For many small and mid-sized organizations, technology can feel like a moving target. Every week brings a new buzzword (AI, automation, digital transformation) while your core systems feel increasingly outdated. Your data may be scattered across tools, workflows may depend on manual processes, and the thought of a major modernization project can feel overwhelming.

You’re not alone. Across industries, small and mid-sized organizations often struggle to keep up with the speed of technological change while balancing day-to-day operations. Yet modernization doesn’t have to be disruptive or intimidating. It is a strategic, step-by-step process that aligns your technology investments with your business goals and, when done right, it can be transformative.

This post will help you reframe modernization as a series of practical, achievable steps that move you from feeling overwhelmed to being ahead of the curve.

Why Modernization Feels Overwhelming

Small businesses frequently cite barriers like cost, lack of expertise, and integration complexity as reasons for delaying tech adoption. A 2023 study from AI Business found that many organizations hesitate to invest in modern solutions because of a lack of in-house expertise and concerns over security and ROI (AI Business, 2023). Similarly, a 2025 TechRadar report noted that small and mid-sized businesses (SMBs) often see technology investment as a financial risk, even while recognizing its necessity for growth (TechRadar, 2025).

40% of business leaders cite lack of expertise as a barrier to technology adoption.
(AI Business, 2023)

Yet, delaying modernization comes with its own costs: slower operations, missed opportunities, and competitive disadvantage. Encouragingly, businesses that have embraced digital tools overwhelmingly report efficiency gains and optimism about future growth (U.S. Chamber of Commerce, 2023).

The good news is that modernization doesn’t need to be a massive leap. A structured, goal-driven approach can help you take small, confident steps forward.

A Practical Approach to Modernization

Modernization should be driven by your strategic business goals, not by chasing the latest technology trend. Here’s an approach to help you get started.

1. Start with Your Goals, Not the Tech

Before looking at software, hardware, or AI models, ask yourself: “What business problems do I need to solve?” Are you trying to speed up workflows, improve decision-making, enhance customer experience, support growth, or a combination of these and other goals?

When organizations align IT initiatives with clear business objectives, they see higher ROI and faster decision-making (Davenport, 2024). Modernization is most effective when it’s a means to an end, not an end in itself.

2. Audit Your Workflows and Data

A modernization journey should start with a clear picture of your current state:

• Workflows: Identify bottlenecks, redundant tasks, or manual processes that slow your team down.
• Data: Evaluate the quality and organization of your data. What’s reliable? What needs cleaning? Are there gaps preventing meaningful analysis?

This audit creates a roadmap for targeted improvements. Often, even simple steps—like consolidating duplicate data sources or automating a repetitive report—can deliver measurable time savings. 

The term “audit” can be overwhelming in itself, as we often picture a laborious, top-down, expensive deconstruction of every process. While that can be useful, it’s not always necessary. Often, you or your staff are already intimately familiar with the inefficiencies in your operations and these can be captured via interviews. This is often enough to get started.

3. Prioritize Small, High-Impact Wins

Modernization doesn’t have to mean overhauling everything at once. Start small:

• Automate a single, repetitive process.
• Consolidate reporting into a single dashboard.
• Migrate one high-value workflow to a cloud-based solution.

These small wins build momentum, reduce team anxiety, and demonstrate clear ROI, paving the way for larger initiatives.

That second benefit, reducing team anxiety, is important. While we can all recognize inefficiencies, the inefficiency you know often causes less anxiety than the improvement you don’t. Getting your team comfortable with some small, early wins builds confidence in the overall process to come.

4. Build a Scalable Technology Strategy

Once you’ve addressed the most obvious inefficiencies, it’s time to lay a scalable foundation for growth. In this context, “scalability” simply means your technology can handle increased demand, complexity, or new functionality without needing a total replacement.

Scalability means your systems should grow as you grow—without costly rebuilds.

For small and medium-sized organizations, this often doesn’t mean buying the most expensive enterprise software. It can mean:

• Choosing cloud platforms and analytics tools that allow you to add users, storage, or features incrementally.
• Using modular systems that make it easy to plug in new capabilities—like automation or AI—when you’re ready.
• Designing workflows so that new services, staff, or customers can be added without reworking everything from scratch.

By focusing on flexibility instead of “big tech,” you create systems that evolve alongside your organization’s needs. This approach can save money in the long run and reduces the stress of future growth.

Make AI and Automation Accessible

Artificial intelligence (AI) and automation are often seen as intimidating, but they don’t have to be. The key is to view AI as a tool to augment decision-making and streamline workflows rather than as a complex, abstract concept.

Practical applications include:

• Automating repetitive reporting or data entry tasks.
• Using predictive analytics to forecast demand or identify trends.
• Applying natural language processing (NLP) to analyze customer feedback.

By starting with small, low-risk pilot projects, you can test AI solutions, measure results, and gain confidence in scaling them further. These pilots often produce immediate efficiency gains, helping you build internal support for deeper adoption.

