Variations of Open

Posted on 2025-11-18 by Cercana

Introduction

The word “open” gets used so often in tech that it starts to feel universal, like everyone must be talking about the same thing. But once you listen closely, it becomes obvious that different groups mean very different things when they say it. A software engineer is thinking about readable source code and licenses. Someone who works with data is thinking about public portals and Creative Commons. People in AI might be picturing model weights you can download even if you can’t see how the model was trained. And increasingly, someone might just mean information that’s publicly visible online, such as social media posts, ship trackers, or livestreams, without any license at all.

None of these interpretations are wrong. They just grew out of different needs. Openness meant one thing when it applied to code, something else entirely when governments began releasing public data, and now it’s shifting again as AI models enter the mix. Meanwhile, the rise of OSINT has blurred things further, with “open” sometimes meaning nothing more than “accessible to anyone with an internet connection.”

The result is that modern systems combine pieces from all these traditions and people often assume they’re aligned when they’re not. The friction shows up not because anyone misunderstands the technology, but because the language hasn’t kept up with how quickly the idea of openness has expanded.

Open-Source Software

In terms of software, “open” means open-source. In that context it has a clear meaning. You can read the code, change it, and share it as long as you follow the license. That predictability is a big part of why the movement grew. People understood the rules and trusted that the rules would hold.

But the full spectrum of open-source shows up in the habits and culture around it. Communities develop their own rhythms for how to submit a pull request, file a useful bug report, talk through disagreements, and decide which features or fixes make it into a release. None of that comes from a license. People learn it by watching others work, answering questions in long issue threads, or showing up in mailing lists and channels where projects live.

There’s also an unspoken agreement inside open-source software. If a project helps you, you try to help it back. Maybe you fix a typo, or you donate, or you answer someone else’s question. It’s not required, but it’s how projects stay healthy.

Anyone who has maintained an open-source project knows it isn’t glamorous. It can be repetitive, sometimes thankless, and often demanding. Good maintainers end up juggling technical decisions, community management, and the occasional bit of diplomacy.

All this shapes a shared understanding of what openness means in software. People think not just about reading code, but about the whole ecosystem built around it: contribution paths, governance models, release practices, and the blend of freedom and responsibility that holds everything together.

Once the idea of openness moved beyond software, that ecosystem didn’t necessarily apply. As other fields developed their own approaches to openness, patterns and practices evolved in alignments with each unique domain.

Open Data

Open data developed along its own path. Instead of code, publishers released information about the world: transit schedules, land use maps, environmental readings, census tables. The goal was simple: make public data easier to access so people could put it to use.

Because software licenses didn’t fit, data and content licenses such as Creative Commons were developed. CC BY and CC0 became common. Open Data Commons created specialized database licenses—ODbL added share-alike requirements specifically for databases, while PDDL offered a public domain dedication. You can see the differences in well known datasets. OpenStreetMap’s ODbL means derived data often has to stay open and always require attribution. USGS datasets, which are mostly public domain, are easy to fold into commercial tools. City transit feeds under CC BY only ask for attribution.

Privacy concerns complicate open data, which isn’t exempt from GDPR, CCPA, or similar laws. Even seemingly innocuous data can reveal personal information—location datasets showing frequent stops at specific addresses, or timestamped transit records that establish movement patterns. Many publishers address this through aggregation, anonymization, or by removing granular temporal and spatial details, but anyone working with open data still ends up checking metadata, tracking where files came from, and thinking about what patterns a dataset might reveal.

Open Source Information (OSINT)

Open-source Intelligence (OSINT) is an overlapping but different concept from open data. Information is considered “open” in OSINT because anyone can access it, not because anyone has the right to reuse it. A ship tracker, a social media post, a livestream from a city camera are examples of data that may fall into this category.

These sources vary widely in reliability. Some come from official databases or verified journalism. Others come from unvetted social media content, fast-moving crisis reporting, or user-generated material with no clear provenance. OSINT analysts rely heavily on validation techniques such as correlation, triangulation, consensus across multiple sources, and structured analytic methods.

