... iii. innovations iv. . new and changing marketing conditions v. new solutions according to customers’ preferences/customization vi. created a new product (pioneer of theme restaurants) vii. risk-oriented viii. measurement of performance and costs ix. on-going reengineering core business processes for greater effectiveness and efficiency x. higher prices (the customer doesn’t just pay for a product but also for a unique experience) xi. inspire competitors 2. What problems did Hardrock in its information management of three core business functions, namely: restaurant operations, merchandising, and financial management? How did this problem impact Hardrock’s ability to transact, manage and innovate? Business Functions: | Information Management Issue...
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...for Domino's is the registration where all the transactions involving the collection, adjustment and recovery of all transaction data are done. The Characteristics of a TPS consist of performance, consistency and dependability. The main aim is to take and customize orders utilizing a touch screen interface, overseeing sales record and assimilating client data. (Bidgoli, 2014) Business process of Transaction processing system Inputs In this system, it take the order from the customers, as which sort of pizza crust, defined flavour of pizza and toppings, any side orders and name of the location where it is to be delivered. Processing All the required information is updated in database and processing of information through machines, so it starts preparing of pizza in the moment Outputs Outputs includes the status of delivery, received money, client feedback, the quantity of pizza's sold (Bidgoli, 2014) 3.2 MANAGEMENT INFORMATION SYSTEM Management information system largely refers to a computer-based system that gives managers with the tools to arrange, assess and productively manage divisions within domino’s pizza. With a specific end goal to give past, present and expectation information, an MIS can incorporate software that helps in choice making, data resources for example, databases that empower the division to run productively. (Information systems and Business Processes,...
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...What is a Data Warehouse • A data warehouse is a relational database that is designed for query and analysis. • It usually contains historical data derived from transaction data, but it can include data from other sources. Finance, Marketing, • Data warehouse can be: Subject Oriented Integrated Nonvolatile Time Variant Inventory SAP, Weblogs, Legacy Identical reports produce same data for different period. daily/monthly/quarterly basis Why Data Warehouse • • • • Provide a consistent information of various cross functional activity. Historical Data. Access, Analyze and Report Information. Augment the Business Processes Why is BI so Important Information Maturity Model Return on Information BI Solution for Everyone BI Framework Business Layer Business goals are met and business value is realized Administration & Operation Layer Business Intelligence and Data Warehousing programs are sustainable Implementation Layer Useful, reliable, and relevant data is used to deliver meaningful, actionable information BI Framework Business Requirements Data Sources Data Sources Data Acquisition, Cleansing,& Integration Data Acquisition, Cleansing, & Integration Data Stores Data Stores Information Services Information Delivery Information Delivery Business Analytics Business Analytics Business Applications Business Applications Business Value Business Value Development Data Resource Administration ...
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...Data Monetization: A Retailer’s Journey Mohammad S. Najjar University of Memphis Department of Management Information Systems 363 Fogelman Administration Building Memphis, TN 38152-3120 (901) 678 2478 msnajjar@memphis.edu William J. Kettinger1 University of Memphis Department of Management Information Systems 346 Fogelman Administration Building Memphis, TN 38152-3120 (901) 678 4547 bill.kettinger@memphis.edu A Paper submitted to the MISQ Executive special issue on “Big Data” Acknowledgements: The authors are indebted to Cynthia Beath and the special issue editors and reviewers for their advice on how to substantially improve this article. An earlier version of the paper was presented at the Pre-ICIS 2012 SIM Academic Workshop on “When Data is Ubiquitous: How to Succeed in a World of Big Data”. We are thankful to Omar El Sawy and other participants at the workshop for their insightful comments. We would also like to offer our sincere gratitude to the anonymous retailer and big data analytics company that provided so much time and insight concerning their experiences with monetization of big data. 1 Corresponding Author 1 Data Monetization: A Retailer’s Journey The ability to monetize a company’s data has been an elusive goal. However, in the era of big data, business intelligence and analytics, and cloud computing, this goal is becoming more achievable. The retail industry, with its exacting merchandising strategies and tight supply chain relationships...
