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Data Profiling by Ziawasch Abedjan,Lukasz Golab,Felix Naumann,Thorsten Papenbrock Pdf
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.
Data Profiling and Insurance Law by Brendan McGurk Pdf
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.
The Data Warehouse Lifecycle Toolkit by Ralph Kimball,Margy Ross,Warren Thornthwaite,Joy Mundy,Bob Becker Pdf
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.
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.
Business Intelligence describes the basic architectural components of a business intelligence environment, ranging from traditional topics such as business process modeling, data modeling, and more modern topics such as business rule systems, data profiling, information compliance and data quality, data warehousing, and data mining. This book progresses through a logical sequence, starting with data model infrastructure, then data preparation, followed by data analysis, integration, knowledge discovery, and finally the actual use of discovered knowledge. The book contains a quick reference guide for business intelligence terminology. Business Intelligence is part of Morgan Kaufmann's Savvy Manager's Guide series. * Provides clear explanations without technical jargon, followed by in-depth descriptions. * Articulates the business value of new technology, while providing relevant introductory technical background. * Contains a handy quick-reference to technologies and terminologies. * Guides managers through developing, administering, or simply understanding business intelligence technology. * Bridges the business-technical gap. * Is Web enhanced. Companion sites to the book and series provide value-added information, links, discussions, and more.
The key to a successful MDM initiative isn’t technology or methods, it’s people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect. Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you’ll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. Presents a comprehensive roadmap that you can adapt to any MDM project Emphasizes the critical goal of maintaining and improving data quality Provides guidelines for determining which data to “master. Examines special issues relating to master data metadata Considers a range of MDM architectural styles Covers the synchronization of master data across the application infrastructure
Principles of Data Wrangling by Tye Rattenbury,Joseph M. Hellerstein,Jeffrey Heer,Sean Kandel,Connor Carreras Pdf
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
SQL Server 2012 Data Integration Recipes by Adam Aspin Pdf
SQL Server 2012 Data Integration Recipes provides focused and practical solutions to real world problems of data integration. Need to import data into SQL Server from an outside source? Need to export data and send it to another system? SQL Server 2012 Data Integration Recipes has your back. You'll find solutions for importing from Microsoft Office data stores such as Excel and Access, from text files such as CSV files, from XML, from other database brands such as Oracle and MySQL, and even from other SQL Server databases. You'll learn techniques for managing metadata, transforming data to meet the needs of the target system, handling exceptions and errors, and much more. What DBA or developer isn't faced with the need to move data back and forth? Author Adam Aspin brings 10 years of extensive ETL experience involving SQL Server, and especially satellite products such as Data Transformation Services and SQL Server Integration Services. Extensive coverage is given to Integration Services, Microsoft's flagship tool for data integration in SQL Server environments. Coverage is also given to the broader range of tools such as OPENDATASOURCE, linked servers, OPENROWSET, Migration Assistant for Access, BCP Import, and BULK INSERT just to name a few. If you're looking for a resource to cover data integration and ETL across the gamut of Microsoft's SQL Server toolset, SQL Server 2012 Data Integration Recipes is the one book that will meet your needs. Provides practical and proven solutions towards creating resilient ETL environments Clearly answers the tough questions which professionals ask Goes beyond the tools to a thorough discussion of the underlying techniques Covers the gamut of data integration, beyond just SSIS Includes example databases and files to allow readers to test the recipes
As organizations deploy business intelligence and analytic systems to harness business value from their data assets, data governance programs are quickly gaining prominence. And, although data management issues have traditionally been addressed by IT departments, organizational issues critical to successful data management require the implementation of enterprise-wide accountabilities and responsibilities. Data Governance: Creating Value from Information Assets examines the processes of using data governance to manage data effectively. Addressing the complete life cycle of effective data governance—from metadata management to privacy and compliance—it provides business managers, IT professionals, and students with an integrated approach to designing, developing, and sustaining an effective data governance strategy. Explains how to align data governance with business goals Describes how to build successful data stewardship with a governance framework Outlines strategies for integrating IT and data governance frameworks Supplies business-driven and technical perspectives on data quality management, metadata management, data access and security, and data lifecycle The book summarizes the experiences of global experts in the field and addresses critical areas of interest to the information systems and management community. Case studies from healthcare and financial sectors, two industries that have successfully leveraged the potential of data-driven strategies, provide further insights into real-time practice. Facilitating a comprehensive understanding of data governance, the book addresses the burning issue of aligning data assets to both IT assets and organizational strategic goals. With a focus on the organizational, operational, and strategic aspects of data governance, the text provides you with the understanding required to leverage, derive, and sustain maximum value from the informational assets housed in your IT infrastructure.
Business Intelligence and Big Data by Esteban Zimányi Pdf
This book constitutes revised tutorial lectures of the 7th European Business Intelligence and Big Data Summer School, eBISS 2017, held in Bruxelles, Belgium, in July 2017. The tutorials were given by renowned experts and covered advanced aspects of business intelligence and big data. This summer school, presented by leading researchers in the field, represented an opportunity for postgraduate students to equip themselves with the theoretical, practical, and collaboration skills necessary for developing challenging business intelligence applications.
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
Intelligent Systems in Big Data, Semantic Web and Machine Learning by Noreddine Gherabi,Janusz Kacprzyk Pdf
This book describes important methodologies, tools and techniques from the fields of artificial intelligence, basically those which are based on relevant conceptual and formal development. The coverage is wide, ranging from machine learning to the use of data on the Semantic Web, with many new topics. The contributions are concerned with machine learning, big data, data processing in medicine, similarity processing in ontologies, semantic image analysis, as well as many applications including the use of machine leaning techniques for cloud security, artificial intelligence techniques for detecting COVID-19, the Internet of things, etc. The book is meant to be a very important and useful source of information for researchers and doctoral students in data analysis, Semantic Web, big data, machine learning, computer engineering and related disciplines, as well as for postgraduate students who want to integrate the doctoral cycle.
Handbook of Data Intensive Computing by Borko Furht,Armando Escalante Pdf
Data Intensive Computing refers to capturing, managing, analyzing, and understanding data at volumes and rates that push the frontiers of current technologies. The challenge of data intensive computing is to provide the hardware architectures and related software systems and techniques which are capable of transforming ultra-large data into valuable knowledge. Handbook of Data Intensive Computing is written by leading international experts in the field. Experts from academia, research laboratories and private industry address both theory and application. Data intensive computing demands a fundamentally different set of principles than mainstream computing. Data-intensive applications typically are well suited for large-scale parallelism over the data and also require an extremely high degree of fault-tolerance, reliability, and availability. Real-world examples are provided throughout the book. Handbook of Data Intensive Computing is designed as a reference for practitioners and researchers, including programmers, computer and system infrastructure designers, and developers. This book can also be beneficial for business managers, entrepreneurs, and investors.
Executing Data Quality Projects by Danette McGilvray Pdf
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
Information Technology: New Generations by Shahram Latifi Pdf
This book collects articles presented at the 13th International Conference on Information Technology- New Generations, April, 2016, in Las Vegas, NV USA. It includes over 100 chapters on critical areas of IT including Web Technology, Communications, Security, and Data Mining.
