Enterprise Data Strategy

20 October, 2012

Anne Marie Smith, Ph.D.

Data Management

An enterprise data strategy should be part of every organization's strategic development.  A data strategy ensures the organization uses its data and information to support business goals. Introduction Most organizations have a business strategy: a plan of how the company will achieve its goals, meet the expectations of its customers, and sustain a competitive advantage in the marketplace.  Once

DAMA-DMBOK© Overview

20 April, 2012

Anne Marie Smith, PhD.

Data Management

The DAMA-DMBOK© is the premier reference guide for all aspects of the data management profession, covering all components of data management with best practices and expert advice. Introduction Every profession has its "body of knowledge," a collection of best practices and proven techniques for performing the functions of the discipline.  Project Management has the PMBOK (Project Management Body

Data Management Maturity Overview

20 October, 2011

Anne Marie Smith, Ph.D.

Data Management

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, many organizations need to

Foundations of Data Stewardship

20 August, 2011

Anne Marie Smith, Ph.D.

Data Governance

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

DATA MANAGEMENT MOMENT VIDEOS

07 July, 2011

Data Management Moment Videos

Enterprise Data Modeling

20 March, 2011

Anne Marie Smith, Ph.D.

Data Architecture

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 ent

Getting Started On a Semantic Data Web Program

01 January, 2010

Pete Stiglich

More Data Geekery

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 t

ISO 8000 Data Quality

01 October, 2009

Peter R. Benson

Data Quality

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 hierarchy, one discovers that the original refere

Metadata Infrastructure Planning Part 1

01 April, 2009

Douglas Viverito

MME

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 will

Data Rationalization and Semantic Resolution

01 April, 2009

Pete Stiglich

More Data Geekery

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 te