...of Big Data. IBM will invest more than $1 billion into the Watson Group, focusing on development and research and bringing Watson-powered cloud-delivered cognitive applications and services to market. About 2,000 professionals will design, develop, and accelerate the adoption of Watson cognitive technologies that transform industries and professions. According to technology research firm Gartner, Inc., smart machines will be the most disruptive change ever brought about by information technology, and can make people more effective, empowering them to do “the impossible.” New Watson-based services IBM is announcing three new services based on Watson’s cognclouditive intelligence: * IBM Watson Discovery Advisor aims to revolutionize how industries such as pharmaceutical and publishing conduct research. It will delve into the influx of data-driven content today’s researchers face, and uncover connections that can speed up and strengthen their work. * IBM Watson Analytics allows users to explore Big Data insights through visual representations, without the need for advanced analytics training. Guided by sophisticated analytics and a natural language interface, Watson Analytics automatically prepares the data, surfaces the most important...
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...view of its critical role in GDP growth and sustainable development strategies. However, the sector’s share of GDP has declined considerably over the years in advanced and emerging economies alike owing to the rapid expansion of the services sector. Today, manufacturing is staging a strong comeback, powered by renewed policy focus, adoption of new technologies and intelligent systems, market demand for higher-end manufactures, and rise of new global markets. MANUFACTURING 19 Certain manufacturing segments are also adopting a services approach, which is a distinct breakaway from conventional manufacturing delivery. For example, Xerox is leasing out machines as a service. This approach spells a paradigm shift in the manufacturing sector’s strategic thinking. Over the next decade, manufacturing competitiveness, growth and development will increasingly hinge on the quality of inputs that go into the strategic planning. Towards this end, the importance of analytics can hardly be overstated. Analytics locates itself in the data that is related to the particular business. With the adoption of new network and communication technologies, companies have access to several terabytes of data about their customers, suppliers and operations. The amount of data in the manufacturing world currently tots up to nearly 1 exabyte per year and growing exponentially (compared to nearly 434 petabytes for healthcare and nearly 619 petabytes for banking). However, manufacturing firms in...
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...Take-off of online marketing: casting the next generation strategies Amalesh Sharma Amalesh Sharma is a Teaching Associate based at the Indian School of Business, Hyderabad, India. Introduction The purpose of the old module of marketing was to dictate a message to the mass market. With changes in time, requirements, technology and expectations of the customers, the field of marketing itself has experienced significant changes. It is noteworthy to mention that marketing dynamics shifted from ‘‘farm centric’’ to ‘‘consumer centric’’ perspectives. With evolution in the market dynamics, it is becoming a difficult issue to choose the correct channel to reach the consumer within a specific target group. The ‘‘dotcom era’’ in the history of marketing will probably be remembered exclusively for its contribution towards innovation based marketing. The development of internet technology has impacted us by changing our behavior, decision making process, relation with one another and the way of doing business. The internet has decreased the distance between places, crossing geographical borders and allowing us to build and bring all the necessary things we require, and discarding all the old communication techniques. Highly intense information based market is forcing marketers to look at the world with new perspectives at the new opportunities. Diverse culture, social restrictions are no longer able to hinder the information flow. At this transitional phase as the world is moving towards...
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...and software ideas along with complementary services. More than onehalf of the company’s employees are engineers and scientists, and there might be hundreds of projects in development at any given time. Many prototypes are made available on the lab’s website for the interested public to download and try in the beta stage. By launching these beta products and services publicly to be tried out, Google can see which ones take off, get feedback, and incrementally improve them without having to scrap a product that might take off with further development. 4.2 Acquisition of talented people Google is among the most active hightech companies in hiring top engineering talent, including technology industry legends and young guru programmers. During the technology bust, Google seized the opportunity to hire bright technologists who focused on search technology (Vise and Malseed 2005). Although Google’s revenue did not grow rapidly at first, its employee brainpower did grow fast. Many of the individuals hired by...
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...INTELLIGENCE TRENDS The Next Generation of Performance Management Updated 2/16/2010 Attivio® 246 Walnut Street Newton, MA 02460 o +1.857.226.5040 | f +1.857.226.5072 | e info@attivio.com www.attivio.com BI Trends: The Next Generation of Performance Management Every year, technology research firm Gartner surveys 1,500 CIOs. As has been the case for several years, the firm’s 2010 survey1 revealed that BI applications are among the top technology focus areas for CIOs. And business process improvement is the top business priority for IT, followed immediately by reducing costs and improving the use of information and analytics. According to Bob Kaplan and David Norton, the creators of the Balanced Scorecard (the most commonly used performance management system2), “breakdowns in a company’s management system, not managers’ lack of ability or effort, are what cause a company’s underperformance.” Kaplan and Norton define a management system as the “integrated set of processes and tools that a company uses to develop its strategy, translate it into operational actions, and monitor and improve the effectiveness of both” and point out that “the failure to balance the tensions between strategy and operations is pervasive…with various studies published in the past 25 years indicating that 60% to 80% of companies are fall short of the success predicted from their strategies.”3 So why are CIOs spending so much time and energy on BI? These technologies are the platform for communicating...
