If you are leading an organization or if you need to communicate with leaders about data management, Navigating the Labyrinth is your guide.

Organizations that want to get value from their data need to manage that data well. But to most executives, data management seems obscure, complicated, and highly technical. You don't have time to learn all the detail or cut through the hype. Navigating the Labyrinth helps you get there. Based on best practices from DAMA's Data Management Body of Knowledge (DMBOK2), it explains the fundamentals and says why they are important. It focuses your attention on what you need to know to help your organization build trust in and get value out of its data.

Si vous êtes à la tête d'une organisation ou si vous devez communiquer avec des dirigeants au sujet de la gestion des données, S'orienter dans le labyrinthe est votre guide.

Les organisations qui veulent tirer de la valeur de leurs données doivent bien les gérer. Mais pour la plupart des cadres, la gestion des données semble obscure, compliquée et très technique. Vous n'avez pas le temps d'apprendre tous les détails ou d'aller au-delà du battage médiatique. S'orienter dans le labyrinthe vous aide à y parvenir. Basé sur les meilleures pratiques du Data Management Body of Knowledge (DMBOK2) de DAMA, il explique les principes fondamentaux et explique pourquoi ils sont importants. Il attire votre attention sur ce que vous devez savoir pour aider votre organisation à faire confiance à ses données et à en tirer de la valeur.

À propos de DAMA

DAMA International est une association à but non lucratif, indépendante des fournisseurs, composée de professionnels techniques et métiers qui se consacrent à l'avancement des concepts et pratiques liés à la gestion des données et des informations au soutien de la stratégie commerciale. Avec des chapitres dans le monde entier, DAMA International encourage les meilleures pratiques à travers un réseau d'individus et d'organisations connectés qui partagent des idées, des tendances, des problèmes et des solutions et qui considèrent DAMA comme une ressource centrale fiable et collaborative pour tout ce qui concerne les données. Visitez dama.org pour en savoir plus.

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.

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.