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...without burdening the amazon.com with the inventory-carrying expenses of traditional retailers. It has also innovated the collection and analysis of customer purchase data. By offering a wide variety of books online and having the procurement and delivery system in place satisfy orders in a timely manner; it has been able to grow substantially. These provided amazon.com a competitive advantage in the online retailers’ marketplace. DESCRIBE THE ACTIVITIES ASSOCIATED WITH THE MANUAL ACCOUNTING SYSTEM. The following activities are associated with the manual accounting system. First is to journalize the business event in a book of original entry. The second activity is to post the business event from the journal to the subsidiary ledger. Third, is to post the total from the journal to the general ledger. Finally, is to summarize the business events by preparing a trial balance. DESCRIBE THE STAGES OF AN AUTOMATED ACCOUNTING PROCESS. The following activities are associated with the automated accounting system. Input stage that includes the capturing of data and converting the data to machine readable form. In the same manner the accounting transactions are recorded in a business event data store. Second is the update AR master data,...
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...INSY 5375 Management of Information Systems Introduction to Big data Every day, 2.5 quintillion bytes of complex, every changing data are generated. (IBM) Data comes from social sites, digital images, transaction records, and countless unknown resources. The amount of data we generate daily is enormous, and the rate it is being generated is accelerating. As we head into a future where technology dominates the global market, this pace will only continue accelerate. Businesses and other entities are aware of this data and its power. In a survey taken by Capgemini and the Economist, over 600 global business leaders identified their companies as data driven and identified data analytics as an integral part of their business. Big Data solutions are considered the answer for handling this data converting it into useful information. According to the O'Reilly Radar Team (Big Data Now), Big Data consists of three variables – size, velocity and variety. Data is considered big if conventional systems cannot handle its size. It is not only that size of Big Data that matters, but also the volume of transactions that come with it. The second issue is how fast the data is generated and how fast if it changes (velocity). New data and updated data is constantly generated, and it must be processed and analyzed quickly to create real value for an organization. The final issue is data structure (variety). Data is typically collected in raw form, unstructured, from a variety of sources. To...
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...ABSTRACT We are also going to discuss a Technical paper on Database Administrator for Department Store. We should know that a Database Administrator is the person who is responsible for planning, organizing, controlling, and monitoring the centralized and shared corporate database. The DBA is the general manager of the database administration department. We are also going to discuss the potential sales of the department store transaction within a database, evaluation of all relationships of the database solution using the Crow Foot notation, justifying that Big Data tools could be used for forecasting sales and inventory of the department store, the SQL functions to help sales predictions, implementing cloud-hosted solution for a database in the cost involved and pricing structure required, ranking the cloud services options of software as a service, the appropriation of DBMS structure, the evaluation of updated and uncommitted data, and the evaluation of concurrency control factors of transactions used within the multiuser environment. I am going to answer each questionnaire from the websites, our textbook, and other academicals resources from the Strayer University LRC. Page 2...
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... August 10, 2011 INTRODUCTION This is a compilation of data lifecycle models and concepts assembled in part to fulfill Committee on Earth Observation Satellites (CEOS) Working Group on Information Systems and Services (WGISS) and the U.S. Geological Survey (USGS) Community for Data Integration Data Management Best Practices needs. It is intended to be a living document, which will evolve as new information is discovered. CONTENTS 1. Digital Curation Centre (DCC) Lifecycle Model 2. Ellyn Montgomery, USGS, Data Lifecycle Diagram 3. FGDC Stages of the Geospatial Data Lifecycle pursuant to OMB Circular A–16 4. University of Oxford Research Data Management Chart 5. NOAA Environmental Data Life Cycle Functions 6. Open Archival Information System (OAIS) Framework 7. USGS Scientific Information Management Workshop Vocabulary 8. Peter Fox Lifecycle Diagrams 9. National Science Foundation 10. NDIIPP Preserving Our Digital Heritage 11. What Researchers Want 12. EPA Project Life Cycle 13. IWGDD’s Digital Data Life Cycle Model 14. Scientific Data Management Plan Guidance 15. Linear Data Life Cycle 16. Generic Science Data Lifecycle 17. Cassandra Ladino Hybrid Data Lifecycle Model 18. Ray Obuch Data Management – A Lifecycle Approach 19. USGS Data Management Plan Framework (DMPf) – Smith, Tessler, and McHale 20. BLM Data Management Handbook 21. ARL Joint Task Force on Library Support for E-Science ...