Targeted Expertise

Many growing organizations delay modernization because they don’t have a CIO, CTO, or internal team member to lead the effort. This is where fractional executive leadership or embedded technical advisors can make a difference.

A skilled advisor provides:

• Strategic vision: Ensuring your technology roadmap supports your goals.
• Vendor neutrality: Recommending the right tools for your needs, not pushing a specific platform.
• Execution support: Overseeing implementation to reduce risk and avoid costly missteps.
• Knowledge transfer: Building your internal team’s capability over time.

At Cercana, we pair executive-grade advisory services with hands-on delivery, helping organizations modernize confidently without the overhead of a full-time executive hire. Our goal is to guide you through modernization while helping you build your internal capacity so that you can move into the future on your own.

Moving Forward with Confidence

Modernization isn’t a single project; it’s an ongoing journey that starts with understanding your goals, streamlining workflows, and building a flexible foundation. By breaking the process into manageable steps and leveraging expert guidance, you can move from feeling overwhelmed to gaining a competitive edge.

The organizations that thrive in today’s landscape are those that see technology as a growth enabler rather than a burden. With the right plan and guidance, modernization can unlock new opportunities, create efficiencies, and position your business for long-term success.

Ready to start your journey? Let’s talk. We’ll help you identify your highest-impact opportunities, design a strategy that works for your business, and deliver clarity at every step.

References

AI Business. (2023, June 21). Small-medium businesses face barriers to technology adoption. AI Business. https://aibusiness.com/verticals/small-medium-businesses-face-barriers-to-technology-adoption

Davenport, L. (2024, October 27). Business and IT alignment: Why is it so important? Davenport Group. https://davenportgroup.com/insights/business-and-it-alignment-why-is-it-so-important/

TechRadar. (2025, April 1). SMBs want to use tech more in order to grow—but costs are proving a big barrier. TechRadar Pro. https://www.techradar.com/pro/smbs-want-to-use-tech-more-in-order-to-grow-but-costs-are-proving-a-big-barrier

U.S. Chamber of Commerce. (2023, September 14). The impact of technology on U.S. small businesses. U.S. Chamber of Commerce. https://www.uschamber.com/small-business/smallbusinesstech

Header Image: Bernd Fiedler, CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0, via Wikimedia Commons

Introduction

When combining open-source GIS tools with the ArcGIS ecosystem, there are a handful of challenges one can encounter. The compatibility of data formats, issues with interoperability, tool chain fragmentation, and performance at scale come to mind quickly. However, the use of the open-source Python library GeoPandas can be an effective way of working around these problems. When working with GeoPandas, there’s a simple series of steps to follow – you start with the data in ArcGIS, process it with the GeoPandas library, and import it back into ArcGIS.

It is worth noting that ArcPy and GeoPandas are not mutually exclusive. Because of its tight coupling with ArcGIS, it may be advantageous to use ArcPy in parts of your workflow and pass your data off to GeoPandas for other parts. This post covers three specific ways GeoPandas can enhance ArcGIS workflows and why it can better than using ArcPy in some cases.

Scenario 1: Spatial Joins Between Large Datasets

Spatial joins in ArcPy can be computationally expensive and time-consuming, especially for large datasets, as they process row by row and write to disk. GeoPandas’ gpd.sjoin() provides a more efficient in-memory alternative for point-to-polygon and polygon-to-polygon joins, leveraging Shapely’s spatial operations. While GeoPandas can be significantly faster for moderately large datasets that fit in memory, ArcPy’s disk-based approach may handle extremely large datasets more efficiently. GeoPandas also simplifies attribute-based filtering and aggregation, making it easier to summarize data—such as joining customer locations to sales regions and calculating total sales per region. Results can be exported to ArcGIS-compatible formats, though conversion is required. For best performance, enabling spatial indexing (gdf.sindex) in GeoPandas is recommended.

Scenario 2: Geometric Operations (Buffering, Clipping, and Dissolving Features)

Buffering and dissolving in ArcPy can be memory-intensive and time-consuming, particularly for large or complex geometries. Using functions like buffer(), clip(), and dissolve() to preprocess geometries before importing them back to ArcGIS is an effective solution to that problem. These functions can help make a multitude of processes more efficient. They can create buffer zones around road networks, dissolve any overlapping zones, and export the results as a new feature class for ArcGIS-based impact analysis. 

These functions can be cleaner and more efficient with regards to geometry processing than ArcPy and require fewer steps to carry out. They also integrate well with data science workflows using pandas-like syntax. 

Below is a detailed side-by-side comparison of GeoPandas and ArcPy for spatial analysis operations, specifically focusing on buffering and dissolving tasks.

