Chapter 2 - Data Governance and IT Architecture Support Long-Term Performance, Analysis Paper writing

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Question Description

Please read attached that describe how information can be used strategically, which is the topic for this week's paper:

  • Chapter 2 - Data Governance and IT Architecture Support Long-Term Performance
  • Chapter 3 - Data Management, Big Data Analytics, and Records Management

Please watch the following videos on Michael Porter's Five Forces model:

Please watch this video: How Does Google Make Money? This video explains how Google uses information to make money. Google collects information from websites and adds it to its search engine. You type information into Google search and Gmail, and Google stores that information about you. Android devices send information to Google that is used to determine traffic patterns for Google Maps and to send you ads for businesses based on your location. Although Google is a technology company, it makes money from information, as this video explains. In your paper for this week, you will explore how other companies use information strategically.

The videos above describe Porter’s Five Competitive Forces model. Use the search term “information sharing in a supply chain” to find 2 peer-reviewed articles from academic journals about the use of information in supply chains or value chains. Write a summary of each article and explain how the use of information in each article relates to Porter’s Five Competitive Forces model. Discuss how information can be used strategically, based on the example(s) in the articles. Your paper should be in APA format and 3-4 pages, not counting the title page and reference pages. Make sure you have in-text citations and a reference page. The rubric for this assignment can be viewed when clicking on the assignment link.

