Unstructured Data Analytics

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Unstructured Data Analytics

Author : Jean Paul Isson
Publisher : John Wiley & Sons
ISBN 13 : 1119129753
Total Pages : 432 pages
Book Rating : 4.1/5 (191 download)

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Book Synopsis Unstructured Data Analytics by : Jean Paul Isson

Download or read book Unstructured Data Analytics written by Jean Paul Isson and published by John Wiley & Sons. This book was released on 2018-03-13 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Turn unstructured data into valuable business insight Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear examples of both traditional business applications and newer, more innovative practices. Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work. Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence. You will learn: How to increase Customer Acquisition and Customer Retention with UDA The Power of UDA for Fraud Detection and Prevention The Power of UDA in Human Capital Management & Human Resource The Power of UDA in Health Care and Medical Research The Power of UDA in National Security The Power of UDA in Legal Services The Power of UDA for product development The Power of UDA in Sports The future of UDA From small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis.

Unstructured Data Analysis

Author : Matthew Windham
Publisher : SAS Institute
ISBN 13 : 1635267099
Total Pages : 193 pages
Book Rating : 4.6/5 (352 download)

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Book Synopsis Unstructured Data Analysis by : Matthew Windham

Download or read book Unstructured Data Analysis written by Matthew Windham and published by SAS Institute. This book was released on 2018-09-14 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unstructured data is the most voluminous form of data in the world, and several elements are critical for any advanced analytics practitioner leveraging SAS software to effectively address the challenge of deriving value from that data. This book covers the five critical elements of entity extraction, unstructured data, entity resolution, entity network mapping and analysis, and entity management. By following examples of how to apply processing to unstructured data, readers will derive tremendous long-term value from this book as they enhance the value they realize from SAS products.

Text Analytics

Author : John Atkinson-Abutridy
Publisher : CRC Press
ISBN 13 : 1000581071
Total Pages : 201 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Text Analytics by : John Atkinson-Abutridy

Download or read book Text Analytics written by John Atkinson-Abutridy and published by CRC Press. This book was released on 2022-05-03 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis is a concise and accessible introduction to the science and applications of text analytics (or text mining), which enables automatic knowledge discovery from unstructured information sources, for both industrial and academic purposes. The book introduces the main concepts, models, and computational techniques that enable the reader to solve real decision-making problems arising from textual and/or documentary sources. Features: Easy-to-follow step-by-step concepts and methods Every chapter is introduced in a very gentle and intuitive way so students can understand the WHYs, WHAT-IFs, WHAT-IS-THIS-FORs, HOWs, etc. by themselves Practical programming exercises in Python for each chapter Includes theory and practice for every chapter, summaries, practical coding exercises for target problems, QA, and sample code and data available for download at https://www.routledge.com/Atkinson-Abutridy/p/book/9781032249797

Data Architecture: A Primer for the Data Scientist

Author : W.H. Inmon
Publisher : Academic Press
ISBN 13 : 0128169176
Total Pages : 434 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Data Architecture: A Primer for the Data Scientist by : W.H. Inmon

Download or read book Data Architecture: A Primer for the Data Scientist written by W.H. Inmon and published by Academic Press. This book was released on 2019-04-30 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. - New case studies include expanded coverage of textual management and analytics - New chapters on visualization and big data - Discussion of new visualizations of the end-state architecture

Big Data, Mining, and Analytics

Author : Stephan Kudyba
Publisher : CRC Press
ISBN 13 : 1466568712
Total Pages : 306 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Big Data, Mining, and Analytics by : Stephan Kudyba

Download or read book Big Data, Mining, and Analytics written by Stephan Kudyba and published by CRC Press. This book was released on 2014-03-12 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book ties together big data, data mining, and analytics to explain how readers can leverage them to transform their business strategy. Illustrating basic approaches of business intelligence to data and text mining, the book guides readers through the process of extracting valuable knowledge from the varieties of data currently being generated in the brick and mortar and Internet environments. It considers the broad spectrum of analytics approaches for decision making, including dashboards, OLAP cubes, data mining, and text mining.

Big Data For Dummies

Author : Judith S. Hurwitz
Publisher : John Wiley & Sons
ISBN 13 : 1118644174
Total Pages : 336 pages
Book Rating : 4.1/5 (186 download)

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Book Synopsis Big Data For Dummies by : Judith S. Hurwitz

Download or read book Big Data For Dummies written by Judith S. Hurwitz and published by John Wiley & Sons. This book was released on 2013-04-02 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization.

Data Analytics and AI

Author : Jay Liebowitz
Publisher : CRC Press
ISBN 13 : 1000094677
Total Pages : 187 pages
Book Rating : 4.0/5 ( download)

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Book Synopsis Data Analytics and AI by : Jay Liebowitz

Download or read book Data Analytics and AI written by Jay Liebowitz and published by CRC Press. This book was released on 2020-08-06 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.

