A picture is worth a thousand words. We’ve all heard this cliché—probably more times than we’d like to count—but it’s an expression that rings true in so many instances. And when it comes to spatial data, there’s no cliché that is more appropriate.
I’ve been in the data business my entire career. It’s safe to say that over the years, I’ve licensed thousands of data sets to use in the products that we’ve built. Like most people who are responsible for purchasing data sets, I’ve never purchased a set without first reviewing a sample of the data. Data samples are the best way to understand the actual content, structure and other information in the file you’re going to get.
When looking at spatial data descriptions in the documentation or in a table, it often looks like quality data. However, if you put that same data on a map, it isn’t always quite as good as you’d originally hoped.
This has happened to me several times. For example, when I was evaluating suppliers for Point of Interest (POI) data, I requested a sample. I was told the sample data contained 10 million business listings within my targeted area. At first pass, the sample data upheld this description—there were 10 million unique records in the sample file that I had received. Before going through with the purchase, I took that data one step further and uploaded it into mapping software so that I could perform a quick visual inspection of the data.
Can you guess what happened?
When the data was displayed on the map, it was quickly apparent that there were major gaps and inaccuracies in the data. We could see that major brands in the area were missing, or lacked standardization, and many of the locations were just flat out wrong. By being able to visualize the data upfront, I had the opportunity to quickly see that the data set wasn’t the best investment of my money.
In another case, I purchased ZIP Code boundary data for a specific analytical project. Hoping to repurpose that same data to create a new delivery schematic for a local flower shop, we loaded the ZIP Code and street data for display. On the screen, I had everything needed to build the delivery map, but it didn’t look like I expected-- it had spikes in and out of the core boundary. While most streets were centralized to the ZIP Code, other streets were “zingers,” moving in and out of the core trade area.
While I had imagined a nice, clean set of boundaries that were generalized for visual appeal, I ended up with boundaries that were highly precise and accurate to the specific ZIP Code. The data was perfect for the original project, but not at all what I needed for display.
Visual inspection is essential when purchasing spatial data—just as important as sample data. It gives you the opportunity in the purchasing process to actually see the value in the data, and assess the quality, completeness, and spatial accuracy of the data. Without visualization capabilities, companies are at risk of making business decisions using misleading or incomplete data, which could ultimately result in major financial risks to the organizations.
As human beings, we do visual inspections all the time when making a decision. You wouldn’t purchase produce in the super market, make an offer on a house, or buy a car without seeing it first, would you? Data should be treated no differently.
Once you can see the data in a visual, like a map, and confirm its accuracy, you can then begin to see relationships between pieces of information and uncover patterns and trends that you wouldn’t have been able to see in a spreadsheet.
Clichés are used because they stand the test of time. Pictures are worth a thousand words. Looking at the data before you buy can protect your data investment over the long term and ensure you’re making data-driven decisions based on quality information.