While OSINT has deep roots in government intelligence work, it is now widely practiced across sectors including journalism, cybersecurity, disaster response, financial services, and competitive intelligence. Marketing technologies have expanded OSINT further into the private sector, making large-scale collection and analysis tools widely accessible.

Confusion can arise when open data and OSINT are treated as interchangeable. Someone may say they used open data, meaning a licensed dataset from a government portal. Someone else hears open and assumes it means scraping whatever is publicly visible.

This distinction matters because the two categories carry fundamentally different permissions and obligations. Open data comes with explicit rights to reuse, modify, and redistribute—legal clarity that enables innovation and collaboration. OSINT, by contrast, exists in a gray area where accessibility doesn’t imply permission, and users must navigate copyright, privacy laws, and terms of service on a case-by-case basis.

Understanding this difference isn’t just semantic precision; it shapes how organizations design data strategies, assess legal risks, and build ethical frameworks for information use. When practitioners clearly specify whether they’re working with licensed open data or publicly accessible OSINT, they help prevent costly misunderstandings and ensure their work rests on solid legal and ethical foundations.

Open AI Models

In AI, openness takes on another meaning entirely. A model is more than code or data. It’s architecture, training data, weights, and the training process that binds everything together. So when a model is described as open, it’s natural to ask which part is actually open.

You see the variety in projects released over the past few years. Some groups publish only the weights and keep the training data private. Meta’s Llama models fall into this category. You can download and fine tune them, but you don’t see what went into them. Others release architectural details and research papers without sharing trained weights—early transformer work from Google and OpenAI showed the approach without providing usable models. GPT-NeoX took a middle path, releasing both architecture and weights but with limited training data transparency.

A few projects aim for full transparency. BLOOM is the most visible example, with its openly released code, data sources, logs, and weights. It took a global effort to pull that off, and it remains the exception, though projects like Falcon and some smaller research models have attempted similar transparency.

This partial openness shapes how people use these models. If you only have the weights, you can run and fine tune the model, but you can’t inspect the underlying data. When the training corpus stays private, the risks and biases are harder to understand. And when licenses restrict use cases, as they do with Llama’s custom license that prohibits certain commercial applications, or research-only models like many academic releases, you might be able to experiment but not deploy. Mistral’s models show another approach—Apache 2.0 licensing for some releases but custom licenses for others.

The idea of contribution looks different too. You don’t patch a model the way you patch a library. You build adapters, write wrappers, create LoRA fine-tunes, or train new models inspired by what came before. Openness becomes less about modifying the original artifact and more about having the freedom to build around it.

So in AI, open has become a spectrum. Sometimes it means transparent. Sometimes it means accessible. Sometimes it means the weights are downloadable even if everything else is hidden. The word is familiar, but the details rarely match what openness means in software or data.

Real World Considerations

These differences are fairly straightforward when they live in their own domains. Complexity can arise when they meet inside real systems. Modern projects rarely stick to one tradition. A workflow might rely on an open-source library, an open dataset, publicly scraped OSINT, and a model with open weights, and each piece brings its own rules.

Teams can run into this without realizing it. Someone pulls in an Apache-licensed geospatial tool and combines it smoothly with CC BY data. These work fine together. But then someone else loads OpenStreetMap data without noticing the share-alike license that affects everything it touches. A third person adds web-scraped location data from social media, not considering the platform’s terms of service or privacy implications. A model checkpoint from Hugging Face gets added on top, even though the license limits commercial fine-tuning. Most of these combinations are manageable with proper documentation, but some create real legal barriers.

Expectations collide too. A software engineer assumes they can tweak anything they pull in. A data analyst assumes the dataset is stable and comes with clear reuse rights. An OSINT analyst assumes publicly visible means fair game for analysis. Someone working with models assumes fine-tuning is allowed. All reasonable assumptions inside their own worlds, but they don’t line up automatically.

