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Planning for effective data management includes recognizing the data's worth, potential sources, and potential uses, as well as identifying an overlying program that efficiently uses and maintains the data. The ultimate sewer system data management program must account for inconsistency in data collection and storage while building capabilities for data analysis and decision-making. Creating a foundation with accurate data, identifying data gaps, and creating a sound information management structure will provide a solid framework to encompass capacity assurance, management, operations and maintenance (CMOM).

Communities have two challenges, effectively compiling existing data and creating a framework, or management system, for the existing data that is applicable to future data gathering efforts. Database technology provides the opportunity to gather and manage large volumes of data for other uses while undertaking “normal” inspection and O&M functions. The long-range result is reduced costs and a more effective system.

The models presented in this paper, supported by “real world” examples, provide insight into the challenges and promise associated with effective data management strategies. Through the real world examples we can reap the benefit of learning from the potholes hit by other programs and successes of other communities.

The opportunities provided by powerful database and GIS tools have resulted in a shift in roles from service providers, where traditional contractors can provide data and assessment in readily usable formats, consultants and analysts can streamline evaluation and design, and clients can quickly and efficiently address management issues. Although cold technology makes this possible, success is only possible through reliable and strong business relationships. This paper provides a description and examples of specific data, data management tools, and projects. We will discuss strategies for reconciling conflicting data, managing huge and sometimes overwhelming volumes of data, and moving from data collection to interactive data analysis. There will be a discussion of projects and project goals that were modified to make use of this strategy.
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Document Type: Research Article

Publication date: 2005-01-01

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