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...INTERNET OF THINGS Introduction: The next wave in the era of computing will be outside the realm of the traditional desktop. In the Internet of Things (IoT) paradigm, many of the objects that surround us will be on the network in one form or another. Radio Frequency IDentification (RFID) and sensor network technologies will rise to meet this new challenge, in which information and communication systems are invisibly embedded in the environment around us. This results in the generation of enormous amounts of data which have to be stored, processed and presented in a seamless, efficient, and easily interpretable form. This model will consist of services that are commodities and delivered in a manner similar to traditional commodities. Cloud computing can provide the virtual infrastructure for such utility computing which integrates monitoring devices, storage devices, analytics tools, visualization platforms and client delivery. The cost based model that Cloud computing offers will enable end-to-end service provisioning for businesses and users to access applications on demand from anywhere. Smart connectivity with existing networks and context-aware computation using network resources is an indispensable part of IoT. With the growing presence of WiFi and 4G-LTE wireless Internet access, the evolution towards ubiquitous information and communication networks is already evident. However, for the Internet of Things vision to successfully...
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...consider and potentially avoid? Data warehouse projects have many risks. Most of them are also found in other IT projects, but data warehousing risks are more serious because data warehouses are expensive, time-and-resource demanding, large-scale projects. Each risk should be assessed at the inception of the project. When developing a successful data warehouse, it is important to carefully consider various risks and avoid the following issues: • Starting with the wrong sponsorship chain. You need an executive sponsor who has influence over the necessary resources to support and invest in the data warehouse. You also need an executive project driver, someone who has earned the respect of other executives, has a healthy skepticism about technology, and is decisive but flexible. You also need an IS/IT manager to head up the project. • Setting expectations that you cannot meet. You do not want to frustrate executives at the moment of truth. Every data warehousing project has two phases: Phase 1 is the selling phase, in which you internally market the project by selling the benefits to those who have access to needed resources. Phase 2 is the struggle to meet the expectations described in Phase 1. For a mere $1 to $7 million, hopefully, you can deliver. • Engaging in politically naive behavior. Do not simply state that a data warehouse will help managers make better decisions. This may imply that you feel they have been making bad decisions until now. Sell the idea that they will...
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...ultimate prize is the consumer purchasing their products. Consumers are more technology savvy and have specific demands when investing in technology. Consumers want devices that are mobile, with faster processing speeds, increased storage capacity, and eco-friendly product. Companies are now faced with a new task of changing their manufacturing, logistics, pricing structure, and communication with the buying market to meet their eco friendly demands. Hewlett Packard relies on technological advances to remain competitive in the market place. HP has created several innovation labs to help the company make the necessary technological advances to complete in the global marketplace. The Systems Research Lab develops radical new approaches to collect, process, store, and analyze data to create the next generation of large-scale high performance infrastructure (HP). This technology will create high capacity storage systems that are highly efficient and reliable. The Analytics Lab is responsible for research in analytic systems, technologies, and infrastructure as well as applications and visualization (HP). These innovations will help business compile large amounts of data and utilize that data to answer fundamental questions essential to their business. The Printing and Content Lab delivers innovations in document and content solutions, and commercial printing, delivering customer values through a new generation of content and data visualization services (HP). Printing services will...
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...1.0 Introduction Business analytics (BA) is the practice of iterative, methodical exploration of an organization’s data with emphasis on statistical analysis. It describes the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics is used by companies committed to data-driven decision making. It focuses on developing new insights and understanding of business performance based on data and statistical methods. BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Data-driven companies treat their data as a corporate asset and leverage it for competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business and an organizational commitment to data-driven decision making. Once the business goal of the analysis is determined, an analysis methodology is selected and data is acquired to support the analysis. Data acquisition often involves extraction from one or more business systems, cleansing, and integration...
