Identify five broad categories of business intelligence/analytics techniques including the specific techniques used in each in two-three paragraphs with valid references.
IS312-Access Group Project IS312-Access Group Project You have recently been hired by Sharbaf and Associates, a small sized firm that offers consulting IT innovative services for the various industries. Your job is to create a database. The database should contain the following criteria information: 1. At least three tables (primary key for each table, and unique name for each table)(20 points). 2. At least 6 attributes (field) for the table 1, at least 5 attributes (field) for the table 2, and 3(10 points). 3. Each table should have at least 10 records (10 points). 4. Establish Entity Relationship Diagram (ERD) for the tables(1:1, 1:M, and M:M)(20 points) 5. Create forms for the tables-each table has its own form (10 points). 6. Create a query for the tables-each table has its own query (establish your criteria for each table)(20 points) 7. Create a report for the tables-each table has its own report.(10 points). Hint: If you are using Microsoft Access, use the wizard to generate the queries. Please be advised not to use student, and faculty for your tables. Access Database Tutorial http://www.quackit.com/microsoft_access/microsoft_access_2016/tutorial/ http://www.gcflearnfree.org/access2016/ https://support.office.com/en-us/article/Access-2016-videos-and-tutorials-a4bd10ea-d5f4-40c5-8b37-d254561f8bce Good Luck! Table 1 Table 2 1 1 1 1 ∞ ∞ Table 3 Establish Entity Relationship Diagram (ERD) for the tables Chapter 6 Business Intelligence: Big Data and Analytics Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Objectives (1 of 3) Identify five key characteristics associated with big data Identify five key challenges associated with big data Distinguish between the terms data warehouse, data mart, and data lake Explain the purpose of each step in the extract, transform, and load process State four ways a NoSQL database differs from an SQL database Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Objectives (2 of 3) Identify the two primary components of the Hadoop computing environment Identify the primary advantage of in-memory database in processing big data State the primary difference between business intelligence and analytics Define the role of a data scientist Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Objectives (3 of 3) Identify three key organizational components that must be in place for an organization to get real value from its BI/analytics efforts Identify five broad categories of business intelligence/analytics techniques including the specific techniques used in each Identify four potential issues that arise with the use of self-service analytics Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Why Learn about Big Data and Analytics? New data coming from all directions Nearly a zettabyte per year 1 trillion gigabytes or a 1 followed by 21 zeros Must analyze large amounts of data Measure past and current performance Predict the future Forecasts drive anticipatory actions Improve business strategies Strengthen business operations Enrich decision making Organization will become more competitive Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Big Data (1 of 2) Big data Enormous (terabytes or more) Complex (sensor data to social media data) Traditional processes incapable of dealing with them Key characteristics Volume Velocity Value Variety Veracity Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Big Data (2 of 2) FIGURE 6.1 Increase in size of the global datasphere Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Sources of Big Data FIGURE 6.2 Sources of an organization’s useful data An organization has many sources of useful data. Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Big Data Uses Organizations use big data to improve: Day-to-day operations Planning Decision making Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Technologies Used to Manage and Process Big Data Technologies used to manage and process big data Data warehouses Extract Transform Load process Data marts Data lakes NoSQL databases Hadoop In-Memory databases Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Data Warehouses, Data Marts, and Data Lakes (1 of 5) Online transaction processing (OLTP) systems Traditionally used to capture data Do not support data analysis required today Data warehouses and data marts Allow organizations to access OLTP data Support decision making more effectively Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Data Warehouses, Data Marts, and Data Lakes (2 of 5) CharacteristicDescription LargeHolds billions of records and petabytes of data Multiple sourcesData comes from many sources both internal and external thus an extract, transform, load process is required to ensure quality data HistoricalTypically 5 years of data or more Cross organizational access and analysisData accessed, used, and analyzed by users across the organization to support multiple business processes and decision making Supports various types of analyses and reportingDrill down analysis, development of metrics, identification of trends TABLE 6.3 Characteristics of a data warehouse Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Data Warehouses, Data Marts, and Data Lakes (3 of 5) Data warehouse Large database Holds business information from many sources in the enterprise Covers all aspects of the company’s processes, products, and customers Extract Transform Load (ETL) process Extracts data from a variety of sources Edits and transforms data into a data warehouse format Loads data into the warehouse Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Data Warehouses, Data Marts, and Data Lakes (4 of 5) FIGURE 6.3 Elements of a data warehouse A data warehouse can help managers and executives relate information in innovative ways to make better decisions. Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Data Warehouses, Data Marts, and Data Lakes (5 of 5) Data mart Subset of a data warehouse Used by small and medium-sized businesses and departments within large companies Supports decision making Data lake Takes a “store everything” approach to big data Saves all data in its raw and unaltered form Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. NoSQL Databases (1 of 3) NoSQL database Differs from a relational database Data modeled without two-dimensional tabular relations Uses horizontal scaling Does not require a predefined schema Does not conform to true ACID properties when processing transactions Structures used by NoSQL databases More flexible than relational database tables Provide improved access speed and redundancy Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. NoSQL Databases (2 of 3) Four categories Key-value NoSQL databases Two columns (“key” and “value”) Document NoSQL databases Store, retrieve, and manage document-oriented information Graph NoSQL databases Well-suited for analyzing interconnections Column NoSQL databases Store data in columns Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. NoSQL Databases (3 of 3) Key-ValueDocumentGraphColumn RedisLotus NotesAllegroAccumulo Couchbase ServerCouchbase ServerNeo4JCassandra Oracle NoSQL DatabaseOracle NoSQL DatabaseInfiniteGraphDruid OrientDBOrientDBOrientDBVertica HyperDEXMongoDBVirtuosoHBase TABLE 6.4 Popular NoSQL database products, by category Stair/Reynolds, Principles of Information Systems, 14th Edition. © 2021 Cengage. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Hadoop (1 of 3) Hadoop Open-source software framework Includes several software modules Stores and processes extremely large data sets Distributed File System (HDFS) Distributed file system