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| Knowledge Integrity trainers conduct one-day seminars on topics relevant to information quality management. To inquire about rates, and for scheduling these seminars at your site, please contact David Loshin at 1-866-249-7853. |
| Building the Information Quality Management Program |
| Many
organizations have recognized the value of data quality improvement,
and are instituting a data quality management program, either as a
function within a line of business, or even at the enterprise level.
However, there are issues that impede the integration of information
quality into the managerial, operational, and technical aspects of
the enterprise, including data ownership issues, vertical system hierarchies,
questionable administrative authority, and limited business case analysis
for data quality improvement. Often data quality personnel are engaged
in an advisory role, making it difficult to properly build the IQ
program.
This seminar presents an Information Quality Blueprint with a limited selection of best practices guidelines that can be effectively communicated to (potentially non-cooperative) data/system managers. The blueprint concept evolves a strategic plan for integrating information quality into the system, as well as describing tactical approaches necessary for advising/influencing application program managers to adopting information quality methods and techniques. Attendees will learn about
A seminar "take-away" is a template for translating best practices into manageable "data quality guidelines" that can be managed as corporate knowledge, as well as an approval process that can be used to integrate these best practices as organizational policy. |
| Introduction to Data Quality Assessment and Data Profiling |
| Because
data quality issues are relevant only within the business context
in which inspected data is used, data quality levels can only be measured
with respect to business data consumer expectations. Relying on subjective
measurements determined by software vendors only provides a subjective assessment from the point of view of an external party with little stake in the ultimate project success. Objective data quality measurement relies on metrics relating directly to how information is being used and how missed expectations impact the business. Once expectations are isolated and understood, we can define assertions that capture those expectations that are used for measuring how information complies with those data quality rules. These rules, which seed our objective data quality metrics, are knowledge-based metadata related to the data sets, suitable for incorporation into the metadata repository. This seminar discusses the process of exploring information using data profiling tools, identifying data quality rules, and isolating noncompliance as a sequence of stages. Attendees will learn about:
|
| Business Rule Based Information Validation |
|
Information
quality revolves around "fitness for use," and as more
information is used for multiple purposes, the perception of fitness
depends on the information consumer and the corresponding application
context. In essence, fitness for use depends on compliance with
the expectations of the knowledge worker, and being able to measure
compliance with those expectations can provide an objective assessment
of the quality of the data. In this seminar we present a framework for defining business rules for information compliance, as well as techniques for using these rules as a component of an information quality and knowledge management program. Attendees will learn:
|
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