The Analytics Process

Examples such as this illustrate the capacity of analytics to correct errors and even uncover infractions when dealing with data. This section has shown three main pieces to connect the analytics process and management.

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Author: Eduardo Rodriguez

Publisher: CRC Press

ISBN: 9781351975636

Category: Computers

Page: 256

View: 653

This book is about the process of using analytics and the capabilities of analytics in today’s organizations. Cutting through the buzz surrounding the term analytics and the overloaded expectations about using analytics, the book demystifies analytics with an in-depth examination of concepts grounded in operations research and management science. Analytics as a set of tools and processes is only as effective as: The data with which it is working The human judgment applying the processes and understanding the output of these processes. For this reason, the book focuses on the analytics process. What is intrinsic to analytics’ real organizational impact are the careful application of tools and the thoughtful application of their outcomes. This work emphasizes analytics as part of a process that supports decision-making within organizations. It wants to debunk overblown expectations that somehow analytics outputs or analytics as applied to other concepts, such as Big Data, are the be-all and end-all of the analytics process. They are, instead, only a step within a holistic and critical approach to management thinking that can create real value for an organization. To develop this holistic approach, the book is divided into two sections that examine concepts and applications. The first section makes the case for executive management taking a holistic approach to analytics. It draws on rich research in operations and management science that form the context in which analytics tools are to be applied. There is a strong emphasis on knowledge management concepts and techniques, as well as risk management concepts and techniques. The second section focuses on both the use of the analytics process and organizational issues that are required to make the analytics process relevant and impactful.

Process Analytics

The POLAP goal is to facilitate the analytics over big process graph through summarizing the process graph and providing multiple views at different granularities. P-OLAP benefits from BP-SPARQL [54] (business process SPARQL), ...

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Author: Seyed-Mehdi-Reza Beheshti

Publisher: Springer

ISBN: 9783319250373

Category: Computers

Page: 178

View: 185

This book starts with an introduction to process modeling and process paradigms, then explains how to query and analyze process models, and how to analyze the process execution data. In this way, readers receive a comprehensive overview of what is needed to identify, understand and improve business processes. The book chiefly focuses on concepts, techniques and methods. It covers a large body of knowledge on process analytics – including process data querying, analysis, matching and correlating process data and models – to help practitioners and researchers understand the underlying concepts, problems, methods, tools and techniques involved in modern process analytics. Following an introduction to basic business process and process analytics concepts, it describes the state of the art in this area before examining different analytics techniques in detail. In this regard, the book covers analytics over different levels of process abstractions, from process execution data and methods for linking and correlating process execution data, to inferring process models, querying process execution data and process models, and scalable process data analytics methods. In addition, it provides a review of commercial process analytics tools and their practical applications. The book is intended for a broad readership interested in business process management and process analytics. It provides researchers with an introduction to these fields by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in business process management to graduate courses in business process analytics. Lastly, it offers professionals a reference guide to the state of the art in commercial tools and techniques, complemented by many real-world use case scenarios.

Marketing Database Analytics

The Marketing Database Analytics Process The process-based approach to effective analyses of large volumes of multi-sourced marketing data is summarized in Figure 2.5. The marketing database analytics (MDA) process shown below is ...

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Author: Andrew D. Banasiewicz

Publisher: Routledge

ISBN: 9781135125691

Category: Business & Economics

Page: 400

View: 533

Marketing Database Analytics presents a step-by-step process for understanding and interpreting data in order to gain insights to drive business decisions. One of the core elements of measuring marketing effectiveness is through the collection of appropriate data, but this data is nothing but numbers unless it is analyzed meaningfully. Focusing specifically on quantitative marketing metrics, the book: Covers the full spectrum of marketing analytics, from the initial data setup and exploration, to segmentation, behavioral predictions and impact quantification Establishes the importance of database analytics, integrating both business and marketing practice Provides a theoretical framework that explains the concepts and delivers techniques for analyzing data Includes cases and exercises to guide students’ learning Banasiewicz integrates his knowledge from both his academic training and professional experience, providing a thorough, comprehensive approach that will serve graduate students of marketing research and analytics well.

Professional SharePoint 2013 Development

In SharePoint Server 2013, the analytics processing component is now directly integrated into the search architecture and is no longer an individual service application. These break down into the following components ➤ Analytics ...

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Author: Reza Alirezaei

Publisher: John Wiley & Sons

ISBN: 9781118495827

Category: Computers

Page: 816

View: 914

Thorough coverage of development in SharePoint 2013 A team of well-known Microsoft MVPs joins forces in this fully updated resource, providing you with in-depth coverage of development tools in the latest iteration of the immensely popular SharePoint. From building solutions to building custom workflow and content management applications, this book shares field-tested best practices on all aspect of SharePoint 2013 development. Offers a thorough look at Windows Azure and SharePoint 2013 Includes new chapters on Application Life Cycle Management, developing apps in SharePoint, and building PerformancePoint Dashboards in SharePoint Professional SharePoint 2013 Development is an essential SharePoint developer title.

Evaluation of the RARE II Public Input Analysis Process

Also some said that the final analysis . at SLAC did not build the qualitative data into the actual data analysis process , which upset many . Not wanting to " get surprised " by the results , several managers pointed out how they kept ...

