...Cloud computing and analytics Name: Course: Tutor: Date: 1. Introduction Organizations and individuals collect vast amounts of data in their day to day operations. Data growth comes from multiple sources: From the use of computing within an enterprise for business functions such as commerce, customer service, and resource management. Use of communication devices, including computers, tablets, and mobile phone by individuals for both work and personal use also increases the daily data collected. The instrumentation of physical infrastructure, such as electrical grids, highways, and buildings for more efficient monitoring and management, opens the opportunity to collect a lot of data. It is expected that the trend will continue as both enterprises and individuals find value in access to information and communication. The challenge lies in managing the dynamic nature of the data, keeping the data secure and applying the right analytic technique to use the information most effectively. New advances in computing technologies make it possible for organizations to take full advantage of the vast amount of data they collect. 2. Architecture Design Cloud computing is an umbrella term. It encompasses many types of services. What cloud computing does is take a process anchored to one company, one data center and one facility. It enables businesses to move from working within their own IT bubble and use the cloud to access technologies they need, when they need them, at the scale they...
Words: 1244 - Pages: 5
...Assignment 1: DDS, BI, Business Analytics, and Predictive Analytics LaShonda Spell Prof. S. Mirajkar CIS 356 Operating a successful business today involves utilizing the correct tools to make the best decisions for that business. The main tools that are used for making critical business decisions are DSS, DDS, BI, Business Analytics and Predictive Analytics systems. The concepts/ systems mentioned assist management in the major decision-making processes by providing crucial operational data in comprehensible formats for monitoring/ reviewing and analyzing. Making the best decisions regarding business operations determine the success or failure of the company and ensures that all business strategies are implemented and effective. In this essay, there is a brief overview of the similarities/ differences, methodologies/ technologies and evaluation of the capabilities of DDS, BI, Business Analytics and Predictive Analytics systems. Similarities and Differences among DDS, BI, Business Analytics, and Predictive Analytics regarding business scope/origins /histories/ methodologies/ technologies DDS (Data Distribution Service) are data communications based on the standards managed by the OMG (Object Management Group). The standards set by the OMG of DDS describe different latency levels of data communications for distributed applications (Twin Oaks Computing, Inc., 2011). DDS standard support data defining applications, dynamic publishing/ subscribing discovery and QoS policy...
Words: 1296 - Pages: 6
...Analytics Concepts and Definitions Types of Analytics Descriptive Analytics: * Post Event Analytics * Add features to website and measure its effectiveness in form of clicks, link sharing, page views * Descriptive Analytics Tools -> Google Analytics, Optimizely Diagnostic Analytics: * Post Event Analytics * Analytics used to diagnose why something/phenomenon happened the way it did * It basically provides a very good understanding of a limited piece of the problem you want to solve. * Usually less than 10% of companies surveyed do this on occasion and less than 5% do so consistently. Predictive Analytics: * Used for Prediction of Phenomenon using past and current data statistics * Essentially, you can predict what will happen if you keep things as they are. * However, less than 1% of companies surveyed have tried this yet. The ones who have, found incredible results that have already made a big difference in their business. * Eg:- SAS, RapidMiner, Statistica Prescriptive Analytics: * Prescriptive analytics automatically synthesizes big data, multiple disciplines of mathematical sciences and computational sciences, and business rules, to make predictions and then suggests decision options to take advantage of the predictions. * It is considered final phase of Analytics Some Analytics Techniques used Linear Regression In statistics, linear regression is an approach for modeling the relationship between a scalar...
Words: 1288 - Pages: 6
...Review of Three Business Analytical Articles Kevin Johnson Grand Canyon University: MGT-820-O101 Using Business Analytics for Competitive Advantage November 26, 2014 Introduction The intent of this paper is to provide doctoral learners an opportunity to analyze three articles of literature as it correlates to the principles of business analytics. This doctoral learner chose the following three articles to review: A New Business Dimension – Business Analytics by Pavel Năstase and Dragos Stoica, The Collaborate / Integrate Business Technology Strategy by Stephen J. Andriole, and Evolving From Information to Insight by Glover Ferguson, Sanjay Mathur and Baiju Shah. A New Business Dimension – Business Analytics by Pavel Năstase and Dragos Stoica This is article focuses on the achievement of the appropriate business outcomes through the utilization of business analytics. According to Năstase and Stoica (2011), to ensure these results necessitates a holistic perspective to evaluate analytic factors and ensure that the appropriate elements are utilized. The methodology of business analytics offers an organization a complete and current data that can improve planning. Therefore; it provides managers with an enhancement of abilities to manage costs and to wisely use sparse funds. Furthermore, it allows managers the capability to leveraging staff resources, placing very valuable, expensive resources to on projects that clearly support organizational goals (Năstase and Stoica...
