Data Profiling

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Data Profiling

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Author : Ziawasch Abedjan
language : en
Publisher: Springer Nature
Release Date : 2022-06-01

Data Profiling written by Ziawasch Abedjan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.

Data profiling refers to the activity of collecting data about data, {i.e.}, metadata. Most IT professionals and researchers who work with data have engaged in data profiling, at least informally, to understand and explore an unfamiliar dataset or to determine whether a new dataset is appropriate for a particular task at hand. Data profiling results are also important in a variety of other situations, including query optimization, data integration, and data cleaning. Simple metadata are statistics, such as the number of rows and columns, schema and datatype information, the number of distinct values, statistical value distributions, and the number of null or empty values in each column. More complex types of metadata are statements about multiple columns and their correlation, such as candidate keys, functional dependencies, and other types of dependencies. This book provides a classification of the various types of profilable metadata, discusses popular data profiling tasks,and surveys state-of-the-art profiling algorithms. While most of the book focuses on tasks and algorithms for relational data profiling, we also briefly discuss systems and techniques for profiling non-relational data such as graphs and text. We conclude with a discussion of data profiling challenges and directions for future work in this area.

Principles Of Data Wrangling

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Author : Tye Rattenbury
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-06-29

Principles Of Data Wrangling written by Tye Rattenbury and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-29 with Business & Economics categories.

A key task that any aspiring data-driven organization needs to learn is data wrangling, the process of converting raw data into something truly useful. This practical guide provides business analysts with an overview of various data wrangling techniques and tools, and puts the practice of data wrangling into context by asking, "What are you trying to do and why?" Wrangling data consumes roughly 50-80% of an analyst’s time before any kind of analysis is possible. Written by key executives at Trifacta, this book walks you through the wrangling process by exploring several factors—time, granularity, scope, and structure—that you need to consider as you begin to work with data. You’ll learn a shared language and a comprehensive understanding of data wrangling, with an emphasis on recent agile analytic processes used by many of today’s data-driven organizations. Appreciate the importance—and the satisfaction—of wrangling data the right way. Understand what kind of data is available Choose which data to use and at what level of detail Meaningfully combine multiple sources of data Decide how to distill the results to a size and shape that can drive downstream analysis

Data Profiling And Insurance Law

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Author : Brendan McGurk KC
language : en
Publisher: Bloomsbury Publishing
Release Date : 2019-03-21

Data Profiling And Insurance Law written by Brendan McGurk KC and has been published by Bloomsbury Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-21 with Law categories.

The winner of the 2020 British Insurance Law Association Book Prize, this timely, expertly written book looks at the legal impact that the use of 'Big Data' will have on the provision – and substantive law – of insurance. Insurance companies are set to become some of the biggest consumers of big data which will enable them to profile prospective individual insureds at an increasingly granular level. More particularly, the book explores how: (i) insurers gain access to information relevant to assessing risk and/or the pricing of premiums; (ii) the impact which that increased information will have on substantive insurance law (and in particular duties of good faith disclosure and fair presentation of risk); and (iii) the impact that insurers' new knowledge may have on individual and group access to insurance. This raises several consequential legal questions: (i) To what extent is the use of big data analytics to profile risk compatible (at least in the EU) with the General Data Protection Regulation? (ii) Does insurers' ability to parse vast quantities of individual data about insureds invert the information asymmetry that has historically existed between insured and insurer such as to breathe life into insurers' duty of good faith disclosure? And (iii) by what means might legal challenges be brought against insurers both in relation to the use of big data and the consequences it may have on access to cover? Written by a leading expert in the field, this book will both stimulate further debate and operate as a reference text for academics and practitioners who are faced with emerging legal problems arising from the increasing opportunities that big data offers to the insurance industry.

Data Quality

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Author : Jack E. Olson
language : en
Publisher: Elsevier
Release Date : 2003-01-09

Data Quality written by Jack E. Olson and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-01-09 with Computers categories.

