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| data quality analysis dashboards: Data Analytics and Visualization in Quality Analysis using Tableau Jaejin Hwang, Youngjin Yoon, 2021-07-28 Data Analytics and Visualization in Quality Analysis using Tableau goes beyond the existing quality statistical analysis. It helps quality practitioners perform effective quality control and analysis using Tableau, a user-friendly data analytics and visualization software. It begins with a basic introduction to quality analysis with Tableau including differentiating factors from other platforms. It is followed by a description of features and functions of quality analysis tools followed by step-by-step instructions on how to use Tableau. Further, quality analysis through Tableau based on open source data is explained based on five case studies. Lastly, it systematically describes the implementation of quality analysis through Tableau in an actual workplace via a dashboard example. Features: Describes a step-by-step method of Tableau to effectively apply data visualization techniques in quality analysis Focuses on a visualization approach for practical quality analysis Provides comprehensive coverage of quality analysis topics using state-of-the-art concepts and applications Illustrates pragmatic implementation methodology and instructions applicable to real-world and business cases Include examples of ready-to-use templates of customizable Tableau dashboards This book is aimed at professionals, graduate students and senior undergraduate students in industrial systems and quality engineering, process engineering, systems engineering, quality control, quality assurance and quality analysis. |
| data quality analysis dashboards: District data quality assurance World Health Organization, 2023-01-30 This training package aims to improve the quality of routine health facility data through building capacity in the monthly use of District Health Information Software (DHIS)2-based data quality (DQ) tools at district level. The package includes tutorials with related exercises and uses a website (https://who-demos.dhis2.org/dq) to demonstrate the tools. |
| data quality analysis dashboards: Toolkit for analysis and use of routine health facility data. Integrated health services analysis World Health Organization, 2023-06-16 This document provides guidance on the integrated analysis and use, at national level, of data collected from health facilities though routine health information systems (RHIS). The integrated approach provides general health service planners and managers with an overarching or “cross-cutting” view of health services, based on a limited set of tracer indicators that represent multiple health programmes and service components. Objectives: The guidance will promote an understanding of: the concept of integrated analysis of health services, using RHIS data; the advantages of using a limited set of standardized indicators and visualizations; analysis and presentation of the data in ways that are easily understood and useful to health service planners and managers; the importance of and approaches to assessing data quality; some considerations for interpretation of RHIS data. Target audience: This guidance targets workers in ministries of health as well as other organizations, including: decision-makers using RHIS data for general planning, management and review of health services; programme staff wanting to share key programme findings with a general audience; staff responsible for the analysis and presentation of health data, including analysts and monitoring and evaluation officers; health information systems staff involved in data management and data quality improvement staff of national health observatories; research institutes and academic institutions involved in RHIS data. |
| data quality analysis dashboards: Salesforce CRM Administration Handbook Krzysztof Nowacki, Mateusz Twarożek, 2024-04-30 Master the core concepts of Salesforce administration and gain a holistic understanding of the SF ecosystem that will also help you prepare for the Salesforce certification exam Key Features Understand Salesforce architecture and security model Integrate Flow Builder with other Salesforce components for seamless automation Understand how to create and manage approval workflows in Salesforce Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDo you aspire to be a successful Salesforce administrator or consultant? If yes, then this step-by-step guide is for you. Written by certified Salesforce professionals, the Salesforce CRM Administration Handbook will take you through the intricacies of Salesforce, covering objects, records, automation, and much more while also prepping you for the Salesforce certification exam. Starting with an overview of the capabilities and limitations of Salesforce, you’ll progress toward a holistic understanding of system architecture with real-world examples, including workarounds that enable the successful completion of projects. As you delve deeper, you’ll learn about the fundamental concepts of a CRM system, its purpose, and the pivotal role of an administrator in the Salesforce lifecycle. You’ll also explore the basic structures of the entire Salesforce system and navigate confidently through the intricacies of security, automation, and reporting in the Sales/Service Cloud. By the end of this book, you’ll have the confidence you need to navigate Salesforce’s backend seamlessly, implement configuration changes effortlessly, and communicate fluently in Salesforce terminology.What you will learn Discover strategies to stay up-to-date with Salesforce releases Integrate Salesforce products seamlessly to meet diverse business needs Explore Sales and Service Cloud and its transformative impact on sales and customer support operations Master opportunity management for successful deal closures and pipeline forecasting Understand Salesforce Orgs, managing users, settings, and customization Explore data import and export methods and data mapping Discover best practices, mock tests, and valuable tips and tricks to prepare for Salesforce certification Who this book is for This book is for aspiring Salesforce administrators and consultants looking to build a solid foundation in Salesforce products, system setup, and customization as well as advance their careers within the Salesforce ecosystem. It’s also a great resource for those preparing to take the Salesforce ADM 201 certification exam, as it serves as an excellent supplement to the existing learning materials. |