Words: 1439 - Pages: 6
...something that can be overcome. www.fullfrontalroi.com 1 Introduction The makers of HootSuite, the social media dashboard, recognized the need for real social metrics and released the new Custom Social . This platform provides a new level of insight for how social media is impacting your business. It provides metrics beyond those available anywhere, like fans and followers, and gives decision making metrics like how many site visits your social media activities generated and how many of your social media contacts converted on your site. Combining this data with basic executive measurement philosophies will provide concrete reports on what is and what isn’t delivering in your social strategy. The following pages outline some core measurement strategies that will transform your conversation about social media measurement. Customize. Automate. Simplify. Get a better view of your social campaigns with new, more powerful analytics tools, more ways to measure, and customizable...
Words: 4885 - Pages: 20
...Analytic Induction as a Qualitative Research Method of Analysis Donald E. Ratcliff (The University of Georgia, 1994) Analytic induction is a method of data analysis described by Florian Znaniecki (1934) who named the method and systematized many of the associated ideas. However Znaniecki was careful to note that the essence of analytic induction has been used repeatedly throughout history (pp. 236237), particularly by scientists in the physical sciences (he cites numerous examples from physics and biology). That essence involves " . . . inducing laws from a deep analysis of experimentally isolated instances" (p. 237). Analytic induction can be contrasted with defining and using terms in advance of research (p. 240). Instead, definitions of terms are considered hypotheses that are to be tested (p. 241). Inductive, rather than deductive, reasoning is involved, allowing for modification of concepts and relationships between concepts occurs throughout the process of doing research, with the goal of most accurately representing the reality of the situation. The goal of research is making universal statements that may need to be modified later if exceptions are discovered (pp. 232-233), but ultimately can reflect fairly exhaustive knowledge of what is researched (pp. 249, 274-275). Causation is a potential goal of such knowledge, although it is causation that can include numerous exceptions (p. 305). Those exceptions, however, add to the base of knowledge as the generalizability...
Words: 2614 - Pages: 11
...Putting Predictive Analytics to Work in Operations James Taylor CEO, Decision Management Solutions More information at: www.decisionmanagementsolutions.com Using Decision Management to maximize the value of predictive analytics Predictive analytics applied to operational decision making is the next major source of competitive advantage. The most successful companies are using Decision Management to put predictive analytics to work powering the day-to-day decisions that impact performance most. ©2011 Decision Management Solutions Maximizing analytic value in operational decisions Insights from predictive analytics Contents: Insights to actions—improving operational decisions Customers—they think your decisions are deliberate Challenge—getting analytics into operations Solution—Decision Management increases analytic value 1 3 5 7 Insights to actions improving operational decisions “Most discussions of decision making assume that only senior executives make decisions or that only senior executives’ decisions matter. This is a dangerous mistake.” Peter Drucker Companies that systematically apply predictive analytics to operational decisions, especially those pertaining to customers, outperform their competitors. Many organizations think of predictive analytics as being valuable primarily for strategic purposes. They look to it as a source of market insights to help guide executives and managers making decisions about where to focus and allocate...
