Executing Data Quality Projects

Executing Data Quality Projects
Author: Danette McGilvray
Publsiher: Academic Press
Total Pages: 376
Release: 2021-05-27
ISBN 10: 0128180161
ISBN 13: 9780128180167
Language: EN, FR, DE, ES & NL

Executing Data Quality Projects Book Review:

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

Executing Data Quality Projects

Executing Data Quality Projects
Author: Danette McGilvray
Publsiher: Elsevier
Total Pages: 352
Release: 2008-09-01
ISBN 10: 0080558399
ISBN 13: 9780080558394
Language: EN, FR, DE, ES & NL

Executing Data Quality Projects Book Review:

Information is currency. Recent 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. In this important and timely new book, Danette McGilvray presents her “Ten Steps approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach—in which she has trained Fortune 500 clients and hundreds of workshop attendees—applies to all types of data and to all types of organizations. * Includes numerous templates, detailed examples, and practical advice for executing every step of the “Ten Steps approach. * 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 planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online.

Executing Data Quality Projects

Executing Data Quality Projects
Author: Danette McGilvray
Publsiher: Morgan Kaufmann
Total Pages: 325
Release: 2008
ISBN 10: 9780123743695
ISBN 13: 0123743699
Language: EN, FR, DE, ES & NL

Executing Data Quality Projects Book Review:

Introduces a systematic, effective approach to enhancing and creating data and information quality that integrates a conceptual framework with essential tools, techniques, and instructions, accompanied by helpful templates, real-world examples, and advice, as well as highlighted definitions, key concepts, checkpoints, warnings, communication activities, and best practices. Original. (Intermediate)

The Practitioner s Guide to Data Quality Improvement

The Practitioner s Guide to Data Quality Improvement
Author: David Loshin
Publsiher: Elsevier
Total Pages: 432
Release: 2010-11-22
ISBN 10: 9780080920344
ISBN 13: 0080920349
Language: EN, FR, DE, ES & NL

The Practitioner s Guide to Data Quality Improvement Book Review:

The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.

Data Quality Assessment

Data Quality Assessment
Author: Arkady Maydanchik
Publsiher: Technics Publications
Total Pages: 336
Release: 2007-04-01
ISBN 10: 163462047X
ISBN 13: 9781634620475
Language: EN, FR, DE, ES & NL

Data Quality Assessment Book Review:

Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting strategies. Quality data is the key to any advancement, whether it’s from the Stone Age to the Bronze Age. Or from the Information Age to whatever Age comes next. The success of corporations and government institutions largely depends on the efficiency with which they can collect, organize, and utilize data about products, customers, competitors, and employees. Fortunately, improving your data quality doesn’t have to be such a mammoth task. DATA QUALITY ASSESSMENT is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organization. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analyzing data errors – the first step in any data quality program. Master techniques in: • Data profiling and gathering metadata • Identifying, designing, and implementing data quality rules • Organizing rule and error catalogues • Ensuring accuracy and completeness of the data quality assessment • Constructing the dimensional data quality scorecard • Executing a recurrent data quality assessment This is one of those books that marks a milestone in the evolution of a discipline. Arkady's insights and techniques fuel the transition of data quality management from art to science -- from crafting to engineering. From deep experience, with thoughtful structure, and with engaging style Arkady brings the discipline of data quality to practitioners. David Wells, Director of Education, Data Warehousing Institute

Measuring Data Quality for Ongoing Improvement

Measuring Data Quality for Ongoing Improvement
Author: Laura Sebastian-Coleman
Publsiher: Newnes
Total Pages: 376
Release: 2012-12-31
ISBN 10: 0123977541
ISBN 13: 9780123977540
Language: EN, FR, DE, ES & NL

Measuring Data Quality for Ongoing Improvement Book Review:

The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You’ll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You’ll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies. Demonstrates how to leverage a technology independent data quality measurement framework for your specific business priorities and data quality challenges Enables discussions between business and IT with a non-technical vocabulary for data quality measurement Describes how to measure data quality on an ongoing basis with generic measurement types that can be applied to any situation

MASTER DATA MANAGEMENT AND DATA GOVERNANCE 2 E

MASTER DATA MANAGEMENT AND DATA GOVERNANCE  2 E
Author: Alex Berson,Larry Dubov
Publsiher: McGraw Hill Professional
Total Pages: 512
Release: 2010-12-06
ISBN 10: 0071744592
ISBN 13: 9780071744591
Language: EN, FR, DE, ES & NL

