Location intelligence: Adding geospatial context to BI

Location intelligence provides powerful “where” perspectives and contextual insights by combining business data, geospatial data and analytics with mapping visualizations, which are easily understood. Being able to visualize the “where” dimension in routes, patterns, trends and other spatial relationships is invaluable for planning and optimizing processes – and enhances many business intelligence applications.

Geospatial data is often collected in conjunction with demographic, business or operational data to gain a better understanding of situational or environmental context. Significant improvements in IoT sensor technologies, cloud ingestion and streaming analytics services enable location data to be collected and analyzed in real-time from connected devices. Due in part to the increasing adoption of Internet of Things (IoT) and digital transformation progress, this segment of the analytics market is estimated to grow to US$16.35 billion by 2021, according to Markets and Markets research.

How location intelligence can be used across different industries

Today, location intelligence is helping transportation, government, retail, energy, supply chain, insurance, healthcare, construction, engineering, manufacturing and other industries to deliver insights needed for improved decision making. From marketing apps on cell phones to connected cars and even self-healing power grids, location is playing a bigger role in our day-to-day lives. Here are a few practical industry applications.

Tips for implementing a location intelligence data strategy

Across numerous industries, location intelligence is helping organizations act smarter and increase productivity. Tapping the potential of location begins with developing a strategy for what level of geospatial data is needed for business processes and how to responsibly collect that data.

• Location data can be captured at the lowest level of granularity (a coordinate pair of latitude and longitude) or at a higher-level area such as ZIP code or city. Geographic location shapes or geospatial polygons might also be needed for computing accurate distances. Third-party location data suppliers currently offer geocoding and geo-enriched data with additional descriptive attributes, time and demographic characteristics.
• Keep security and personal data privacy top of mind when gathering location data. Individual location data may require disclosure and opt-in prior to collection. Location data can also become a threat to personal safety. Summarizing location data at higher levels or anonymizing the data may mitigate some of those issues.
• Another thing to consider as you capture location data is what elements will be needed to relate spatial data to other data your organization has saved. Since regions, territories, ZIP codes and other location areas can change over time, you may want to add identifiers to location data along with foreign keys to other data for analytical queries. To avoid mapping issues, save geospatial latitude and longitude points along with city, state, country and other location information. Many BI solutions cannot map street addresses correctly unless a latitude and longitude has been provided.
• To make location data useful for the business, review who should have access to this data and how the information will be used for decision making. Custom geospatial shape polygons and other location data are complex. They can’t be easily queried or displayed in analytics tools. Depending on where the location data is stored, there may or may not be SQL geospatial functions available for location, points, distance or shape.
• To make location data operational, geographic information system (GIS) analysts and analytics professionals should store location data in a format that can be used. If distance functions are not supported but needed, consider storing location coordinates and distances in a lookup table along with related business-ready data. 

Bridging the gap between GIS analysts and BI roles

Embedding geospatial context into data-driven decisions and delivering insights in time to the right person requires close collaboration between GIS analysts and those with BI and analytics roles. Historically, these functional roles have operated in siloes, but the integration of location intelligence capabilities into BI and analytics solutions has helped surface location-driven insights to a wider set of employees.

Marketing analysts should collaborate with GIS analysts who understand spatial and geographic data to explore location data and visually identify promising customer segments for suitable campaigns. GIS analysts know how to create and use multilayered or custom-built maps for exploratory analysis. Similarly, data scientists might want to integrate geospatial data into predictive models for specific use cases. They should also collaborate with GIS analysts, who can use their expertise to help data scientists blend
spatial data with other enterprise data to boost the quality of their predictive models.

GIS teams can also provide insights into the information infrastructure needed to collect and dynamically explore data from things, people or locations, and identify additional data needed to support new analytics use cases such as the IoT and analytics at the edge.

Both GIS and BI teams need to agree on objectives and collectively decide how the organization’s information infrastructure can incorporate geospatial context across all relevant uses for improved decision making.

Time to add a new dimension to your BI strategy

Location data reveals vital context for optimizing operational workflows and understanding proximity relationships that might be missed with traditional graphs and charts. Vastly improved technologies have eased the collection, processing, analysis and visualization of location data. Now it’s time to start applying it in your analytics and BI initiatives.