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Chapter 3 Data Management, Big Data Analytics, and Records Management Prepared by Dr. Derek Sedlack, South University Learning Objectives Data Warehouse and Big Data Analytics Database Management Systems Electronic Records Management Data and Text Mining Business Intelligence Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems • Databases – Collections of data sets or records stored in a systematic way. – Stores data generated by business apps, sensors, operations, and transaction-processing systems (TPS). – The data in databases are extremely volatile. – Medium and large enterprises typically have many databases of various types. Volatile data changes frequently Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems • Data Warehouses – Integrate data from multiple databases and data silos, and organize them for complex analysis, knowledge discovery, and to support decision making. – May require formatting processing and/or standardization. – Loaded at specific times making them non-volatile and ready for analysis. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems • Data Marts – Small-scale data warehouses that support a single function or one department. – Enterprises that cannot afford to invest in data warehousing may start with one or more data marts. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems • Business intelligence (BI) – Tools and techniques that process data and conduct statistical analysis for insight and discovery. – Used to discover meaningful relationships in the data, keep informed of real time, detect trends, and identify opportunities and risks. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems • Database Management System (DBMS) – Integrate with data collection systems such as TPS and business applications. – Stores data in an organized way. – Provides facilities for accessing and managing data. – Standard database model adopted by most enterprises. – Store data in tables consisting of columns and rows, similar to the format of a spreadsheet. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems • Relational Management System (DBMS) – Provides access to data using a declarative language. • Declarative Language – Simplifies data access by requiring that users only specify what data they want to access without defining how they will be achieved. – Structured Query Language (SQL) is an example of a declarative language: SELECT column_name(s) FROM table_name WHERE condition Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems • DBMS Functions – Data filtering and profiling – Data integrity and maintenance – Data synchronization – Data security – Data access Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems Online Transaction Processing and Online Analytics Processing • Online Transaction Processing (OLTP) – Designed to manage transaction data, which are volatile & break down complex information into simpler data tables to strike a balance between transaction-processing efficiency and query efficiency. – Cannot be optimized for data mining • Online Analytics Processing (OLAP) – A means of organizing large business databases. – Divided into one or more cubes that fit the way business is conducted. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems • DBMSs (mid-2014) – Oracle’s MySQL – Microsoft’s SQL Server – PostgreSQL – IBM’s DB2 – Teradata Database. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems • Trend Toward NoSQL Systems – Higher performance – Easy distribution of data on different nodes • enables scalability and fault tolerance – Greater flexibility – Simpler administration Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems Centralized and Distributed Database Architecture • Centralized Database Architecture – Better control of data quality. – Better IT security. • Distributed Database Architecture – Allow both local and remote access. – Use client/server architecture to process requests. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems Garbage In, Garbage Out • Dirty Data – Lacks integrity/validation and reduces user trust. – Incomplete, out of context, outdated, inaccurate, inaccessible, or overwhelming. Cost of Poor Quality Data Lost Business Cost to Prevent Errors Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Cost to Correct Errors Chapter 3 Database Management Systems • Principle of Diminishing Data Value – The value of data diminishes as they age. – Blind spots (lack of data availability) of 30 days or longer inhibit peak performance. – Global financial services institutions rely on nearreal-time data for peak performance. • Principle of 90/90 Data Use – As high as 90 percent, is seldom accessed after 90 days (except for auditing purposes). – Roughly 90 percent of data lose most of their value after 3 months. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems • Principle of data in context – The capability to capture, process, format, and distribute data in near real time or faster requires a huge investment in data architecture. – The investment can be justified on the principle that data must be integrated, processed, analyzed, and formatted into “actionable information.” Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems Data Life Cycle Figure 3.11 Data life cycle. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems Figure 3.12 An enterprise has transactional, master, and analytical data. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Database Management Systems 1. Describe a database and a database management system (DBMS). 2. Explain what an online transaction-processing (OLAP) system does. 3. Why are data in databases volatile? 4. Explain what processes DBMSs are optimized to perform. 5. What are the business costs or risks of poor data quality? 6. Describe the data life cycle. 7. What is the function of master data management (MDM)? Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Learning Objectives Data Warehouse and Big Data Analytics Database Management Systems Electronic Records Management Data and Text Mining Business Intelligence Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data Warehouse and Big Data Analytics • Market share – Percentage of total sales in a market captured by a brand, product, or company. • Operating Margin – A measure of the percent of a company’s revenue left over after paying variable costs: wages, raw materials, etc. – Increased margins mean earning more per dollar of sales. – The higher the operating margin, the better. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data Warehouse and Big Data Analytics TORTURE DATA LONG ENOUGH AND IT WILL CONFESS . . . BUT MAY NOT TELL THE TRUTH Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data Warehouse and Big Data Analytics • Human Expertise and Judgment Required – Data are worthless if you cannot analyze, interpret, understand, and apply the results in context. – Data need to be prepared for analysis. – Dirty data degrade the value of analytics. – Data must be put into meaningful context. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data Warehouse and Big Data Analytics • Enterprise data warehouses (EDW) – Data warehouses that pull together data from disparate sources and databases across an entire. – Warehouses are the primary source of cleansed data for analysis, reporting, and Business Intelligence (BI). – Their high costs can be subsidized by using Data marts. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data Warehouse and Big Data Analytics • Procedures to Prepare EDW Data for Analytics – Extract from designated databases. – Transform by standardizing formats, cleaning the data, integration. – Loading into a data warehouse. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data Warehouse and Big Data Analytics Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data Warehouse and Big Data Analytics • Active Data Warehouse (ADW) – Real-time data warehousing and analytics. – Transform by standardizing formats, cleaning the data, integration. • They Provide – Interaction with a customer to provide superior customer service. – Respond to business events in near real time. – Share up-to-date status data among merchants, vendors, customers, and associates. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data Warehouse and Big Data Analytics • Supporting Actions as well as Decisions – Marketing and Sales – Pricing and Contracts – Forecasting – Sales – Financial Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data Warehouse and Big Data Analytics • Really Big Data – Low-cost sensors collect data in real time in all types of physical things (machine-generated sensor data): • Regulate temperature and climate • Detect air particles for contamination • Machinery conditions/failures • Engine wear/maintenance Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data Warehouse and Big Data Analytics Figure 3.16 Machine generated data from physical objects are becoming a much larger portion of big data and analytics.. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data Warehouse and Big Data Analytics • Hadoop and MapReduce – Hadoop is an Apache processing platform that places no conditions on the processed data structure. – MapReduce provides a reliable, fault-tolerant software framework to write applications easily that process vast amounts of data (multi-terabyte datasets) in-parallel on large clusters (thousands of nodes) of commodity hardware. • Map stage: breaks up huge data into subsets • Reduce stage: recombines partial results Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data Warehouse and Big Data Analytics 1. Why are human expertise and judgment important to data analytics? Give an example. 2. What is the relationship between data quality and the value of analytics? 3. Why do data need to be put into a meaningful context? 4. What are the differences between databases and data warehouses? 5. Explain ETL and CDC. 6. What is an advantage of an active data warehouse (ADW)? 7. Why might a company invest in a data mart? 8. How can manufacturers and health care benefit from data analytics? 9. Explain how Hadoop implements MapReduce in two stages. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Learning Objectives Data Warehouse and Big Data Analytics Database Management Systems Electronic Records Management Data and Text Mining Business Intelligence Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data and Text Mining • Creating Business Value – Business Analytics: the entire function of applying technologies, algorithms, human expertise, and judgment. – Data Mining: software that enables users to analyze data from various dimensions or angles, categorize them, and find correlative patterns among fields in the data warehouse. – Text Mining: broad category involving interpreted words and concepts in context. – Sentimental Analysis: trying to understand consumer intent. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data and Text Mining • Text Analytics (Mining) Procedure – Exploration • Simple word counts • Topics consolidation – Preprocessing • Standardization • May be 80% of processing time • Grammar and spell checking – Categorizing and Modelling • Create business rules and train models for accuracy and precision Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data and Text Mining • Text Analytics Procedure – Exploration • Simple word counts • Topics consolidation – Preprocessing • Standardization • May be 80% of processing time • Grammar and spell checking – Categorizing and Modelling • Create business rules and train models for accuracy and precision Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Data and Text Mining 1. Describe data mining. 2. How does data mining generate or provide value? Give an example. 3. What is text mining? 4. Explain the text mining procedure. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Learning Objectives Data Warehouse and Big Data Analytics Database Management Systems Electronic Records Management Data and Text Mining Business Intelligence Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Business Intelligence • Key to competitive advantage – Across industries in all size enterprises – Used in operational management, business process, and decision making – Provides moment of value to decision makers – Unites data, technology, analytics, & human knowledge to optimize decisions Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Business Intelligence • Challenges – Data Selection & Quality – Alignment with Business Strategy and BI Strategy • Alignment – Clearly articulates business strategy – Deconstructs business strategy into targets – Identifies PKIs – Prioritizes PKIs – Creates a plan based on priorities – Transform based on strategic results and changes Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Business Intelligence Smart Devices Everywhere have created demand for effortless 24/7 access to insights. Advanced BI and Analytics help to ask questions that were previously unknown and unanswerable. Data is Big Business when they provide insight that supports decisions and action. Cloud Enabled BI and Analytics are providing low-cost and flexible solutions. Figure 3.20 Four factors contributing to increased use of BI. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Business Intelligence • BI Architecture and Analytics – Advances in response to big data and end-user performance demands. – Hosted on public or private clouds. – Limits IT staff and controls costs – May slow response time, add security and backup risks Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Business Intelligence 1. How has BI improved performance management at Quicken Loans? 2. What are the business benefits of BI? 3. What are two data-related challenges that must be resolved for BI to produce meaningful insight? 4. What are the steps in a BI governance program? 5. What is a business-driven development approach? 6. What does it mean to drill down, and why is it important? 7. What four factors are contributing to increased use of BI? 8. How did BI help CarMax achieve record-setting revenue growth? Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Learning Objectives Data Warehouse and Big Data Analytics Database Management Systems Electronic Records Management Data and Text Mining Business Intelligence Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Electronic Records Management • Business Records – Documentation of a business event, action, decision, or transaction. • Electronic Records Management (EMR) – Workflow software, authoring tools, scanners, and databases that manage and archive electronic documents and image paper documents. – Index and store documents according to company policy or legal compliance. – Success depends on partnership of key players. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Electronic Records Management • Best Practices – Effective systems capture all business data. – Input from online forms, bar codes, sensors, websites, social sites, copiers, e-mails, and more. • Industry Standards – Association for Information and Image Management (AIIM; www.aiim.org) – National Archives and Records Administration (NARA; www.archives.gov) – ARMA International (formerly the Association of Records Managers and Administrators; www.arma.org) Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Electronic Records Management • Primary Benefits – Access and use the content contained in documents. – Cut labor costs by automating business processes. – Reduce time and effort to locate required information for decision making. – Improve content security, thereby reducing intellectual property theft risks. – Minimizes content printing, storing, and searching costs. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Electronic Records Management • DISASTER RECOVERY, BUSINESS CONTINUITY, AND COMPLIANCE 1. Does the software meet the organization’s needs? For example, can the DMS be installed on the existing network? Can it be purchased as a service? 2. Is the software easy to use and accessible from Web browsers, office applications, and e-mail applications? If not, people will not use it. 3. Does the software have lightweight, modern Web and graphical user interfaces that effectively support remote users? 4. Before selecting a vendor, it is important to examine workflows and how data, documents, and communications flow throughout the company. Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 3 Electronic Records Management 1. What are business records? 2. Why is ERM a strategic issue rather than simply an IT issue? 3. Why might a company have a legal duty to retain records? Give an example. 4. Why is creating backups an insufficient way to manage an organization’s documents? 5. What are the benefits of ERM? Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 2 Data Governance and IT Architecture Support LongTerm Performance Prepared by Dr. Derek Sedlack, South University Learning Objectives Enterprise Architecture and Data Governance Information Management Cloud Services Add Agility Information Systems: The Basics Data Centers, Cloud Computing, and Virtualization Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 2 Information Management INFORMATION MANAGEMENT HARNESSES SCATTERED DATA Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 2 Information Management • Information Management – The use of IT tools and methods to collect, process, consolidate, store, and secure data from sources that are often fragmented and inconsistent. – Why a continuous plan is needed to guide, control, and govern IT growth. – Information management is critical to data security and compliance with continually evolving regulatory requirements, such as the Sarbanes-Oxley Act, Basel III, the Computer Fraud and Abuse Act (CFAA), the USA PATRIOT Act, and the Health Insurance Portability and Accountability Act (HIPAA). Copyright © 2015 John Wiley & Sons, Inc. All rights reserved. Chapter 2 Information Manageme ...
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henryprofessor
School: Duke University

