What You'll Learn Discover how the open source business model works and how to make it work for you See how cloud computing completely changes the economics of analytics Harness the power of Hadoop and its ecosystem Find out why Apache ...
Author: Thomas W. Dinsmore
Learn all you need to know about seven key innovations disrupting business analytics today. These innovations—the open source business model, cloud analytics, the Hadoop ecosystem, Spark and in-memory analytics, streaming analytics, Deep Learning, and self-service analytics—are radically changing how businesses use data for competitive advantage. Taken together, they are disrupting the business analytics value chain, creating new opportunities. Enterprises who seize the opportunity will thrive and prosper, while others struggle and decline: disrupt or be disrupted. Disruptive Business Analytics provides strategies to profit from disruption. It shows you how to organize for insight, build and provision an open source stack, how to practice lean data warehousing, and how to assimilate disruptive innovations into an organization. Through a short history of business analytics and a detailed survey of products and services, analytics authority Thomas W. Dinsmore provides a practical explanation of the most compelling innovations available today. What You'll Learn Discover how the open source business model works and how to make it work for you See how cloud computing completely changes the economics of analytics Harness the power of Hadoop and its ecosystem Find out why Apache Spark is everywhere Discover the potential of streaming and real-time analytics Learn what Deep Learning can do and why it matters See how self-service analytics can change the way organizations do business Who This Book Is For Corporate actors at all levels of responsibility for analytics: analysts, CIOs, CTOs, strategic decision makers, managers, systems architects, technical marketers, product developers, IT personnel, and consultants.
Categorization of some known tools used for Big Data Analytics Platform Local
Hadoop, MapReduce, Cloudera, Hortonworks, BigInsights Cloud AWS, Google
Compute Engine, Azure Data Base SQL Greenplum, Aster data, Vertica NO-SQL
Author: L. Grandinetti
Publisher: IOS Press
The realization that the use of components off the shelf (COTS) could reduce costs sparked the evolution of the massive parallel computing systems available today. The main problem with such systems is the development of suitable operating systems, algorithms and application software that can utilise the potential processing power of large numbers of processors. As a result, systems comprising millions of processors are still limited in the applications they can efficiently solve. Two alternative paradigms that may offer a solution to this problem are Quantum Computers (QC) and Brain Inspired Computers (BIC). This book presents papers from the 14th edition of the biennial international conference on High Performance Computing - From Clouds and Big Data to Exascale and Beyond, held in Cetraro, Italy, from 2 - 6 July 2018. It is divided into 4 sections covering data science, quantum computing, high-performance computing, and applications. The papers presented during the workshop covered a wide spectrum of topics on new developments in the rapidly evolving supercomputing field – including QC and BIC – and a selection of contributions presented at the workshop are included in this volume. In addition, two papers presented at a workshop on Brain Inspired Computing in 2017 and an overview of work related to data science executed by a number of universities in the USA, parts of which were presented at the 2018 and previous workshops, are also included. The book will be of interest to all those whose work involves high-performance computing.
This book provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors.
Author: Aaron K. Baughman
This book provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors. The work describes how the history of multimedia data processing can be viewed as a sequence of disruptive innovations. Across the chapters, the discussion covers the practical frameworks, libraries, and open source software that enable the development of ground-breaking research into practical applications. Features: reviews how innovations in mobile, social, cognitive, cloud and organic based computing impacts upon the development of multimedia data mining; provides practical details on implementing the technology for solving real-world problems; includes chapters devoted to privacy issues in multimedia social environments and large-scale biometric data processing; covers content and concept based multimedia search and advanced algorithms for multimedia data representation, processing and visualization.
Cover subtitle: Disruptive technologies for changing the game.
Author: Arvind Sathi
Publisher: Mc PressLlc
Bringing a practitioner’s view to big data analytics, this work examines the drivers behind big data, postulates a set of use cases, identifies sets of solution components, and recommends various implementation approaches. This work also addresses and thoroughly answers key questions on this emerging topic, including What is big data and how is it being used? How can strategic plans for big data analytics be generated? and How does big data change analytics architecture? The author, who has more than 20 years of experience in information management architecture and delivery, has drawn the material from a large breadth of workshops and interviews with business and information technology leaders, providing readers with the latest in evolutionary, revolutionary, and hybrid methodologies of moving forward to the brave new world of big data.
