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Information Management

Real-time measurements systems require real-time methods for managing information.

Triad-based work activities impose greater demands on information management than traditional activities. There are two reasons for this. The first is that real-time decision-making requires the timely delivery of data, which in turn assumes that the information management infrastructure is in place to guarantee delivery of that data. Depending on the types of real-time data collection technologies employed, Triad data collection efforts can generate thousands of individual data points every working day. The second is that the Triad makes much heavier use of field deployable data collection technologies than traditional approaches, and in many cases the accompanying information management protocols are not as developed as one would expect when working with a commercial laboratory facility. This, in turn, will likely require developing project-specific information management protocols for field-deployable technologies.

The concept of an electronic data deliverable (EDD) is now commonplace among commercial laboratory service providers. Unfortunately, for most field deployable systems the definition and use of appropriate EDDs is not as common. For most historical applications of field deployable systems, results were transcribed to field notebooks for later reference. For techniques that produced large volumes of raw data (e.g., non-intrusive geophysics or gamma walkover surveys), data reduction took place after data collection was complete, with electronic data deliverables available weeks or months after field work was finished. The definition of EDDs and their implementation for every real-time measurement system that will feed decision-making will greatly increase the likelihood of a successful Triad implementation.

Common weak points in information management for Triad-based activities include when data from different sources need to be integrated to support decisions. An example is matching a result with a sample identifier, and then matching a sample identifier with a sample station or location. A second example is when multiple data sets with spatial information need to be merged to form maps or other visual displays. A third is associating QA/QC results with the data sets to which they apply. Small details such as a standardized sample naming convention, the use of key data fields for tying different data sets together, pre-specified and well understood formats for data deliverables, and a common, well-defined coordinate system that is systematically used for all data collection, can become major obstacles to timely decision-making if not in place. For a traditional data collection program, spending several days untangling these types of problems after field work is complete is unwelcome extra work, but does not necessarily affect project success. For a Triad-based program, however, these types of problems can mean significant delays and increased project costs.

Data flow diagrams can be useful for information planning purposes. Annotated data flow diagrams identify:

• the origination of data streams,
• the "paths" information must follow to get to the hands of decision-makers,
• who is responsible for and controls data at each step of the way,
• what QA/QC must be applied and where in the data flow process,
• what data analyses or manipulations (e.g., statistical analyses, mapping, visualization) are required and where in the process,
• where data streams will need to be integrated,
• which decisions particular data streams support, and what the scheduling requirements are for data delivery to those decisions, and finally,
• the ultimate repository for data sets for archiving purposes.

Data flow diagrams should be able to support at least an informal critical path analysis that can determine where potential bottlenecks exist that may prevent timely decision-making. Readiness reviews, as discussed in the next section, can assist in verifying that information management plans are workable.

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