Tapping into Unstructured Data

2007-12-11
Tapping into Unstructured Data

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  • Author: William H. Inmon
  • Publisher: Pearson Education
  • ISBN: 0132712911
  • Category : Business & Economics
  • Languages : en
  • Pages : 362

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

Unstructured Data Analytics

2018-03-13
Unstructured Data Analytics

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  • Author: Jean Paul Isson
  • Publisher: John Wiley & Sons
  • ISBN: 1119129753
  • Category : Computers
  • Languages : en
  • Pages : 432

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 ANALYTICS USING MACHINE LEARNING

2022-02-02
UNSTRUCTURED DATA ANALYTICS USING MACHINE LEARNING

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  • Author: M. Sangeetha
  • Publisher:
  • ISBN:
  • Category :
  • Languages : en
  • Pages : 58

We can find easily structured data in our database system such as profile records, transaction records, item records. As time goes by, people think about how to handle unstructured like text, image, data satellite, audio, etc. That might give you something useful to make decisions in your business. In this book various unstructured machine learning techniques for data analytics are discussed.

Practical Text Analytics

2015-07-03
Practical Text Analytics

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  • Author: Steven Struhl
  • Publisher: Kogan Page Publishers
  • ISBN: 0749474025
  • Category : Business & Economics
  • Languages : en
  • Pages : 272

In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys and phone logs. Practical Text Analytics is an essential daily reference resource, providing real-world guidance on the effective application of text analytics. The book presents the analysis process so that it is immediately understood by the marketing professionals who must use it, so they can apply proven concepts and methods correctly and with confidence. By decoding industry terminology and demonstrating practical application of data models once reserved for experts, Practical Text Analytics shows marketers how to frame the right questions, identify key themes and find hidden meaning from unstructured data. Readers will learn to develop powerful new marketing strategies to elevate customer experience, solidify brand value and elevate reputation. Online resources include self-test questions, chapter review Q&A and an Instructor's Manual with text sources and instructions.

Data Analytics

2017-05
Data Analytics

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  • Author: Vernon Barrett
  • Publisher: Createspace Independent Publishing Platform
  • ISBN: 9781546418443
  • Category :
  • Languages : en
  • Pages : 82

Data Analytics - A Comprehensive Beginner's Guide The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data. It emphasizes more on machine learning and mining methods required for processing and decision-making. The text begins with the introduction to the subject and explores the concept of data mining methods and models along with the applications. It then goes into detail on other aspects of Big Data analytics, such as clustering, incremental learning, multi-label association and knowledge representation. The readers are also made familiar with business analytics to create value. The book finally ends with a discussion on the areas where research can be explored. The book is designed for the senior level undergraduate, and postgraduate students of computer science and engineering. Here Is A Preview Of What Inside The Book: Big data Statistics in practice Descriptive and Inferential Statistics Parameters and Statistics Statistical data analysis Variables SUMMARY OF THE GENERAL METHOD OF DECISION ANALYSIS ANOTHER DECISION TREE MODEL AND ITS ANALYSIS Making Data Work for You Predictive Modeling Techniques Take Action Today and Learn Data Analytics in no time! Click the "Buy now with 1-Click" to the right and get this guide immediately.

BIG DATA ANALYTICS

2016-07-07
BIG DATA ANALYTICS

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  • Author: Parag Kulkarni
  • Publisher: PHI Learning Pvt. Ltd.
  • ISBN: 8120351169
  • Category : Language Arts & Disciplines
  • Languages : en
  • Pages : 208

The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data. It emphasizes more on machine learning and mining methods required for processing and decision-making. The text begins with the introduction to the subject and explores the concept of data mining methods and models along with the applications. It then goes into detail on other aspects of Big Data analytics, such as clustering, incremental learning, multi-label association and knowledge representation. The readers are also made familiar with business analytics to create value. The book finally ends with a discussion on the areas where research can be explored.

Unstructured Data Analysis

2018-09-14
Unstructured Data Analysis

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  • Author: Matthew Windham
  • Publisher: SAS Institute
  • ISBN: 1635267099
  • Category : Computers
  • Languages : en
  • Pages : 166

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.

Building the Unstructured Data Warehouse

2011
Building the Unstructured Data Warehouse

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  • Author: William H. Inmon
  • Publisher:
  • ISBN: 9781935504047
  • Category : Business intelligence
  • Languages : en
  • Pages : 0

Learn essential techniques from data warehouse legend Bill Inmon on how to build the reporting environment your business needs now Answers for many valuable business questions hide in text. How well can your existing reporting environment extract the necessary text from email, spreadsheets, and documents, and put it in a useful format for analytics and reporting? Transforming the traditional data warehouse into an efficient unstructured data warehouse requires additional skills from the analyst, architect, designer, and developer. This book will prepare you to successfully implement an unstructured data warehouse and, through clear explanations, examples, and case studies, you will learn new techniques and tips to successfully obtain and analyze text. Master these ten objectives: Build an unstructured data warehouse using the 11-step approach Integrate text and describe it in terms of homogeneity, relevance, medium, volume, and structure Overcome challenges including blather, the Tower of Babel, and lack of natural relationships Avoid the Data Junkyard and combat the "Spider's Web" Reuse techniques perfected in the traditional data warehouse and Data Warehouse 2.0, including iterative development Apply essential techniques for textual Extract, Transform, and Load (ETL) such as phrase recognition, stop word filtering, and synonym replacement Design the Document Inventory system and link unstructured text to structured data Leverage indexes for efficient text analysis and taxonomies for useful external categorization Manage large volumes of data using advanced techniques such as backward pointers Evaluate technology choices suitable for unstructured data processing, such as data warehouse appliances

Data Analytics and AI

2020-08-06
Data Analytics and AI

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  • Author: Jay Liebowitz
  • Publisher: CRC Press
  • ISBN: 1000094650
  • Category : Computers
  • Languages : en
  • Pages : 242

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.

Analysis of Structured and Unstructured Data Using Data Science

2023-03-19
Analysis of Structured and Unstructured Data Using Data Science

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  • Author: Shagufta Parveen
  • Publisher:
  • ISBN:
  • Category :
  • Languages : en
  • Pages : 0

Data is an important asset for every plant working over data process. There are number of organizations presently working on data process specifically. These organizations are in need of the desired data and their format. These data can only be achieved by proper data conversion and data transformation. Basically there are three kinds of data present in the computer science world. These are structured, unstructured and semi-structured data. Data that can easily be stored in text form. Organizations like banks has the excel sheet which consist of all structured data. Data stored in excel and csv file and can be extracted for any kind of information purpose. The analysis of structured data is quite easy as from the beginning scientist are working on structured data for data process. Like most of the forms of data research survey has fields that consist of data like numbers, text and figures.