Emerging Business Intelligence and Analytic Trends for Today's Businesses
Michael Minelli, Michele Chambers, Ambiga Dhiraj ... “Disruptive Analytics” The
changing health care landscape is an excellent example of where data science
Author: Michael Minelli
Publisher: John Wiley & Sons
Unique prospective on the big data analytics phenomenon for both business and IT professionals The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability. The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics. Learn more about the trends in big data and how they are impacting the business world (Risk, Marketing, Healthcare, Financial Services, etc.) Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insights Explores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more.
METHODOLOGY The analysis presented in this chapter is based on a novel
database of disruptive agricultural technologies (DATs) in Sub-Saharan Africa
that has been curated through secondary research. This database of 194 DATs
Author: Jeehye Kim
Publisher: World Bank Publications
This study—which includes a pilot intervention in Kenya—aims to further the state of knowledge about the emerging trend of disruptive agricultural technologies (DATs) in Africa, with a focus on supply-side dynamics. The first part of the study is a stocktaking analysis to assess the number, scope, trend, and characteristics of scalable disruptive technology innovators in agriculture in Africa. From a database of 434 existing DAT operations, the analysis identified 194 as scalable. The second part of the study is a comparative case study of Africa’s two most successful DAT ecosystems in Kenya and Nigeria, which together account for half of Sub-Saharan Africa’s active DATs. The objective of these two case studies is to understand the successes, challenges, and opportunities faced by each country in fostering a conducive innovation ecosystem for scaling up DATs. The case study analysis focuses on six dimensions of the innovation ecosystem in Kenya and Nigeria: finance, regulatory environment, culture, density, human capital, and infrastructure. The third part of the study is based on the interactions and learnings from a pilot event to boost the innovation ecosystem in Kenya. The Disruptive Agricultural Technology Innovation Knowledge and Challenge Conference in Nairobi, Kenya, brought together more than 300 key stakeholders from large technology companies, agribusiness companies, and public agencies; government representatives and experts from research and academic institutions; and representatives from financial institutions, foundations, donors, and venture capitalists. Scaling Up Disruptive Agricultural Technologies in Africa concludes by establishing that DATs are demonstrating early indications of a positive impact in addressing food system constraints. It offers potential entry points and policy recommendations to facilitate the broader adoption of DATs and improve the overall food system.
Therefore, firms could create a void at the lower price points of the market place (
i.e. bottom-of-the-pyramid), hence making it easy for disruptive technologies to
enter (Lewis, 2001). Here, analytics gleaned from big data fits into this concept of
Author: Dr. Joseph Aluya, D.B.A.
Publisher: Author House
The theoretical framework for this book was our ground-up theory of the Scope, Size, Speed, and Skill (4Ss) and Technological Situational Happenstances (TSHs) applied to Big data analytics. With in-depth research, we catechized the effects of the coalesced insights from big data influencing the architectures of incremental and radical business models. We discussed data inflation and the global impact of TSHs. We showed how deft leadership used insights gleaned from big data analytics to make strategic decisions. The big data syndrome led to Microsoft's acquisition of Nokia in our case study. Our study of APPLE Corporation's use of large datasets was explicitly analyzed. Leaderships' failure to incorporate those contextual elements afforded by insights gleaned from big data analytics, concomitant with the associated costs led to acute forms of irrational rationalism, groupthink, and faulty decision making. We explained the statistics used to essentially describe this paradigm shift, such as high dimensionality, incidental endogeneity, noise accumulation, spurious correlation, and computational costs. Significantly, machine learning challenged the status quo by effectively changing the existing technological landscape. To scholarly critics, how would supervised and un-supervised learning algorithms advance the trajectory of perspectives in applied knowledge under the umbrella of big data? Further, political and socio-economics tied to big data was examined. We recommended leaders should have a shared cognition on how to leverage analytics from large datasets for competitive advantages. Most significantly, leaders or managers should be cognizant of the inextricable synergies that seamlessly flow from adroitly implementing a strategy to profit from the speed, size, skill, and scope (i.e. the 4Ss) of the big data environment, conditioned by the leveraging of those transactional situational happenstances generated by increases in market volatility. We concluded the algorithmic processes of leveraging insights from big data have globally resulted in a disruption of current technological pathways.
The Challenge of Disruptive Change Taking advantage of time-based
information and social network information is disruptive innovation at its best.
When the competition follows the same core process, these augmentations can
Author: Evan Stubbs
Publisher: John Wiley & Sons
AVOID THE MISTAKES THAT OTHERS MAKE – LEARN WHAT LEADS TO BEST PRACTICE AND KICKSTART SUCCESS This groundbreaking resource provides comprehensive coverage across all aspects of business analytics, presenting proven management guidelines to drive sustainable differentiation. Through a rich set of case studies, author Evan Stubbs reviews solutions and examples to over twenty common problems spanning managing analytics assets and information, leveraging technology, nurturing skills, and defining processes. Delivering Business Analytics also outlines the Data Scientist’s Code, fifteen principles that when followed ensure constant movement towards effective practice. Practical advice is offered for addressing various analytics issues; the advantages and disadvantages of each issue’s solution; and how these solutions can optimally create organizational value. With an emphasis on real-world examples and pragmatic advice throughout, Delivering Business Analytics provides a reference guide on: The economic principles behind how business analytics leads to competitive differentiation The elements which define best practice The Data Scientist’s Code, fifteen management principles that when followed help teams move towards best practice Practical solutions and frequent missteps to twenty-four common problems across people and process, systems and assets, and data and decision-making Drawing on the successes and failures of countless organizations, author Evan Stubbs provides a densely packed practical reference on how to increase the odds of success in designing business analytics systems and managing teams of data scientists. Uncover what constitutes best practice in business analytics and start achieving it with Delivering Business Analytics.
Accidents (Excessive accidents are disruptive and decrease productivity.) 2.
Employee turnover (Excessive turnover is disruptive operationally and increases
costs.) 3. Unplanned absences (Excessive unplanned absences are disruptive, ...
Author: Jack Phillips
Publisher: McGraw Hill Professional
PROVE THE VALUE OF YOUR HR PROGRAM WITH HARD DATA While corporate leaders may well know the value of human capital, they don’t always understand the extent to which the HR function contributes to the bottom line. So when times get tough and business budgets get cut, HR departments often take the first hit. In this groundbreaking guide, the cofounders of ROI Institute, Jack Phillips and Patti Phillips, provide the tools and techniques you need to use analytics to show top decision makers the value of HR in your organization. Focusing on three types of analytics--descriptive, predictive, and prescriptive--Making Human Capital Analytics Work shows how you can apply analytics by: Developing relationships between variables Predicting the success of HR programs Determining the cost of intangibles that are otherwise diffi cult to value Showing the business value of particular HR programs Calculating and forecasting the ROI of various HR projects and programs Much more than a guide to using data collection and analysis, Making Human Capital Analytics Work is a template for spearheading large-scale change in your organization by dramatically influencing your department's overall image within the organization. The authors take you step-by-step through the processes of using hard data to drive decisions and demonstrate the tangible value of HR. You know that your department is more than administrative and transactional--that it's an integral player in your company's strategy. Apply the lessons in Making Human Capital Analytics Work and ensure that all other stakeholders know too.
... as well as the relationship with the target market. Initially, this can be
intentionally distant, but as the disruptive product becomes part of the host, the
handoff must be graceful. CHAPTER 30 : LEAN FROM WITHIN:
Author: Alistair Croll
Publisher: "O'Reilly Media, Inc."
Whether you’re a startup founder trying to disrupt an industry or an intrapreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without. Understand Lean Startup, analytics fundamentals, and the data-driven mindset Look at six sample business models and how they map to new ventures of all sizes Find the One Metric That Matters to you Learn how to draw a line in the sand, so you’ll know it’s time to move forward Apply Lean Analytics principles to large enterprises and established products
3.1.4 Scenario Analysis Scenario analysis consciously attempts to draft various
scenarios relating to the future. ... of a scenario Progression changed by
disruptive event Trend Scenario Worst-Case Scenario Future 1 Disruptive event ^
Point of ...