Aspect	GeoPandas 🐍	ArcPy 🌎
Processing Speed	Faster for medium-sized datasets due to vectorized NumPy/Shapely operations. Slows down with very large datasets.	Slower for smaller datasets but optimized for large-scale GIS processing due to disk-based operations.
Memory Usage	Fully in-memory, efficient for moderately large data but can struggle with very large datasets.	Uses ArcGIS’s optimized storage and caching mechanisms, which help handle large datasets without running out of RAM.
Ease of Use	Requires fewer lines of code; syntax is cleaner for many operations.	More verbose; requires handling geoprocessing environments and ArcPy-specific data structures.
Buffering Capabilities	Uses GeoSeries.buffer(distance), efficient but requires a projected CRS.	arcpy.Buffer_analysis(), supports geodesic buffers and larger datasets more reliably.
Dissolve Functionality	GeoDataFrame.dissolve(by=”column”), vectorized and fast for reasonably large data.	arcpy.Dissolve_management(), slower for small datasets but scales better for massive datasets.
Coordinate System Handling	Requires explicit CRS conversion for accurate distance-based operations.	Natively supports geodesic buffering (without requiring projection changes).
Data Formats	Works with GeoDataFrames, exports to GeoJSON, Shapefile, Parquet, etc.	Works with File Geodatabases (.gdb), Shapefiles, and enterprise GIS databases.
Integration with ArcGIS	Requires conversion (e.g., gdf.to_file(“data.shp”)) before using results in ArcGIS.	Seamless integration with ArcGIS software and services.
Parallel Processing Support	Limited parallelism (can use Dask or multiprocessing for workarounds).	Can leverage ArcGIS Pro’s built-in multiprocessing tools.
License Requirements	Open-source, free to use.	Requires an ArcGIS license.

Scenario 3: Bulk Updates and Data Cleaning

When performing bulk updates (e.g., modifying attribute values, recalculating fields, or updating geometries), ArcPy and GeoPandas have different approaches and performance characteristics. ArcPy uses a cursor-based approach, applying updates row-by-row. GeoPandas uses an in-memory GeoDataframe and vectorized operations via the underlying Pandas library. This can make GeoPandas orders of magnitude faster on bulk updates than ArcPy, but it can be memory intensive. Modern computing systems generally have a lot of memory so this is rarely a concern but, if you are working in a memory-constrained environment, ArcPy may suit your needs better.

Here is a side-by-side comparison:

Feature	GeoPandas 🐍	ArcPy 🌎
Processing Model	Uses in-memory GeoDataFrame for updates (vectorized with Pandas).	Uses a cursor-based approach (UpdateCursor), modifying records row by row.
Speed	Faster for large batch updates (leverages NumPy, vectorized operations).	Slower for large datasets due to row-by-row processing but scales well with large file geodatabases.
Memory Usage	Higher, since it loads the entire dataset into memory.	Lower, as it processes one row at a time and writes directly to disk.
Ease of Use	Simpler, using Pandas-like syntax.	More complex, requiring explicit cursor handling.
Parallel Processing	Can use multiprocessing/Dask to improve performance.	Limited, but ArcGIS Pro supports some multiprocessing tools.
Spatial Database Support	Works well with PostGIS, SpatiaLite, and other open formats.	Optimized for Esri File Geodatabases (.gdb) and enterprise databases.
File Format Compatibility	Reads/writes GeoJSON, Shapefiles, Parquet, etc.	Reads/writes File Geodatabase, Shapefile, Enterprise Databases.

5. When to Use ArcPy Instead

There are still times that using ArcPy would be the better solution. Things like network analysis, topology validation, or tasks that require a deeper integration with ArcGIS Enterprise in some other capacity are better done in ArcPy as opposed to GeoPandas. In the case of network analysis, ArcPy integrates ArcGIS’s native network analyst extension. On its own, it supports finding the shortest path between locations, calculating service areas, origin-destination cost analysis, vehicle routing problems, and closest facility analysis. It also works natively with ArcGIS’s advanced network datasets such as turn restrictions, traffic conditions, one-way streets, and elevation-based restrictions. 

6. Conclusion

GeoPandas offer greater efficiency, speed, flexibility, and simplicity when working with open-source tools in ArcGIS workflows, especially with regard to custom analysis and preprocessing. If you haven’t tried using GeoPandas before, it is more than worth your time to play around with. 

Have you had your own positive or negative experiences using GeoPandas with ArcGIS? Feel free to leave them in the comments, or give us a suggestion of other workflows you would like to see a blog post about! 

What is Strategic Planning and Why Does it Matter?

Strategic planning is one of the most important things you can do for your organization. It helps you not only paint the picture of where you want your organization to be in the future, but also draws the roadmap for how you’re going to get there. 

Having goals is crucial for any business. Companies generally don’t thrive and grow by accident. However, having those goals is only the first step. You need to know what the steps to reaching them are, what challenges lie in the way, and what you’re going to do when those challenges arise. The better your plans, the quicker you can address the challenges.

Key Factors in Developing a Strategic Plan

It is equally important to know both where you want your organization to be in five years as well as next year. Your short-term goals will act as checkpoints along the journey to your long-term goals. While articulating these, you’ll want to make sure to craft a clear mission statement to help bring your entire organization into alignment on where the ship is trying to steer and why.