| data quality analysis dashboards: The Practitioner's Guide to Data Quality Improvement David Loshin, 2010-11-22 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 analysis dashboards: Data Quality Prashanth Southekal, 2023-02-01 Discover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you’ll learn techniques to define and assess data quality, discover how to ensure that your firm’s data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. The author shows you how to: Profile for data quality, including the appropriate techniques, criteria, and KPIs Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization. Formulate the reference architecture for data quality, including practical design patterns for remediating data quality Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the business An essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest. |
| data quality analysis dashboards: Data Quality Fundamentals Barr Moses, Lior Gavish, Molly Vorwerck, 2022-09 Do your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you. Many data engineering teams today face the good pipelines, bad data problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies. Build more trustworthy and reliable data pipelines Write scripts to make data checks and identify broken pipelines with data observability Learn how to set and maintain data SLAs, SLIs, and SLOs Develop and lead data quality initiatives at your company Learn how to treat data services and systems with the diligence of production software Automate data lineage graphs across your data ecosystem Build anomaly detectors for your critical data assets |
| data quality analysis dashboards: Performance Dashboards Wayne W. Eckerson, 2005-10-27 Tips, techniques, and trends on how to use dashboard technology to optimize business performance Business performance management is a hot new management discipline that delivers tremendous value when supported by information technology. Through case studies and industry research, this book shows how leading companies are using performance dashboards to execute strategy, optimize business processes, and improve performance. Wayne W. Eckerson (Hingham, MA) is the Director of Research for The Data Warehousing Institute (TDWI), the leading association of business intelligence and data warehousing professionals worldwide that provide high-quality, in-depth education, training, and research. He is a columnist for SearchCIO.com, DM Review, Application Development Trends, the Business Intelligence Journal, and TDWI Case Studies & Solution. |
| data quality analysis dashboards: Data Quality Rupa Mahanti, 2019-03-18 This is not the kind of book that youll 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. |
| data quality analysis dashboards: Salesforce End-to-End Implementation Handbook Kristian Margaryan Jorgensen, Tameem Bahri, 2023-03-10 Packed with practical examples, case studies, and proven recommendations, this handbook helps you manage enterprise-grade Salesforce programs Key Features Explore the end-to-end Salesforce implementation lifecycle with templates and practical examples Discover common issues and strategies for mitigation and prevention Work with checklists to assess the state of your Salesforce implementation in each phase Purchase of the print or Kindle book includes a free PDF eBook Book Description With ever-growing digital transformation programs involving Salesforce, there is a greater need for a comprehensive overview of the phases and activities specific to Salesforce implementations. This book will act as a detailed guide for your Salesforce implementation journey, including common issues and pitfalls to mitigate and prevent errors. The Salesforce End-to-End Implementation Handbook starts with the pre-development phase. Here you'll understand how to define the vision and nature of your project, determine your change management strategy and delivery methodology, learn to build a business case for your project, get insights on engaging with Salesforce and implementation partners, and learn to establish a governance framework. As you progress, you'll gain insights on the necessary activities, milestones, and common issues faced in salesforce implementation, along with strategies to mitigate them. At the end of each section, you'll find evaluation checklists to assess the state of your Salesforce implementation. By the end of this book, you'll be well-equipped to set up Salesforce projects and programs effectively and deliver maximum ROI. What you will learn Discover the critical activities in Salesforce implementation Address common issues faced in implementing Salesforce Explore appropriate delivery methodology Understand the importance of a change management strategy Govern Salesforce implementation through all its phases Gain insights on key activities in the continuous improvement phase Leverage customer 360 for analytics, AI and automation Who this book is for This book is for salesforce consultants, architects, project/program managers, delivery leaders for salesforce implementation, or product owners- planning to implement Salesforce or already implementing Salesforce for their organizations. If you are switching to a role in salesforce development, this book will act as a guide on what to expect on the job and how to ace it. Familiarity with basic business, CRM, and project management concepts is required. |