The same thing happens in procurement. Leadership hears open and thinks it means lower cost or fewer restrictions. But an open-source library under Apache is not the same thing as a CC BY dataset, neither is the same as scraped public data that’s accessible but not licensed, and none of those are the same as an open-weight model with a noncommercial license.

Geospatial and AI workflows feel these tensions even more. They rarely live cleanly in one domain. You might preprocess imagery with open-source tools, mix in open government data, correlate it with ship tracking OSINT, and run everything through a model that’s open enough to test but not open enough to ship. Sometimes careful documentation and attribution solve the problem. Other times, you discover a share-alike clause or terms-of-service violation that requires rethinking the entire pipeline.

This is when teams slow down and start sorting things out. Not because anyone did something wrong, but because the word open did more work than it should have and because “publicly accessible” got mistaken for “openly licensed.”

Clarifying Open

A lot of this gets easier when teams slow down just enough to say what they actually mean by open. It sounds almost too simple, but it helps. Are we talking about open code, open data, open weights, open access research, or just information that’s publicly visible? Each one carries its own rules and expectations, and naming it upfront clears out a lot of the fog.

Most teams don’t need a formal checklist, though those in regulated industries, government contracting, or high-stakes commercial work often do. What every team needs is a little more curiosity about the parts they’re pulling in—and a lightweight way to record the answers. If someone says a dataset is open, ask under what license and note it in your README or project docs. If a model is open, check whether that means you can fine-tune it, use it commercially, or only experiment with it—and document which version you’re using, since terms can change. If a library is open-source, make sure everyone knows what the license allows in your context. If you’re using publicly visible information—social media posts, ship trackers, livestreams—be clear that this is OSINT, not licensed open data, and understand what legal ground you’re standing on.

These questions matter most at project boundaries: when you’re about to publish results, share with partners, or move from research to production. A quick decision log—even just a shared document listing what you’re using and under what terms—prevents expensive surprises. It also helps when someone new joins the team or when you revisit the project months later.

The more people get used to naming the specific flavor of openness they’re dealing with and writing it down somewhere searchable, the smoother everything else goes. Projects move faster when everyone shares the same assumptions. Compliance reviews become straightforward when the licensing story is already documented. Teams stop discovering deal-breakers right when they’re trying to ship something. It’s not about being overly cautious or building heavy process. It’s just about giving everyone a clear, recorded starting point before the real work begins.

Conclusion

If there’s a theme running through all of this, it’s that the word open has grown far beyond its original boundaries. It meant one thing in software, something different in the world of public data, another in AI, and gets stretched even further when people conflate it with simply being publicly accessible. Each tradition built its own norms and expectations, and none of them are wrong. They just don’t automatically line up the way people sometimes expect.

Most of the friction we see in real projects doesn’t come from bad decisions. It comes from people talking past one another while using the same word. A workflow can look straightforward on paper but fall apart once you realize each component brought its own version of openness, or that some parts aren’t “open” at all, just visible. By the time a team has to sort it out, they’ve already committed to choices they didn’t realize they were making.

The good news is that this is manageable. When people take a moment to say which kind of open they mean, or acknowledge when they’re actually talking about OSINT or other public information, everything downstream gets smoother: design, licensing, procurement, expectations, even the conversations themselves. It turns a fuzzy idea into something teams can actually work with. It requires ongoing attention, especially as projects grow and cross domains, but the effort pays off.

Openness is a powerful idea, maybe more powerful now than ever. But using it well means meeting it where it actually is, not where we assume it came from.

At Cercana Systems, we have deep experience with the full open stack and can help you navigate the complexities as you implement open assets in your organization. Contact us to learn more.

Header image credit: Aaron Pruzaniec, CC BY 2.0 https://creativecommons.org/licenses/by/2.0, via Wikimedia Commons

Data Stewardship in AI, Geospatial, and Security Operations

Posted on 2025-04-30 by Cercana

In today’s AI-driven and geospatially enabled world, data is an organization’s most valuable asset — yet it is often treated as an afterthought until issues arise. Poor data quality, incomplete metadata, and inconsistent governance can quickly derail even the most sophisticated projects. At Cercana, we believe that data stewardship must be intentional, continuous, strategic, and embedded into every phase of the project lifecycle.