Geospatial tools can be helpful in accomplishing your goals. There are a variety of ways they can help you provide useful insights that support your organization’s operations. Geospatial tools can help you determine the best areas for expansion, understand your supply chains in greater depth, reveal location-based trends in your consumers, and so much more. The more exact your understanding of your business and customers is, the more sure you can be in your next steps and your plan. 

A key step in formulating your strategic plan is conducting a SWOT analysis. All four components – your strengths, weaknesses, opportunities, and threats – can be examined in greater depth through geospatial insights. 

If one of your strengths is a particular location of your operations, there are many geospatial factors that can be contributing to that. Spatial analysis using demographic data such as census blocks and local parcel data can help you understand the population within proximity to the store. This information can be used to characterize new locations with similar populations. Geospatial tools can create an easily understood map that helps your leadership visualize results without having to sift through pages and pages of data.

Perhaps a weakness of your operation is high distribution cost and long turn-around time for acquiring inventory. Geospatial tools can provide a deeper understanding of your supply chain in a manner that’s easy to understand. They can help to optimize distribution points in relation to upstream suppliers and downstream retail locations. It can also help to identify gaps that may require new supplier relationships to fill.

When analyzing the opportunities, understanding geospatial factors can help capitalize on them. If you want to expand your operations in the Northeast, a geospatial analysis of commercial locations could tell you what parcels are ideal to target based on municipal taxes, proximity to distribution channels, demographic make-up for sourcing labor and finding consumers, and environmental factors such as frequency of natural disasters. 

Similar analysis can help identify potential threats that may have been previously unrecognized. As you’re looking at expanding in the Northeast, perhaps you notice there’s a very limited number of properties that ideally suit your needs, and the majority of them that exist are already owned by one of your competitors. This presents you with an opportunity to reassess your market entry strategy. 

Devise a Strategy

After completing your SWOT analysis, you’ll want to put pen to paper on your strategic plan. Be sure to make the steps in your plan actionable, clear, and measurable. Your steps and strategies should align with your organization’s values and mission statement. Incorporating geospatial analysis into your plan of action is important, as well. Geospatial insights can provide excellent visualizations of data and progress towards your goals. 

Execute Your Strategy

Executing a strategic plan across all business departments ensures alignment, efficiency, and goal achievement. Effective communication fosters collaboration, breaking down silos between teams. Proper resource allocation optimizes budgets, personnel, and technology, preventing inefficiencies. A well-integrated plan leverages geospatial skills and tools, placing them where they add the most value—whether in logistics, marketing, or risk assessment. 

This enhances decision-making, improves operational efficiency, and boosts competitiveness. Geospatial insights drive location-based strategies, ensuring businesses optimize site selection, route planning, and customer targeting. A strategic, cross-departmental approach maximizes the impact of geospatial tools, leading to smarter business decisions and sustainable growth.

Evaluate and Control

Monitoring progress on a strategic plan requires tracking KPIs to measure success and identify areas for improvement. Geospatial KPIs, such as delivery efficiency, store performance by location, or market penetration, provide location-based insights to optimize decisions. Regular analysis ensures alignment with business goals, allowing for timely adjustments. By leveraging real-time geospatial data, businesses can refine strategies, improve resource allocation, and adapt to changing market conditions for sustained success.

Common Challenges and Solutions

Change is often met with uncertainty. It’s important to foster a culture of open communication and honesty about the direction of the organization with all your players on all levels of all departments in order to get everyone on the same page. Here, geospatial analysis can contribute to ensuring that decisions are data-driven. Maps are a great communication tool for aligning your organization around your goals.

Another common problem with effective implementation of a strategic plan is working with inadequate data. Data collection has to be the number one priority of your organization at all levels in order to ensure that data-driven decisions can be made accurately and effectively. In a geospatial context, ensuring that all tools being used enable location as a first-class citizen is paramount. This way, all data collected is location aware from the onset of your plan’s implementation. 

Commonly, geospatial tools and data are stovepiped and underutilized. Modern geospatial systems can live side-by-side with other line-of-business tools used in strategic planning and monitoring, such as ERP and CRM. It is no longer necessary to have fully dedicated geospatial tools and expertise that resides outside of mainstream business systems. Ensuring that geospatial analysis is tightly integrated will help ensure that strategic data-driven decisions are also location-aware.

Conclusion

Most organizations understand how location affects their operation, but the cost and effort of integrating geospatial analysis into strategic decision-making is often seen as too high. Modern geospatial tools and practices, when deployed in targeted ways, can ensure that location is properly accounted for in goal-setting and execution. If you’d like to learn more about how Cercana can help you maximize the value of location in your strategic planning, contact us here.

Many organizations rely on geospatial technology to derive insights based on location and spatial relationship. Whether they are mapping infrastructure, analyzing environmental changes, or optimizing logistics, managing geospatial investments effectively is imperative. Two strategies, IT portfolio management and IT rationalization, can help organizations maximize the value of their geospatial assets while reducing inefficiencies. Leveraging the right tools and techniques ensures these strategies are implemented effectively.

