A New Way of Thinking – October 2005

Published in TDAN.com October 2005

What usually happens when senior management at a company begins to understand the impacts cause by poor data quality? A predictable reaction is to immediately attempt to “solve the
problem” through technology acquisition. A typical approach is to form a “data quality tools evaluation project,” which is followed by 6 months developing a Request for Proposals
(RFP) based on a quick and dirty assessment of needs and environmental technology constraints (e.g., “it has to integrate with our query and reporting tools”). Once the RFP is out, and
responses have been collected, the team will spend 3 months evaluating responses and sitting through demos and proofs-of-concept, followed by a decision to spend the $100-250K its costs for a data
quality tool. After an additional 3 months of integration and training, what is the result? Basically this:

One year later your company is $300+K poorer and the quality of data is not significantly improved.

Clearly, data quality is rapidly being recognized as a fundamental competency upon which most business-relevant applications rely, and savvy senior managers are interested in instituting
information quality management programs. However, purchasing a tool as the first activity of the group may not be the best first step in the process. As I started to explore in my January, 2004
column (“Challenges in Coordinating Data Quality Management”), issues such as questionable data ownership, vertical system hierarchies, questionable administrative authority, and
limited business case analysis for data quality improvement impede the integration of information quality. And often, data quality practitioners are engaged as mere advisors, compounding their
ability to effect change.

In our client engagements, we have devised an approach to evaluating these technical and organizational impediments, to explore the critical technical and management techniques to improve the
chances of success. By collecting these techniques into an “Information Quality Blueprint,” we are able to incorporate a selection of best practices and guidelines that can be
effectively communicated to potentially non-cooperative data/system managers and gain not only their acceptance and approval, but also their active participation. The blueprint lays out a strategy
for integrating information quality into the system, coupled with the tactics for advising/influencing application program managers to adopt information quality methods and techniques that achieve
short-term benefits along the way.

The goal of building an information quality management program is not to correct bad data; instead, it is to effectively incorporate information quality as part of the enterprise system development
lifecycle. The intention of planning a “blueprint” is to lay out those fundamental activities that compose and information quality management program:

  • Assessing the business impact of poor data quality. Often, data flaws are, by default, assumed to be “bad,” yet not all flaws have business impacts, nor are all
    impacts specifically related to a single flaw. As a prelude to being able to effectively communicate the value of improved information quality, it is necessary to have a process sin place for
    assessing impact within your organization’s business context.
  • Developing ROI models for Information Quality improvement. Once you understand the business impacts, the next task is to prioritize them with respect to the potential costs to
    remediate each issue. Occasionally, the cost to fix a problem is greater than the cost to ignore it – your management would certainly want to know that ahead of time!
  • Documenting the information architecture showing data models, metadata, information usage, and information flow throughout enterprise. Once we begin to repurpose data created
    in operational applications for other purposes, such as analytical systems and BI applications, it is important to document how information flows across the enterprise, where data accountability
    lies, and how modifications to data elements and models impact the rest of the enterprise.
  • Identifying, documenting, and validating Data Quality expectations. Ultimately, the business clients’ dissatisfaction relates to information flaws, and their input should
    be encouraged in formally stating the rules that specify their data quality expectations, as well as how each rule contributes to recognized business impacts. A formal specification of business
    rules can then be used to measure data quality and its impact aggregated into a defined data quality performance metric.
  • Educating your staff in ways to integrate Information Quality as an integral component of system development lifecycle. Evolving into a proactive organization with respect to
    high quality information is a significant change, and ongoing education of data quality concepts (e.g., information value, data cleansing, objective data quality assessment, performance metrics)
    will contribute to effecting that change.
  • Develop a Management Framework for Information Quality event tracking and ongoing Information Quality measurement, monitoring, and reporting of compliance with customer
    expectations. These defined performance metrics and reports help in communicating both where the critical points of pain are, as well as where the most significant improvements are being achieved.
    The management framework is the set of tools used to demonstrate that the projected returns on investment are actually being achieved.
  • Consolidate current and planned Information Quality management guidelines, policies, and activities. An information quality management guideline is intended to influence
    management, processes, activities, or system implementations so as to integrate information quality into the system development lifecycle, and describes:

    • An information quality management directive or activity,
    • The concepts that support it,
    • Any associated roles and responsibilities,
    • Any technical, operational, or administrative implementation details

In implementing an information quality program, try to use these ideas as guiding principles for deployment, but it is always to keep this one concept in mind: strategies are all well and good, but
individual managers still expect that the acute needs are addressed. Therefore, as you develop your information quality strategy, make sure that your implementation roadmap that details incremental
value and tactical approaches that will continue to satisfy your business partners’ needs.

Copyright © 2005 Knowledge Integrity, Inc.

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About David Loshin

David is the President of Knowledge Integrity, Inc., a consulting and development company focusing on customized information management solutions including information quality solutions consulting, information quality training and business rules solutions. Loshin is the author of The Practitioner's Guide to Data Quality Improvement, Master Data Management, Enterprise Knowledge Management: The Data Quality ApproachÊand Business Intelligence: The Savvy Manager's Guide. He is a frequent speaker on maximizing the value of information. David can be reached at loshin@knowledge-integrity.com or at (301) 754-6350.

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