Measuring the maturity of an organization’s data management capabilities against a standard model provides structure and the ability to gauge progress over time Introduction Organizations do not know how to treat data as an asset. As evidenced by the fact that many organizations do not or cannot control the data they capture and store for it to produce business value,
Data stewardship is the role practiced by business staff that implements the policies and standards developed by data governance professionals. Data stewards ensure the quailty of data for use. Introduction Data stewardship is the management and oversight of an organization’s data assets according to established data governance practices. Selected business users ensure that the organization has high-quality data that is
Data Management Moment videos are short (2-3 minutes) presentations about one topic in enterprise data management. Delivered by David Marco, president of EWSolutions, each video offers an information and entertaining view of a point in data management.
Enteprise data modeling is an essential component of strong enterprise data architecture, with subject, conceptual and enterprise logical models based on business concepts and requirements Introduction In many organizations, there is no way to understand the scope and variety of the data captured, stored and used by the applications and business activities that need information. An enterprise data model
The Semantic data web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries Introduction Interest in the Semantic Web has skyrocketed, along with interest in the implications of semantic technologies on traditional data management. The promises of the Semantic Web – for example, being able to find information regardless of the
ISO 8000 defines the parameters of data quality, so one can ask for quality data and verify that one has received quality data Introduction It is common to see the relationship between data and information represented as a pyramid with data as the base rising through information and knowledge to wisdom as the apex. In researching the origin of this
Metadata infrastructure planning should start by focusing on the “big picture”, which is the objective of the metadata program, and plan all the MME layers to maintain focus on the objective. Introduction When planning to implement a Managed Metadata Environment (MME) infrastructure, it is important to maintain perspective overall objective of the MME program and the fact that all the users
Data rationalization through managed metadata and data modeling, can support semantic resolution, enabling improved analysis and knowledge transfer. Introduction Across the Internet and within internal systems, ontologies are used to improve search capabilities and make inferences for improved human or computer reasoning. By relating terms in ontology, the user doesn’t need to know the exact term actually stored in the
Project Management Office (PMO) implementations need project portfolio management and project management tools to sustain the PMO function Introduction Part 1 covered requirements of creating a PMO and the steps needed for a successful PMO. This part also addressed a breakdown of the elements and processes which constitute the many components of a PMO. Part 2 addressed the challenges that
To be agile, business intelligence and analytics systems need frameworks and metrics to enable stable evolution. Introduction Enterprise Business Intelligence and analytics solutions are complex implementation efforts because of the Develop – Support (Growth-Sustain) cycles followed concurrently. Every enterprise wide BI system continuously evolves over a period with new business functionality added at regular intervals, and they need to be in
