Affordable Access

Publisher Website

The Ten Steps Process-Chapter 3

Authors
Publisher
Elsevier Inc.
Identifiers
DOI: 10.1016/b978-012374369-5.50005-2
Disciplines
  • Communication

Abstract

Publisher Summary This chapter provides detailed instructions for each of the steps of the Ten Steps Process for completing information and data quality improvement projects. Step 1 of the process, Define Business Need and Approach, involves defining goals, strategies, issues, opportunities, requirements, and constraints affecting the business. Step 2, Analyze Information Environment, is used to understand relevant requirements, understand the four key components in the Framework for Information Quality, document the Information Life Cycle, and understand the life cycle through the POSMAD (Plan, Obtain, Store and Share, Maintain, Apply, Dispose) phases, and develop a realistic data capture and assessment plan. Step 3, Assess Data Quality, assesses and evaluates data quality for dimensions applicable to the issues. Step 4, Assess Business Impact, establishes the impact of data quality issues on the business. In Step 5, Identify Root Causes, the causes of data quality problems are identified and recommendations for addressing them are developed. Step 6, Develop Improvement Plans, aims to develop an action plan based on the recommendations from the data quality and/or business impact assessment results and from root cause analysis. In Step 7, Prevent Future Data Errors, appropriate solutions that address root causes of the data quality problems in a business are implemented. The objective of Step 8, Correct Current Data Errors, is to implement solutions that correct the existing data errors that are causing problems for the business. Step 9, Implement Controls, involves planning and implementing controls, obtaining buy-ins for implementations, and evaluating the improvements that have been implemented. Step 10, Communicate Actions and Results, involves communicating about the data quality project with sponsors of the data quality project, stakeholders, process owners, and knowledge workers, to let them know how continuous improvement will affect how they use the data.

There are no comments yet on this publication. Be the first to share your thoughts.