Words: 5078 - Pages: 21
...Thought Leadership White Paper 2 Reengineering IT discovery with analytics and visualization Contents 2 Introduction 3 The inevitable push towards greater efficiency 3 The need for better IT discovery 4 Building a more comprehensive snapshot of the data center 6 Changing the parameters for IT discovery 6 How ALDM works 8 Identifying issues that hinder operational efficiency and resilience 9 Compiling affinity groups automatically 11 Identifying the best candidates for virtualization 12 Extending insights with data visualization 12 The confluence of discovery analytics and human analysis 15 Conclusion 16 For more information Introduction An intimate knowledge of IT assets and dependencies has always been imperative to mitigating the risk of data center migrations and improving the resiliency of the IT environment. But the IT discovery process can be slow, costly and prone to error. And for all their value in helping organizations determine where and how to plan a migration or improve IT resiliency, traditional asset inventories and dependency maps provide only part of the picture. With modern IT infrastructures an intricate web of interdependencies, uncovering the total IT environment, including the logical relationships between physical, virtual and cloud elements, has never been more important—or more complex. IBM’s analytics for logical dependency mapping, ALDM, reengineers the IT discovery process...
Words: 5168 - Pages: 21
...asdfThis document examines the role of big data in the enterprise as it relates to network design considerations. It describes the rise of big data and the transition of traditional enterprise data models with the addition of crucial building blocks to handle the dramatic growth of data in the enterprise. According to IDC estimates, the size of the "digital universe" in 2011 will be 1.8 zettabytes (1.8 trillion gigabytes). With information growth exceeding Moore's Law, the average enterprise will need to manage 50 times more information by the year 2020 while increasing IT staff by only 1.5 percent. With this challenge in mind, the integration of big data models into existing enterprise infrastructures is a critical element when considering the addition of new big data building blocks while considering the efficiency, economics and privacy. This document also shows that the Cisco Nexus ® architectures are optimized to handle big data while providing integration into current enterprise infrastructures. In reviewing multiple data models, this document examines the effects of Apache Hadoop as a building block for big data and its effects on the network. Hadoop is an open source software platform for building reliable, scalable clusters in a scaled-out, "shared-nothing" design model for storing, processing, and analyzing enormous volumes of data at very high performance. The information presented in this document is based on the actual network traffic patterns of the Hadoop framework...
Words: 1384 - Pages: 6
...Accenture Insurance Reaping the Benefits of Analytics Six Ways to Make Your Business Intelligence Smarter 1 Gartner research shows that many insurers are still struggling to achieve a 360-degree view of their customers, and part of the issue they are grappling with is the completeness or the accuracy of the data they have on customers across their various repositories.1 Despite their hefty and increasing investments in data warehouses, architectures, analytics, and business intelligence (BI) platforms, many insurance companies still are not getting the value they want, and need, from their BI initiatives. Over the past 10 years, insurers have spent hundreds of millions of dollars on data warehousing and BI. One of the prime areas for insurer’s IT investments from 2010 to 2011 was in data warehousing; half of the 29 propertycasualty executives surveyed by the Ward Research Center indicated they were budgeting more money to better house their data.2 Despite the focus and investment, insurers saw little change; the same huge, bulky, flat report outputs from the old systems were simply migrated to the new systems. As a result, carrier information remains disjointed, conflicting, slow, and backwards-looking. Executives and managers continue to be overwhelmed by ominous reports. They have a hard time deciphering what the information means relative to business change. They either have too much or too little information, rarely at the point of need, and never with the ability...
Words: 3218 - Pages: 13
...extent are the Intelligence Community’s Analytic Standards, contained in Intelligence Community Directive 203 (ICD 203), an effective framework and set of core principles for improving the quality of intelligence analysis? What, in your view, are the two most important standards, and why? Comprised of the intelligence community’s (IC) core principles, the Analytic Standards clearly convey expectations, guidelines, ethics, and responsibilities for effective analysts to follow. 1 The five analytic standards, along with the nine Analytic Tradecraft Standards, detail the desirable attributes that increase the probability of successful analysis. Objective, independent of political consideration, timely, based on all available sources...
Words: 1689 - Pages: 7
...Facebook launched their new Insights analytics for pages in June 2013. The rollout has replaced the old system gradually from this point, with a more modern-looking platform with more available data. Facebook Insights is a really useful system to understand the more basic elements of your social media analytics requirements. However, if you do want to pull out reams of data purely on your page, Facebook Insights does have its uses. There are six main tabs containing data on your Likes, Reach, Visits, Posts, and People, as well as an Overview tab covering some headline figures. There is also the ability to download extensive spreadsheets containing all of this data broken down by day at a page or post content level. This data is useful, but can be quite time consuming to analyse and comprehend, as well as not having that critical context of comparing it to other pages. If you’re new to Facebook as a business, we’d definitely recommend exploring Insights and seeing what is there for yourself as a first step. Our recommendation would be to not stop there though in your quest to truly understand your performance. FACEBOOK INSIGHTS Facebook Insights covers some of the key metrics mentioned below and contains both public and private metrics; however, it’s one major flaw is that it doesn’t include any real data to compare to on the public metrics, especially in terms of other companies. Insights has a new element where you can compare your page demographics against...
