If you work with environmental or engineering data, you’ve probably found yourself in this situation: a dataset gets refreshed, and you need to recreate the related grids, maps, and reports. This recreation process can eat up many hours of your day and lead to multiple mistakes. That’s where automation comes in to help. 

Automation is a practical way to streamline work you’re already doing. When repetition starts slowing you down, using scripts to automate your workflow can remove manual steps, improve consistency, and give you time to focus on what matters most: analysis and visualizing insights. One Surfer user experienced this firsthand. His project required regenerating dozens of deliverables each time new data arrived—until he implemented scripts that transformed the process. 

Before we talk about automation, it’s important to understand what repetitive, manual workflows actually cost you. When you’re recreating grids, contour maps, cross-sections, or reports every time new data arrives, the process itself becomes a bottleneck that often leads to the following consequences:

• Human error: Even if you follow the same steps each time, manually repeating dozens of actions increases the chance that a setting is missed, a parameter is applied incorrectly, or a file is exported with the wrong configuration.
• Inconsistency across outputs: Small variations in settings or formatting can create noticeable differences between deliverables. Over time, that inconsistency can affect credibility and make comparisons between datasets harder.
• Lower efficiency: Manual repetition consumes time without adding analytical value. Instead of interpreting results or visualizing insights, you’re re-executing steps you’ve already taken dozens of times.

Of course, repetitive work isn’t inherently bad—but when you consistently take manual steps to complete it, you’ll start to notice friction. And that friction is exactly what automation is designed to remove.

One person who experienced the power of automation is Michael Petrozzi. As Principal Scientist at Edison Environmental & Engineering Pty Ltd, he was analyzing concentration data for a large and complex site. The massive dataset was frequently refreshed with new data, and each update triggered the same time consuming chain reaction.

“Essentially, this job involves a large dataset that’s refreshed regularly,” Michael explained. “We produce a high volume of deliverables for the project: concentration contour maps, concentration gradient maps, cross-validation reports, and gridding reports. We’re talking dozens of them every time. I wanted to do this quickly and consistently.”

Before introducing automation, manually running a single round of updates would consume one to two days of work. Even when relying on saved grid settings files, the repetitive workflow introduced constant operational risk, particularly when moving between different datasets. That changed when Michael designed a customized automation script to handle the initial heavy lifting.

Instead of requiring manual execution for every single dataset, Michael’s primary script automates the core map-creation sequence from start to finish. The script reads the incoming data, generates the grid files, and creates both the gridding and cross-validation reports—automatically log-transforming the data and saving it back to the file as part of the cross-validation process. Once the math is complete, the script finishes the job by posting the completed contour layers directly onto a Surfer map.

“By hard-coding the grid settings into a script, you take the potential for human error out of the equation,” he said. “Even if you use a grid settings file, if you’re doing it 40 times manually, there’s always room for a mistake, so a script significantly reduces the error rate.”

Once the main map objects are populated, Michael triggers a second companion script that exports all the contour layers directly into Esri 2D shapefiles. These assets are then pulled into ArcGIS for further use. The result? According to Michael, “A process that used to take days now only takes two to four hours to allow for cross checking and saving files, depending on how many new datasets need processing.”

Michael’s improved workflow illustrates the impact of automation in practice, but what benefits did he experience in a plain sense? Here are the main advantages that he’s seen: 

• Significant time savings: An intensive 24-to-48-hour manual process was compressed into a swift two-to-four-hour window. This timeline allows for faster updates and quicker decision-making.
• Reduced error rates: Hard-coding parameters into a script removes the need to manually manage grid settings files when moving between different datasets. This eliminates repetitive manual steps in the gridding process, ensuring that the exact same settings are applied perfectly every time without any easy-to-miss variations creeping in.
• Greater confidence in deliverables: When routine workflows are fully standardized, there is no need to second-guess whether a single log-transformation was missed or if a report was exported with the wrong configuration. That confidence carries directly into client communication.

Ultimately, when routine processes are automated, workflows become more productive, reliable, and standardized. There are no more inefficiencies, preventable mistakes, or uncertainty.  

Related Resources

• Where to Find Surfer Script Examples
• 3 Types of Scripts That Can Automate Some of Your Map-Making Workflow
• Let Automation Do the Heavy Lifting: Say Goodbye to Manual Mapping Steps