Making sense of GIS data: A practical guide for teams
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Whether you're managing assets, reviewing drone surveys, or coordinating field teams, you’ve probably noticed how often GIS data ends up at the center of the conversation. It’s the information everyone needs to make decisions, but it’s also the information that often lives in different systems, different formats, and different people’s heads. For teams who rely on clear spatial insights, understanding and using GIS data well is one of the biggest steps toward working together smoothly.
What is GIS data?
At its core, GIS data is information tied to a specific location. It could be a power pole’s coordinates, the elevation of a tailings dam, or the outline of a construction zone. If it exists somewhere in the world — and you want to measure it, map it, or analyze it — GIS data is doing the heavy lifting.
Most GIS data fits into two main types: vector and raster data.
1. Vector data
Vector data is the geometry layer — lines, points, and shapes that describe where things are and how they relate. Common examples include:
- Points: asset locations, sample sites, trees
- Lines: roads, pipelines, transmission lines
- Polygons: lease areas, exclusion zones, property boundaries
If you're looking for a simple breakdown, GIS Lounge gives a concise overview of how vector data works.
2. Raster data
Raster data is pixel-based and ideal for visuals and terrain information. It is often used when you need to analyze continuous surfaces like elevation, temperature, or vegetation.Examples include:
- Orthomosaic maps
- Digital Elevation Models
- Satellite imagery
- Thermal or multispectral imagery
For more detail, check out this article by Birdi Partner, GIS Geography: Vector vs Raster in GIS: What's the Difference?
Why GIS data matters for teams working across the same site or assets
GIS data becomes the shared “source of truth” that multiple people and departments rely on. Here’s how it helps teams collaborate more easily.
Better visibility for shared decision-making
When everyone is looking at the same orthomosaic or DEM, it becomes much easier to align on questions like:
- Where a design needs adjusting
- How much a stockpile has changed
- Whether a slope looks stable
- Which assets need follow-up
Visual context removes the usual round of confused emails.
Faster communication between the field and office
GIS data helps connect onsite observations with desktop analysis. Field teams can capture photos, GPS positions, and condition notes, which are later viewed against current imagery or asset layers. Engineers and managers can then review and act on the information without waiting for someone to piece together a summary.
Repeatable workflows that scale
Once GIS data is processed and accessible, teams can run the same workflows at any frequency they need — weekly, monthly, quarterly. Stockpile checks, vegetation reviews, progress monitoring, and land movement assessments all become easier to repeat with consistency and confidence.
Where GIS data typically comes from
Most organizations use a blend of sources, such as:
- Drone mapping
- Satellite imagery like Landsat
- Environmental monitoring tools such as Copernicus Sentinel
- Surveying equipment: GNSS, total stations, ground control
- Government datasets (e.g., elevation or land information from Geoscience Australia)
- Field capture: photos, notes, GPS points
How to work with GIS data effectively (even if you’re not a GIS specialist)
Collecting GIS data isn’t the hard part — using it well as a team is where things can fall apart. These simple practices help keep the data usable and collaborative.
1. Centralize your data
Keeping spatial files spread across folders, laptops, and inboxes is one of the quickest ways to lose track of the current version of anything. A single Birdi workspace makes it easy for anyone — engineers, surveyors, asset managers, contractors — to work from the same information.
2. Visualize before you analyze
Seeing the data mapped out in 2D or 3D immediately adds context. It’s one of the easiest ways for mixed-discipline teams to collaborate without needing deep, specialist GIS skills.
3. Use annotations to communicate clearly
Marking up layers with measurements, comments, labels, or polygons transforms raw spatial data into a clear shared picture. This is often the difference between aligned teams and crossed wires.
4. Track changes over time
Temporal GIS data helps teams:
- Detect land movement
- Review vegetation growth
- Compare stockpile changes
- Monitor environmental changes
- Track construction progress
Patterns become visible much faster when datasets can be viewed side by side. (Check out Birdi's Split Screen view tool, for how to easily compare datasets side by side).
5. Share insights, not just files
Sending exported PDFs or huge data packages often invites confusion. Letting people jump directly into the mapped data keeps communication clear, consistent, and up to date.
Common GIS data formats you’ll run into
These formats appear frequently in spatial workflows:
- GeoTIFF: Raster imagery like orthomosaics or DEMs
- SHP: Vector format for points, lines, and polygons
- KML/KMZ: Easy for visualizing in Google Earth
- LAS/LAZ: Common lidar formats
- DXF/DWG: Used in engineering and design
- WMS/WMTS: Live map services
Seeing GIS data come together in one shared space
When GIS layers — drone imagery, lidar, asset data, surveys, field notes — come together in a single workspace, interdisciplinary teams can work far more smoothly. Everyone sees the same information, understands the same context, and contributes without needing specialist software.
From there, teams can measure, comment, compare, export, and progress work without losing time or clarity.
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At its best, GIS data helps people understand the world they’re responsible for — whether that’s a construction corridor, a mine site, a network of power assets, or a landscape under environmental review.
The real value isn’t just in the data itself. It’s in how easily your team can see it, communicate around it, and make decisions from it. When GIS data becomes a shared resource instead of a technical silo, the work naturally becomes clearer, faster, and far more collaborative.