When we talk about “geospatial investments,” we are talking about infrastructure, software, and data. Assets in those categories may be acquired through proprietary licenses, subscriptions such as SaaS or DaaS, or by onboarding open-source solutions. Regardless of the provenance of the asset, its acquisition and incorporation into operations brings costs that must be managed like any other investments.

What Is IT Portfolio Management?

IT portfolio management is a structured process for evaluating and aligning IT assets, including geospatial tools and data, with organizational goals. Think of it like managing a financial portfolio—prioritizing investments to maximize returns while managing risks.

In practice, effective IT portfolio management involves a combination of strategic planning, resource allocation, and continuous assessment. Organizations leverage portfolio management to ensure IT investments, including geospatial tools and data, align with long-term objectives while remaining adaptable to changing priorities. This often entails mapping projects to business outcomes, identifying dependencies, and evaluating performance metrics to measure success. Additionally, fostering collaboration between IT and operational teams enhances decision-making, ensuring geospatial initiatives address both technical and organizational needs. By applying these principles, organizations can maximize the value of their geospatial assets while mitigating risks associated with resource misallocation or misaligned goals.

Tools and Techniques for Geospatial Portfolio Management

1. Portfolio Management Software:
   • Tools like Planview, SailPoint, or Smartsheet can help track geospatial technology assets. Some integrate with ERP systems to identify spend. This is useful for tracking commercial software licenses, subscriptions, and even support contracts from open-source tools. They can be especially useful for identifying “shadow IT” in which staff onboard SaaS tools and then expense the subscription fees.
   • Mobile Device Management (MDM) tools such as JAMF or JumpCloud can be effective at tracking or deploying software on managed devices. This can help with license optimization for commercial software, patch management for commercial and  open-source tools, or data inventory management at the edge.
2. Geospatial Data Inventory:
   • Platforms like GeoNode, CKAN, or Esri’s Data Catalog helps centralize and manage spatial datasets across multiple teams and locations.
   • Search tools such as Voyager, have features that enable discovery of geospatial data and the assessment of data redundancy.
3. Prioritization Frameworks:
   • Weighted Scoring Models enable the use of organization-specific criteria to provide consistent evaluation of alternatives.
   • Benefit-Cost Analysis provides a relatively simple way to objectively evaluate and rank geospatial investments.

By utilizing these tools and techniques, organizations can align their geospatial investments with business goals and make data-driven decisions.

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What Is IT Rationalization?

IT rationalization focuses on simplifying and streamlining IT assets to eliminate redundancy and reduce costs. It requires a systematic approach to evaluate the relevance, efficiency, and performance of IT assets. It involves cataloging all technology assets, assessing their value and usage, and identifying areas of overlap or obsolescence. For geospatial technology, this process includes analyzing the lifecycle of geospatial tools, evaluating data quality and relevance, and determining the efficiency of current workflows. Organizations often use rationalization to create a unified technology ecosystem by consolidating systems, integrating data sources, and phasing out redundant or underperforming applications. This ensures that geospatial investments support operational needs while reducing costs and improving overall agility.

Geospatial rationalization involves a systematic approach to streamlining geospatial technology and data assets, ensuring they align with organizational goals while reducing inefficiencies and costs. The process begins with inventorying assets using tools like an MDM platform or Voyager, which can track software, hardware, and data. Identifying redundancies is a critical next step, where tools like FME or Voyager can uncover duplicate data for cleanup, while GDAL/OGR standardizes and consolidates diverse datasets to ensure consistency. Migration and consolidation further enhance efficiency by moving geospatial data to modern, scalable platforms like Apache Sedona with Spark, PostGIS, or a data warehouse, often leveraging ELT/ETL tools. 

Application rationalization frameworks help organizations evaluate and classify geospatial applications for retention or retirement. Finally, performance monitoring tools like ensure applications operate efficiently, allowing for proactive identification of bottlenecks and optimization of resources. Together, these steps enable organizations to create a unified, cost-effective geospatial technology ecosystem tailored to their operational needs.

Challenges of Geospatial Technology in Portfolio Management and Rationalization

Managing and rationalizing geospatial tools and data present unique challenges due to the specialized nature and complexity of these systems. For instance, the vast volumes and diverse formats of geospatial data—such as vector layers, satellite imagery, and real-time sensor feeds—require robust storage and processing solutions. Organizations often grapple with ensuring data integrity, accessibility, and compatibility, especially when datasets come in formats like shapefiles, GeoJSON, or KML. For example, a municipality managing urban planning projects might need to consolidate data from various sources into a unified format using tools like GDAL/OGR. Apache Sedona, integrated with Spark in a data lake employing a medallion architecture, provides an efficient framework for managing large-scale geospatial datasets. This architecture allows organizations to organize raw data into bronze, silver, and gold layers, enabling a scalable and structured approach to data cleansing, integration, and analysis while maintaining high performance and flexibility.