New Opportunities for Sentiment Analysis and Information Processing

Author : Sharaff, Aakanksha
Publisher : IGI Global
ISBN 13 : 179988063X
Total Pages : 311 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis New Opportunities for Sentiment Analysis and Information Processing by : Sharaff, Aakanksha

Download or read book New Opportunities for Sentiment Analysis and Information Processing written by Sharaff, Aakanksha and published by IGI Global. This book was released on 2021-06-25 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multinational organizations have begun to realize that sentiment mining plays an important role for decision making and market strategy. The revolutionary growth of digital marketing not only changes the market game, but also brings forth new opportunities for skilled professionals and expertise. Currently, the technologies are rapidly changing, and artificial intelligence (AI) and machine learning are contributing as game-changing technologies. These are not only trending but are also increasingly popular among data scientists and data analysts. New Opportunities for Sentiment Analysis and Information Processing provides interdisciplinary research in information retrieval and sentiment analysis including studies on extracting sentiments from textual data, sentiment visualization-based dimensionality reduction for multiple features, and deep learning-based multi-domain sentiment extraction. The book also optimizes techniques used for sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic analysis, this book is essential for data scientists, data analysts, IT specialists, scientists, researchers, academicians, and students.

The Text Mining Handbook

Author : Ronen Feldman
Publisher : Cambridge University Press
ISBN 13 : 0521836573
Total Pages : 423 pages
Book Rating : 4.5/5 (218 download)

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Book Synopsis The Text Mining Handbook by : Ronen Feldman

Download or read book The Text Mining Handbook written by Ronen Feldman and published by Cambridge University Press. This book was released on 2007 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher description

Tapping into Unstructured Data

Author : William H. Inmon
Publisher : Pearson Education
ISBN 13 : 0132712911
Total Pages : 353 pages
Book Rating : 4.1/5 (327 download)

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Book Synopsis Tapping into Unstructured Data by : William H. Inmon

Download or read book Tapping into Unstructured Data written by William H. Inmon and published by Pearson Education. This book was released on 2007-12-11 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Definitive Guide to Unstructured Data Management and Analysis--From the World’s Leading Information Management Expert A wealth of invaluable information exists in unstructured textual form, but organizations have found it difficult or impossible to access and utilize it. This is changing rapidly: new approaches finally make it possible to glean useful knowledge from virtually any collection of unstructured data. William H. Inmon--the father of data warehousing--and Anthony Nesavich introduce the next data revolution: unstructured data management. Inmon and Nesavich cover all you need to know to make unstructured data work for your organization. You’ll learn how to bring it into your existing structured data environment, leverage existing analytical infrastructure, and implement textual analytic processing technologies to solve new problems and uncover new opportunities. Inmon and Nesavich introduce breakthrough techniques covered in no other book--including the powerful role of textual integration, new ways to integrate textual data into data warehouses, and new SQL techniques for reading and analyzing text. They also present five chapter-length, real-world case studies--demonstrating unstructured data at work in medical research, insurance, chemical manufacturing, contracting, and beyond. This book will be indispensable to every business and technical professional trying to make sense of a large body of unstructured text: managers, database designers, data modelers, DBAs, researchers, and end users alike. Coverage includes What unstructured data is, and how it differs from structured data First generation technology for handling unstructured data, from search engines to ECM--and its limitations Integrating text so it can be analyzed with a common, colloquial vocabulary: integration engines, ontologies, glossaries, and taxonomies Processing semistructured data: uncovering patterns, words, identifiers, and conflicts Novel processing opportunities that arise when text is freed from context Architecture and unstructured data: Data Warehousing 2.0 Building unstructured relational databases and linking them to structured data Visualizations and Self-Organizing Maps (SOMs), including Compudigm and Raptor solutions Capturing knowledge from spreadsheet data and email Implementing and managing metadata: data models, data quality, and more

Semantic Modeling for Data

Author : Panos Alexopoulos
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492054224
Total Pages : 330 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Semantic Modeling for Data by : Panos Alexopoulos

Download or read book Semantic Modeling for Data written by Panos Alexopoulos and published by "O'Reilly Media, Inc.". This book was released on 2020-08-19 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the right data and the technology to extract business gold—but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications. You’ll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data. Understand the fundamental concepts, phenomena, and processes related to semantic data modeling Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and tools Avoid mistakes and bad practices that can undermine your efforts to create good data models Learn about model development dilemmas, including representation, expressiveness and content, development, and governance Organize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges

Visual Analytics for Data Scientists

Author : Natalia Andrienko
Publisher : Springer Nature
ISBN 13 : 3030561461
Total Pages : 440 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Visual Analytics for Data Scientists by : Natalia Andrienko

Download or read book Visual Analytics for Data Scientists written by Natalia Andrienko and published by Springer Nature. This book was released on 2020-08-30 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail. The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows, organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified. The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows.