Why Data Stewardship Matters More Than Ever

The rapid adoption of artificial intelligence and machine learning means that models are only as good as the data that train them. In geospatial systems, slight inaccuracies — such as misaligned coordinates or outdated basemaps — can cascade into serious operational errors. Furthermore, organizations are increasingly judged not only on the outcomes they produce but also the quality and governance of the data they maintain, particularly under frameworks such as GDPR and emerging AI regulations (Voigt & von dem Bussche, 2017). Effective data stewardship is now essential for both technical success and regulatory compliance.

Research indicates that up to 80% of AI project failures can be traced back to issues of poor data quality (Gartner, 2021). Organizations that invest early in stewardship practices stand a much greater chance of building reliable, resilient systems.

Common Pitfalls When Stewardship Is Ignored

When stewardship is neglected common problems emerge quickly. Disparate data sources lead to inconsistencies that degrade model performance and decision-making. Metadata is often incomplete, particularly for spatial attributes like projection information or temporal validity, limiting future usability. Without lineage tracking, teams cannot verify the origin or reliability of their data, making validation nearly impossible. Additionally, mismatches in coordinate systems, uncontrolled enrichment, and poorly managed access rights introduces risks that compound over time. Without a defined stewardship process even the most promising initiatives can stagnate or fail outright.

Security Risks Tied to Poor Stewardship

In addition to operational challenges, poor data stewardship also introduces serious security risks. Mismanaged datasets can unintentionally expose sensitive information, particularly when metadata or spatial attributes reveal more than intended. Without proper lineage tracking and access control, organizations are vulnerable to unauthorized data manipulation, leakage, or corruption. Furthermore, compliance with emerging security and privacy standards increasingly depends on maintaining disciplined data governance practices (NIST, 2020). Strong stewardship is not only essential for quality and reliability — it is also critical for protecting organizational and national security interests.

Retrieval-Augmented Generation (RAG) systems, which combine data retrieval with AI driven content generation, are particularly vulnerable to a form of attack known as RAG poisoning. In this scenario, malicious or inaccurate data is intentionally inserted into the knowledge base leading the AI to retrieve and generate harmful or misleading outputs. Without strong data stewardship practices, including strict validation, provenance tracking and controlled ingestion pipelines, organizations may unwittingly expose themselves to these sophisticated new threats.

What Effective Data Stewardship Looks Like

Effective stewardship begins with discovery; cataloging all datasets including third-party and open-source feeds. Standardization of schemas, metadata fields and coordinate systems follows, ensuring consistency across applications. Enrichment activities such as gap-filling, normalization, and validation against authoritative sources elevates the quality of data available for analysis. Governance frameworks define who can create, edit, validate and retire datasets, providing necessary accountability. Finally, continuous monitoring using audits and stewardship KPIs ensures that quality standards are sustained overtime (Redman, 2018).

Many modern data platforms implement these stewardship principles using structured frameworks like the Medallion Architecture, which organizes data into Bronze (raw), Silver (cleaned) and Gold (curated) layers. This structured progression enforces discovery, standardization, enrichment and governance practices in a scalable way (Armbrust, Das, Zhu, & Xin, 2021). By applying stewardship systematically across each stage, organizations can build more resilient and trustworthy AI and geospatial systems.

Organizations leveraging open datasets must pay particular attention to validation as not all external sources meet the same quality thresholds required for mission-critical work.

How Cercana Helps Clients Get It Right

At Cercana, data stewardship is foundational not optional. We work with leading technologies such as Apache Airflow for orchestration, dbt for data transformation, Delta Lake for storage reliability, and PostGIS for advanced geospatial data management. We embed stewardship practices at the earliest phases of our projects ensuring strong, reliable data pipelines from day one. Our team brings expertise across metadata cataloging, ETL/ELT pipelines, geospatial validation and stewardship strategy development. Beyond tools and processes, we assist organizations in building a sustainable stewardship culture through team training and change management. We believe that good stewardship is as much about people and processes as it is about technology.