Data Quality: The Accuracy Dimension is about assessing the quality of corporate data and improving its accuracy using the data profiling method. Corporate data is increasingly important as companies continue to find new ways to use it. Likewise, improving the accuracy of data in information systems is fast becoming a major goal as companies realize how much it affects their bottom line. Data profiling is a new technology that supports and enhances the accuracy of databases throughout major IT shops. Jack Olson explains data profiling and shows how it fits into the larger picture of data quality.* Provides an accessible, enjoyable introduction to the subject of data accuracy, peppered with real-world anecdotes. * Provides a framework for data profiling with a discussion of analytical tools appropriate for assessing data accuracy. * Is written by one of the original developers of data profiling technology. * Is a must-read for any data management staff, IT management staff, and CIOs of companies with data assets.

Artificial Intelligence Applications And Innovations Aiai 2024 Ifip Wg 12 5 International Workshops

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Author : Ilias Maglogiannis
language : en
Publisher: Springer Nature
Release Date : 2024-06-22

Artificial Intelligence Applications And Innovations Aiai 2024 Ifip Wg 12 5 International Workshops written by Ilias Maglogiannis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-22 with Computers categories.

This book constitutes the refereed proceedings of three International Workshops held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024, held in Corfu, Greece, during June 27-30, 2024. The 30 full papers and 4 short papers presented in this book were carefully reviewed and selected from 69 submissions. AIAI 2024 Workshop volume presents papers from the following three workshops: 13th event of the International Mining Humanistic Data Workshop (MHDW 2024) 9th 5G-PINE Workshop (5G-PINE 2024) 1st Workshop on AI Applications for Achieving the Green Deal Targets (ΑΙ4GD 2024).

The Data Warehouse Lifecycle Toolkit

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Author : Ralph Kimball
language : en
Publisher: John Wiley & Sons
Release Date : 2008-01-10

The Data Warehouse Lifecycle Toolkit written by Ralph Kimball 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 2008-01-10 with Computers categories.

A thorough update to the industry standard for designing, developing, and deploying data warehouse and business intelligence systems The world of data warehousing has changed remarkably since the first edition of The Data Warehouse Lifecycle Toolkit was published in 1998. In that time, the data warehouse industry has reached full maturity and acceptance, hardware and software have made staggering advances, and the techniques promoted in the premiere edition of this book have been adopted by nearly all data warehouse vendors and practitioners. In addition, the term "business intelligence" emerged to reflect the mission of the data warehouse: wrangling the data out of source systems, cleaning it, and delivering it to add value to the business. Ralph Kimball and his colleagues have refined the original set of Lifecycle methods and techniques based on their consulting and training experience. The authors understand first-hand that a data warehousing/business intelligence (DW/BI) system needs to change as fast as its surrounding organization evolves. To that end, they walk you through the detailed steps of designing, developing, and deploying a DW/BI system. You'll learn to create adaptable systems that deliver data and analyses to business users so they can make better business decisions.

Testing The Data Warehouse Practicum

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Author : Wayne Yaddow Doug Vucevic &
language : en
Publisher: Trafford Publishing
Release Date : 2012-08

Testing The Data Warehouse Practicum written by Wayne Yaddow Doug Vucevic & and has been published by Trafford Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08 with Business & Economics categories.

The quality of a data warehouse (DWH) is the elusive aspect of it, not because it is hard to achieve [once we agree what it is], but because it is difficult to describe. We propose the notion that quality is not an attribute or a feature that a product has to possess, but rather a relationship between that product and each and every stakeholder. More specifically, the relationship between the software quality and the organization that produces the products is explored. Quality of data that populates the DWH is the main concern of the book, therefore we propose a definition for data quality as: "fitness to serve each and every purpose". Methods are proposed throughout the book to help readers achieve data warehouse quality.

Executing Data Quality Projects

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Author : Danette McGilvray
language : en
Publisher: Academic Press
Release Date : 2021-05-27

Executing Data Quality Projects written by Danette McGilvray and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-27 with Computers categories.

Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online

Data Wrangling With Sql

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Author : Raghav Kandarpa
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-07-31

Data Wrangling With Sql written by Raghav Kandarpa and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-31 with Computers categories.