Advanced Data Analytics Using Python

Author : Sayan Mukhopadhyay
Publisher : Apress
ISBN 13 : 1484234502
Total Pages : 195 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Advanced Data Analytics Using Python by : Sayan Mukhopadhyay

Download or read book Advanced Data Analytics Using Python written by Sayan Mukhopadhyay and published by Apress. This book was released on 2018-03-29 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. What You Will Learn Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP Who This Book Is For Data scientists and software developers interested in the field of data analytics.

Win with Advanced Business Analytics

Author : Jean-Paul Isson
Publisher : John Wiley & Sons
ISBN 13 : 1118417089
Total Pages : 416 pages
Book Rating : 4.1/5 (184 download)

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Book Synopsis Win with Advanced Business Analytics by : Jean-Paul Isson

Download or read book Win with Advanced Business Analytics written by Jean-Paul Isson and published by John Wiley & Sons. This book was released on 2012-09-25 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Plain English guidance for strategic business analytics and big data implementation In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice. Provides the essential concept and framework to implement business analytics Written clearly for a nontechnical audience Filled with case studies across a variety of industries Uniquely focuses on integrating multiple types of big data intelligence into your business Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition.

Financial Data Analytics

Author : Sinem Derindere Köseoğlu
Publisher : Springer Nature
ISBN 13 : 3030837998
Total Pages : 393 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Financial Data Analytics by : Sinem Derindere Köseoğlu

Download or read book Financial Data Analytics written by Sinem Derindere Köseoğlu and published by Springer Nature. This book was released on 2022-04-25 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics.

Applications of Machine Learning in Big-Data Analytics and Cloud Computing

Author : Subhendu Kumar Pani
Publisher : CRC Press
ISBN 13 : 1000793559
Total Pages : 346 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Applications of Machine Learning in Big-Data Analytics and Cloud Computing by : Subhendu Kumar Pani

Download or read book Applications of Machine Learning in Big-Data Analytics and Cloud Computing written by Subhendu Kumar Pani and published by CRC Press. This book was released on 2022-09-01 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions. This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.

Cataloging Unstructured Data in IBM Watson Knowledge Catalog with IBM Spectrum Discover

Author : Joseph Dain
Publisher : IBM Redbooks
ISBN 13 : 073845902X
Total Pages : 108 pages
Book Rating : 4.7/5 (384 download)

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Book Synopsis Cataloging Unstructured Data in IBM Watson Knowledge Catalog with IBM Spectrum Discover by : Joseph Dain

Download or read book Cataloging Unstructured Data in IBM Watson Knowledge Catalog with IBM Spectrum Discover written by Joseph Dain and published by IBM Redbooks. This book was released on 2020-08-11 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redpaper publication explains how IBM Spectrum® Discover integrates with the IBM Watson® Knowledge Catalog (WKC) component of IBM Cloud® Pak for Data (IBM CP4D) to make the enriched catalog content in IBM Spectrum Discover along with the associated data available in WKC and IBM CP4D. From an end-to-end IBM solution point of view, IBM CP4D and WKC provide state-of-the-art data governance, collaboration, and artificial intelligence (AI) and analytics tools, and IBM Spectrum Discover complements these features by adding support for unstructured data on large-scale file and object storage systems on premises and in the cloud. Many organizations face challenges to manage unstructured data. Some challenges that companies face include: Pinpointing and activating relevant data for large-scale analytics, machine learning (ML) and deep learning (DL) workloads. Lacking the fine-grained visibility that is needed to map data to business priorities. Removing redundant, obsolete, and trivial (ROT) data and identifying data that can be moved to a lower-cost storage tier. Identifying and classifying sensitive data as it relates to various compliance mandates, such as the General Data Privacy Regulation (GDPR), Payment Card Industry Data Security Standards (PCI-DSS), and the Health Information Portability and Accountability Act (HIPAA). This paper describes how IBM Spectrum Discover provides seamless integration of data in IBM Storage with IBM Watson Knowledge Catalog (WKC). Features include: Event-based cataloging and tagging of unstructured data across the enterprise. Automatically inspecting and classifying over 1000 unstructured data types, including genomics and imaging specific file formats. Automatically registering assets with WKC based on IBM Spectrum Discover search and filter criteria, and by using assets in IBM CP4D. Enforcing data governance policies in WKC in IBM CP4D based on insights from IBM Spectrum Discover, and using assets in IBM CP4D. Several in-depth use cases are used that show examples of healthcare, life sciences, and financial services. IBM Spectrum Discover integration with WKC enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of data. The integration improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research.