| data quality analysis dashboards: Executing Data Quality Projects Danette McGilvray, 2021-05-27 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 quality analysis dashboards: Data Quality for Analytics Using SAS Gerhard Svolba, 2012-04-01 Analytics offers many capabilities and options to measure and improve data quality, and SAS is perfectly suited to these tasks. Gerhard Svolba's Data Quality for Analytics Using SAS focuses on selecting the right data sources and ensuring data quantity, relevancy, and completeness. The book is made up of three parts. The first part, which is conceptual, defines data quality and contains text, definitions, explanations, and examples. The second part shows how the data quality status can be profiled and the ways that data quality can be improved with analytical methods. The final part details the consequences of poor data quality for predictive modeling and time series forecasting. With this book you will learn how you can use SAS to perform advanced profiling of data quality status and how SAS can help improve your data quality. This book is part of the SAS Press program. |
| data quality analysis dashboards: Microsoft Certified Exam guide - Power Platform Fundamentals (PL-900) Cybellium Ltd, Unlock the Power of the Microsoft Power Platform! Are you ready to embark on a journey into the dynamic world of Microsoft's Power Platform? Look no further than the Microsoft Certified Exam Guide - Power Platform Fundamentals (PL-900). This comprehensive book is your essential companion on the path to mastering the fundamentals of the Power Platform and acing the PL-900 exam. In today's rapidly evolving business landscape, organizations seek to empower their employees to create solutions and automate workflows. Microsoft's Power Platform, comprising Power Apps, Power Automate, Power BI, and Power Virtual Agents, is at the forefront of this revolution. Whether you're a business professional, an aspiring developer, or an IT enthusiast, this book equips you with the knowledge and skills needed to leverage the Power Platform's capabilities. Inside this book, you will discover: ✔ Comprehensive Coverage: A detailed exploration of all the key components and capabilities of the Power Platform, ensuring you have a solid understanding of its potential. ✔ Real-World Scenarios: Practical examples and case studies that showcase how the Power Platform is used to solve real business challenges, making learning both engaging and relevant. ✔ Exam-Ready Preparation: Thorough coverage of PL-900 exam objectives, along with practice questions and expert tips to ensure you're fully prepared for the test. ✔ Proven Expertise: Authored by Power Platform experts who hold the certification and have hands-on experience in building solutions, offering you valuable insights and practical wisdom. Whether you aspire to enhance your skills, advance your career, or simply master the Power Platform, Microsoft Certified Exam Guide - Power Platform Fundamentals (PL-900) is your trusted companion on this journey. Don't miss this opportunity to become a proficient Power Platform user in a competitive job market. Prepare, practice, and succeed with the ultimate resource for PL-900 certification. Order your copy today and unlock a world of possibilities with the Microsoft Power Platform! © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| data quality analysis dashboards: Automating Data Quality Monitoring Jeremy Stanley, Paige Schwartz, 2024-01-09 The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records. Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately. This book will help you: Learn why data quality is a business imperative Understand and assess unsupervised learning models for detecting data issues Implement notifications that reduce alert fatigue and let you triage and resolve issues quickly Integrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systems Understand the limits of automated data quality monitoring and how to overcome them Learn how to deploy and manage your monitoring solution at scale Maintain automated data quality monitoring for the long term |
| data quality analysis dashboards: Data Lakes Anne Laurent, Dominique Laurent, Cédrine Madera, 2020-04-09 The concept of a data lake is less than 10 years old, but they are already hugely implemented within large companies. Their goal is to efficiently deal with ever-growing volumes of heterogeneous data, while also facing various sophisticated user needs. However, defining and building a data lake is still a challenge, as no consensus has been reached so far. Data Lakes presents recent outcomes and trends in the field of data repositories. The main topics discussed are the data-driven architecture of a data lake; the management of metadata supplying key information about the stored data, master data and reference data; the roles of linked data and fog computing in a data lake ecosystem; and how gravity principles apply in the context of data lakes. A variety of case studies are also presented, thus providing the reader with practical examples of data lake management. |