Conclusion

Data stewardship is no longer a back-office concern; it is a mission critical capability that underpins the success of AI, machine learning and geospatial analytics initiatives. Organizations that prioritize stewardship today will be best positioned to lead in an AI-driven, regulation-conscious future. To learn how Cercana can help you strengthen your data stewardship practices, contact us today.

References

Armbrust, M., Das, T., Zhu, X., & Xin, R. (2021). Delta Lake: High-performance ACID table storage over cloud object stores. Proceedings of the VLDB Endowment, 13(12), 3411–3424. https://doi.org/10.14778/3415478.3415560

Gartner. (2021). Gartner predicts 80% of AI projects will remain alchemy, run by wizards whose talents won’t scale. Retrieved from https://www.gartner.com/en/newsroom/press-releases/2021-03-17-gartner-predicts-80–of-ai-projects-will-stagnate

NIST. (2020). Security and Privacy Controls for Information Systems and Organizations (NIST Special Publication 800-53 Rev. 5). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-53r5

Redman, T. C. (2018). Data Driven: Profiting from Your Most Important Business Asset. Harvard Business Review Press.

Voigt, P., & von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR): A Practical Guide. Springer.

Applying Porter’s Five Forces to Open-Source Geospatial

Posted on 2025-02-06 by Cercana

Introduction

The geospatial industry has seen significant transformation with the rise of open-source solutions. Tools like QGIS, PostGIS, OpenLayers, and GDAL have provided alternatives to proprietary GIS software, providing cost-effective, customizable, and community-driven mapping and spatial analysis capabilities. While open-source GIS thrives on collaboration and accessibility, it still operates within a competitive landscape influenced by external pressures.

Applying Porter’s Five Forces, a framework for competitive analysis developed by Michael E. Porter in 1979, allows us to analyze the industry dynamics and understand the challenges and opportunities open-source GIS solutions face. The five forces include the threat of new entrants, bargaining power of suppliers, industry rivalry, bargaining power of buyers, and the threat of substitutes. We will explore how these forces shape the world of open-source geospatial technology.

Porter’s Five Forces was conceived to analyze traditional market-driven dynamics. While open-source software development is not necessarily driven by a profit motive, successful open-source projects require thriving, supportive communities. Such communities still require resources – either money or, even more importantly and scarce, time. As a result, a certain amount of market thinking can be useful when considering adoption of open-source into your operations or starting a new project.

Porter articulated the five forces in terms of “threats” and “power” and “rivalry.” We have chosen to retain that language here for alignment with the model but, in the open-source world, many of these threats can represent opportunities for greater collaboration.

1. Threat of New Entrants: Low to Moderate

The barriers to entry in open-source geospatial solutions are low for basic tool development compared to proprietary software development. Developers can utilize existing open-source libraries, open geospatial data, and community-driven documentation to build new tools with minimal investment.

However, gaining significant adoption or community traction presents higher barriers than described in traditional new entrant scenarios. Well-established open-source solutions like QGIS, PostGIS, and OpenLayers have strong community backing and extensive documentation, making it challenging for new entrants to attract users.

New players may find success by focusing on novel or emerging use case areas like AI-powered GIS, cloud-based mapping solutions, or real-time spatial analytics. Companies that provide specialized integrations or enhancements to existing open-source GIS tools may also gain traction. DuckDB and its edge-deployability is a good example of this.

While new tools are relatively easy to develop, achieving broad community engagement often requires differentiation, sustained innovation, and compatibility with established standards and ecosystems.

2. Bargaining Power of Suppliers: Low to Moderate

Unlike proprietary GIS, where vendors control software access, open-source GIS minimizes supplier dependence due to its open standards and community-driven development. The availability of open geospatial datasets (e.g., OpenStreetMap, NASA Earthdata, USGS) further reduces the influence of traditional suppliers.