Become a data wrangling expert and make well-informed decisions by effectively utilizing and analyzing raw unstructured data in a systematic manner Purchase of the print or Kindle book includes a free PDF eBook Key Features Implement query optimization during data wrangling using the SQL language with practical use cases Master data cleaning, handle the date function and null value, and write subqueries and window functions Practice self-assessment questions for SQL-based interviews and real-world case study rounds Book DescriptionThe amount of data generated continues to grow rapidly, making it increasingly important for businesses to be able to wrangle this data and understand it quickly and efficiently. Although data wrangling can be challenging, with the right tools and techniques you can efficiently handle enormous amounts of unstructured data. The book starts by introducing you to the basics of SQL, focusing on the core principles and techniques of data wrangling. You’ll then explore advanced SQL concepts like aggregate functions, window functions, CTEs, and subqueries that are very popular in the business world. The next set of chapters will walk you through different functions within SQL query that cause delays in data transformation and help you figure out the difference between a good query and bad one. You’ll also learn how data wrangling and data science go hand in hand. The book is filled with datasets and practical examples to help you understand the concepts thoroughly, along with best practices to guide you at every stage of data wrangling. By the end of this book, you’ll be equipped with essential techniques and best practices for data wrangling, and will predominantly learn how to use clean and standardized data models to make informed decisions, helping businesses avoid costly mistakes.What you will learn Build time series models using data wrangling Discover data wrangling best practices as well as tips and tricks Find out how to use subqueries, window functions, CTEs, and aggregate functions Handle missing data, data types, date formats, and redundant data Build clean and efficient data models using data wrangling techniques Remove outliers and calculate standard deviation to gauge the skewness of data Who this book is forThis book is for data analysts looking for effective hands-on methods to manage and analyze large volumes of data using SQL. The book will also benefit data scientists, product managers, and basically any role wherein you are expected to gather data insights and develop business strategies using SQL as a language. If you are new to or have basic knowledge of SQL and databases and an understanding of data cleaning practices, this book will give you further insights into how you can apply SQL concepts to build clean, standardized data models for accurate analysis.

Mastering Power Bi

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Author : Chandraish Sinha
language : en
Publisher: BPB Publications
Release Date : 2024-05-28

Mastering Power Bi written by Chandraish Sinha and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-28 with Computers categories.

Take a deep dive into the dynamic world of Power BI! KEY FEATURES ● In-depth knowledge of Power BI, demonstrated through step-by-step exercises. ● Covers data modeling, visualization, and implementing security with complete hands-on training. ● Includes a project that simulates a realistic business environment from start to finish. ● This version teaches about Artificial Intelligence visuals in Power BI. DESCRIPTION Mastering Power BI covers the entire Power BI implementation process. The readers will be able to understand all the concepts covered in this book, from data modeling to creating powerful visualizations. This book begins with concepts and terminology such as the star-schema, dimensions, and facts. It explains multi-table dataset and demonstrates how to load these tables into Power BI. It shows how to load stored data in various formats and create relationships. Readers will also learn more about Data Analysis Expressions (DAX). This book is a must for developers to learn how to extend the usability of Power BI, to explore meaningful and hidden data insights. Throughout the book, you keep on learning about the concepts, techniques, and expert practices on loading and shaping data, visualization design, and security implementation. The second edition of Mastering Power BI book adheres to the first edition in terms of providing the basics of business intelligence and Power BI; however, it introduces new concepts and features in terms of data transformation, data profiling, custom hierarchies, AI visuals, and many more. WHAT YOU WILL LEARN ● Learn about Business Intelligence (BI) concepts and their contribution in business analytics. ● Learn to connect, load, and transform data from disparate data sources. ● Create and execute powerful DAX calculations. ● Design various visualizations to prepare insightful reports and dashboards. WHO THIS BOOK IS FOR This book is for anyone interested in learning how to use Power BI desktop or starting a career in business intelligence and analytics. While it covers all the fundamentals, it is recommended that the reader be familiar with MS Excel and database concepts. TABLE OF CONTENTS 1. Understanding the Basics 2. Connect and Shape 3. Advanced Data Transformations 4. Optimize Your Data Model 5. Data Analysis Expressions 6. Visualizations in Power BI 7. Drill Through and Drill Down Reports 8. Artificial Intelligence in Power BI 9. Power BI Service 10. Securing Your Application