MASTER DATA MANAGEMENT AND DATA GOVERNANCE 2 E Book Review:

The latest techniques for building a customer-focused enterprise environment "The authors have appreciated that MDM is a complex multidimensional area, and have set out to cover each of these dimensions in sufficient detail to provide adequate practical guidance to anyone implementing MDM. While this necessarily makes the book rather long, it means that the authors achieve a comprehensive treatment of MDM that is lacking in previous works." -- Malcolm Chisholm, Ph.D., President, AskGet.com Consulting, Inc. Regain control of your master data and maintain a master-entity-centric enterprise data framework using the detailed information in this authoritative guide. Master Data Management and Data Governance, Second Edition provides up-to-date coverage of the most current architecture and technology views and system development and management methods. Discover how to construct an MDM business case and roadmap, build accurate models, deploy data hubs, and implement layered security policies. Legacy system integration, cross-industry challenges, and regulatory compliance are also covered in this comprehensive volume. Plan and implement enterprise-scale MDM and Data Governance solutions Develop master data model Identify, match, and link master records for various domains through entity resolution Improve efficiency and maximize integration using SOA and Web services Ensure compliance with local, state, federal, and international regulations Handle security using authentication, authorization, roles, entitlements, and encryption Defend against identity theft, data compromise, spyware attack, and worm infection Synchronize components and test data quality and system performance

Data Stewardship

Data Stewardship
Author: David Plotkin
Publsiher: Newnes
Total Pages: 248
Release: 2013-09-16
ISBN 10: 0124104452
ISBN 13: 9780124104457
Language: EN, FR, DE, ES & NL

Data Stewardship Book Review:

Data stewards in business and IT are the backbone of a successful data governance implementation because they do the work to make a company’s data trusted, dependable, and high quality. Data Stewardship explains everything you need to know to successfully implement the stewardship portion of data governance, including how to organize, train, and work with data stewards, get high-quality business definitions and other metadata, and perform the day-to-day tasks using a minimum of the steward’s time and effort. David Plotkin has loaded this book with practical advice on stewardship so you can get right to work, have early successes, and measure and communicate those successes, gaining more support for this critical effort. Provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on company structure, business functions, and data ownership Shows how to gain support for your stewardship effort, maintain that support over the long-term, and measure the success of the data stewardship effort and report back to management 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

The Decision Model

The Decision Model
Author: Barbara von Halle,Larry Goldberg
Publsiher: CRC Press
Total Pages: 553
Release: 2009-10-27
ISBN 10: 9781420082821
ISBN 13: 1420082825
Language: EN, FR, DE, ES & NL

The Decision Model Book Review:

In the current fast-paced and constantly changing business environment, it is more important than ever for organizations to be agile, monitor business performance, and meet with increasingly stringent compliance requirements. Written by pioneering consultants and bestselling authors with track records of international success, The Decision Model: A Business Logic Framework Linking Business and Technology provides a platform for rethinking how to view, design, execute, and govern business logic. The book explains how to implement the Decision Model, a stable, rigorous model of core business logic that informs current and emerging technology. The authors supply a strong theoretical foundation, while succinctly defining the path needed to incorporate agile and iterative techniques for developing a model that will be the cornerstone for continual growth. Because the book introduces a new model with tentacles in many disciplines, it is divided into three sections: Section 1: A Complete overview of the Decision Model and its place in the business and technology world Section 2: A Detailed treatment of the foundation of the Decision Model and a formal definition of the Model Section 3: Specialized topics of interest on the Decision Model, including both business and technical issues The Decision Model provides a framework for organizing business rules into well-formed decision-based structures that are predictable, stable, maintainable, and normalized. More than this, the Decision Model directly correlates business logic to the business drivers behind it, allowing it to be used as a lever for meeting changing business objectives and marketplace demands. This book not only defines the Decision Model and but also demonstrates how it can be used to organize decision structures for maximum stability, agility, and technology independence and provide input into automation design.