Moderate supplier power can arise in scenarios where users depend heavily on specific service providers for enterprise-level support, long-term maintenance, or proprietary enhancements (e.g., enterprise hosting or AI-powered extensions). Companies offering such services, like Red Hat’s model for Linux, could gain localized influence over organizations that require continuous, tailored support.

However, competition among service providers ensures that no single vendor holds significant leverage. This can work to the benefit of users, who often require lifecycle support. Localized supplier influence can grow in enterprise settings where long-term support contracts are critical, making it a consideration in high-complexity deployments.

3. Industry Rivalry: Moderate to High

While open-source GIS tools are developed with a collaborative ethos, competition still exists, particularly in terms of user adoption, funding, and enterprise contracts. Users typically don’t choose multiple solutions in a single category, so a level of de facto competition is implied even though open-source projects don’t explicitly and directly compete with each other in the same manner as proprietary software.

Open-source projects compete for users: QGIS, GRASS GIS, and gvSIG compete in desktop GIS; OpenLayers, Leaflet, and MapLibre compete in web mapping.
Enterprise support: Companies providing commercial support for open-source GIS tools compete for government and business contracts.
Competition from proprietary GIS: Esri, Google Maps, and Hexagon offer integrated GIS solutions with robust support, putting pressure on open-source tools to keep innovating.

However, open-source collaboration reduces direct rivalry. Many projects integrate with one another (e.g., PostGIS works alongside QGIS), creating a cooperative rather than competitive environment. While open-source GIS projects indirectly compete for users and funding, collaboration mitigates this and creates shared value.

Emerging competition from cloud-native platforms and real-time analytics tools, such as SaaS GIS and geospatial AI services, increases rivalry. As geospatial technology evolves, integrating AI and cloud functionalities may determine long-term competitiveness.

When looking to adopt open-source, consider that loose coupling through the use of open standards can add greater value. When considering starting a new open-source project, have integration and standardization in mind to potentially increase adoption.

4. Bargaining Power of Buyers: Moderate

In the case of open-source, “bargaining” refers to the ability of the user to switch between projects, rather than a form of direct negotiation. The bargaining power of buyers in the open-source GIS space is significant, primarily due to the lack of upfront capital expenditure. This financial flexibility enables users to explore and switch between tools without major cost concerns. While both organizational and individual users have numerous alternatives across different categories, this flexibility does not necessarily translate to strong influence over the software’s development.

Key factors influencing buyer power:

Minimal financial lock-in: In the early stages of adoption, users can easily migrate between open-source tools. However, as organizations invest more time in customization, workflow integration, and user training, switching costs increase, gradually reducing their flexibility.
Community-driven and self-support options: Buyers can access free support through online forums, GitHub repositories, and community-driven resources, lowering their dependence on paid services.
Customizability and adaptability: Open-source GIS software allows organizations to tailor the tools to their specific needs without vendor constraints. However, creating a custom version (or “fork”) requires caution, as it could result in a bespoke solution that the organization must maintain independently.

To maximize their influence, new users should familiarize themselves with the project’s community and actively participate by submitting bug reports, fixes, or documentation. Consistent contributions aligned with community practices can gradually enhance a user’s role and influence over time.

For large enterprises and government agencies, long-term support requirements – especially for mission-critical applications – can reduce their flexibility and bargaining power over time. This dependency highlights the importance of enterprise-level agreements in managing risk.

5. Threat of Substitutes: Moderate to High

Substitutes for open-source GIS tools refer to alternatives that provide similar functionality. These substitutes include:

Proprietary GIS software: Tools like ArcGIS, Google Maps, and Hexagon are preferred by many organizations due to their perceived stability, advanced features, and enterprise-level support.
Cloud-based and SaaS GIS platforms: Services such as Felt, MapIdea, Atlas, Mapbox, and CARTO offer user-friendly, web-based mapping solutions with minimal infrastructure requirements.
Business Intelligence (BI) and AI-driven analytics: Platforms like Tableau, Power BI, and AI-driven geospatial tools can partially or fully replace traditional GIS in certain applications.
Other open-source GIS tools: Users can switch between alternatives like QGIS, GRASS, OpenLayers, or MapServer with minimal switching costs.

