Image showing descending computer code in multiple colors.Sometimes, answers to data quality questions are readily available. We can discuss trustworthy data providers, offer ideas for data processing or cleanup workflows, describe metadata, or reference industry-standard datasets. However, when we think about this question a bit deeper, we often find it difficult to answer.
The question, where do I go to find good data is hard to answer because it depends on an entirely different set of questions related to our project: what do we want to do with the data, how do we want to present it, to whom will we present it, when do we need to finish it, etc. In short, the answer to these questions depend on project considerations: we need to map out a vision, scope, budget, audience, and time for our project before we dive into the data.
Let us assume we have answers to project-level questions and turn our attention to principles that guide our search for data, especially how to use metadata and choose data that meet our needs.
Data as material
ArcGIS Pro is a toolbox that gives us an amazing array of tools to perform operations on and with data. In other words, if our GIS is the tool, then data is the material we work with. The geoprocessing, editing, and cartographic design tools we have in our GIS let us create, modify, modulate, and represent data in unique ways that allow us to tell a story about spatial phenomena. The goal is to use these data to inform decisions we make for ourselves, our organizations, and the broader world.
Clip art image depicting a hammer on top of a wrench.
At the same time, not all data is created equal. Sometimes, the dataset we need does not exist. At other times it might be the wrong format or out of date. Maybe it contains the wrong attributes or covers a different location than we need. We might find a dataset from an excellent data provider, and later discover that it contains use limitations that prevent us from using it to answer our spatial question.
In this sense, data can be thought of as an analog to the materials a carpenter works with: as the wood and fasteners and glue that together comprise an object such as a table.
One mistake that we make as GIS analysts is to gather any material that could be relevant to a given project without identifying why we need it, how it will be used, and what purpose it serves. It would be as if a carpenter runs to the hardware store to buy wood and glue and nails and screws of all different sizes, types, and shapes without understanding what their client wants.
Project Considerations
Image showing an audience seated at a conference.First and foremost, when we search for data we have to understand the vision, scope, budget, audience, and timelines involved for our project. What will we deliver? Who will we deliver it to? Do we have financial constraints that limit the data we can buy? Do we have short deadlines that limit the amount of processing or editing we can perform? How will we maintain our deliverables after the project ends, or if we should maintain our deliverables at all? By finding answers to these kinds of questions, we set ourselves up to streamline data collection and clean-up processes.
Now, because there are so many data providers across sectors and industries, this post will not dive into specific data providers. I assume that you know where to find the data you need to work with.
Instead, we will use the following scenario: we have identified two datasets we could use in our project. They come from providers we trust, but we still need to know which one to use.
Check the Metadata
No matter what the data is, we should always look at the metadata record first.
A good metadata record will tell us a ton of information: when was the dataset created; when was it last updated; what’s the spatial reference; how accurate is the dataset and at what level of detail; does the data have legally binding use limitations; and what attributes does it have.
Using metadata records helps to narrow down our search for data and understand how the data will contribute to our project.
Let’s say the metadata on both of our datasets has good attribution and we think it would be possible to use either one. What do we do next?