Data Governance

Data Governance
Author: John Ladley
Publsiher: Academic Press
Total Pages: 352
Release: 2019-11-08
ISBN 10: 0128158328
ISBN 13: 9780128158326
Language: EN, FR, DE, ES & NL

Data Governance Book Review:

Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program. Incorporates industry changes, lessons learned and new approaches Explores various ways in which data analysts and managers can ensure consistent, accurate and reliable data across their organizations Includes new case studies which detail real-world situations Explores all of the capabilities an organization must adopt to become data driven Provides guidance on various approaches to data governance, to determine whether an organization should be low profile, central controlled, agile, or traditional Provides guidance on using technology and separating vendor hype from sincere delivery of necessary capabilities Offers readers insights into how their organizations can improve the value of their data, through data quality, data strategy and data literacy Provides up to 75% brand-new content compared to the first edition

Quality Management in Construction Projects

Quality Management in Construction Projects
Author: Abdul Razzak Rumane
Publsiher: CRC Press
Total Pages: 538
Release: 2017-10-17
ISBN 10: 1498781683
ISBN 13: 9781498781688
Language: EN, FR, DE, ES & NL

Quality Management in Construction Projects Book Review:

The first edition published in 2010. The response was encouraging and many people appreciated a book that was dedicated to quality management in construction projects. Since it published, ISO 9000: 2008 has been revised and ISO 9000: 2015 has published. The new edition will focus on risk-based thinking which must be considered from the beginning and throughout the project life cycle. There are quality-related topics such as Customer Relationship, Supplier Management, Risk Management, Quality Audits, Tools for Construction Projects, and Quality Management that were not covered in the first edition. Furthermore, some figures and tables needed to be updated to make the book more comprehensive.

A Guide to the Project Management Body of Knowledge PMBOK Guide Seventh Edition and The Standard for Project Management BRAZILIAN PORTUGUESE

A Guide to the Project Management Body of Knowledge  PMBOK   Guide      Seventh Edition and The Standard for Project Management  BRAZILIAN PORTUGUESE
Author: Project Management Institute Project Management Institute
Publsiher: Project Management Institute
Total Pages: 368
Release: 2021-08-01
ISBN 10: 1628256885
ISBN 13: 9781628256888
Language: EN, FR, DE, ES & NL

A Guide to the Project Management Body of Knowledge PMBOK Guide Seventh Edition and The Standard for Project Management BRAZILIAN PORTUGUESE Book Review:

PMBOK&® Guide is the go-to resource for project management practitioners. The project management profession has significantly evolved due to emerging technology, new approaches and rapid market changes. Reflecting this evolution, The Standard for Project Management enumerates 12 principles of project management and the PMBOK&® Guide &– Seventh Edition is structured around eight project performance domains.This edition is designed to address practitioners' current and future needs and to help them be more proactive, innovative and nimble in enabling desired project outcomes.This edition of the PMBOK&® Guide:•Reflects the full range of development approaches (predictive, adaptive, hybrid, etc.);•Provides an entire section devoted to tailoring the development approach and processes;•Includes an expanded list of models, methods, and artifacts;•Focuses on not just delivering project outputs but also enabling outcomes; and• Integrates with PMIstandards+™ for information and standards application content based on project type, development approach, and industry sector.

Meeting the Challenges of Data Quality Management

Meeting the Challenges of Data Quality Management
Author: Laura Sebastian-Coleman
Publsiher: Academic Press
Total Pages: 352
Release: 2022-01-25
ISBN 10: 0128217561
ISBN 13: 9780128217566
Language: EN, FR, DE, ES & NL

Meeting the Challenges of Data Quality Management Book Review:

Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly. The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage. This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses. Describes the importance of high-quality data to organizations wanting to leverage their data and, more generally, to people living in today’s digitally interconnected world Explores the five challenges in relation to organizational data, including "Big Data," and proposes approaches to meeting them Clarifies how to apply the core capabilities required for an effective data quality management program (data standards definition, data quality assessment, monitoring and reporting, issue management, and improvement) as both stand-alone processes and as integral components of projects and operations Provides Data Quality practitioners with ways to communicate consistently with stakeholders

Data Quality

Data Quality
Author: Rupa Mahanti
Publsiher: Quality Press
Total Pages: 526
Release: 2019-03-18
ISBN 10: 0873899776
ISBN 13: 9780873899772
Language: EN, FR, DE, ES & NL

Data Quality Book Review:

“This is not the kind of book that you’ll read one time and be done with. So scan it quickly the first time through to get an idea of its breadth. Then dig in on one topic of special importance to your work. Finally, use it as a reference to guide your next steps, learn details, and broaden your perspective.” from the foreword by Thomas C. Redman, Ph.D., “the Data Doc” Good data is a source of myriad opportunities, while bad data is a tremendous burden. Companies that manage their data effectively are able to achieve a competitive advantage in the marketplace, while bad data, like cancer, can weaken and kill an organization. In this comprehensive book, Rupa Mahanti provides guidance on the different aspects of data quality with the aim to be able to improve data quality. Specifically, the book addresses: -Causes of bad data quality, bad data quality impacts, and importance of data quality to justify the case for data quality-Butterfly effect of data quality-A detailed description of data quality dimensions and their measurement-Data quality strategy approach-Six Sigma - DMAIC approach to data quality-Data quality management techniques-Data quality in relation to data initiatives like data migration, MDM, data governance, etc.-Data quality myths, challenges, and critical success factorsStudents, academicians, professionals, and researchers can all use the content in this book to further their knowledge and get guidance on their own specific projects. It balances technical details (for example, SQL statements, relational database components, data quality dimensions measurements) and higher-level qualitative discussions (cost of data quality, data quality strategy, data quality maturity, the case made for data quality, and so on) with case studies, illustrations, and real-world examples throughout.

