Data Sharing Using a Common Data Architecture Wouldn’t it be a pleasure to know and understand all the data in your organization? Wouldn’t it be great to easily identify and readily share those data to develop information that supports business strategies? Wouldn’t it be wonderful to have a formal data resource that provides just-in-time data for developing just-in-time information to support just-in-time decision making? Data Sharing Using a Common Data Architecture shows you how by: Defining a common data architecture, its contents, and its uses Refining data to a common data architecture Discussing disparate data, its structure, quality, and how to identify it Describing how Data Sharing Reality is achieved Focusing on the importance of people and creating a win-win situation Providing a data lexicon and extensive glossary Data Sharing Using a Common Data Architecture is must reading for data administrators, database administrators, MIS project leaders, application programmers, systems analysts, MIS trainers and instructors, and graduate students.

Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh.

Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment.

Enterprise architecture is leading IT's way to the executive boardroom, as CIOs are now taking their place at the management table. Organizations investing their time, money, and talent in enterprise architecture (EA) have realized significant process improvement and competitive advantage. However, as these organizations discovered, it is one thing

As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata

The huge volume of neuroscience data and the wide variety of data formats generated across different neuroscience communities has posed a challenge to traditional methods of data management, data sharing and data mining. Mandates on data sharing and the demand for using open data has driven the development of advanced methodologies and tools to effectively explore, mine and integrate data. However, the growing number of resources make it harder for researchers to navigate this landscape. Awareness of these tools and resources is vital for effective data mining and unlocking new discoveries. The goal of this research collection is to provide an overview of available resources, centred around making data findable, accessible, interoperable and reusable (FAIR).

Besides the courseware publication (ISBN: 9789401811491), you are advised to obtain the DAMA DMBOK publication (ISBN: 9781634622349). Optionally, you can use the publication Data management: a gentle introduction (ISBN: 9789401805506) as inspiration for examples and quotes about the field of data management. This material is intended to prepare participants for the CDMP exam by DAMA International. The courseware can only be ordered by partners and is based on the current version of the DAMA DMBOK. The material will be updated when new versions of DMBOK are published. DAMA DMBOK is the industry reference for data management. It is published by DAMA International and is currently in its second version. The DMBOK is developed by professionals and can be seen as a collection of best practices. The domain of data management is divided into functional areas which are discussed in terms of definitions (what is it), goals (what are we trying to achieve), steps (what are typical activities), inputs/outputs, and participating roles. Developing and sustaining an effective data management function is far from an easy task. The DMBOK framework is adopted by many organizations as the foundation for their data management function: standardized language and good practices speed up the learning process. After the training, you have an overview of the field of data management, its terminology, and current best practices.