| data quality analysis dashboards: Performing Information Governance Anthony David Giordano, 2015 Using case studies and hands-on activities, this book discusses topics in information governance (IG): recognizing hidden development and operational implications of IG--and why it needs to be integrated in the broader organization; integrating IG activities with transactional processing, BI, MDM, and other enterprise information management functions; the information governance organization: defining roles, launching projects, and integrating with ongoing operations; performing IG in transactional projects, including those using agile methods and COTS products; bringing stronger information governance to MDM: strategy, architecture, development, and beyond; governing information throughout the BI or big data project lifecycle; performing ongoing IG and data stewardship operational processes; auditing and enforcing data quality management in the context of enterprise information management; maintaining and evolving metadata management for maximum business value. -- $c Edited summary from book. |
| data quality analysis dashboards: WHO Malaria Policy Advisory Group (MPAG) meeting report, 18–20 April 2023 World Health Organization, 2023-06-01 On 18–20 April 2023, the WHO Malaria Policy Advisory Group (MPAG) convened to review updates and progress, and to provide guidance on thematic areas of work by the Global Malaria Programme. The meeting focused on the following topics: 1) the RBM Partnership to End Malaria evaluation of the “High burden to high impact” (HBHI) approach; 2) updates on the vector control, treatment and diagnostic recommendations in the WHO guidelines for malaria; 3) revisiting comparative effectiveness in the context of the arrival of new vector control products; 4) an update on certification of malaria elimination and the E-2025 Global Forum; 5) an update on the RTS,S/AS01 Malaria Vaccine Implementation Programme and WHO evidence review for the R21/Matrix-M vaccine; 6) an update on the work areas of the Strategic Information for Response Unit; 7) a report from the technical consultation on the effectiveness of rectal artesunate (RAS) and field implementation manual; 8) a report from the technical consultation on community-based delivery of intermittent preventive treatment of malaria in pregnancy (IPTp); 9) an update on the WHO/TDR field implementation manual for seasonal malaria chemoprevention (SMC); 10) an update on the Anopheles stephensi regional strategy; 11) an update on histidine-rich protein 2 (HRP2) gene deletions and global response plan; and 12) an update on antimalarial drug resistance in Africa. |
| data quality analysis dashboards: WHO technical brief for countries preparing malaria funding requests for the Global Fund (2020–2022) , 2020-03-19 |
| data quality analysis dashboards: Consumer-Centered Computer-Supported Care for Healthy People H.-A. Park, P.J. Murray, C. Delaney, 2006-06 This publication, initiated by the Korean Society of Medical Informatics (KOSMI) and its Nursing Informatics Specialist Group, and the Special Interest Group in Nursing Informatics of the International Medical Informatics Association (IMIA-NI), is published for nurses and informatics experts working with informatics applications in nursing care, administration, research and education, bringing together the worlds of nursing informatics community. Korea is well known for having the highest level of Information and Communication Technology (ICT) accessibility in the world. Advances in ICT in Korea have lead Korean health care sectors to fully utilize the benefit of ICT for health care. The theme of the book, ‘Consumer-Centered Computer-Supported Care for Healthy People’, emphasizes the central role of the consumer and the function of information technology in health care. It reflects the major challenge in our time, which is developing and using information technology for the improvement of consumer oriented health care. I would seriously recommend that this book – in text form – should be available in all nursing libraries as a resource for study and reference in the expanding area of nursing and health care.”--Paula M. Procter, Reader in Informatics and Telematics in Nursing, The University of Sheffield, United Kingdom. |
| data quality analysis dashboards: Digital Responses to Covid-19 Christian Hovestadt, Jan Recker, Janek Richter, Karl Werder, 2021-03-10 This book presents ten essays that examine the potential of digital responses to the COVID-19 pandemic. The essays explore new digital concepts for learning and teaching, provide an overview of organizational responses to the crisis through digital technologies, and examine digital solutions developed to manage the crisis. Scientists from many disciplines work together in the fight against the virus and its numerous consequences. This book explores how information systems researchers can contribute to these global efforts. The book will be of interest to researchers and scholars in the field of digital business and education. |
| data quality analysis dashboards: Consolidated HIV strategic information guidelines World Health Organization, 2020-03-30 |
| data quality analysis dashboards: Cognition-Driven Decision Support for Business Intelligence Li Niu, Jie Lu, Guangquan Zhang, 2009-09-14 Cognition-driven decision support system (DSS) has been recognized as a paradigm in the research and development of business intelligence (BI). Cognitive decision support aims to help managers in their decision making from human cognitive aspects, such as thinking, sensing, understanding and predicting, and fully reuse their experience. Among these cognitive aspects, decision makers’ situation awareness (SA) and mental models are considered to be two important prerequisites for decision making, particularly in ill-structured and dynamic decision situations with uncertainties, time pressure and high personal stake. In today’s business domain, decision making is becoming increasingly complex. To make a successful decision, managers’ SA about their business environments becomes a critical factor. This book presents theoretical models as well practical techniques of cognitiondriven DSS. It first introduces some important concepts of cognition orientation in decision making process and some techniques in related research areas including DSS, data warehouse and BI, offering readers a preliminary for moving forward in this book. It then proposes a cognition-driven decision process (CDDP) model which incorporates SA and experience (mental models) as its central components. The goal of the CDDP model is to facilitate cognitive decision support to managers on the basis of BI systems. It also presents relevant techniques developed to support the implementation of the CDDP model in a BI environment. Key issues addressed of a typical business decision cycle in the CDDP model include: natural language interface for a manager’s SA input, extraction of SA semantics, construction of data warehouse queries based on the manger’s SA and experience, situation information retrieval from data warehouse, how the manager perceives situation information and update SA, how the manager’s SA leads to a final decision. Finally, a cognition-driven DSS, FACETS, and two illustrative applications of this system are discussed. |
| data quality analysis dashboards: Data Pipelines Pocket Reference James Densmore, 2021-02-10 Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting |
| data quality analysis dashboards: Using Digital Analytics for Smart Assessment Tabatha Farney, 2018-12-13 Tracking the library user's journey is no simple task in the digital world; users can often navigate through a series of different websites, including library websites, discovery tools, link resolvers, and more just to view a single journal article. Your library collects massive amounts of data related to this journey—probably more than you realize, and almost certainly more than you analyze. Too often library analytic programs simplify data into basic units of measurements that miss useful insights. Here, data expert Farney shows you how to maximize your efforts: you’ll learn how to improve your data collection, clean your data, and combine different data sources. Teaching you how to identify and analyze areas that fit your library’s priorities, this book covers case studies of library projects with digital analytics;ways to use email campaign data from MailChimp or ConstantContact;how to measure click-through rates from unavailable items in the catalog to the ILL module;getting data from search tools such as library catalogs, journal search portals, link resolvers, and digital repositories;using COUNTER compliant data from your electronic resources;techniques for using Google Tag Manager for custom metrics and dimensions;descriptions of analytics tools ranging from library analytics tools like Springshare’s LibInsights and Orangeboy’s Savannah to more focused web analytics tools like Google Analytics, Piwik, and Woopra; anddata visualization tools like Tableau or Google Data Studio. Focusing on digital analytics principles and concepts, this book walks you through the many tools available, including step-by-step examples for typical library needs. |
| data quality analysis dashboards: Modern Data Strategy Mike Fleckenstein, Lorraine Fellows, 2018-02-12 This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits. Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues. This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management. |
| data quality analysis dashboards: Big Data Imperatives Soumendra Mohanty, Madhu Jagadeesh, Harsha Srivatsa, 2013-06-24 Big Data Imperatives, focuses on resolving the key questions on everyone's mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this book intends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data. |
| data quality analysis dashboards: Enterprise Dashboards Shadan Malik, 2005-09-01 Enterprise Dashboards: Design and Best Practices for IT is a one-stop resource of methodology and best practices for this dynamic and relevant information platform, packed with charts, reports, visual indicators, and alert mechanisms, all consolidated in a rich computer interface. This powerful book is both a guide and a handbook. It helps business leaders understand dashboarding while considering dashboards for their information needs and outlines a tested and proven, step-by-step implementation framework. Enterprise Dashboards covers strategies, vendor selections, execution steps, project milestones, dashboard types, case studies contributed by DaimlerChrysler, Emergency Medical Associates, and ING Direct, and more. Enterprise Dashboards: Design and Best Practices for IT is the perfect tool to help COOs, CIOs, CFOs, CTOs, IT managers, business intelligence managers, information analysts, and software consultants be successful at dashboard implementation. |
| data quality analysis dashboards: Metadata Management with IBM InfoSphere Information Server Wei-Dong Zhu, Tuvia Alon, Gregory Arkus, Randy Duran, Marc Haber, Robert Liebke, Frank Morreale Jr., Itzhak Roth, Alan Sumano, IBM Redbooks, 2011-10-18 What do you know about your data? And how do you know what you know about your data? Information governance initiatives address corporate concerns about the quality and reliability of information in planning and decision-making processes. Metadata management refers to the tools, processes, and environment that are provided so that organizations can reliably and easily share, locate, and retrieve information from these systems. Enterprise-wide information integration projects integrate data from these systems to one location to generate required reports and analysis. During this type of implementation process, metadata management must be provided along each step to ensure that the final reports and analysis are from the right data sources, are complete, and have quality. This IBM® Redbooks® publication introduces the information governance initiative and highlights the immediate needs for metadata management. It explains how IBM InfoSphereTM Information Server provides a single unified platform and a collection of product modules and components so that organizations can understand, cleanse, transform, and deliver trustworthy and context-rich information. It describes a typical implementation process. It explains how InfoSphere Information Server provides the functions that are required to implement such a solution and, more importantly, to achieve metadata management. This book is for business leaders and IT architects with an overview of metadata management in information integration solution space. It also provides key technical details that IT professionals can use in a solution planning, design, and implementation process. |
| data quality analysis dashboards: Perspectives in digital health and big data in medicine: Current trends, professional challenges, and ethical, legal, and social implications João Valente Cordeiro, Liliana Laranjo, 2023-10-25 |
| data quality analysis dashboards: The Data Warehouse ETL Toolkit Ralph Kimball, Joe Caserta, 2011-04-27 Cowritten by Ralph Kimball, the world's leading data warehousing authority, whose previous books have sold more than 150,000 copies Delivers real-world solutions for the most time- and labor-intensive portion of data warehousing-data staging, or the extract, transform, load (ETL) process Delineates best practices for extracting data from scattered sources, removing redundant and inaccurate data, transforming the remaining data into correctly formatted data structures, and then loading the end product into the data warehouse Offers proven time-saving ETL techniques, comprehensive guidance on building dimensional structures, and crucial advice on ensuring data quality |
| data quality analysis dashboards: Business Intelligence Career Master Plan Eduardo Chavez, Danny Moncada, 2023-08-31 Learn the foundations of business intelligence, sector trade-offs, organizational structures, and technology stacks while mastering coursework, certifications, and interview success strategies Purchase of the print or Kindle book includes a free PDF eBook Key Features Identify promising job opportunities and ideal entry point into BI Build, design, implement, and maintain BI systems successfully Ace your BI interview with author's expert guidance on certifications, trainings, and courses Book DescriptionNavigating the challenging path of a business intelligence career requires you to consider your expertise, interests, and skills. Business Intelligence Career Master Plan explores key skills like stacks, coursework, certifications, and interview advice, enabling you to make informed decisions about your BI journey. You’ll start by assessing the different roles in BI and matching your skills and career with the tech stack. You’ll then learn to build taxonomy and a data story using visualization types. Additionally, you’ll explore the fundamentals of programming, frontend development, backend development, software development lifecycle, and project management, giving you a broad view of the end-to-end BI process. With the help of the author’s expert advice, you’ll be able to identify what subjects and areas of study are crucial and would add significant value to your skill set. By the end of this book, you’ll be well-equipped to make an informed decision on which of the myriad paths to choose in your business intelligence journey based on your skill set and interests.What you will learn Understand BI roles, roadmap, and technology stack Accelerate your career and land your first job in the BI industry Build the taxonomy of various data sources for your organization Use the AdventureWorks database and PowerBI to build a robust data model Create compelling data stories using data visualization Automate, templatize, standardize, and monitor systems for productivity Who this book is for This book is for BI developers and business analysts who are passionate about data and are looking to advance their proficiency and career in business intelligence. While foundational knowledge of tools like Microsoft Excel is required, having a working knowledge of SQL, Python, Tableau, and major cloud providers such as AWS or GCP will be beneficial. |
| data quality analysis dashboards: Analysis of Large and Complex Data Adalbert F.X. Wilhelm, Hans A. Kestler, 2016-08-03 This book offers a snapshot of the state-of-the-art in classification at the interface between statistics, computer science and application fields. The contributions span a broad spectrum, from theoretical developments to practical applications; they all share a strong computational component. The topics addressed are from the following fields: Statistics and Data Analysis; Machine Learning and Knowledge Discovery; Data Analysis in Marketing; Data Analysis in Finance and Economics; Data Analysis in Medicine and the Life Sciences; Data Analysis in the Social, Behavioural, and Health Care Sciences; Data Analysis in Interdisciplinary Domains; Classification and Subject Indexing in Library and Information Science. The book presents selected papers from the Second European Conference on Data Analysis, held at Jacobs University Bremen in July 2014. This conference unites diverse researchers in the pursuit of a common topic, creating truly unique synergies in the process. |
| data quality analysis dashboards: Fundamentals of Analytics Engineering Dumky De Wilde, Fanny Kassapian, Jovan Gligorevic, Juan Manuel Perafan, Lasse Benninga, Ricardo Angel Granados Lopez, Taís Laurindo Pereira, 2024-03-29 Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineering Key Features Discover how analytics engineering aligns with your organization's data strategy Access insights shared by a team of seven industry experts Tackle common analytics engineering problems faced by modern businesses Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer. After conquering data ingestion and techniques for data quality and scalability, you’ll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You’ll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You’ll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance. By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.What you will learn Design and implement data pipelines from ingestion to serving data Explore best practices for data modeling and schema design Scale data processing with cloud based analytics platforms and tools Understand the principles of data quality management and data governance Streamline code base with best practices like collaborative coding, version control, reviews and standards Automate and orchestrate data pipelines Drive business adoption with effective scoping and prioritization of analytics use cases Who this book is for This book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing. |
| data quality analysis dashboards: The Tyranny of Metrics Jerry Z. Muller, 2019-04-30 How the obsession with quantifying human performance threatens business, medicine, education, government—and the quality of our lives Today, organizations of all kinds are ruled by the belief that the path to success is quantifying human performance, publicizing the results, and dividing up the rewards based on the numbers. But in our zeal to instill the evaluation process with scientific rigor, we've gone from measuring performance to fixating on measuring itself—and this tyranny of metrics now threatens the quality of our organizations and lives. In this brief, accessible, and powerful book, Jerry Muller uncovers the damage metrics are causing and shows how we can begin to fix the problem. Filled with examples from business, medicine, education, government, and other fields, the book explains why paying for measured performance doesn't work, why surgical scorecards may increase deaths, and much more. But Muller also shows that, when used as a complement to judgment based on personal experience, metrics can be beneficial, and he includes an invaluable checklist of when and how to use them. The result is an essential corrective to a harmful trend that increasingly affects us all. |
| data quality analysis dashboards: Tableau For Dummies Jack A. Hyman, 2023-06-20 Discover how visualization turns data into action Tableau gives you the power to understand your data and put it in a format that is appealing and meaningful for everyone who needs to see it. Tableau For Dummies walks you through the steps to turn your data into a story that inspires action. This easy-to-understand guide offers insights from an enterprise data pro on how to transform data into a clear and memorable visual presentation. Navigate the Tableau user interface and connect to data sources Use drag-and-drop features to create stunning visualizations Work with templates, add graphs, and create clear charts Export your visualizations to multiple formats for easy sharing This is the perfect Dummies software guide for business professionals who need to better derive value from that all-important data. |
| data quality analysis dashboards: Eddy Covariance Method For Scientific, Regulatory, and Commercial Applications George Burba, 2022-09-06 Eddy covariance method is a modern high-precision technique for direct measurements of the movement of gases, water vapor, heat, and momentum between the surface and the atmosphere. It is used for measurements of carbon sequestration and emission rates in natural, agricultural, industrial, and urban environments, atmospheric exchange rates of greenhouse gases, direct measurements of evapotranspirative water loss, heat exchange, turbulence rates, and momentum fluxes. The wide range of applications includes numerous fundamental and applied sciences, regulatory and industrial monitoring, multiple aspects of agricultural management, as well as carbon trading and offsets, corporate sustainability and neutrality, among many others. The book “Eddy Covariance Method for Scientific, Regulatory, and Commercial Applications” has been created to familiarize the reader with the general theoretical principles, requirements, applications, planning, processing, and analysis steps of the eddy covariance method. It is intended to assist readers in furthering their understanding of the method and provide references such as academic textbooks, flux network guidelines, and journal papers. In particular, it is designed to help scientific, industrial, agricultural, and regulatory projects and monitoring programs with experiment design and field deployment of the eddy covariance method. Some of the topics covered in “Eddy Covariance Method for Scientific, Regulatory, and Commercial Applications” include: · Overview of eddy covariance principles · Planning and design of an eddy covariance experiment · Implementation of an eddy covariance experiment · Processing and analysis of eddy covariance data · Networking multiple flux stations · Alternative flux methods · Useful resources, training and knowledge base · Example of planning, design and implementation of a comprehensive automated flux station |
| data quality analysis dashboards: The Big Book of Dashboards Steve Wexler, Jeffrey Shaffer, Andy Cotgreave, 2017-04-24 The definitive reference book with real-world solutions you won't find anywhere else The Big Book of Dashboards presents a comprehensive reference for those tasked with building or overseeing the development of business dashboards. Comprising dozens of examples that address different industries and departments (healthcare, transportation, finance, human resources, marketing, customer service, sports, etc.) and different platforms (print, desktop, tablet, smartphone, and conference room display) The Big Book of Dashboards is the only book that matches great dashboards with real-world business scenarios. By organizing the book based on these scenarios and offering practical and effective visualization examples, The Big Book of Dashboards will be the trusted resource that you open when you need to build an effective business dashboard. In addition to the scenarios there's an entire section of the book that is devoted to addressing many practical and psychological factors you will encounter in your work. It's great to have theory and evidenced-based research at your disposal, but what will you do when somebody asks you to make your dashboard 'cooler' by adding packed bubbles and donut charts? The expert authors have a combined 30-plus years of hands-on experience helping people in hundreds of organizations build effective visualizations. They have fought many 'best practices' battles and having endured bring an uncommon empathy to help you, the reader of this book, survive and thrive in the data visualization world. A well-designed dashboard can point out risks, opportunities, and more; but common challenges and misconceptions can make your dashboard useless at best, and misleading at worst. The Big Book of Dashboards gives you the tools, guidance, and models you need to produce great dashboards that inform, enlighten, and engage. |
| data quality analysis dashboards: Internet of Things and Big Data Analytics Toward Next-Generation Intelligence Nilanjan Dey, Aboul Ella Hassanien, Chintan Bhatt, Amira S. Ashour, Suresh Chandra Satapathy, 2017-08-14 This book highlights state-of-the-art research on big data and the Internet of Things (IoT), along with related areas to ensure efficient and Internet-compatible IoT systems. It not only discusses big data security and privacy challenges, but also energy-efficient approaches to improving virtual machine placement in cloud computing environments. Big data and the Internet of Things (IoT) are ultimately two sides of the same coin, yet extracting, analyzing and managing IoT data poses a serious challenge. Accordingly, proper analytics infrastructures/platforms should be used to analyze IoT data. Information technology (IT) allows people to upload, retrieve, store and collect information, which ultimately forms big data. The use of big data analytics has grown tremendously in just the past few years. At the same time, the IoT has entered the public consciousness, sparking people’s imaginations as to what a fully connected world can offer. Further, the book discusses the analysis of real-time big data to derive actionable intelligence in enterprise applications in several domains, such as in industry and agriculture. It explores possible automated solutions in daily life, including structures for smart cities and automated home systems based on IoT technology, as well as health care systems that manage large amounts of data (big data) to improve clinical decisions. The book addresses the security and privacy of the IoT and big data technologies, while also revealing the impact of IoT technologies on several scenarios in smart cities design. Intended as a comprehensive introduction, it offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in IoT and big data technologies. |
| data quality analysis dashboards: Practical Data Quality Robert Hawker, 2023-09-29 Identify data quality issues, leverage real-world examples and templates to drive change, and unlock the benefits of improved data in processes and decision-making Key Features Get a practical explanation of data quality concepts and the imperative for change when data is poor Gain insights into linking business objectives and data to drive the right data quality priorities Explore the data quality lifecycle and accelerate improvement with the help of real-world examples Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionPoor data quality can lead to increased costs, hinder revenue growth, compromise decision-making, and introduce risk into organizations. This leads to employees, customers, and suppliers finding every interaction with the organization frustrating. Practical Data Quality provides a comprehensive view of managing data quality within your organization, covering everything from business cases through to embedding improvements that you make to the organization permanently. Each chapter explains a key element of data quality management, from linking strategy and data together to profiling and designing business rules which reveal bad data. The book outlines a suite of tried-and-tested reports that highlight bad data and allow you to develop a plan to make corrections. Throughout the book, you’ll work with real-world examples and utilize re-usable templates to accelerate your initiatives. By the end of this book, you’ll have gained a clear understanding of every stage of a data quality initiative and be able to drive tangible results for your organization at pace.What you will learn Explore data quality and see how it fits within a data management programme Differentiate your organization from its peers through data quality improvement Create a business case and get support for your data quality initiative Find out how business strategy can be linked to processes, analytics, and data to derive only the most important data quality rules Monitor data through engaging, business-friendly data quality dashboards Integrate data quality into everyday business activities to help achieve goals Avoid common mistakes when implementing data quality practices Who this book is for This book is for data analysts, data engineers, and chief data officers looking to understand data quality practices and their implementation in their organization. This book will also be helpful for business leaders who see data adversely affecting their success and data teams that want to optimize their data quality approach. No prior knowledge of data quality basics is required. |
| data quality analysis dashboards: Toolkit for analysis and use of routine health facility data World Health Organization, 2023-04-21 |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnationa…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes …
