Adopting Geospatial Analysis in BI Tools

March 31, 2023 - Aptaworks

Geospatial analysis is a powerful tool for businesses to understand spatial relationships and patterns in their data. Traditionally, GIS (Geographic Information Systems) software has been used to handle and analyze spatial data. However, as businesses generate more location-based data, the integration of geospatial analysis into BI tools becomes increasingly important. While GIS software is designed for geospatial analysis, BI tools offer more user-friendly interfaces and intuitive data visualization capabilities, putting other types of analysis together to create a more holistic view of business operations. However, BI tools may lack the same level of spatial data integration, accuracy, analysis, and processing capabilities as GIS software. Yet, there are challenges and opportunities to carry out geospatial analysis without using GIS software. 

 

Data Integration

One major challenge is integrating geospatial data with other types of data within a BI tool. Geospatial data often requires specific formats, such as KML or GPX, which may not be compatible with the BI tool’s data formats. 

Cons: 

• Over 200 geospatial data types may require transformation to be recognized by BI tools for proper integration and analysis. 
• While most BI tools can acknowledge locations through geocoding, the information may be outdated or biased when determining administrative levels. 

Pros: 

• Plenty of external connectors support geospatial data integration which is user-friendly and easy to use. 

 

Data Quality

Geospatial data can be complex and may require careful processing to ensure data quality. Data quality can also be a challenge as geospatial data is often collected in various forms such as point, line, and polygons with its own quality criteria. The accuracy of geospatial data can be influenced by various factors such as geocoding parameters, CRS type, and data processing methods. 

Cons: 

• BI tools rarely have their own geospatial database that can directly interpret and process geospatial data, which could limit their capabilities in handling complex spatial data. 

Pros: 

• Centralized data, which separates geospatial data processing outside the BI environment, creates a single source of truth for both geospatial and non-geospatial data. This can make it easier to manage and analyze large volumes of data. 

 

Spatial Algorithm

BI tools may not have the same level of functionality as dedicated geospatial analysis tools. For example, they may lack the ability to perform complex spatial analysis, such as network analysis or isochrone analysis, which can limit the types of geospatial analysis that can be performed. 

Cons: 

• Geospatial visualization in BI tools requires ready-made data, as most tools lack a geospatial engine to perform geospatial manipulation 

Pros: 

• Combining with other types of analysis, such as time-series, rank, and composition. Since most analysis needs to be explicitly illustrated for easier understanding of the information. 
• Interactive visualization. BI tools are optimized for interactive data visualization. Many BI tools offer powerful mapping and interaction with other visualization capabilities that make it easy to explore geospatial data and uncover insights. 

 

Processing Geospatial Data

The amount of data generated by businesses is increasing rapidly, and geospatial analysis requires high computing power and storage. Analyzing big data can be a challenge for BI tools that are not optimized for geospatial analysis. This can lead to slow performance and inefficient use of resources. 

Cons: 

• Limited data points. Visualizing up to 1000 data points is the most recommended approach to maintain the relevance of the visual and ensure optimal performance without losing any data points. 

Pros: 

• Many BI tools support analysis tools such as Python & R, which we can use to perform geospatial processing and specify data needs. 

 

Overall, adopting geospatial analysis in BI tools can provide businesses with valuable insights into spatial relationships and patterns in their data. However, there are challenges to integrating geospatial data with other types of data within BI tools, ensuring data quality, performing complex spatial analysis, and processing large volumes of geospatial data efficiently. Despite these challenges, BI tools offer benefits such as user-friendly interfaces, interactive data visualization capabilities, and support for analysis tools like Python and R. 

Eventually, geospatial analysis has significantly evolved from its military and scientific research roots, and is now being adopted by businesses across various sectors, including finance, land management, product development, insurance, and logistics. Integrating geospatial data into business intelligence tools helps organizations uncover hidden insights and patterns, enabling them to make informed decisions about resource allocation, marketing campaigns, and logistics optimization. With the growing availability of geospatial data and advanced data processing and visualization tools, even businesses without specialized geospatial analysis skills can harness the power of geospatial analysis. As the field continues to evolve and new datasets become available, we can expect to see further adoption of this technology as businesses strive to gain a competitive advantage and make better-informed decisions. Geospatial analysis is set to become an increasingly vital component of business intelligence in the future.