Another significant concern is aligning tools and resources with organizational priorities while managing costs and governance. Differing project requirements can lead to overlapping software tools. For example multiple desktop GIS software or mobile data collection platforms can exist across a portfolio. Additionally, ensuring data governance is important, particularly when handling sensitive geospatial information, such as infrastructure as-built data or parcel boundaries. For instance, a transportation agency may use GeoNetwork to manage metadata securely while employing encryption and role-based access controls to comply with privacy regulations. Collaboration platforms, such as ArcGIS Enterprise or GeoNode, can help bring together diverse stakeholders—urban planners, emergency responders, and environmental analysts—by centralizing geospatial data and tools, fostering better alignment, and ensuring efficient resource utilization.

Conclusion

Geospatial technology is critical for modern organizations but presents unique challenges that demand careful management. Combining geospatial tools with standard strategies for handling data volume, interoperability, and governance, organizations can streamline their geospatial systems and integrate geospatial assets into larger organizational governance frameworks. IT portfolio management and rationalization not only optimize costs but also ensure geospatial investments align with strategic goals, delivering long-term value.

To learn more about how Cercana can help you optimize your geospatial portfolio, contact us.

At Cercana, we have worked with geospatial systems that have run the gamut—from all-in proprietary stacks to pure open-source toolchains. As the technology landscape evolves, many organizations are blending both proprietary and open-source solutions. These hybrid architectures aim to capitalize on the best of each world, providing flexibility in how users store data, serve maps, run analyses, or deploy applications – but making this approach work requires thinking through a few key considerations.

Whether you’re starting from a pure proprietary environment and eyeing open-source options, or you’re heavily invested in open-source but considering some proprietary tools for specific tasks, it helps to understand where each may fit best.

The Realities of Hybrid Architecture

No two organizations have the exact same requirements. Some rely on legacy systems tied to proprietary platforms. Others have in-house developers more comfortable with taking on the full lifecycle maintenance of open-source code – which includes contributing back to projects. Others – especially in the public sector – may face strict procurement rules or governance models that dictate one approach or the other. A hybrid stack can acknowledge these constraints while providing flexibility. It says: “We’ll pick the right tool for the right job, from whatever ecosystem makes sense.”

Of course, “right tool for the right job” sounds simple. But deciding what’s “right” can be tricky.

Where Proprietary Tools Fit Well

Tightly-Coupled Stacks

One of the biggest strengths of proprietary solutions is the cohesiveness of their ecosystems. Vendors spend a lot of time enabling end-to-end data and application integration. If an organization is willing to put aside preconceived notions about the uniqueness of its workflows, it can achieve productivity quickly by simply adopting a proprietary stack and its embedded processes and methods. This approach essentially trades money for time. The organization pays the vendor on the premise that it will get up to speed quickly.

For example, Esri’s ArcGIS platform integrates desktop, server, cloud, and mobile components. If your organization leans heavily on complex, out-of-the-box analytics or well-supported data management workflows, going with this solution can shorten the learning curve. Tools like ArcGIS Pro or ArcGIS Enterprise can handle data ingestion, manage user access, provide advanced analytics, and generate polished cartography—all within a single environment.

Easily Available Support and Roadmaps

Commercial vendors often provide guaranteed support and clearly stated product roadmaps. If your organization prioritizes the idea of risk reduction, having a help desk and service-level agreement (SLA) behind you can tip the scales toward proprietary platforms.

There’s a lot to be said about the quality of help desk support, the timeliness of remedies under an SLA, and the speed and availability of things like security patches. That said, organizations that are very process-oriented place a lot of value in the existence of the agreement itself, which gives them a place to start, even if it is inefficient in execution.

User-Friendly Interfaces

This is far less of an issue than it used to be, but it is a misconception that persists among adherents to proprietary systems. There was a time when GUIs – especially on the desktop – were superior with proprietary software. Open-source was the domain of developers who were happy to work via APIs or the CLI and other such users were their target audience. That distinction has mostly evaporated – especially with the move of applications and processing to the web and cloud. The recent advent of natural-language interfaces will continue to close this gap.

Where proprietary GUIs still shine has more to do with the end-to-end workflow integration discussed earlier. Vendors do a good job of exposing tools and using consistent nomenclature throughout their stacks, which helps users follow their nose through a workflow. In ArcGIS, it is relatively easy to to chart the journey of a feature class, through to being a map package, and finally a map service exposed via ArcGIS Online.

In the end, it is important to recognize the distinction between an “interface that I know how to use” versus an “interface that is better.” Market dominance has a strong effect on perception (see Windows v. Mac, or iPhone v. any other mobile device).

