Search results for: analytics-best-practices

Analytics Best Practices

Author : Prashanth Southekal
File Size : 82.46 MB
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Deliver enterprise data analytics success by following Prashanth's prescriptive and practical techniques. Today, organizations across the globe are looking at ways to glean insights from data analytics and make good business decisions. However, not many business enterprises are successful in data analytics. According to Gartner, 80% of analytics programs do not deliver business outcomes. Mckinsey consulting says, less than 20% of the companies have achieved analytics at scale. So, how can a business enterprise avoid analytics failure and deliver business results? This book provides ten key analytics best practices that will improve the odds of delivering enterprise data analytics solutions successfully. It is intended for anyone who has a stake and interest in deriving insights from data analytics. The three key differentiating aspects of this book are: Practicality. This book offers prescriptive, superior, and practical guidance. Completeness. This book looks at data analytics holistically across the four key data analytics domains - data management, data engineering, data science, and data visualization. Neutrality. This book is technologically agnostic and looks at analytics concepts without any reference to commercial analytics products and technologies. Dr. Southekal proves why he is one of the leading thinkers on data and analytics today. 'Analytics Best Practices' is an indispensable guide for business leaders and those looking to get into the analytics field on the nuances, challenges, and immense opportunities with data. Douglas B. Laney Principal, Data & Analytics Strategy, Caserta, and author of "Infonomics"

Analytics Best Practices A Business driven Playbook for Creating Value through Data Analytics

Author : Prashanth H Southekal, PhD, MBA
File Size : 55.65 MB
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Deliver enterprise data analytics success by following Prashanth’s prescriptive and practical techniques. Today, organizations across the globe are looking at ways to glean insights from data analytics and make good business decisions. However, not many business enterprises are successful in data analytics. According to Gartner, 80% of analytics programs do not deliver business outcomes. Mckinsey consulting says, less than 20% of the companies have achieved analytics at scale. So, how can a business enterprise avoid analytics failure and deliver business results? This book provides ten key analytics best practices that will improve the odds of delivering enterprise data analytics solutions successfully. It is intended for anyone who has a stake and interest in deriving insights from data analytics. The three key differentiating aspects of this book are: · Practicality. This book offers prescriptive, superior, and practical guidance. · Completeness. This book looks at data analytics holistically across the four key data analytics domains - data management, data engineering, data science, and data visualization. · Neutrality. This book is technologically agnostic and looks at analytics concepts without any reference to commercial analytics products and technologies. Dr. Southekal proves why he is one of the leading thinkers on data and analytics today. ‘Analytics Best Practices’ is an indispensable guide for business leaders and those looking to get into the analytics field on the nuances, challenges, and immense opportunities with data. Douglas B. Laney Principal, Data & Analytics Strategy, Caserta, and author of “Infonomics” Dr. Southekal’s book is a treasure trove of best practices and practical examples from the field of Data Analytics. Upen Varanasi CEO & Co-Founder, Riversand Technologies What I like about this book is its focus on real-world best practices with an extensive set of practical tips and tricks. It provides an important bridge between the data management and business operations sides of a business. Michael Wade Professor of Innovation & Strategy, IMD Business School Prashanth’s book is accessible and practical – an excellent guide for corporate leaders who want to produce meaningful business results from the use of data and analytics to create true business value. Irina Pelphrey Senior Director, Walmart Corporation This book is a must on the desks of business executives and decision makers at all levels in an organization who want to truly understand what it takes to become a successful data driven organization. Ram Kumar Senior Vice President, Quantium Dr. Prashanth Southekal has created a practical guidebook for realizing business value from data and analytics investments. Highly recommended. Randy Bean Founder & CEO, NewVantage Partners Prashanth’s second book starts with the right title – it is always about BUSINESS VALUE. The practices explored here will help anyone interested to achieve these goals. Mario Faria Gartner Research Board The Analytics Best Practices book is one of the most comprehensive and well-researched books I have come across on data analytics. Ameet Shetty Former Chief Data and Analytics Officer, McDonald's Corporation I would encourage all professionals to read this easy to navigate, thoughtful and pragmatic book as it is relevant to all of us seeking to maximize the ROI for our organizations. Lisa M. Wardlaw Former EVP, Global Chief Digital Strategy Officer, MunichRe Deriving actionable insights from data requires that linkage to be clear between art and science and this book does just that. Chris Leonard, Director, Digital Strategy & Transformation, Plains Midstream Prashanth’s book simplifies the complex world of data analytics, and one to understand the drivers of bringing valued results to an organization. Matthew Joyce Senior Solution Architect, SAS-Institute

TIMAF Information Management Best Practices Volume 1

Author :
File Size : 56.95 MB
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Analysis of Current Mobile Marketing Applications Selected Best Practices and Future Development

Author : Asligul Aktas
File Size : 45.87 MB
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Master's Thesis from the year 2010 in the subject Business economics - Marketing, Corporate Communication, CRM, Market Research, Social Media, grade: 1.3, University of Applied Sciences Giessen, language: English, abstract: This study fundamentally attempts to investigate current mobile marketing and advertising market, to examine current mobile marketing applications and to analyze three selected Best Practices and the future development of mobile marketing. The study consists of five main sections. First section consists of two parts, namely Introduction and Problem Statement. Introduction part covers a brief presentation about the subject of the study and the research objectives. And Problem Statement part discusses the stimulating factors to apply this research. The next section, which is Background Information, discusses the structural frame and the distinctive aspects of Mobile Marketing, the factors influencing consumer attitude towards mobile marketing, as well as measurement fundamentals of mobile marketing campaigns. This is followed by an Analysis of Current Mobile Marketing Applications including three selected Best Practices. Subsequently, Future Development of Mobile Marketing section explores the anticipations of the way towards which Mobile Marketing progresses and how the mobile technologies shape the development. Too, Key Success Factors of Mobile Marketing are investigated in this section. Finally, the fifth section concludes with a discussion about key findings of the study. Limitations of the Research and Recommendations for Further Research are provided to highlight the study. The writer pays attention that the study comprises her own reflections and original contributions. Keywords: Mobile Advertising, Mobile Marketing Applications, Best Practices in Mobile Marketing, Key Success Factors of Mobile Marketing, Future Developments on Mobile Marketing

SAP Business Analytics

Author : Sudipa DuttaRoy
File Size : 59.10 MB
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Focus on SAP business analytics business gains, key features, and implementation. The book includes example implementations of SAP business analytics, the challenges faced, and the solutions implemented. SAP Business Analytics explains both the strategy and technical implementation for gathering and analyzing all the information pertaining to an organization. Key features of the book are: A 360-degree view of an organization’s data and the methods to gather and analyze that data The strategies that need to be in place to gather relevant data from disparate systems Details about the SAP business analytics suite of products The technical implementations used to gather data from disparate systems such as ERP and CRM Real business cases as examples Analytics is the driving force in today’s business, be it healthcare, marketing, telecommunications, or retail and hence the most vital part of any organization’s strategy. What You'll Learn Gain an understanding of business analytics in general Absorb the technical details of the SAP business analytics suite of products Discover the challenges faced during an enterprise-level analytics project implementation Learn the key points to be kept in mind during the technical implementation of an SAP business analytics project Who This Book Is For Analytics strategists, BI managers, BI architects, business analysts, and BI developers.

Best Practices for Architecting Visual Exploratory Data Analytics Applications

Author : David Camilo Mera David
File Size : 79.16 MB
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Data Analytics is one of the activities that is currently booming due to the increasing use of advanced techniques such as Machine Learning and the constant growth of computational capacity. Because of this, it is possible to obtain insights from large volumes of data, make decisions in real time and anticipate changes and trends in the markets. Although with current technologies it is possible to explore large volumes of data, the challenge remains to decrease the computational complexity to manipulate small and large volumes of data interactively. This thesis contributes to review the current panorama of approaches and proposals that have originated in the Visual Analytics and Data Integration communities, the current technologies that allow storing and manipulating currently large volumes of data. Also, this thesis records the results of different benchmarking tests whose purpose was to determine the technologies that allow to explore and visualize large volumes of data in the shortest possible time. Based on these results, this thesis proposes recommendations on how to design applications for the exploration and visualization of multivariable and streaming data.

Managerial Analytics

Author : Michael Watson
File Size : 88.44 MB
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The field of analytics is rapidly evolving, making it difficult for professionals and students to keep up the most current and effective applications. Managerial Analytics will help readers sort through all these new options and identify the appropriate solution. In this reference, authors Watson, Nelson and Cacioppi accurately define and identify the components of analytics and big data, giving readers the knowledge needed to effectively assess new aspects and applications. Building on this foundation, they review tools and solutions, identify the offerings best aligned to one’s requirements, and show how to tailor analytics applications to an organization’s specific needs. Drawing on extensive experience implementing, planning, and researching advanced analytics for business, the authors clearly explain all this, and more: What analytics is and isn’t: great examples of successful usage – and other examples where the term is being degraded into meaninglessness The difference between using analytics and “competing on analytics” How to get started with big data, by analyzing the most relevant data Components of analytics systems, from databases and Excel to BI systems and beyond Anticipating and overcoming “confirmation bias” and other pitfalls Understanding predictive analytics and getting the high-quality random samples necessary Applying game theory, Efficient Frontier, benchmarking, and revenue management models Implementing optimization at the small and large scale, and using it to make “automatic decisions”

IBM Business Analytics and Cloud Computing

Author : Anant Jhingran
File Size : 87.53 MB
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Business intelligence and analytics software enable businesses to analyze performance data in order to make better decisions through the use of cloud computing—an Internet-based model for convenient, on-demand network access to a shared pool of configurable computing resources. This book is a practitioner’s guide for successful evaluation and design for implementation of Cognos Business Intelligence cloud solution, for either Cognos 8 BI or Cognos Business Intelligence Version 10. With pragmatic and practical information about the best practices and guidelines, as well as specific software and configuration steps, this guide for solutions and IT architects includes detailed screen shots, code samples, and input instructions.

Big Data Analytics Methods

Author : Peter Ghavami
File Size : 34.91 MB
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Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.

Analytics in Healthcare and the Life Sciences

Author : Thomas H. Davenport
File Size : 76.89 MB
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Make healthcare analytics work: leverage its powerful opportunities for improving outcomes, cost, and efficiency.This book gives you thepractical frameworks, strategies, tactics, and case studies you need to go beyond talk to action. The contributing healthcare analytics innovators survey the field’s current state, present start-to-finish guidance for planning and implementation, and help decision-makers prepare for tomorrow’s advances. They present in-depth case studies revealing how leading organizations have organized and executed analytic strategies that work, and fully cover the primary applications of analytics in all three sectors of the healthcare ecosystem: Provider, Payer, and Life Sciences. Co-published with the International Institute for Analytics (IIA), this book features the combined expertise of IIA’s team of leading health analytics practitioners and researchers. Each chapter is written by a member of the IIA faculty, and bridges the latest research findings with proven best practices. This book will be valuable to professionals and decision-makers throughout the healthcare ecosystem, including provider organization clinicians and managers; life sciences researchers and practitioners; and informaticists, actuaries, and managers at payer organizations. It will also be valuable in diverse analytics, operations, and IT courses in business, engineering, and healthcare certificate programs.

Analytics in Practice

Author : Soumendra Mohanty
File Size : 81.43 MB
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This book provides an insight into the mechanics of Analytics and how to use it to identify and leverage the competitive advantage in the era of ever-growing data increment needs. This book discusses topics such as Foundations of Analytics, Business Analytics, Sentiment Analysis and Opinion Mining, methodology, and technical architecture. It also contains useful case studies, tips, techniques, and best practices. This book would be a hands-on reference for practitioners for dealing with the type of information often called for by practitioners, developers, and specialists working in the Data Mining and Analytics area. It would also be highly useful for the students of Computer Science, Information Technology and MBA-Information Technology at various universities.

Transforming Data Into Action

Author : Tony Pericle
File Size : 27.58 MB
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Business Week

Author :
File Size : 52.80 MB
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High Performance Big Data Analytics

Author : Pethuru Raj
File Size : 44.99 MB
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This book presents a detailed review of high-performance computing infrastructures for next-generation big data and fast data analytics. Features: includes case studies and learning activities throughout the book and self-study exercises in every chapter; presents detailed case studies on social media analytics for intelligent businesses and on big data analytics (BDA) in the healthcare sector; describes the network infrastructure requirements for effective transfer of big data, and the storage infrastructure requirements of applications which generate big data; examines real-time analytics solutions; introduces in-database processing and in-memory analytics techniques for data mining; discusses the use of mainframes for handling real-time big data and the latest types of data management systems for BDA; provides information on the use of cluster, grid and cloud computing systems for BDA; reviews the peer-to-peer techniques and tools and the common information visualization techniques, used in BDA.

The Complete Guide to Business Analytics Collection

Author : Thomas H. Davenport
File Size : 81.19 MB
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A brand new collection of business analytics insights and actionable techniques… 3 authoritative books, now in a convenient e-format, at a great price! 3 authoritative eBooks deliver comprehensive analytics knowledge and tools for optimizing every critical business decision! Use business analytics to drive maximum value from all your business data! This unique 3 eBook package will help you harness your information, discover hidden patterns, and successfully act on what you learn. In Enterprise Analytics, analytics pioneer Tom Davenport and the world-renowned experts at the International Institute for Analytics (IIA) bring together the latest techniques, best practices, and research on large-scale analytics strategy, technology, implementation, and management. Using real-world examples, they cover everything from building better analytics organizations to gathering data; implementing predictive analytics to linking analysis with organizational performance. You'll find specific insights for optimizing supply chains, online services, marketing, fraud detection, and many other business functions; plus chapter-length case studies from healthcare, retail, and financial services. Next, in the up-to-the-minute Analysis Without Paralysis, Second Edition, Babette E. Bensoussan and Craig S. Fleisher help you succeed with analysis without getting mired in advanced math or arcane theory. They walk you through the entire business analysis process, and guide you through using 12 core tools for making better decisions about strategy and operations -- including three powerful tools covered for the first time in this new Second Edition. Then, in Business and Competitive Analysis, Fleisher and Bensoussan help you apply 24 leading business analysis models to gain deep clarity about your business environment, answer tough questions, and make tough choices. They first walk you through defining problems, avoiding pitfalls, choosing tools, and communicating results. Next, they systematically address both “classic” techniques and the most promising new approaches from economics, finance, sociology, anthropology, and the intelligence and futurist communities. For the first time, one book covers Nine Forces, Competitive Positioning, Business Model, Supply Chain Analyses, Benchmarking, McKinsey 7S, Shadowing, Product Line, Win/Loss, Strategic Relationships, Corporate Reputation, Critical Success Factors, Driving Forces, Country Risk, Technology Forecasting, War Gaming, Event/Timeline, Indications, Warning Analyses, Competitor Cash Flow, ACH, Linchpin Analyses, and more. Whether you're an executive, strategist, analyst, marketer, or operations professional, this eBook collection will help you make more effective, data-driven, profitable decisions! From world-renowned analytics and competitive/business intelligence experts Thomas H. Davenport, Babette E. Bensoussan, and Craig S. Fleisher

The Best Thinking in Business Analytics from the Decision Sciences Institute

Author : Decision Sciences Institute
File Size : 87.70 MB
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Today, business success depends on making great decisions - and making them fast. Leading organizations apply sophisticated business analytics tools and technologies to evaluate vast amounts of data, glean new insights, and increase both the speed and quality of decision making. In The Best Thinking and Practices in Business Analytics from the Decision Sciences Institute , DSI has compiled award-winning and award-nominated contributions from its most recent conferences: papers that illuminate exceptionally high-value applications and research on analytics for decision-making. These papers have appeared in no other DSI collection. Explore them here, and you'll discover powerful new opportunities for competitive advantage through analytics. For all business, academic, and organizational professionals concerned with the science of more effective decision-making; and for undergraduate students, graduate students, and certification candidates in all related fields.

Applied Business Analytics

Author : Nathaniel Lin
File Size : 57.35 MB
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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.

Big Data

Author : Nathan Marz
File Size : 82.13 MB
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Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth

Master Competitive Analytics with Oracle Endeca Information Discovery

Author : Helen Sun
File Size : 31.10 MB
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Oracle Endeca Information Discovery Best Practices Maximize the powerful capabilities of this self-service enterprise data discovery platform. Master Competitive Analytics with Oracle Endeca Information Discovery reveals how to unlock insights from any type of data, regardless of structure. The first part of the book is a complete technical guide to the product's architecture, components, and implementation. The second part presents a comprehensive collection of business analytics use cases in various industries, including financial services, healthcare, research, manufacturing, retail, consumer packaged goods, and public sector. Step-by-step instructions on implementing some of these use cases are included in this Oracle Press book. Install and manage Oracle Endeca Server Design Oracle Endeca Information Discovery Studio visualizations to facilitate user-driven data exploration and discovery Enable enterprise-driven data exploration with Oracle Endeca Information Discovery Integrator Develop and implement a fraud detection and analysis application Build a healthcare correlation application that integrates claims, patient, and operations analysis; partners; clinical research; and remote monitoring Use an enterprise architecture approach to incrementally establish big data and analytical capabilities

Big Data Analytics

Author : Frank Ohlhorst
File Size : 83.34 MB
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Takes an in-depth look at the financial value of big data analytics and offers tools and best practices for working with big data. Once the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries.