Buch, Englisch, 376 Seiten, Format (B × H): 216 mm x 270 mm, Gewicht: 1014 g
Ten Steps to Quality Data and Trusted Information (TM)
Buch, Englisch, 376 Seiten, Format (B × H): 216 mm x 270 mm, Gewicht: 1014 g
ISBN: 978-0-12-818015-0
Verlag: Elsevier Science Publishing Co Inc
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.
Zielgruppe
<p>Anyone responsible for or who cares about the quality of data and information in their organization. This includes individual contributors and practitioners, along with project or program managers, and managers of those doing the data quality work. Job titles of practitioners who can benefit from this book include data analysts, data quality analysts, data stewards, business analysts, subject-matter experts, developers, programmers, business process and data modelers/designers, database administrators, users of data (a.k.a. information consumers or knowledge workers) who have felt the pain of poor data quality in their responsibilities, and data scientists who have found themselves in a position of dealing with poor quality data before they can start the "real" job for which they were hired. In addition, those who are accountable for business processes such as supply chain management or who lead data-driven analytics initiatives will benefit from knowing that such a resource exists to help their teams. Using a proven approach gives everyone a jumpstart so the bulk of their efforts are spent applying the methodology to develop solutions which fit their particular needs, not reinventing the basics. </p>
Autoren/Hrsg.
Weitere Infos & Material
1. Data Quality and the Data-Dependent World2. Data Quality in Action3. Key Concepts4. The Ten Steps Process5. Structuring Your Project6. Other Techniques and Tools7. A Few Final WordsAppendix: Quick References
