to execute/run queries. Query tools included in many RDBMS include crosstab queries, action queries, parameter queries, and SQL-specific queries. |
| Cross tab - | Cross tab queries calculate the sum, average, or other aggregate functions, then group the results by two sets of values. The two groupings include one set of data/results on the side of a datasheet and the other set across the top of the sheet. |
| Action - | There are four types of action queries included in many RDBMS and include append, delete, update, and make-table queries. These queries (except for make-table queries, which create new tables) make changes or move the data in tables (records) they are bas |
| Parameter - | Parameter queries prompt the user for values in order to run/execute the query. When a value is supplied, the parameter query applies the field as a criterion. If a value is not supplied, it is interpreted as an empty string. |
| Structured Query Language (SQL) - | Structured Query Language (SQL)-specific queries use specific SQL statements to execute the query. SQL statements are translated by the RDBMS to create output. |
| Forms - | are used to control how data are entered into a database. Forms structure data input to ensure data integrity. Data are entered into the blank areas of the form. Forms turn data into information. Forms are created using a database management system (DBMS) |
| Reports - | offer a way to view, format, and summarize the information in a database. Reports can be used to display or distribute a summary of data and archive snapshots of the data. Reports can also be used to provide details about individual records and to create |
| Entities in entity relationship diagrams (ERD) - | are the various business objects that make up the database. These objects include: |
| Roles/people (employees and customers) | |
| Tangible business objects (different types of products or services) | |
| Intangible business objects (logs, system information) | |
| Relationships refer to how these various entities relate to each other within the database/system. | |
| Data normalization - | is a method of organizing various types of data in the database. |
| Normalization is an organized approach of breaking down/simplifying tables to eliminate data redundancy and undesirable data characteristics. | |
| Entity relationship diagrams - | ERD are a method of structurally representing database design via the use of diagrams. |
| An ERD involves the use of different symbols and connectors that help to visualize two different types of information: - | The entities within the system |
| The interrelationships among these entities | |
| For many years, relational databases have been the most popular choice for businesses. Due to increasing volumes of data, the increased use of web services, and the need for data storage, alternatives to relational databases are starting to emerge. Instea | nonrelational databases (NoSQL). |
| NoSQL Databases description - | These databases are designed to manage large data sets across many platforms and have the ability to analyze structured and nonstructured data. |
| They are also useful for creating queries from the data created from social media platforms, web apps, and other emerging forms of digital content | |
| variety of NoSQL databases are available on the market today. | |
| NoSQL Databases example - | According to Amazon, DynamoDB can handle more than 10 trillion requests per day and can support peaks of more than 20 million requests per second. Many of the world's fastest growing businesses, such as Lyft, Airbnb, and Redfin, as well as enterprises suc |
| A Cloud database - | a type of database that is built and accessed via a Cloud platform. |
| A Cloud platform - | includes the hardware and operating environment of servers in an Internet-based datacenter. Cloud databases have many of the same functions of traditional databases, but with added features supported by the Cloud. |
| cloud database features - | Ability for enterprise users to host databases without having to buy and maintain dedicated hardware. |
| Can be self-managed or maintained and managed by a provider. | |
| Support SQL and NoSQL databases | |
| Accessed through the web or a vendor-provided API (application programming interface) | |
| Big data - | encompasses all of the analysis tools and processes related to applying and managing large volumes of data. |
| was conceived out of the need of organizations to better understand trends, patterns, and preferences that emerge from the interaction with different systems and databases. | |
| How does Big Data help? - | Big data allows organizations to use analytics to help uncover a variety of predictive behaviors to help create new offerings. |
| The Four Vs of Big Data include. - | volume, variety, veracity, and velocity. |
| Volume: Scale of data - | Enormous amounts of data are created every day. Most companies have over 120 terabytes of information stored (that's 120,000 gigabytes!). It is estimated that 2.3 trillion gigabytes of information are created each day. |
| Variety: Different forms of data - | Data comes from many structured and unstructured sources. These sources include social media platforms, email, photos, videos, and point-of-sale interactions. YouTube estimates that over 1 billion hours of video content is viewed each day. We're now at a |
| Veracity: Uncertainty of data - | With all of the data being generated and stored, it is important to ensure that data is meaningful and useful. Poor quality data is estimated to cost the U.S. economy over $3.3 trillion dollars per year. A recent survey found that one in three business le |
| Velocity: Analysis of streaming data - | The pace at which data is generated is mind-blowing. Ninety percent of the data on the Internet has been created since 2016. Every minute on Facebook: |
| 510,000 comments are posted | |
| 293,000 statuses are updated | |
| 136,000 photos are uploaded | |
| Over 3.5 billion Google searches are conducted worldwide each minute of every day. That is 2 trillion searches per year worldwide. | |
| Business intelligence (BI) - | includes the technologies, computer applications, and procedures for the collection, analysis, and presentation of business information to help support decision making. |
| Fundamentally, business intelligence systems are data-driven decision support systems (DSS) that aid businesses to make better strategic decisions. | |
| BI systems provide businesses a picture of historic, current, and future views of operations. | |
| BI systems use information stored in data warehouses, data marts, in-memory computing, and other analytic platforms to create information output. | |
