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Data and Models: Pay Attention to What is Behind the Curtain


Commentary by Jim Smith, LANDFIRE Project Lead
Many are familiar with George E. P. Box’s statement, “All models are wrong, but some are useful.”  That said, his seemingly cynical view doesn’t stop us from using them every day, whether we know it or not. For instance, do you check the weather? Do you pay attention to economic forecasts?  Did you know that your grocery store arrangement was modeled? Underneath all critical decisions are models, and behind every one of those models are data. 
Models support the world of conservation as well, much like they do weather or economic forecasting. Restoration plans, environmental assessments and The Nature Conservancy’s Portfolio are models. As is every map.  But just as Dorothy uncovered the real Wizard in Oz, we must pay attention to what is behind the curtain (model) -- data.
Because all models depend on data, they should match in application scale and quality. What I believe (and fear) is that data are hidden so far behind the curtain that they are invisible, forgotten. No good policy, plan or project was created or monitored without good data. A nearly perfect model is nearly perfectly useless without the right kind of data used in the right way.
I am a member of the TNC LANDFIRE Team. We are in the data business, so that while this commentary may seem like a self-serving diatribe, my personal concern goes way beyond our program. LANDFIRE relies on the Gap Analysis Program, the Landsat Program, Forest Inventory and Analysis, Forest Health Technology Enterprise Team, the NRCS National Resource Inventory, Bureau of Land Management monitoring plots, and many other resources. Local data are absolutely vital to LANDFIRE. It is critical that we don’t plan to create and use mission-critical models or systems without understanding and providing for their current and future data requirements.
Despite my obvious love of data (after all, it is my life’s work,) I also know that George Box’s statement about models applies to data. That is, while all data ARE [in effect] wrong, some are useful. I know the value of models, and am a true worshiper of useful data.  As strange as it may seem, I savor a rich data set (map, spreadsheet, etc.) like a piece of chocolate cake with chocolate chips and cream cheese frosting.  To me, investigating a new data set is like opening a wrapped gift -- you never know what exciting things await.
However, it is so important to remember that no data set is effective or efficient for every application. The bottom line is that users must take responsibility for making the decision about the data you use. Our responsibility as data producers is to tell you everything we know about our products, but we cannot -- indeed must not -- make decisions for you about which data sets are the “right” ones for your purposes.
Instead of ‘Readin’, Ritin’ and Rithmetic’, here are my “Three “Rs”: Remember the importance of data. Respect the value of that data. Make the Right decisions about data. 
Pay attention to the data behind the model!