GIS Complexity Is Leaving Geoscientists Behind

Thierry Gregorius contends that the current state of GIS, data management, and IT infrastructure has become a source of mounting frustration—particularly within the geoscience community. Despite decades of reliance on geospatial tools, geoscientists increasingly feel that the technology designed to support them has drifted away from their practical needs.

GIS as a Tool, Not an Identity

In the global energy industry, geoscientists—whether geologists, geophysicists, or environmental specialists—work with GIS daily. Yet they do not define themselves as GIS professionals any more than they identify as spreadsheet or software specialists. For them, GIS is a supporting instrument in the pursuit of earth science objectives.

When one practitioner recently remarked that “GIS is not as simple as it used to be,” it encapsulated a broader sentiment. The challenges associated with GIS usage have not diminished over time; in many respects, they have intensified. This paradox is striking given the rapid technological evolution of geospatial software.

Data Reality: Far from the Google Model

Unlike consumer mapping platforms such as Google Maps, geoscientists operate in a vastly more complex data landscape. Their datasets originate from digitized paper archives, field notes, spreadsheets, intelligence reports, legacy databases, and web feeds. Formats vary widely, and data quality is inconsistent.

Where internet companies benefit from natively digital data streams, geoscientists must assemble fragmented information into coherent narratives—whether assessing environmental impacts or evaluating subsurface resource potential. After painstakingly consolidating disparate sources, the last obstacle they need is a GIS environment that is difficult to operate or poorly aligned with their workflows.

Promises Unfulfilled

Over the past decade, several major GIS trends have promised simplification and integration. Standardization initiatives and spatial data infrastructures were envisioned as solutions to persistent data conversion headaches. Yet tools like FME and GDAL remain essential, underscoring the reality that interoperability remains elusive.

Web-based GIS platforms followed, often transforming simple browser interfaces into complex mapping systems. Only with the arrival of Google Maps in 2005 did mainstream web mapping achieve intuitive usability.

Desktop GIS applications have similarly oscillated between professional specialization and attempts at mass adoption. Modern platforms such as ArcMap evolved into sophisticated environments, yet sophistication often translated into increased complexity. Even open-source alternatives like QGIS—while powerful—are expanding in scope and feature density, risking the same usability challenges.

The open source movement, initially heralded as liberation from proprietary lock-in, has not fundamentally altered the usability paradigm. Open source GIS tools remain comprehensive and feature-rich, but they often mirror the complexity of commercial counterparts.

Empowerment vs. Infrastructure Burden

GIS historically aimed to democratize spatial analysis. However, when routine workflows demand dedicated GIS departments and extensive IT support stacks, that original promise is undermined. Instead of empowering domain experts, complex GIS architectures can invert the relationship, forcing users to adapt to the system rather than the system serving the user.

This tension was reflected in a comment by Brian Timoney, who suggested that many enterprise GIS requirements could be satisfied using Google Earth combined with shared KML files. While unconventional, such a lightweight configuration may in some cases prove more practical than deploying a full enterprise GIS stack.

Nostalgia for earlier tools such as ArcView 3 reflects a perceived balance between usability and functionality. Although outdated by modern standards, it embodied a clarity of purpose that many users feel has not been replaced.

The Missing Productivity Layer

The core critique centers on unmet needs. Geoscientists require tools capable of handling messy, heterogeneous datasets without extensive preprocessing. They need flexible data models that accommodate thematic analysis across varying formats, intuitive interfaces that surface relevant options, and workflows that allow rapid assembly of georeferenced information.

Yet GIS platforms often assume clean, structured datasets as a starting point. The burden of preparation falls on users, consuming time and resources before meaningful analysis even begins.

Meanwhile, analytics ecosystems are absorbing spatial capabilities. From open-source environments like R to proprietary platforms such as SAS, mapping functionality is increasingly embedded within broader data science toolsets. Specialized geoscience platforms from companies like Schlumberger and Landmark integrate spatial visualization directly into domain-specific workflows.

These systems may not replicate the full capabilities of dedicated GIS software, but they do not need to. When data integration and analysis occur within a unified environment, mapping becomes one representation among many rather than a separate technological domain.

First the Foundation, Then the Decoration

The argument concludes with a metaphor: GIS has focused on embellishments before solidifying the foundation. Advanced cartographic features and expanded functionality matter little if the underlying workflow remains cumbersome.

For geoscientists confronting complex, imperfect datasets, the pressing need is not additional layers of sophistication, but streamlined, purpose-built tools that accommodate the realities of their work. Until GIS platforms address these structural usability gaps, frustration within the geoscience community is unlikely to diminish.