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...6. Managing the security of information 6.1 Control over data integrity, privacy and security 6.1.1 Information Classification: is the conscious decision to assign a level of sensitivity to data as it is being created, amended, enhanced, stored, or transmitted. The classification of the data should then determine the extent to which the data needs to be controlled / secured and is also indicative of its value in terms of Business Assets. The classification of data and documents is essential if you are to differentiate between that which is a little (if any) value, and that which is highly sensitive and confidential. When data is stored, whether received, created or amended, it should always be classified into an appropriate sensitivity level. For many organizations, a simple 5 scale grade will be sufficient as follows: Document / Data Classification | Description | Top Secret | Highly sensitive internal documents e.g. pending mergers or acquisitions; investment strategies; plans or designs; that could seriously damage the organization if such information were lost or made public. Information classified as Top Secret has very restricted distribution and must be protected at all times. Security at this level is the highest possible. | Highly Confidential | Information that, if made public or even shared around the organization, could seriously impede the organization’s operations and is considered critical to its ongoing operations. Information would include...
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... hngo @cs.umn.edu, ¡ 1 Introduction A statistical database management system (SDBMS) is a database management system that can model, store and manipulate data in a manner well suited to the needs of users who want to perform statistical analyses on the data. Statistical databases have some special characteristics and requirements that are not supported by existing commercial database management systems. For example, while basic aggregation operations like SUM and AVG are part of SQL, there is no support for other commonly used operations like variance and co-variance. Such computations, as well as more advanced ones like regression and principal component analysis, are usually performed using statistical packages and libraries, such as SAS [1] and SPSS [2]. From the end user’s perspective, whether the statistical calculations are being performed in the database or in a statistical package can be quite transparent, especially from a functionality viewpoint. However, once the datasets to be analyzed grow beyond a certain size, the statistical package approach becomes infeasible, either due to its inability to handle large volumes of data, or the unacceptable computation times which make interactive analysis impossible. With the increasing sophistication of data collection instrumentation, and the cheap availability of large volume and high speed storage devices, most applications are today collecting data at unprecedented rates. In addition, an increasing number of applications...
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...Find more on www.studymaterial.ca ADMS 2511 MIS Notes Ch 1 – Modern Organization in the Global, Web-Based Environment Management information systems (MIS)- deals with the planning of info tech to help people perform tasks related to info processing and management Information technology (IT)- any computer-based tool used with info to support the needs of an org Importance of Planning for IT -a new info system can apply to the whole org, or a specific area of the org Application portfolios- are groups of new system proposals (apps that have to be added/modified) IT Planning -begins with an organizational strategic plan -states the firm’s mission, goals, and steps to reach those goals -IT architecture describes the way an org’s info resources should be used to accomplish its mission -includes both technical (hardware operating systems) and managerial aspects (managing the IT dpt, how area managers will be involved) IT strategic plan- LT goals that describe the IT infrastructure and major IT initiatives to achieve the organization’s goals -it must meet three main objectives: -must be aligned with the org’s strategic plan -must provide for an IT architecture that networks users, apps, and databases -must efficiently allocate IS resources among different projects so they can all be completed on time, within budget, and function properly IT steering committee- composed of managers/staff who rep diff organizational units -they establish IT priorities...
