Why Location Data Matters
For businesses, location data often comes from external sources in different formats and coordinate systems. Finding, downloading, and converting it into analytics-ready form requires time and expertise. Yet, leveraging location data offers significant opportunities:
“When you add a geographical dimension to business analysis, you gain a better understanding of where and why things happen. This supports decisions in areas such as resource allocation, planning, and risk management,” says Smart Data Hub Co-Founder Karri Pulkkinen.
The Challenge: Diverse Sources and Formats
According to Pulkkinen, many organisations have moved to cloud-based analytics platforms where they consolidate up-to-date operational data for analysis. Bringing location data into these environments isn’t always easy. Spatial data APIs, coordinate systems, and data formats can be challenging even for experienced data professionals who haven’t worked with location data before. In addition, the data integration tools used in cloud analytics environments often don’t support them well.
“Without automation, integrating location data is labor-intensive and slow, making its use expensive and complex,” he adds.
The Solution: Automated Integration
Smart Data Hub simplifies location data integration so businesses know where critical events occur. Its Geo Data Connector software suite, designed for data professionals, automates the extract, transformation, and load of location data directly into analytics environments such as Snowflake or Databricks. Users don’t need to handle raw data themselves – the application manages the process and keeps data up to date on schedule.
“The role of location data experts doesn’t diminish; it shifts toward interpreting content and applying it to business needs, as well as performing advanced spatial analyses that AI models cannot yet handle with sufficient accuracy or reliability,” Pulkkinen notes.
Dynamic Pricing: A Common Use Case
Smart Data Hub serves both enterprises and public-sector organisations. The most common use case is dynamic pricing based on location data. Factors influencing pricing may include population density, distance to competitors, demographic data such as income levels, regional demand forecasts based on mobility data, and environmental factors like weather.
Pulkkinen shares an example of a solution that was delivered to a client in the manufacturing industry:
“Location datasets were collected from multiple sources with varying resolutions, which differed from the client’s own boundary definitions. Harmonization required expertise and kriging interpolation to combine data into a usable, comparable whole. Automation handles the technical work, but quality assurance and suitability assessment require human expertise.”
AI Completes Metadata
AI is a key part of the solution. To make effective decisions, the source data must be of the highest possible quality. Geo Data Connector uses AI to automatically fill in missing metadata and convert data into an analytics-ready, machine-learning-compatible format. This allows analysts and AI models to use location data immediately without manual preprocessing.
“AI works well for enriching descriptive metadata. It can generate missing descriptions, keywords, and classifications, improving discoverability and helping data experts select suitable datasets,“ Pulkkinen explains.
Users always have access to both the original source data and the added data, ensuring quality assessment and preserving the original purpose. Pulkkinen believes that compliance with standards like INSPIRE is likely possible since it relies on linguistic analysis, though they haven’t tested it.
‘In principle, metadata sections concerning accuracy, official responsibilities or legal obligations should not be determined automatically by artificial intelligence,’ states Pulkkinen.
Interface Stability and Risk Management
Interface stability is a major challenge in open-data-based business because providers can discontinue or change APIs without notice. Pulkkinen stresses that preparation cannot rely solely on technology, but organizations must identify critical data sources and define contingency plans for changes or outages. Technology, however, provides an edge:
“Our product detects API disruptions and alerts users, enabling pre-planned actions to minimize business and analytics interruptions. We also actively monitor the development of interface standards to ensure our tools support key APIs well before new standards are adopted,” Pulkkinen says.
Next Steps: Broader Compatibility
Smart Data Hub’s near-term goal is to offer broader compatibility with various location data sources and popular analytics platforms, as well as add AI-assisted tools that make finding, combining, and analyzing location data faster. Pulkkinen concludes:
“When companies use location data effectively, they can respond to market changes faster, optimize operations, and develop new services or products based on data. Our mission is clear: to make location datasets accessible to every data professional.”