| A data warehouse. - | is a repository of data and information that organizations analyze to make informed business and operational decisions. |
| Data flows into a - | data warehouse from a variety of transactional systems (point-of-sale, online transactions, etc.), databases, and other data-generating sources. |
| Information flows into - | a data warehouse at regular intervals and is stored for later processing. |
| variety of people within an organization have access to the data warehouse, including data scientists, key decision makers (KDMs), and data specialists. | |
| Data is analyzed using - | business intelligence (BI) tools, Structured Query Language (SQL) clients, and a variety of analytics applications designed to interpret the data. |
| The output created from data warehouses includes reports, dashboards, and queries. | |
| Data and the analytics provided from the analysis of data allow organizations to create/maintain a competitive advantage. | |
| data mart - | is a subsection of a data warehouse that is designed and built specifically for individual departments or business functions. There are three types of data marts: |
| Dependent Data Mart - | Constructed from existing data warehouses and utilize a top-down approach where organizational data is stored in a centralized location, then specific data is extracted when analysis is needed. |
| Independent Data Mart - | A stand-alone system that is created separate from a data warehouse and focuses on specific organizational functions. |
| Hybrid Data Mart - | Assimilates data from a data warehouse as well as other data collection systems. It incorporates a top-down approach, end-user inputs, and enterprise-level integration. |
| Data warehouses - | help to create a decision support system (DSS) environment that allows businesses to gauge the performance of an enterprise over measurable periods of time. |
| data warehouse use - | contain large amounts of historical data (data is stored in a series of snapshots) that represent data points at a specific time. This gives organizations the ability to compare different time periods to make more informed business decisions. |
| One of the advantages of data warehouses is their ability to provide access and analysis of information from a variety of subject areas. | |
| Data marts - | are designed to collect and measure data from specific operational areas of a business and are used by individual departments or groups. |
| data marts use - | used to track inventories, purchase transactions, and the supply chain. |
| Data marts assist with the analysis of what data a user needs rather than focusing on existing data. | |
| In-memory computing - | is the use of middleware software to assist in the storage of data in random-access memory (RAM) across a group of different computers. |
| Middleware - | is software that provides processing capabilities outside what is offered by the user's operating system. This allows for the processing of data in parallel, which leads to enhanced data processing and accessing speeds. |
| RAM storage and parallel processing are - | two of the main components of in-memory computing. |
| Function: In-memory computing - | is used by businesses to help unite transactional and analytical processing to provide real-time insights and analytics. This creates an environment that increases the amount and speed at which data can be ingested and analyzed. |
| Analytics platforms - | are designed to assist large data-driven companies in the analysis and interpretation of organizational data. A variety of database software providers have developed high-speed platforms that are used in relational and nonrelational database technologies |
| Analytics platforms purpose - | Analytics platforms provide information about a variety of business and operational areas including: |
| Customer analytics | |
| Sales and marketing analytics | |
| Social media analytics | |
| Cybersecurity | |
| Plant and facilities data | |
| One of the popular analytics platforms is IBM's Integrated Analytics System. According to IBM, "The IBM Integrated Analytics System": - | Is a unified hybrid data management analytics solution providing massively parallel processing (MPP) |
| Comprises a high-performance hardware platform, optimized database query engine software and networking capabilities that work together to support various data analysis and business-reporting capabilities | |
| Online analytical processing (OLAP) - | is included in many business intelligence (BI) software applications and is used for a variety of data discovery activities. The activities include report creation and analysis, analytical calculations, forecasting, budgeting, planning, and what-if predic |
| Allows users to perform multidimensional analysis - | OLAP software allows users to perform multidimensional analysis of a wide range of business data, complex calculations, and trend analysis, as well as data modeling. Whereas relational databases store information in rows and columns (similar to a workshee |
| Assists businesses in a wide range of areas - | OLAP is used to assist businesses in a wide range of areas including: |
| Performance management | |
| Financial reporting | |
| Simulation models | |
| Data warehouse reporting | |
| Using OLAP, end-users can perform analysis in multiple dimensions (looking at data in different ways), which leads to insights and understanding of how to make better business decisions. | |
| Allows for delivery of reports and analytics - | Newly developed OLAP systems maintain a constant connection with back-end systems and allow for the delivery of reports and analytics in Microsoft Excel and other front-end tools used to collect data. |
| Data mining - | (also referred to as knowledge discovery in data—KDD) is the searching of large data stores and sets to uncover patterns and trends that cannot be executed using OLAP or simple analysis techniques. Data mining is executed using mathematical algorithms tha |
| Data Mining Key Properties - | Automatic discovery of patterns |
| Prediction of likely outcomes | |
| Creation of actionable information | |
| Focus on large data sets and databases | |
| The Data mining Process - | includes: |
| Problem definition | |
| Data gathering and preparation. | |
| Model building and evaluation. | |
| Knowledge deployment | |
| Key ideas about data mining - | While data mining is a powerful tool, it does not replace the need to have an intimate knowledge of the organization, the data that is produced, and analytical methods employed to turn data into information. |