Author: Marco Meier
Publisher: Springer Verlag
In order to make strategy happen there is a need for powerful management information systems. SAP focuses on the application of modern business administration concepts, e.g. Value Based Management, the Balanced Scorecard, the Management Cockpit or flexible planning methods. The book describes the methodology and implementation of a powerful tool for enterprise management. Practical examples show how SAP Strategic Enterprise Management/Business Analytics (SAP SEM/BA) can help to improve cross functional planning, reporting and analyzing. SAP SEM/BA is a leading edge IT-solution for top management and related departments in large enterprises and groups. It demonstrates the state of the art of modern management information and decision support systems.
Even though a complete and rigorous DOE is analytically desirable, it might be
too costly or disruptive as a first option. An analytics decider should try to work
within the given business reality and construct a good-enough solution that gives
Author: Nathaniel Lin
Publisher: FT Press
Bridge the gap between analytics and execution, and actually translate analytics into better business decision-making! Now that you've collected data and crunched numbers, Applied Business Analytics reveals how to fully apply the information and knowledge you've gleaned from quants and tech teams. Nathaniel Lin explains why "analytics value chains" often break due to organizational and cultural issues, and offers "in the trenches" guidance for overcoming these obstacles. You'll discover why a special breed of "analytics deciders" is indispensable for any organization that seeks to compete on analytics… how to become one of those deciders… and how to identify, foster, support, empower, and reward others to join you. Lin draws on actual cases and examples from his own experience, augmenting them with hands-on examples and exercises to integrate analytics at all levels: from top-level business questions to low-level technical details. Along the way, you'll learn how to bring together analytics team members with widely diverse goals, knowledge, and backgrounds. Coverage includes: How analytical and conventional decision making differ — and the challenging implications How to determine who your analytics deciders are, and ought to be Proven best practices for actually applying analytics to decision-making How to optimize your use of analytics as an analyst, manager, executive, or C-level officer Applied Business Analytics will be invaluable to wide audiences of professionals, decision-makers, and consultants involved in analytics, including Chief Analytics Officers, Chief Data Officers, Chief Scientists, Chief Marketing Officers, Chief Risk Officers, Chief Strategy Officers, VPs of Analytics and/or Big Data, data scientists, business strategists, and line of business executives. It will also be exceptionally useful to students of analytics in any graduate, undergraduate, or certificate program, including candidates for INFORMS certification.
Disruptive Procurement is a radical new approach to creating value and innovation by challenging the status quo in the entire product and service line.
Author: Michael F. Strohmer
Publisher: Springer Nature
Disruptive Procurement is a radical new approach to creating value and innovation by challenging the status quo in the entire product and service line. It requires going far beyond conventional desktop procurement to understand the value the company brings to its customers as well as the value that suppliers bring to the company. By combining knowledge of these two dimensions, companies become far more flexible and they move closer to disrupting the environment in ways that create value. To move toward Disruptive Procurement, companies need a holistic view and a complete new set of capabilities for staff in marketing, sales, R&D, manufacturing, innovation, and, of course, procurement. This will only happen if procurement is fully backed by the Chief Executive Officer and companies embrace digital tools that will help make procurement slimmer and smarter.
I thank disruptive thinkers including Eric Topol, who demonstrated in his book,
The Creative Destruction of Medicine, how people can take more control of their
health through the digital transformation of medicine; and Clayton Christensen et
Author: Dwight McNeill
Publisher: FT Press
The American way of producing health is failing. It continues to rank very low among developed countries on our most vital need…to live a long and healthy life. Despite the well-intentioned actions on the part of government, life sciences, and technology, the most important resource for achieving our full health potential is ourselves. This book is about how you can do so, and how others can help you. Dwight McNeill introduces person-centered health analytics (pchA) and shows how you can use it to master five everyday behaviors that cause and perpetuate most chronic diseases. Using Person-Centered Health Analytics to Live Longer combines deep insight, a comprehensive framework, and practical tools for living longer and healthier lives. It offers a clear path forward for both individuals and stakeholders, including providers, payers, health promotion companies, technology innovators, government, and analytics practitioners.
Inside this book, technologists, executives, and data managers will find information and inspiration to adopt blockchain as a big data tool.