Words: 282 - Pages: 2
...Chapter 6 HR MetRics and WoRkfoRce analytics Kevin D. Carlson anD MiChael J. Kavanagh EDITORS’ NOTE The capacity to manage is limited by the accessible information in our possession. Research on goal setting confirms that being able to articulate the specific goal for a task and the level of the goal we want to achieve enhances performance of that task. Better information about the expectations of customers, the actions of competitors, and the state of the economy provides strong support for the strategic direction of organizations. Information about levels of output, for example, numbers of defects and efficiency of processes, positions line managers to produce high-quality products in the right amounts at the right time to meet customer needs. The same is true for the effective management of human capital in organizations. As discussed in this chapter, effective approaches to the measurement of human capital and the impact of people on organization processes, for example, HR programs such as recruiting, will enable both HRM professionals and line managers to utilize the human capital in organizations effectively. This measurement is accomplished by focusing on the development of systems of workforce analytics and supporting HR metrics that meet the needs of organization decision makers. This chapter offers a brief history of the efforts involved in the development of HR metrics and workforce analytics and of how these efforts have been enhanced by the advent of integrated...
Words: 9337 - Pages: 38
...Introduction to Analytics Hal Hagood u01a1 The article used was found on Forbes and reports how UPS (United Parcel Service) uses predictive analytics to replace routine maintenance. It addresses a problem that UPS, one of the largest logistics operations in the world faces constantly as they deliver millions of packages every day, a feat which is a small miracle in and of itself. If even one of the trucks in their fleet has so much as even a minor breakdown, it can be a big problem with unpleasant consequences. This can result in driver downtime, late packages and angry customers. The data analytics solution used was that of predictive analytics. United Parcel Service, Inc. (UPS) is the world's largest package delivery company and a provider of supply chain management solutions. It is a global logistics company headquartered in Sandy Springs, Georgia, which is part of the Greater Atlanta metropolitan area. UPS delivers more than 15 million packages a day to more than 6.1 million customers in more than 220 countries and territories around the world (UPS, 2015). The challenges associated with this problem and the information that required analysis concerned maintenance of its fleet. In the past UPS used to replace important parts every few years. This was the solution they used to ensure that its vehicles stayed on the road and in good working order. The new approach however, is to collect data from hundreds of sensors in each vehicle. They then use various algorithms...
Words: 769 - Pages: 4
...August 2013 Big Data Analytics How Cisco IT Built Big Data Platform to Transform Data Management EXECUTIVE SUMMARY CHALLENGE ● Unlock the business value of large data sets, including structured and unstructured information ● Provide service-level agreements (SLAs) for internal customers using big data analytics services ● Support multiple internal users on same platform SOLUTION ● Implemented enterprise Hadoop platform on Cisco UCS CPA for Big Data - a complete infrastructure solution including compute, storage, connectivity and unified management ● Automated job scheduling and process orchestration using Cisco Tidal Enterprise Scheduler as alternative to Oozie RESULTS ● Analyzed service sales opportunities in one-tenth the time, at one-tenth the cost ● $40 million in incremental service bookings in the current fiscal year as a result of this initiative ● Implemented a multi-tenant enterprise platform while delivering immediate business value LESSONS LEARNED ● Cisco UCS can reduce complexity, improves agility, and radically improves cost of ownership for Hadoop based applications ● Library of Hive and Pig user-defined functions (UDF) increases developer productivity. ● Cisco TES simplifies job scheduling and process orchestration ● Build internal Hadoop skills ● Educate internal users about opportunities to use big data analytics to improve data processing and decision making NEXT STEPS ● Enable NoSQL Database and advanced analytics capabilities...
Words: 3053 - Pages: 13