Role Of Data In Business
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Internet Of Things In Business Transformation
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Author : Parul Gandhi
language : en
Publisher: John Wiley & Sons
Release Date : 2021-02-03
Internet Of Things In Business Transformation written by Parul Gandhi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-03 with Computers categories.
The objective of this book is to teach what IoT is, how it works, and how it can be successfully utilized in business. This book helps to develop and implement a powerful IoT strategy for business transformation as well as project execution. Digital change, business creation/change and upgrades in the ways and manners in which we work, live, and engage with our clients and customers, are all enveloped by the Internet of Things which is now named "Industry 5.0" or "Industrial Internet of Things." The sheer number of IoT(a billion+), demonstrates the advent of an advanced business society led by sustainable robotics and business intelligence. This book will be an indispensable asset in helping businesses to understand the new technology and thrive.
Data Perspective In Business Process Management
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Author : Andreas Meyer
language : en
Publisher:
Release Date : 2015
Data Perspective In Business Process Management written by Andreas Meyer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Business process management (BPM) is a systematic and structured approach to model, analyze, control, and execute business operations also referred to as business processes that get carried out to achieve business goals. Central to BPM are conceptual models. Most prominently, process models describe which tasks are to be executed by whom utilizing which information to reach a business goal. Process models generally cover the perspectives of control flow, resource, data flow, and information systems. Execution of business processes leads to the work actually being carried out. Automating them increases the efficiency and is usually supported by process engines. This, though, requires the coverage of control flow, resource assignments, and process data. While the first two perspectives are well supported in current process engines, data handling needs to be implemented and maintained manually. However, model-driven data handling promises to ease implementation, reduces the error-proneness through graphical visualization, and reduces development efforts through code generation. This thesis addresses the modeling, analysis, and execution of data in business processes and presents a novel approach to execute data-annotated process models entirely model-driven. As a first step and formal grounding for the process execution, a conceptual framework for the integration of processes and data is introduced. This framework is complemented by operational semantics through a Petri net mapping extended with data considerations. Model-driven data execution comprises the handling of complex data dependencies, process data, and data exchange in case of communication between multiple process participants. This thesis introduces concepts from the database domain into BPM to enable the distinction of data operations, to specify relations between data objects of the same as well as of different types, to correlate modeled data nodes as well as received messages to the correct run-time process instances, and to generate messages for inter-process communication. The underlying approach, which is not limited to a particular process description language, has been implemented as proof-of-concept. Automation of data handling in business processes requires data-annotated and correct process models. Targeting the former, algorithms are introduced to extract information about data nodes, their states, and data dependencies from control information and to annotate the process model accordingly. Usually, not all required information can be extracted from control flow information, since some data manipulations are not specified. This requires further refinement of the process model. Given a set of object life cycles specifying allowed data manipulations, automated refinement of the process model towards containment of all data manipulations is enabled. Process models are an abstraction focusing on specific aspects in detail, e.g., the control flow and the data flow views are often represented through activity-centric and object-centric process models. This thesis introduces algorithms for roundtrip transformations enabling the stakeholder to add information to the process model in the view being most appropriate. Targeting process model correctness, this thesis introduces the notion of weak conformance that checks for consistency between given object life cycles and the process model such that the process model may only utilize data manipulations specified directly or indirectly in an object life cycle. The notion is computed via soundness checking of a hybrid representation integrating control flow and data flow correctness checking. Making a process model executable, identified violations must be corrected. Therefore, an approach is proposed that identifies for each violation multiple, alternative changes to the process model or the object life cycles. Utilizing the results of this thesis, business processes can be executed entirely model-driven from the data perspective in addition to the control flow and resource perspectives already supported before. Thereby, the model creation is supported by algorithms partly automating the creation process while model consistency is ensured by data correctness checks
Taxmann S Business Analytics Underscoring The Pivotal Role Of Data In The Contemporary Business Landscape For Data Analysis And Strategic Implementation Ms Excel Tableau R Nep
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Author : H.K. Dangi
language : en
Publisher: Taxmann Publications Private Limited
Release Date : 2024-04-15
Taxmann S Business Analytics Underscoring The Pivotal Role Of Data In The Contemporary Business Landscape For Data Analysis And Strategic Implementation Ms Excel Tableau R Nep written by H.K. Dangi and has been published by Taxmann Publications Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-15 with Education categories.
This book emphasises the critical role of data in today's evolving business landscape. It highlights the increasing complexity of the business environment and the growing demand for professionals adept at analysing data patterns and translating them into actionable strategies. This book is designed to progressively build the reader's knowledge in business analytics, from fundamental concepts to specialised techniques and ethical considerations, complete with practical applications and exercises for reinforcement. The Present Publication is the Latest Edition, focusing on the latest syllabus under UGCF 2022, aligning with the National Education Policy (NEP) adopted by the University of Delhi. This book is authored by Prof. H.K. Dangi and Gurveen Kaur, with the following noteworthy features: • [Balanced Approach Between Theory and Practice] The book maintains an equilibrium between theoretical knowledge and practical application. It lays a solid theoretical foundation in Business Analytics while also emphasising its practical aspects • [Real-World Application and Hands-On Learning] Incorporating real-life case studies, hands-on examples, and exercises, the book ensures that students can connect theoretical concepts with their implementation in the real world • [Educational Journey in Business Analytics] This book offers insights into data-driven decision-making and strategic thinking The structure of the book is as follows: • [Learning Outcomes] Every chapter begins with the list of learning outcomes which the readers will achieve after the completion of the chapter • [Headings/Sub-headings] Chapters are further divided into headings and sub-headings to increase the reader's comprehension • [Practice & Discussion Questions] Each chapter contains a series of practice/discussion questions to help the reader review the material • [Case Studies] are provided at the end of each chapter to help readers implement their learning into hypothetical real-life situations The content is methodically divided into eight chapters, covering a broad range of topics such as: • Introduction o Begins with a historical overview and the architectural framework of business analytics o Definitions, distinctions between analysis and analytics, and types (descriptive, predictive, prescriptive) are discussed o Applications across finance, marketing, human resources, and healthcare are explored alongside a case study and summary, followed by exercises and multiple-choice questions • Data Preparation o Focuses on the data preparation process, using MS-Excel for cleaning and validation, identifying outliers, and understanding covariance and correlation matrix o Practical application to business, summary, exercises, and multiple-choice questions are included • Data Summarisation and Visualisation o Covers types of data summarisation and visualisation, with an emphasis on using Tableau o The chapter concludes with exercises and multiple-choice questions • Getting Started with R o Introduces R and R Studio, highlighting the advantages of R, installation processes, data structures in R, and their application to business o Summarised with exercises and multiple-choice questions • Descriptive Statistics Using R o Measures of central tendency, dispersion, and relationship between variables are explored o Focuses on data visualisation using R through various plots and business applications, followed by a summary, exercises, and questions • Predictive Analytics o Discusses simple and multiple linear regression models, confidence and prediction intervals, regression analysis using R, and their applications in business o A summary, exercises, and multiple-choice questions are provided • Textual Analysis o Highlights the significance, applications, and challenges of textual data analysis o Introduces methods and techniques like word clouds, tree maps, and sentiment analysis using R, with a focus on business applications, summarised with exercises and questions • Ethics in Business Analytics o Addresses the meaning and importance of ethics in analytics, ethical issues, and considerations for ethical conduct o Concludes with practical applications to business, a summary, exercises, and multiple-choice questions
Data Driven
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Author : Thomas C. Redman
language : en
Publisher: Harvard Business Press
Release Date : 2008
Data Driven written by Thomas C. Redman and has been published by Harvard Business Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Business & Economics categories.
Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that.
The Business Of Data
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Author : Martin De Saulles
language : en
Publisher: Routledge
Release Date : 2020-06-08
The Business Of Data written by Martin De Saulles and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-08 with Business & Economics categories.
This book is about the rise of data as a driver of innovation and economic growth. It charts the evolution of business data as a valuable resource and explores some of the key business, economic and social issues surrounding the data-driven revolution we are currently going through. Readers will gain an understanding of the historical underpinnings of the data business and why the collection and use of data has been driven by commercial needs. Readers will also gain insights into the rise of the modern data-driven technology giants, their business models and the reasons for their success. Alongside this, some of the key social issues including privacy are considered and the challenges these pose to policymakers and regulators. Finally, the impact of pervasive computing and the Internet of Things (IoT) is explored in the context of the new sources of data that are being generated. This book is useful for students and practitioners wanting to better understand the origins and drivers of the current technological revolution and the key role that data plays in innovation and business success.
Understanding The Role Of Business Analytics
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Author : Hardeep Chahal
language : en
Publisher: Springer
Release Date : 2018-09-14
Understanding The Role Of Business Analytics written by Hardeep Chahal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-14 with Business & Economics categories.
This book encompasses empirical evidences to understand the application of data analytical techniques in emerging contexts. Varied studies relating to manufacturing and services sectors including healthcare, banking, information technology, power, education sector etc. stresses upon the systematic approach followed in applying the data analytical techniques; and also analyses how these techniques are effective in decision-making in different contexts. Especially, the application of regression modeling, financial modelling, multi-group modeling, cluster analysis, and sentiment analysis will help the readers in understanding critical business scenarios in the best possible way, and which later can help them in arriving at best solution for the business related problems. The individual chapters will help the readers in understanding the role of specific data analytic tools and techniques in resolving business operational issues experienced in manufacturing and service organisations in India and in developing countries. The book offers a relevant resource that will help readers in the application and interpretation of data analytical statistical practices relating to emerging issues like customer experience, marketing capability, quality of manufactured products, strategic orientation, high-performance human resource policy, employee resilience, financial resources, etc. This book will be of interest to a professional audience that include practitioners, policy makers, NGOs, managers and employees as well as academicians, researchers and students.
Data Driven Business Decisions
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Author : Chris J. Lloyd
language : en
Publisher: Wiley
Release Date : 2012-01-13
Data Driven Business Decisions written by Chris J. Lloyd and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-13 with Business & Economics categories.
A hands-on guide to the use of quantitative methods and software for making successful business decisions The appropriate use of quantitative methods lies at the core of successful decisions made by managers, researchers, and students in the field of business. Providing a framework for the development of sound judgment and the ability to utilize quantitative and qualitative approaches, Data Driven Business Decisions introduces readers to the important role that data plays in understanding business outcomes, addressing four general areas that managers need to know about: data handling and Microsoft Excel, uncertainty, the relationship between inputs and outputs, and complex decisions with trade-offs and uncertainty. Grounded in the author's own classroom approach to business statistics, the book reveals how to use data to understand the drivers of business outcomes, which in turn allows for data-driven business decisions. A basic, non-mathematical foundation in statistics is provided, outlining for readers the tools needed to link data with business decisions; account for uncertainty in the actions of others and in patterns revealed by data; handle data in Excel; translate their analysis into simple business terms; and present results in simple tables and charts. The author discusses key data analytic frameworks, such as decision trees and multiple regression, and also explores additional topics, including: Use of the Excel functions Solver and Goal Seek Partial correlation and auto-correlation Interactions and proportional variation in regression models Seasonal adjustment and what it reveals Basic portfolio theory as an introduction to correlations Chapters are introduced with case studies that integrate simple ideas into the larger business context, and are followed by further details, raw data, and motivating insights. Algebraic notation is used only when necessary, and throughout the book, the author utilizes real-world examples from diverse areas such as market surveys, finance, economics, and business ethics. Excel add-ins StatproGo and TreePlan are showcased to demonstrate execution of the techniques, and a related website features extensive programming instructions as well as insights, data sets, and solutions to problems included in the material. The enclosed CD contains the complete book in electronic format, including all presented data, supplemental material on the discussed case files, and links to exercises and solutions. Data Dr...
Becoming Data Literate
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Author : David Reed
language : en
Publisher: Harriman House Limited
Release Date : 2021-08-31
Becoming Data Literate written by David Reed and has been published by Harriman House Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Business & Economics categories.
Data is a must-have for any business looking to thrive. So is having leadership who 'get' data and use it to support their decision-making. But how do you embed the use of data and analytics across your organisation so they truly support every process end-to-end? Becoming data literate in this way is a journey that involves vision, strategy, value creation, culture and data foundations. With an evidence-based framework to guide you, this book lays out a roadmap to ensure you get where you need to go.
Corporate Data Quality
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Author : Boris Otto
language : en
Publisher: epubli
Release Date : 2015-12-08
Corporate Data Quality written by Boris Otto and has been published by epubli this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-08 with Business & Economics categories.
Data is the foundation of the digital economy. Industry 4.0 and digital services are producing so far unknown quantities of data and make new business models possible. Under these circumstances, data quality has become the critical factor for success. This book presents a holistic approach for data quality management and presents ten case studies about this issue. It is intended for practitioners dealing with data quality management and data governance as well as for scientists. The book was written at the Competence Center Corporate Data Quality (CC CDQ) in close cooperation between researchers from the University of St. Gallen and Fraunhofer IML as well as many representatives from more than 20 major corporations. Chapter 1 introduces the role of data in the digitization of business and society and describes the most important business drivers for data quality. It presents the Framework for Corporate Data Quality Management and introduces essential terms and concepts. Chapter 2 presents practical, successful examples of the management of the quality of master data based on ten cases studies that were conducted by the CC CDQ. The case studies cover every aspect of the Framework for Corporate Data Quality Management. Chapter 3 describes selected tools for master data quality management. The three tools have been distinguished through their broad applicability (method for DQM strategy development and DQM maturity assessment) and their high level of innovation (Corporate Data League). Chapter 4 summarizes the essential factors for the successful management of the master data quality and provides a checklist of immediate measures that should be addressed immediately after the start of a data quality management project. This guarantees a quick start into the topic and provides initial recommendations for actions to be taken by project and line managers. Please also check out the book's homepage at cdq-book.org/
Data Stewardship
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Author : David Plotkin
language : en
Publisher: Academic Press
Release Date : 2020-10-31
Data Stewardship written by David Plotkin and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-31 with Computers categories.
Data stewards in any organization are the backbone of a successful data governance implementation because they do the work to make data trusted, dependable, and high quality. Since the publication of the first edition, there have been critical new developments in the field, such as integrating Data Stewardship into project management, handling Data Stewardship in large international companies, handling "big data" and Data Lakes, and a pivot in the overall thinking around the best way to align data stewardship to the data—moving from business/organizational function to data domain. Furthermore, the role of process in data stewardship is now recognized as key and needed to be covered. Data Stewardship, Second Edition provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on organizational/company structure, business functions, and data ownership. The book shows data managers how to gain support for a stewardship effort, maintain that support over the long-term, and measure the success of the data stewardship effort. It includes detailed lists of responsibilities for each type of data steward and strategies to help the Data Governance Program Office work effectively with the data stewards. Includes an enhanced section on data governance/stewardship structure for companies that do business internationally, including the structure of business terms to account for country differences Outlines the advantages and disadvantages of "data domains," details on suggested data domains and data domain structures, as well as data governance by data domains Integrates data governance into Project methodology, defining roles on a project, adding Data Governance tasks to the Work Breakdown Structure, as well as advantages of working closely with the Project management Office Covers the data stewardship involved in implementing national and international data privacy regulations