Where Open-Source Tools Shine

Flexibility and Interoperability

Open-source geospatial tools often align tightly with open standards like WMS, WFS, and GeoPackage. This makes it easier to integrate with other systems, add new capabilities, or swap out components without rewriting everything. For instance, using PostGIS as your spatial database allows you to connect easily with GeoServer for serving OGC web services or with QGIS for editing and analysis. Especially in geospatial, open-source tools tend to align heavily around open standards, such as those from the OGC, as a first principle. This streamlines integration of systems that are mostly developed by independent or loosely-coupled project teams.

Cost Savings and Scalability

Open-source tools don’t carry licensing fees, which can be a big plus for tight budgets. And since you can run them on your own hardware or in the cloud, scaling up often involves fewer financial hurdles. For massive datasets or complex operations, you might spin up multiple PostGIS instances or deploy a cluster of servers running GeoServer to handle load—all without worrying about additional per-core or per-seat licenses.

That said, open-source tools aren’t (or shouldn’t be) entirely free of cost. If you are using open-source and deriving business value from it, you should contribute back in some way. That can take many forms. You can let your staff spend part of their time developing enhancements to open-source code, documentation, or tests that can be contributed back. You could at least partially employ someone who maintains projects that you use. You could simply donate funds to projects. Regardless of how you choose to support open-source, there will be a cost, but it will most likely be far less than what you’d spend on a per-seat/core/whatever licensing model.

Finally, organizations can procure support for open-source tools from third parties who often employ maintainers. This begins to approach the help-desk/SLA model discussed above in relation to proprietary systems. It is often not an exact match for that model, but it is a good way for an organization that doesn’t think of software as its “day job” to simultaneously get support and contribute back to the open-source from which it derives value.

Deep Customization

Because the code is open, if you have development resources, you can tailor these tools to your exact needs. We’ve seen teams customize QGIS plugins to automate their entire workflow or tweak GeoServer configurations for specialized queries. You’re not stuck waiting for a vendor to implement that one feature you need.

That said, think through how you approach such customizations before you jump in. The moment you fork a project and change its core code, you own it – especially if the maintainers reject your changes. You’ll want to think about a modular approach that isolates your changes in a way that leaves you able to continue to receive updates to the core code from the maintainers while preserving your customizations. QGIS is a great example – build a plug-in rather than changing the QGIS code itself. Many open-source geospatial tools have extensible models – like GDAL or GeoServer. Understand how those work and have a plan for your customizations before you get going on them.

Taking a Hybrid Approach

A hybrid architecture tries to find balance. Consider the following patterns that we’ve seen work well:

Pattern 1: Proprietary Desktop + Open-Source Backend

In this scenario, you might run ArcGIS Pro for your cartographic and analytic workflows—especially if your staff is well-versed in it—while managing all your spatial data in PostGIS. You maintain the user-friendly environment that your team knows, but you also gain the scalability and interoperability of a robust spatial database. ArcGIS Pro can connect to PostGIS tables, perform analyses, and visualize results. Meanwhile, data integration and sharing can happen through open formats and APIs.

Pattern 2: Open-Source Desktop + Proprietary Web Services

This might sound like a twist, but we’ve seen teams rely on an open-source desktop tool like QGIS while they serve their data through a proprietary server product or a cloud-based hosted solution. Perhaps your organization invested heavily in a proprietary web platform (like ArcGIS Enterprise) that integrates with enterprise user management, security, and BI tools. QGIS users can consume services from that server, taking advantage of familiar open-source editing tools while benefiting from a managed, well-supported data environment.

Pattern 3: Proprietary Spatial Analysis + Open-Source Front-Ends

If you’re dealing with complex spatial modeling—maybe you’re working with advanced network analysis or 3D analytics that a proprietary tool excels at—you can still present and distribute those results through open-source web maps or dashboards. For example, run your analysis in ArcGIS Pro or FME, then publish the output as a service via GeoServer and visualize it in a Leaflet-based web app. Now your end users interact through a lightweight, custom UI that’s easy to update.

Pattern 4: Open-Source Core with Proprietary Add-Ons

Alternatively, your core environment might be open-source—PostGIS for data storage, QGIS for editing, GeoServer for OGC services—but maybe you integrate a proprietary imagery analysis tool because it handles specific sensor data or advanced machine learning models out-of-the-box. This “best-of-breed” approach can deliver specialized capabilities without forcing your entire stack into one ecosystem.

Key Considerations

Governance and Security

A hybrid environment means more moving parts. You’ll need clear policies on data governance, security practices, version control, and how updates get rolled out. Vetting open-source tools for security and licensing compliance is essential, as is ensuring that proprietary components don’t introduce unexpected vendor constraints. 

There are two important points here. The first is that open-source is not less secure than proprietary software – in fact, it is often demonstrably more secure. Acquisition policies often (fairly or not) have extra processes for the use of open-source. You’ll need to be aware of how your organization approaches this. As part of that, you’ll need a plan to show how you’ll integrate security patches as they become available, since there’s usually not a vendor-provided system that automatically pushes them.