| Data mining assists businesses in uncovering information that may be hidden in data sets, but does not offer an organization why this information may be valuable. This step is usually executed by managers and data analysts who take reports and interpret t | |
| Predictive information and relationships that are produced from data mining are not causal relationships. | |
| Data mining yields probabilities, not exact answers | |
| Silver is produced by silver mining. It is extracted from silver ore, yielding the final outcome of a precious metal. ________ takes data and refines it down into precious information. Data mining is a means of analyzing data that can help organizations f | Data Mining |
| Data mining example - | might determine that females with incomes between $75,000 and $100,000 who subscribe to certain female-targeted magazines may be more likely to purchase various products. It is important that analysts not assume that the population identified through data |
| Web mining - | uses the principles of data mining to uncover and extract information from websites, social media sites, e-commerce platforms, and web services. Organizations use web mining to assist them in gaining a better understanding of consumer behavior, website ef |
| Web content mining (WCM) - | Includes extraction of information from web pages/documents, including text, images, videos, and interactives. |
| Web structure mining (WSM) - | Includes analysis of hyperlinks, nodes, and related web pages. |
| Web usage mining (WUM) - | Also called log mining, includes analysis of web access logs or the when, how, and frequency of website access. |
| Common web mining applications used by today's organizations include - | Google Analytics, Data Miner, and Tableau. |
| The use of web mining by organizations can lead to improved website - | visibility, usability, and accessibility. |
| Site visibility - | includes how/when the site surfaces when queries are executed in search engines. Search engine optimization (SEO) can be enhanced through the information gained by web mining. This information can also assist marketers in online ad placement and search en |
| Usability - | refers to how easily website users/visitors can interact with the site. Data gained from web mining can help web designers to optimize website navigation and structure of website information. |
| Accessibility - | includes the structure of websites and pages to ensure device/platform access and scalability, ensuring information is available to users, and access for those with physical and cognitive limitations. |
| Web mining helps businesses improve experiences for users. | |
| Database policies - | should be created and implemented by all organizations, regardless of the size of the organization. The data an organization has is a critical asset, and ensuring its security and structure is critical. |
| Database policies are important for ensuring data security. | |
| Information policies specify: - | The rules used in database design (how data is structured) |
| Who has access to the data | |
| How the data is collected and maintained | |
| Where information and data are distributed | |
| Data administration - | is responsible for the policies and procedures that are used to manage an organization's data. Data administration policies and procedures are the most commonly used/implemented by large organizations. |
| Common data administration tasks include: - | Development of information policies |
| Data planning | |
| Database design | |
| Security | |
| How internal users and end users use data | |
| Data administration also includes the development of data modes and data dictionaries. | |
| Data governance (DG) - | is an important component of a data management plan. Data governance includes the personnel, processes, and technology needed to oversee and secure an organization's data and data assets. |
| How do data governance policies help? - | Data governance policies help to ensure an organization's data is: |
| Valid | |
| Understandable | |
| Complete | |
| Accessible | |
| What is covered under digital governance? - | Areas covered in digital governance include: |
| Data architecture | |
| Data quality | |
| Data modeling | |
| Data warehousing and BI | |
| Data security | |
| What are the key goals of data governance? - | The key goals of data governance include: |
| Risk mitigation | |
| Rules for data use | |
| Compliance to requirements | |
| Cost reduction | |
| High-quality data | |
| What are the key considerations in creating a data governance plan? - | When creating a data governance plan, an organization should determine: |
| Goals and objectives of the organization (Where/Who) | |
| Aspects of business governance should cover (What) | |
| Technical aspects and specifications (How) | |
| A database administrator (DBA) - | Ensures an organization's information is accessible. |
| Monitors database systems performance. | |
| Develops and maintains databases. | |
| Establishes user privileges. | |
| Most DBAs have a bachelor's degree in - | computer science or database administration and experience as a data analyst. |
| Data that is of poor quality and validity presents many risks. In order to ensure an organization's data are of the highest quality, a - | data quality audit can be used. |
| Data Quality Audit - | uses statistical analysis to test data, variabilities, and outcomes against test data. |
| Data Quality Audit purpose - | Data quality audits help test the accuracy and completeness of an organization's data and provide a complete picture of the state of data and what improvements can be made to clean up data issues. |
| Key benefits of a data quality audit - | Reduced risk of data inconsistency |
| Reduced data storage costs | |
| Data improvement recommendations | |
| data quality audits - | helps to ensure data being used by business intelligence and other applications are of the highest quality. The process of analyzing data includes quality assessment, design, transformation, and monitoring. |
| Quality assessment - | Analyzes the quality of source data. Data sources, including data warehouses and metadata, are analyzed during this phase. Data profiling, which is the process of investigating data abnormalities and redundancies through the analysis of the content, struc |
| Data design - | Involves the creation of quality processes used to manage data. |
| Quality transformation - | Incorporates correction maps that are designed to correct issues that are present in source data. |
| Quality monitoring - | Uses an established process to examine data over a given amount of time to ensure data rules are being followed and that data is valid. |
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