However, open-source GIS tools often complement rather than fully replace proprietary systems. For instance, libraries like GDAL and GeoPandas are frequently used alongside proprietary solutions like ArcGIS. Additionally, many SaaS platforms incorporate open-source components, offering organizations a hybrid approach that minimizes infrastructure investment while leveraging open-source capabilities.

The emergence of AI-driven spatial analysis and real-time location intelligence platforms is increasingly positioning them as partial substitutes to traditional GIS, intensifying this threat. As these technologies mature, hybrid models integrating both open-source and proprietary elements will become more common.

Conclusion

Porter’s Five Forces analysis reveals that open-source geospatial solutions exist in a highly competitive and evolving landscape. While they benefit from free access, strong community support, and low supplier dependence, they also face competition from proprietary GIS, SaaS-based alternatives, and substitutes like AI-driven geospatial analytics.

To remain competitive, open-source GIS projects must not only innovate in cloud integration and AI-enhanced spatial analysis but also respond to the shifting landscape of real-time analytics and SaaS-based delivery models. Strengthening enterprise support, improving user-friendliness, and maintaining strong community engagement will be key to their long-term sustainability.

As geospatial technology advances, open-source GIS will continue to play a crucial role in democratizing access to spatial data and analytics, offering an alternative to fully proprietary systems while fostering collaboration and technological growth.

To learn more about how Cercana can help you develop your open-source geospatial strategy, contact us here.

Do You Need a Data Pipeline?

Posted on 2023-05-17 by Cercana

Do you need a data pipeline? That depends on a few things. Does your organization see data as an input into its key decisions? Is data a product? Do you deal with large volumes of data or data from disparate sources? Depending on the answers to these and other questions, you may be looking at the need for a data pipeline. But what is a data pipeline and what are the considerations for implementing one, especially if your organization deals heavily with geospatial data? This post will examine those issues.

A data pipeline is a set of actions that extract, transform, and load data from one system to another. A data pipeline may be set up to run on a specific schedule (e.g., every night at midnight), or it might be event-driven, running in response to specific triggers or actions. Data pipelines are critical to data-driven organizations, as key information may need to be synthesized from various systems or sources. A data pipeline automates accepted processes, enabling data to be efficiently and reliably moved and transformed for analysis and decision-making.

A data pipeline can start small – maybe a set of shell or python scripts that run on a schedule – and it can be modified to grow along with your organization to the point where it may be driven my a full-fledged event-driven platform like AirFlow or FME (discussed later). It can be confusing, and there are a lot of commercial and open-source solutions available, so we’ll try to demystify data pipelines in this post.

Geospatial data presents unique challenges in data pipelines. Geospatial data are often large and complex, containing multiple dimensions of information (geometry, elevation, time, etc.). Processing and transforming this data can be computationally intensive and may require significant storage capacity. Managing this complexity efficiently is a major challenge. Data quality and accuracy is also a challenge. Geospatial data can come from a variety of sources (satellites, sensors, user inputs, etc.) and can be prone to errors, inconsistencies, or inaccuracies. Ensuring data quality – dealing with missing data, handling noise and outliers, verifying accuracy of coordinates – adds complexity to standard data management processes.

Standardization and interoperability challenges, while not unique to geospatial data, present additional challenges due to the nature of the data. There are many different formats, standards, and coordinate systems used in geospatial data (for example, reconciling coordinate systems between WGS84, Mercator, state plane, and various national grids). Transforming between these can be complex, due to issues such as datum transformation. Furthermore, metadata (data about the data) is crucial in geospatial datasets to understand the context, source, and reliability of the data, which adds another layer of complexity to the processing pipeline.