Author: Michael G. Solomon
Publisher: John Wiley & Sons
Get ahead of the curve—learn about big data on the blockchain Blockchain came to prominence as the disruptive technology that made cryptocurrencies work. Now, data pros are using blockchain technology for faster real-time analysis, better data security, and more accurate predictions. Blockchain Data Analytics For Dummies is your quick-start guide to harnessing the potential of blockchain. Inside this book, technologists, executives, and data managers will find information and inspiration to adopt blockchain as a big data tool. Blockchain expert Michael G. Solomon shares his insight on what the blockchain is and how this new tech is poised to disrupt data. Set your organization on the cutting edge of analytics, before your competitors get there! Learn how blockchain technologies work and how they can integrate with big data Discover the power and potential of blockchain analytics Establish data models and quickly mine for insights and results Create data visualizations from blockchain analysis Discover how blockchains are disrupting the data world with this exciting title in the trusted For Dummies line!
Predicting the EconomicValue of Your Company's Human Capital Investments
Jac FITZ-ENZ. I P A R T 1 Introduction to Predictive Analytics This page
intentionally left blank Disruptive Technology: The Power to PART ONE:
Author: Jac FITZ-ENZ
In his landmark book The ROI of Human Capital, Jac Fitz-enz presented a system of powerful metrics for quantifying the contributions of individual employees to a company’s bottom line. The New HR Analytics is another such quantum leap, revealing how to predict the value of future human capital investments. Using Fitz-enz’s proprietary analytic model, readers learn how to measure and evaluate past and current returns. By combining those results with focused business intelligence and applying the exclusive analytical tools in the book, they will be able to: Evaluate and prioritize the skills needed to sustain performance • Build an agile workforce through flexible Capability Planning • Determine how the organization can stimulate and reward behaviors that matter • Apply a proven succession planning strategy that leverages employee engagement and drives top-line revenue growth • Recognize risks and formulate responses that avoid surprises • Support decision making by predicting the actions that will yield the best returns Brimming with real-world examples and input from thirty top HR practitioners and thought leaders, this groundbreaking book ushers in a new era in human resources and human capital management.
Hadoop comes with a scheduling capability that enables several diverse forms of
processing across the entire file system. Sometimes that processing is for
analytics, sometimes for transcoding, and sometimes it enables databases and
Author: Jeffrey Needham
Publisher: "O'Reilly Media, Inc."
Big data has more disruptive potential than any information technology developed in the past 40 years. As author Jeffrey Needham points out in this revealing book, big data can provide unprecedented visibility into the operational efficiency of enterprises and agencies. Disruptive Possibilities provides an historically-informed overview through a wide range of topics, from the evolution of commodity supercomputing and the simplicity of big data technology, to the ways conventional clouds differ from Hadoop analytics clouds. This relentlessly innovative form of computing will soon become standard practice for organizations of any size attempting to derive insight from the tsunami of data engulfing them. Replacing legacy silos—whether they’re infrastructure, organizational, or vendor silos—with a platform-centric perspective is just one of the big stories of big data. To reap maximum value from the myriad forms of data, organizations and vendors will have to adopt highly collaborative habits and methodologies.
Featuring coverage on a broad range of topics such as semantic mapping, ethics in AI, and big data governance, this book is ideally designed for IT specialists, industry professionals, managers, executives, researchers, scientists, and ...
Author: Strydom, Moses
Publisher: IGI Global
Big data and artificial intelligence (AI) are at the forefront of technological advances that represent a potential transformational mega-trend—a new multipolar and innovative disruption. These technologies, and their associated management paradigm, are already rapidly impacting many industries and occupations, but in some sectors, the change is just beginning. Innovating ahead of emerging technologies is the new imperative for any organization that aspires to succeed in the next decade. Faced with the power of this AI movement, it is imperative to understand the dynamics and new codes required by the disruption and to adapt accordingly. AI and Big Data’s Potential for Disruptive Innovation provides emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative technologies in a variety of sectors including business, transportation, and healthcare. Featuring coverage on a broad range of topics such as semantic mapping, ethics in AI, and big data governance, this book is ideally designed for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research on the production of new and innovative mechanization and its disruptions.
In other words , if you have a culture of analysis , you schedule and launch
projects based on their value in your business . ... Strict prioritization may turn out
to be so disruptive that you won ' t be able to implement it without mass
Author: Jason Burby
Provides information on developing a Web analytics strategy to help make strategic business decisions, plan a website, develop effective marketing, and create a culture of analysis within an organization.