Location analytics: Why adding "where" makes BI better

Location analytics blends business data with geographic data to reveal the relationship of location to people, events, transactions, facilities and assets. And even though most business data contains a location aspect, not many organizations are using location data and spatial analytics in their BI and analytical workflows.

Traditionally, location data has been relegated to mapping and GIS purposes. But geo-mapping software, like other business software, has become easier to use. And BI and analytics software vendors are integrating mapping and spatial data analysis into their products to provide additional context to visualizations, reports and analysis. Murali Nori leads the location analytics/visualization efforts at SAS. In this interview, he answers some questions about location analytics and explains how it adds a valuable dimension to traditional BI and analytics endeavors.

Organizations are already collecting vast amounts of data. Why should they add location data to the analytical mix?

Nori: It’s estimated that 70 to 80 percent of business data already has a location aspect included, and plus, all business happens somewhere! There are many insights that can be gleaned by adding location context to business intelligence. Also, the Internet of Things has created connected cars, connected people and analytics at the edge. IoT is really making businesses rethink their use cases. Driverless cars, Uber, drones – all rely on location as an important facet.

But location is becoming important to many types of business analysis. Organizations need to pay more attention to it. And we’re not just about taking location-enabled data sets and displaying them on a map with simple layers. Additional value lies in considering distances, median drive-times and drive-distance calculations; combining location data with analytical techniques, like clustering, to better segment customer; and using spatial data in predictive and prescriptive analytics.

What business issues can be solved using location analytics?

Nori: Some are covered above. Add to that, business scenarios where location is a primary factor, such as property insurance companies trying to determine the price of insurance based on location, health care organizations trying to understand the types of services needed based on the demographic data or new transportation companies, like Uber and Lyft, analyzing and understanding use of their services.

What are some challenges organizations might face as they begin implementing processes and technology for analyzing location data, and how they can be overcome?

Nori: Location information has specific requirements that are needed to add it to business intelligence or data visualization applications. For example, to represent a bank on a geographic map, you need latitude and longitude information. To represent a region like a zip code, county, state or sales territory, you’ll need shape files that are used to draw the boundaries on a map. Organizations need to plan to implement software tools that can be used to geocode a location to obtain latitude and longitude information and to create the shape files necessary to draw the shapes on maps. Getting the correct tools is the first step to creating and using location information in an organization.

How can organizations best develop a strategy for using location data?

Nori: Organizations using location data need to collect, access and prepare data. At a basic level, the organization will already have access to the geographic definitions of entities they want to analyze – such as geotagged social media data and latitude/longitudes for customer addresses. They’ll need to augment that information with third-party external data sources like weather data, crime statistics or consumer spending. And then, integrate the different data sets.

For additional value, organizations should consider collecting location data over time from an array of new data sources, including GPS, NFC and Beacons, to understand the movement of entities such as customers and assets. In many cases, consumers are voluntarily offering location-aware data through their mobile devices, which provides much more contextual depth.

Finally, location data often includes personally identifiable information. Organizations must keep in mind privacy and data protection regulations.

How do organizations identify the right use cases for location analytics?

Nori: Location analytics is applicable to all types of industries. It’s also relevant to all functional units within an organization. So, focus on projects or use cases that fulfill high-priority requirements and deliver the highest returns. And, keep in mind data preparation challenges, the availability of skills and competencies, and data privacy, security and ethics issues. Possible use cases include:

• Growing your business with proper site or channel selection. Expand your market footprint, capture the next generation of customers and identify where future growth will come from by quickly finding and qualifying all potential new locations or online channels.
• Improving customer experience or targeted marketing. Combine geospatial location, ease of access, context and proximity to make relevant marketing offers or improve experiences. This enables marketers to proactively meet customer needs.
• Improving public services and government planning. Whether it’s responding to emergency situations, alleviating traffic congestion, offering social services to needy families or improving law enforcement, local, state and federal governments are prime candidates for location analytics.
• Optimizing business processes. Adding the location context of assets – manufacturing, assembling, logistics, distribution and servicing – helps optimize businesses process and adds value for organizations.
  

How will the use of location analytics transform common approaches to business intelligence, analytics or big data?

Nori: At present, location analytics is primarily being used in a traditional manner – to analyze data in an interactive geo-map through reports, dashboards or explorations. It needs to go further. With new scenarios supported by digitalization, mobility and IoT, location-dependent decision making offers more potential for all organizations. But they need to go beyond reporting items, events or performance across locations. More value can be derived if location data can be correlated with historical behavior or if location data is used as a continuous variable in analyses to predict future outcomes.

For example, with location analytics, you can understand the impact of drive-time and drive-distance on trends you are trying to explore, determine optimal locations based on demographics and available resources, or combine location data with analytical techniques like clustering to segment customers in a more useful and relevant way. Location analytics adds the “where” element to the traditional set of business intelligence and analytics questions and allows for new insights that you might have missed or not thought of.

SAS sees location analytics as an extension of BI, so it was only natural for us to partner with GIS leader Esri to bring GIS and BI together. Now our customers can access self-service mapping and spatial analytics from within SAS® Visual Analytics – an intuitive tool they use every day.