Attached.

Analysis Paper writing – Outline
Thesis Statement: Among the numerous strategic options at the disposal of all entities,
information is a critical tool that a business can exploit to establish a competitive edge over and
above any existing threats in the industry, and boost its vigor towards the achievement of all
business goals and objectives.
I.
II.

Introduction
Summary of articles

III.

Strategic use of information

IV.

Conclusion


Running head: ANALYSIS PAPER WRITING

Analysis Paper writing
Name
Institution

1

ANALYSIS PAPER WRITING

2
Analysis Paper writing

Introduction
The modern business world is characterized by a highly dynamic market in which there
exists stiff competition waged by the numerous business players and the challenges posed by the
continuously evolving consumer needs, tastes, and preferences. As such, businesses strive to
achieve the highest levels of competitiveness so as to remain relevant in the entirety of their
operations. The failure to establish a strong competitive edge is often a sufficient reason for
business failure, which is never among the motives of all entities. In the context of
competitiveness, Porter identified five forces that play a critical role in the process of shaping
every industry including competition, new entrants, supplier power, customer power, and the
threat posed by substitutes (Investopedia, n.d.). In a bid to overcome the imminent business
challenges highlighted by Porter, businesses adopt various strategic considerations, all geared
towards increased business efficiency. A business may, therefore, exploit information
strategically so as to counter the threats of competition, new entrants, and substitutes, as well as
align well with the power of suppliers as well as the cl...

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Anonymous
Thanks, good work

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