Python for Data Analysis

Python for Data Analysis
Author: Wes McKinney
Publsiher: "O'Reilly Media, Inc."
Total Pages: 676
Release: 2017-09-25
ISBN 10: 1491957611
ISBN 13: 9781491957615
Language: EN, FR, DE, ES & NL

Python for Data Analysis Book Review:

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Data Quality

Data Quality
Author: Jack E. Olson
Publsiher: Elsevier
Total Pages: 300
Release: 2003-01-09
ISBN 10: 0080503691
ISBN 13: 9780080503691
Language: EN, FR, DE, ES & NL

Data Quality Book Review:

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.

Competing with High Quality Data

Competing with High Quality Data
Author: Rajesh Jugulum
Publsiher: John Wiley & Sons
Total Pages: 304
Release: 2014-03-10
ISBN 10: 111841649X
ISBN 13: 9781118416495
Language: EN, FR, DE, ES & NL

Competing with High Quality Data Book Review:

Create a competitive advantage with data quality Data is rapidly becoming the powerhouse of industry, butlow-quality data can actually put a company at a disadvantage. Tobe used effectively, data must accurately reflect the real-worldscenario it represents, and it must be in a form that is usable andaccessible. Quality data involves asking the right questions,targeting the correct parameters, and having an effective internalmanagement, organization, and access system. It must be relevant,complete, and correct, while falling in line with pervasiveregulatory oversight programs. Competing with High Quality Data: Concepts, Tools andTechniques for Building a Successful Approach to Data Qualitytakes a holistic approach to improving data quality, fromcollection to usage. Author Rajesh Jugulum is globally-recognizedas a major voice in the data quality arena, with high-levelbackgrounds in international corporate finance. In the book,Jugulum provides a roadmap to data quality innovation,covering topics such as: The four-phase approach to data quality control Methodology that produces data sets for different aspects of abusiness Streamlined data quality assessment and issue resolution A structured, systematic, disciplined approach to effectivedata gathering The book also contains real-world case studies to illustrate howcompanies across a broad range of sectors have employed dataquality systems, whether or not they succeeded, and what lessonswere learned. High-quality data increases value throughout theinformation supply chain, and the benefits extend to the client,employee, and shareholder. Competing with High Quality Data:Concepts, Tools and Techniques for Building a Successful Approachto Data Quality provides the information and guidance necessaryto formulate and activate an effective data quality plan today.

Ten Steps to a Results Based Monitoring and Evaluation System

Ten Steps to a Results Based Monitoring and Evaluation System
Author: Jody Zall Kusek,Ray C. Rist
Publsiher: World Bank Publications
Total Pages: 264
Release: 2004-06-15
ISBN 10: 0821389076
ISBN 13: 9780821389072
Language: EN, FR, DE, ES & NL

Ten Steps to a Results Based Monitoring and Evaluation System Book Review:

This Handbook provides a comprehensive ten-step model that will help guide development practitioners through the process of designing and building a results-based monitoring and evaluation system.

Computational Genomics with R

Computational Genomics with R
Author: Altuna Akalin
Publsiher: CRC Press
Total Pages: 462
Release: 2020-12-16
ISBN 10: 1498781861
ISBN 13: 9781498781862
Language: EN, FR, DE, ES & NL

Computational Genomics with R Book Review:

Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Handbook of Financial Data and Risk Information II

Handbook of Financial Data and Risk Information II
Author: Margarita S. Brose,Mark D. Flood,Dilip Krishna,Bill Nichols
Publsiher: Cambridge University Press
Total Pages: 574
Release: 2014-01-09
ISBN 10: 1107012023
ISBN 13: 9781107012028
Language: EN, FR, DE, ES & NL

Handbook of Financial Data and Risk Information II Book Review:

A comprehensive resource for understanding the issues involved in collecting, measuring and managing data in the financial services industry.