Location data, and the intelligence that geospatial analysis can provide business decision makers, is the missing link that ties together various data sets from Big Data sources.
By Thomas Walk, COO
Turtler GPS Ltd.
While the mantra of real estate has long been “Location, Location, Location,” that focus is just becoming more obvious in data analysis. Location analytics has the potential to render the complex data landscape more orderly. Location is a common link between seemingly disconnected dumps of Big Data analytics. Data sets that are disconnected and seem to have no relevance to each other can suddenly make sense once the dimension of location is added. Relationships between data sets with no obvious connection can and will emerge once you geo-enrich them, giving you a better view of customer behavior.
Geo-enriching disconnected data sets is an effective way to reveal relationships that are not obvious and, by doing so, to arrive at the types of analytic insights that help business decision makers improve their bottom lines. The following are just a few ways in which geo-enriching with location data can improve the bottom line:
- Reduce costs
- Augment address verification
- Improve customer experience with in-store alerts
- Develop civic and community participation in local government
Location data is now mainstream, with business intelligence platforms adding it to their offerings. However, to get deeper knowledge of location data and the analytic insights it offers, you still need specialized vendors who can provide more advanced analytics such as demographics. For the best results, geo-spatial data must be combined with more traditional business data to unlock business value.
Geo-Enriching Business Data
Location data is fundamental for enriching business data. It starts with geocoding — getting latitude and longitude from an address. With these sets of data, you can do geo-enriching. Geo-enrichment produces the geocoded data points with authoritative attributes to present a more detailed understanding of the real environment of business data.
In the real estate insurance industry, an address or parcel of land is geocoded. Then it is geo-enriched with data about the land such as the type and number of buildings, the property age, construction, residential or commercial use, sales value, etc. Accurately geocoding location data can put a property inside or outside a designated natural hazard area. In real location, it is only a difference of a few hundred feet, but it could have huge cost implications if the insurer undercharges or overcharges for a policy or exposes itself to too much risk.
Regardless of industry, raw data is much more useful if it is geo-enriched. In the financial sector, geo-enrichment of accounts can protect against fraud and identify links between different accounts. This is done by locating customers and transactions, incidents and areas of risk, thereby spotting geographic connections between accounts.
For civic organization, raw data on pollution can be monitored and analyzed against demographic information, such as density of vehicles, which may be a factor in causing the pollution. The location of health problems caused by pollution allows officials to do something about it.
Creating data sets is such a massive undertaking many businesses are choosing to buy data sets, with intelligence vendors differentiating themselves by the degree to which their data is geo-enriched. In the logistics industry, accurate addressing can make a huge difference in company performance. It sounds like a small thing, but it can have huge implications. Inputting correct and accurate addresses is a major bottleneck in logistics because of national difference in address standards. This pain point starts out small but can result in huge wastes of time and money.
For example, global addresses are not standardized across borders. Incorrect input of an address can result in futile trips, custom holds, compliance failures, unwanted costs and delays, substantial fines, and even the loss of trading rights. This can be fixed by geo-enriching data that already includes location coordinates, making sure the address metadata is correct and accurate.
Understanding Business Data
Every object exists in space and time and there is always something to be learned from analyzing those locations in conjunction with other data sets. In fact, location is a common key to link different data sets, revealing a previously unknowable relationship.
For example, it could link the relationship between the path of an actual physical tornado and the resources an insurance company should marshal in the aftermath. People are influenced by geographic considerations when they consume products or services, so organizations are using location analytics to gain insight when choosing the placement of an asset such as a company store or what products to offer in different geographic regions. Consultants and business intelligence experts agree that those businesses which can link location analytics with traditional intelligence and data will receive the greatest benefits.
In retail, stores that use location technology such as Bluetooth Low Energy (BLE) beacons, Wi-Fi or other means to analyze the in-store shopping behavior of a customer, can use this data to offer automated in-store alerts and offerings based on the customer’s past shopping history. This will give a personal touch to sales offerings based on actual customer preference in an individualized way.
Starting in 2014 and 2015, there was an increasing awareness of the importance of leveraging location analytics among business leaders. Geospatial analytics had expanded beyond its base in GIS (Geographic Information Systems) platforms. The companies that can link location analytics with other types of analysis, such as predictive and machine learning, are the ones that can provide the broadest value to customers. They are also the ones best able to handle the large data dumps from Big Data.
There are few enterprises at the geospatial level that can analyze location analytics in the context of broader data. The industry still relies primarily on open source platforms and, thus, there is a broad opportunity for vendors and ancillary companies to exploit this space.
Something like 80% of business data has a hidden location component, but traditionally few operators are working out this location intelligence and the connections it can illuminate. The revolution of change brought by the advent of the Internet gave companies access to millions of IP addresses, and then the advent of cellphones—and especially smartphones—gave companies real-time access to the location of hundreds of millions of users. In addition, with the mainstreaming of the IoT (Internet of Things) revolution, there will be a wider and wider array of everyday devices connected to the Internet; that can feed location analytics into the big location data dump.
Presenting Business Data
Maps are a time-tested tool for visualizing data. Mercator, cylindrical, and conic projections have been around for hundreds of years to share information about location. This location data was used by explorers, traders and business leaders to share business data from antiquity to today.
Traditional business analytics that focus on text search, machine learning, and algorithm dumps tend to present information in boring, hard-to-read spreadsheets or charts. The use of maps, however, to present location analytics and the insight they bring to other business data, makes it easy to visualize the underlying relationships between different business vectors. Because maps are so widely used and readily recognizable by even the layperson, using them to present complex business analytics opens this business analysis to people far beyond the technical professionals who have analyzed this information in the past.
One example of how this form of visualization through maps can make complex analytics much easier for regular people to grasp is in the case of home assurances. Buying and selling houses is a process that involves mountain of documents, all of which are related to a certain location. With an interactive map, an assurance organization can make it possible to see at a glance how close a given property is to natural hazards such as a flood zone, an earthquake zone, lands that are prone to forest fire, erosion areas, etc.
Location data and GPS analytics are the key that can tie seemingly unconnected data together to gain valuable business insight. Companies that can best utilize the vast stores of available location data to geo-enrich their business intelligence are best suited to thrive in the new economy. With these insights, they can make better decision on how, what and where to provide products and services to their customers. They can improve their bottom lines by cutting unnecessary costs. And they can better and more accurately present information to their shareholders and customers alike.