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...Introduction to Data Warehousing and Business Intelligence Slides kindly borrowed from the course “Data Warehousing and Machine Learning” Aalborg University, Denmark Christian S. Jensen Torben Bach Pedersen Christian Thomsen {csj,tbp,chr}@cs.aau.dk Course Structure • Business intelligence Extract knowledge from large amounts of data collected in a modern enterprise Data warehousing, machine learning Acquire theoretical background in lectures and literature studies Obtain practical experience on (industrial) tools in practical exercises Data warehousing: construction of a database with only data analysis purpose • Purpose Business Intelligence (BI) Machine learning: find patterns automatically in databases 2 •1 Literature • Multidimensional Databases and Data Warehousing, Christian S. Jensen, Torben Bach Pedersen, Christian Thomsen, Morgan & Claypool Publishers, 2010 • Data Warehouse Design: Modern Principles and Methodologies, Golfarelli and Rizzi, McGraw-Hill, 2009 • Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications, Elzbieta Malinowski, Esteban Zimányi, Springer, 2008 • The Data Warehouse Lifecycle Toolkit, Kimball et al., Wiley 1998 • The Data Warehouse Toolkit, 2nd Ed., Kimball and Ross, Wiley, 2002 3 Overview • • • • Why Business Intelligence? Data analysis problems Data Warehouse (DW) introduction DW topics Multidimensional modeling ETL Performance optimization 4 •2 What is Business Intelligence (BI)? • From...
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... . . . . . . . . . . . .7 Benefits and Barriers to Implementation 10 Business Benefits Sought from Customer Analytics . . . . . . . . . . 10 Barriers to Adoption of Customer Analytics . . . . . . . . . . . . . . 12 role of Analytics in Increasing Marketing roI . . . . . . . . . . . . . 13 Analytics Tools, Data Sources, and Techniques 17 BI, olAP, and data discovery for Customer Analytics . . . . . . . . . 18 In-Memory Computing for More rapid discovery Analysis . . . . . . . 20 Predictive Analytics, data Mining, and Advanced Statistics Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 filling the role of the data Scientist for Customer Analytics . . . . . . 23 Applying Technologies for Social Media Data Analysis 24 Applying Analytics to find and Influence the Influencers . . . . . . . . 26 Selecting and Accessing Internal and External Social Media data . . . 27 finding the right role for Hadoop and Mapreduce . . . . . . . . . . 28 Data Management and Integration...
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...McKinsey Global Institute June 2011 Big data: The next frontier for innovation, competition, and productivity The McKinsey Global Institute The McKinsey Global Institute (MGI), established in 1990, is McKinsey & Company’s business and economics research arm. MGI’s mission is to help leaders in the commercial, public, and social sectors develop a deeper understanding of the evolution of the global economy and to provide a fact base that contributes to decision making on critical management and policy issues. MGI research combines two disciplines: economics and management. Economists often have limited access to the practical problems facing senior managers, while senior managers often lack the time and incentive to look beyond their own industry to the larger issues of the global economy. By integrating these perspectives, MGI is able to gain insights into the microeconomic underpinnings of the long-term macroeconomic trends affecting business strategy and policy making. For nearly two decades, MGI has utilized this “micro-to-macro” approach in research covering more than 20 countries and 30 industry sectors. MGI’s current research agenda focuses on three broad areas: productivity, competitiveness, and growth; the evolution of global financial markets; and the economic impact of technology. Recent research has examined a program of reform to bolster growth and renewal in Europe and the United States through accelerated productivity growth; Africa’s economic potential;...
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...McKinsey Global Institute June 2011 Big data: The next frontier for innovation, competition, and productivity The McKinsey Global Institute The McKinsey Global Institute (MGI), established in 1990, is McKinsey & Company’s business and economics research arm. MGI’s mission is to help leaders in the commercial, public, and social sectors develop a deeper understanding of the evolution of the global economy and to provide a fact base that contributes to decision making on critical management and policy issues. MGI research combines two disciplines: economics and management. Economists often have limited access to the practical problems facing senior managers, while senior managers often lack the time and incentive to look beyond their own industry to the larger issues of the global economy. By integrating these perspectives, MGI is able to gain insights into the microeconomic underpinnings of the long-term macroeconomic trends affecting business strategy and policy making. For nearly two decades, MGI has utilized this “micro-to-macro” approach in research covering more than 20 countries and 30 industry sectors. MGI’s current research agenda focuses on three broad areas: productivity, competitiveness, and growth; the evolution of global financial markets; and the economic impact of technology. Recent research has examined a program of reform to bolster growth and renewal in Europe and the United States through accelerated productivity growth; Africa’s economic potential;...
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