The second point is that open-source licenses are legally-binding licenses. Because you do not pay for the software does not mean the licenses do not apply. You’ll want to understand the nuances of open-source licenses (permissive vs. restrictive, copy-left, etc.) to ensure you remain compliant as you integrate open-source into your stack.

Skill Sets and Training

Your team may need to learn new tools. If everyone is fluent in ArcGIS Pro but you introduce PostGIS, you’ll need to provide SQL training. Conversely, if you bring in ArcGIS Enterprise to complement your open-source stack, your team may need guidance on navigating that environment. Don’t skimp on professional development—investing in training pays off in smoother operations down the line.

This is simply best practice in terms of lifecycle management of your technology stack. Give your staff the knowledge they need to be productive. There is ample information and training available for both proprietary and open-source geospatial tooling, so the provenance of a software solution should not affect the availability of training to get your staff up to speed.

Performance and Scalability

As you blend tools, test performance early and often. Proprietary solutions may have certain hardware recommendations or licensing constraints that impact scaling. Open-source tools can scale horizontally, but you may need devops practices to manage containers, virtual machines, security patching, or cloud deployments. Think through how you’ll handle bigger data volumes or higher user traffic before it becomes an urgent issue.

Long-Term Viability and Community Support

Open-source tools thrive on community involvement. Check activity on GitHub repos or forums—are they lively? Do updates happen regularly? Proprietary vendors usually maintain formal roadmaps and documentation. Balancing these factors ensures you’re not tied to a dead-end project or a product that doesn’t meet your evolving needs.

Wrapping Up

We’re long past the days when an all-in proprietary approach was the only game in town. At the same time, not everyone is ready (or able) to go fully open-source. A hybrid architecture acknowledges that each technology ecosystem brings something different to the table, and there is value in mixing and matching.

If you want stable support and integrated workflows right out of the box, proprietary tools might be your go-to. If you’re looking to scale rapidly, and stay agile, open-source solutions are hard to beat. Most organizations find themselves somewhere in between. By thoughtfully picking where you deploy proprietary versus open-source tools, you can build a geospatial architecture that’s both pragmatic and innovative—ready for whatever challenges (and opportunities) lie ahead.

To learn more about how Cercana can help you optimize your geospatial architecture, contact us.

At Cercana, we’re excited by the constant evolution of geospatial technology. AI and its related technologies are becoming increasingly important components of geospatial workflows. Recently, our founder, Bill Dollins, has shared some of his explorations into AI through his personal blog, geoMusings, where he has written about topics like Named Entity Recognition (NER), image similarity using pgVector, and retrieval-augmented generation (RAG). These explorations reflect Cercana’s commitment to helping our clients understand emerging technologies and consider how best to integrate them into their operations.

Coarse Geocoding with ChatGPT

In his May 2024 post, Bill explored the application of ChatGPT for Named Entity Recognition (NER), a vital tool in the AI toolkit. NER can extract key information from unstructured text, such as identifying people, locations, and organizations. At Cercana Systems, we see potential in using AI to streamline geospatial data processing, particularly in the context of large-scale data integration tasks. By using AI tools like ChatGPT, we can automate the extraction of spatial information from textual data, making it easier for our clients to analyze and take action.

This capability is particularly relevant in scenarios where large volumes of data need to be sifted through quickly—whether for real-time monitoring or in-depth analysis. As we continue to refine our capabilities, Cercana is positioned to offer more precise and scalable solutions.

Exploring Image Similarity with pgVector

In a July 2024 post, Bill explored analyzing image similarity using pgVector, a vector database extension for PostgreSQL. This post examines the creation of direct vector embeddings from images, rather than solely using image metadata such as EXIF tags. Combined with such metadata, including location, direct embeddings enable a more discreet kind of “looks like” analysis on a corpus of images.

By integrating pgVector with existing geospatial data pipelines, we are enhancing our ability to process and analyze visual data more efficiently. This capability not only speeds up workflows but also opens new avenues for our clients to derive actionable insights from their image datasets.

Experimenting with RAG Using ChatGPT and DuckDuckGo

Most recently, in August 2024, Bill explored the concept of retrieval-augmented generation (RAG) by combining ChatGPT with DuckDuckGo for information retrieval. RAG models are a quickly-developing capability in AI because they blend the generative capabilities of models like LLMs with the precision of traditional search engines and database queries. This fusion enables more accurate and contextually relevant information retrieval, which can be valuable for analytical tasks and other data-intensive operations.

At Cercana, we’re looking at RAG to enable AI-enhanced geospatial solutions. By integrating RAG, we seek to provide clients with more discreet, context-aware tools that can make sense out of large volumes of unstructured data.

Moving Forward

As Cercana grows and evolves, we are finding new ways of integrating AI into our geospatial services. Bill’s explorations into NER, image similarity, and RAG are examples of this. We believe that AI tools and related technologies, when paired with more traditional tools such as data pipelines and geospatial databases, provide the opportunity to improve data quality and shorten the time-to-market for actionable information.

To learn more about how Cercana can help you integrate AI into your operations, please contact us.