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...Analytics as a Service POV Bhagyashree V1 The no-pain route to analytics Analytics, the trend sweeping businesses There is a new corner office and it belongs to the Chief Analytics Officer (CAO). For businesses with access to large data streams, analytics holds the key to making fast and accurate decisions. CAOs can now use data for a variety of purposes. It can help manage operational efficiencies, discover customer needs, identify new markets, give shape to new products and value-added services and develop defensible differentiators. In other words, the onus to support the organizational business strategy now falls upon the CAO. And assisting CAOs with this goal is the science of analytics. Analytics is being recognized as a key business differentiator across the industries. All business functions in every organization recognize the need of quality data to drive decisions. In this competitive world, every critical decision counts CAOs are tasked to make this data and insights to make it available to anyone, anytime and anywhere. —In the current digital era, lack of actionable insights means loss of business momentum, customer attrition and erosion of market share. CAOs are therefore careful when they embark upon the analytics transformation journey. They know it requires the ability to balance the business landscape with technological developments. Through targeted business and technology capabilities realized by a proven methodology that prepares , analyses and visualizes...
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...INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 U.S.A. {vstorey@gsu.edu} Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework. Keywords: Business intelligence and analytics, big data analytics, Web 2...
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...Spotlight on Making Your Company Data-Friendly Spotlight 64 Harvard Business Review December 2013 Artwork Chad Hagen Nonsensical Infographic No. 5 2009, digital hbr.org Analytics 3.0 In the new era, big data will power consumer products and services. by Thomas H. Davenport T hose of us who have spent years studying “data smart” companies believe we’ve already lived through two eras in the use of analytics. We might call them BBD and ABD—before big data and after big data. Or, to use a naming convention matched to the topic, we might say that Analytics 1.0 was followed by Analytics 2.0. Generally speaking, 2.0 releases don’t just add some bells and whistles or make minor performance tweaks. In contrast to, say, a 1.1 version, a 2.0 product is a more substantial overhaul based on new priorities and technical possibilities. When large numbers of companies began capitalizing on vast new sources of unstructured, fast-moving information—big data—that was surely the case. Some of us now perceive another shift, fundamental and farreaching enough that we can fairly call it Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis December 2013 Harvard Business Review 65 Spotlight on Making Your Company Data-Friendly methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy. I’ll develop this argument in what follows, making...
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...AND ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 U.S.A. {vstorey@gsu.edu} Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework. Keywords: Business intelligence and analytics, big...
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...Financial Services Practice Journey III: The Next Frontier in Property and Casualty Insurance The Challenge of Profitable Growth Journey III: The Next Frontier in Property and Casualty Insurance The Challenge of Profitable Growth Contents Introduction Dramatic improvement and a paradigm shift 1 A Paradigm Shift The exit of undisciplined capacity Strengthened risk management skills Stronger foundational pillars The Implications The risk journey continues – with profitable growth as the new frontier 7 9 10 13 17 Continuous improvement of risk management Growing profitably in a competitive industry Lessons and barriers Levers to drive growth Upgrading the foundational pillars Conclusion Looking forward 17 21 28 38 43 Journey III: The Next Frontier in Property and Casualty Insurance The Challenge of Profitable Growth 1 Introduction Dramatic improvement and a paradigm shift In our first Journey report, covering 1979 to 1993, we reached three main conclusions about performance in the property and casualty insurance industry: 1. Risk management is the primary driver of success, not investment performance, expense management or financial leverage. We suggested that risk management had four components: enterprise risk management, capital management, product market management, and transaction excellence. 2. Performance is remarkably consistent and selfreinforcing. Winners keep winning, while persistent underperformers tend to be acquired...
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...verticals have amassed burgeoning amounts of digital data, capturing trillions of bytes of information about their customers, suppliers and operations. Data volume is also growing exponentially due to the explosion of machine-generated data (data records, web-log files, sensor data) and from growing human engagement within the social networks. The growth of data will never stop. According to the 2011 IDC Digital Universe Study, 130 exabytes of data were created and stored in 2005. The amount grew to 1,227 exabytes in 2010 and is projected to grow at 45.2% to 7,910 exabytes in 2015.3 The growth of data constitutes the “Big Data” phenomenon – a technological phenomenon brought about by the rapid rate of data growth and parallel advancements in technology that have given rise to an ecosystem of software and hardware products that are enabling users to analyse this data to produce new and more granular levels of insight. Figure 1: A decade of Digital Universe Growth: Storage in Exabytes Error! Reference source not found.3 1 2 3 Ovum. What is Big Data: The End Game. [Online] Available from: http://ovum.com/research/what-is-big-data-theend-game/ [Accessed 9th July 2012]. IBM. Data growth and standards. [Online] Available from: http://www.ibm.com/developerworks/xml/library/xdatagrowth/index.html?ca=drs- [Accessed 9th July 2012]. IDC. The 2011 Digital Universe Study: Extracting Value from Chaos. [Online]...
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