While these challenges make the design, implementation, and management of data pipelines for geospatial data a complex task, they can provide significant benefits to organizations that process large amounts of geospatial data:

Efficiency and automation: Data pipelines can automate the entire process of data extraction, transformation, and loading (ETL). Automation is particularly powerful in the transformation stage. “Transformation” is a deceptively simple term for a process that can contain many enrichment and standardization tasks. For example, as the coordinate system transformations described above are validated, they can be automated and included in the transformation stage to remove human error. Additionally, tools like Segment Anything can be called during this stage to turn imagery into actionable, analyst-ready information.
Data quality and consistency: The transformation phase includes steps to clean and validate data, helping to ensure data quality. This can include resolving inconsistencies, filling in missing values, normalizing data, and validating the format and accuracy of geospatial coordinates. By standardizing and automating these operations in a pipeline, an organization can ensure that the same operations are applied consistently to all data, improving overall data quality and reliability.
Data Integration: So far, we’ve talked a lot about the transformation phase, but the extract phase provides integration benefits. A data pipeline allows for the integration of diverse data sources, such as your CRM, ERP, or support ticketing system. It also enables extraction from a wide variety of formats (shapefile, GeoParquet, GeoJSON, GeoPackage, etc). This is crucial for organizations dealing with geospatial data, as it often comes from a variety of sources in different formats. Integration with data from business systems can provide insights into performance as relates to the use of geospatial data.
Staging analyst-ready data: With good execution, a data pipeline produces clean, consistent, integrated data that enables people to conduct advanced analysis, such as predictive modeling, machine learning, or complex geospatial statistical analysis. This can provide valuable insights and support data-driven decision making.

A data pipeline is first and foremost about automating accepted data acquisition and management processes for your organization, but it is ultimately a technical architecture that will be added to your portfolio. The technology ecosystem for such tools is vast, but we will discuss a few with which we have experience.

Apache Airflow: Developed by Airbnb and later donated to the Apache Foundation, Airflow is a platform to programmatically author, schedule, and monitor workflows. It uses directed acyclic graphs (DAGs) to manage workflow orchestration. It supports a wide range of integrations and is highly customizable, making it a popular choice for complex data pipelines. AirFlow is capable of being your entire data pipeline.
GDAL/OGR: The Geospatial Data Abstraction Library (GDAL) is an open-source, translator library for raster and vector geospatial data formats. It provides a unified API for over 200 types of geospatial data formats, allowing developers to write applications that are format-agnostic. GDAL supports various operations like format conversion, data extraction, reprojection, and mosaicking. It is used in GIS software like QGIS, ArcGIS, and PostGIS. As a library it can also be used in large data processing tasks and in AirFlow workflows. Its flexibility makes it a powerful component of a data pipeline, especially where support for geospatial data is required.
FME: FME is a data integration platform developed by Safe Software. It allows users to connect and transform data between over 450 different formats, including geospatial, tabular, and more. With its visual interface, users can create complex data transformation workflows without coding. FME’s capabilities include data validation, transformation, integration, and distribution. FME in the geospatial information market and is the most geospatially literate commercial product in the data integration segment. In addition it supports a wide range of non-spatial sources, including proprietary platforms such as Salesforce. FME has a wide range of components, making it possible for it to scale up to support enterprise-scale data pipelines.

In addition to the tools listed above, there is a fairly crowded market segment for hosted solutions, known as “integration platform as a service” or IPaaS. These platforms all generally have ready-made connectors for various sources and destinations, but spatial awareness tends to be limited, as does customization options for adding spatial. A good data pipeline is tightly coupled to the data governance procedures of your organization, so you’ll see greater benefits from technologies that allow you customize to your needs.

Back to the original question: Do you need a data pipeline? If data-driven decisions are key to your organization, and consistent data governance is necessary to have confidence in your decisions, then you may need a data pipeline. At Cercana, we have experience implementing data pipelines and data governance procedures for organizations large and small. Contact us today to learn more about how we can help you.