Metadata management is one of the foundational components of an enterprise data management initiative. Many organizations struggle with its incorporation into their data management processes. Introduction Metadata is “data about data.” It is the context for the raw data content. As stated in the DAMA-DMBOK©, metadata can be thought of with the metaphor of a library’s card catalog. The card
Enterprise Data Management is the global function that facilitates the management of data as a valuable asset of an enterprise. Its components align to provide the capabilities to manage data as an organization resource. Introduction Enterprise Data Management (EDM) is the global function that facilitates the management of data as a valuable asset of an enterprise. As defined in the
Logical and physical data modeling are essential components of every organization’s enterprise data architecture, and should form the foundation of every database design. Standard techniques for logical and physical data modeling enable consistent development and usability. Even though the concept of data modeling has been around for a long time, in many organizations it is interpreted differently. According to the
Organizations can find increased value in business intelligence and analytics efforts through the use of various data delivery strategies. Introduction Like many organizations, your company probably has invested significant time, effort, and money into establishing your data warehousing and business intelligence environment. You’ve built the databases and the ETL programs to move, clean and integrate data from multiple sources into
Many project management challenges are centered on the people involved in a project; part of the “people, process, technology” triangle as the basis of all organizations Introduction There are still some common project management challenges, despite the huge efforts from the project management consortiums, project management Subject Matter Experts (SMEs), and many companies that are embracing project management as a
Challenges to successful data governance deployment are numerous, but can be avoided or overcome through attention to best practices and industry standards Introduction Corporate interest in data governance often starts with recognition within data integration projects that there is a high variability in success and that this can be attributed, partially, to a lack of standardized approaches to data management. Historically
Metadata management should start with the identification of business subject areas to enable prioritization of all enterprise data management initiatives and related projects. Introduction Metadata is commonly defined as “data about data.” In fact, a more accurate definition might be “information about information assets.” These information assets may be data, processes, applications, or information technology. Metadata may be “business metadata”
A well-modeled data dictionary can provide lasting value and ensure consistency of use throughout an organization for all data elements. Introduction The primary purpose organizations create and maintain a data dictionary is to standardize data content , context, and definitions to achieve consistency, reusability, and quality of the data that drives or supports the organization’s tactical and strategic business initiatives.
Metadata infrastructure planning should include all the MME layers while maintaining focus on the objective. Introduction It is essential to retain focus on the overall objectives of the organization’s managed metadata environment (MME) to have a successful implementation. Within an MME’s infrastructure, there are six layers, and each layer has specific purposes. A previous article explained overall MME planning, and gave
Do you know the numerous benefits and the effect that having a managed metadata environment will bring to your organization? Introduction Many organizations continue to struggle to understand the meaning and value of one of their most important and sustainable assets, data (and its companion, information). Data are raw facts, presented without context. Metadata is the “data about data,” the