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Author: United States. Forest Service

Publisher:

ISBN: MINN:31951D00269097R

Category: Forest reserves

Page: 67

View: 510

Enterprise Business Process and Information Systems Modeling

Data Integration Optimized Process Analysis Business Process Process Model Deployment BPMS Process Execution Process Optimization Metric Calc. Runtime Utilization Cost Graph analysis & matching Preprocessing Process Analytics Process ...

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Author: Terry Halpin

Publisher: Springer Science & Business Media

ISBN: 9783642217586

Category: Business & Economics

Page: 538

View: 510

This book contains the refereed proceedings of the 12th International Conference on Business Process Modeling, Development and Support (BPMDS 2011) and the 16th International Conference on Exploring Modeling Methods for Systems Analysis and Design (EMMSAD 2011), held together with the 23rd International Conference on Advanced Information Systems Engineering (CAiSE 2011) in London, UK, in June 2011. The 22 papers accepted for BPMDS were selected from 61 submissions and cover a wide spectrum of issues related to business processes development, modeling, and support. They are grouped into sections on BPMDS in practice, business process improvement, business process flexibility, declarative process models, variety of modeling paradigms, business process modeling and support systems development, and interoperability and mobility. The 16 papers accepted for EMMSAD were chosen from 31 submissions and focus on exploring, evaluating, and enhancing current information modeling methods and methodologies. They are grouped in sections on workflow and process modeling extensions, requirements analysis and information systems development, requirements evolution and information systems evolution, data modeling languages and business rules, conceptual modeling practice, and enterprise architecture.

Data Science Fundamentals and Practical Approaches

Social media analytics is one of the emerging topics in the area of research in data science and it is still in its infancy. Let us quickly explore the social media analytics process which comprises of three stages – data capturing, ...

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Author: Nandi Dr. Rupam Dr. Gypsy, Kumar Sharma

Publisher: BPB Publications

ISBN: 9789389845679

Category: Language Arts & Disciplines

Page: 634

View: 514

Learn how to process and analysis data using Python Key Features a- The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. a- The book is quite well balanced with programs and illustrative real-case problems. a- The book not only deals with the background mathematics alone or only the programs but also beautifully correlates the background mathematics to the theory and then finally translating it into the programs. a- A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. Description This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic. What will you learn a- Understand what machine learning is and how learning can be incorporated into a program. a- Perform data processing to make it ready for visual plot to understand the pattern in data over time. a- Know how tools can be used to perform analysis on big data using python a- Perform social media analytics, business analytics, and data analytics on any data of a company or organization. Who this book is for The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. Table of Contents 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics About the Authors Dr. Gypsy Nandi is an Assistant Professor (Sr) in the Department of Computer Applications, Assam Don Bosco University, India. Her areas of interest include Data Science, Social Network Mining, and Machine Learning. She has completed her Ph.D. in the field of 'Social Network Analysis and Mining'. Her research scholars are currently working mainly in the field of Data Science. She has several research publications in reputed journals and book series. Dr. Rupam Kumar Sharma is an Assistant Professor in the Department of Computer Applications, Assam Don Bosco University, India. His area of interest includes Machine Learning, Data Analytics, Network, and Cyber Security. He has several research publications in reputed SCI and Scopus journals. He has also delivered lectures and trained hundreds of trainees and students across different institutes in the field of security and android app development.

Summary and Evaluation of Economic Consequences of Highway Improvements

Throughout the program analysis effort is directed toward ( 1 ) identifying the best alternatives in the program mix ... The analytical process involved results in the key output of a planning , programming , budgeting system .

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Author:

Publisher:

ISBN: OSU:32435030673024

Category: Highway research

Page: 324

View: 133

92 3165 92 3199

MCAIR icing Analysis Process experimental limits. Both the predicted and experimental sets of data. diction methods for modern day aircraft systems. Recent advances in computer technology have lead to the development of analytical codes ...

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Author:

Publisher:

ISBN: PSU:000022694393

Category: Airplanes

Page:

View: 348

Building a Digital Analytics Organization

Unbiased and product-independent, this guide is replete with practitioner's knowledge and examples based on Phillips' own experience.

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Author: Judah Phillips

Publisher: Pearson Education

ISBN: 9780133372786

Category: Business & Economics

Page: 354

View: 328

Drive maximum business value from digital analytics, web analytics, site analytics, and business intelligence! In Building a Digital Analytics Organization, pioneering expert Judah Phillips thoroughly explains digital analytics to business practitioners, and presents best practices for using it to reduce costs and increase profitable revenue throughout the business. Phillips covers everything from making the business case through defining and executing strategy, and shows how to successfully integrate analytical processes, technology, and people in all aspects of operations. This unbiased and product-independent guide is replete with examples, many based on the author's own extensive experience. Coverage includes: key concepts; focusing initiatives and strategy on business value, not technology; building an effective analytics organization; choosing the right tools (and understanding their limitations); creating processes and managing data; analyzing paid, owned, and earned digital media; performing competitive and qualitative analyses; optimizing and testing sites; implementing integrated multichannel digital analytics; targeting consumers; automating marketing processes; and preparing for the revolutionary “analytical economy.” For all business practitioners interested in analytics and business intelligence in all areas of the organization.