Recently, I took an in-depth look into Enterprise Resource Planning (ERP) software. I was interested to see that the vendors were making moves to not only provide versions of software that processed real-time transactions, but also real-time analytics that ran alongside those transactions.

After seeing that, I concluded that most companies wouldn’t need these real-time analytics—but to some, it would be indispensable.

Consider, for example, an online retailer that wants to use real-time analytics to see the minute to minute buying trends of customers so they could offer them instant deals. Or a hospitality chain that sees vacant rooms at certain locations and can instantaneously find customers who might be interested in booking one if they are presented with a compelling offer.

For these and other companies that want real-time intelligence, analytics are invaluable. And the geospatial information realm is no different.

Here are some real-time geospatial use case examples:

Dynamic Auto Insurance Pricing

By plugging geospatial encoding engines into company databases, insurers can use geospatial data to overlay addresses and locations, routes most often taken by insured vehicles (thanks to geo-tracking sensors on the vehicles themselves), and even braking and speed habits of drivers. 

“The most immediate question that insurance companies ask is, ‘How good are they at evaluating risk and data?’” says Dror Katzav, CEO of Atidot, which provides analytics for life insurance. “The object is to use your data to its fullest, because the greatest advantage is when you use that information to arrive at new insights that can drive your business.” 

Geospatially charged information is equally as important in auto insurance pricing because it enables insurers to get a clearer picture of each individual driver’s risk, driving habits, locations frequented and miles traveled.

Emergency Responder Placement for Utilities

During power outages, utilities and government agencies work to get power going again. To do this, they need real-time data about storm system locations and timing so they can effectively plan repair crew deployments. 

Helping to facilitate disaster response efforts, sensors are placed on assets such as transformers, substations and power lines. The sensors transmit location coordinates of the assets and can also be used to ascertain their condition.

Real-time geospatial data about storm systems can then be superimposed on maps of these asset coordinates, so companies and government agencies can see what’s happening in real-time with storms and other disruptive occurrences that potentially impact these assets. From here, companies can determine the best way to proceed with the restoration of power based upon the real-time data they are getting from the field and the geolocations of the information. If geospatial data is being updated in real-time, they can see the paths of storms and also detect unexpected events, such as when a storm front makes a sudden change in direction that could impact repair activity.

Food Freshness

When Zest Labs, a Silicon Valley-based AgTech company, performed an analysis of data collected on California strawberries harvested during the warm summer months of August and September, the analysis revealed that pallets experienced very different cut-to-cool times. Some were also exposed to high temperatures for long periods of time before reaching the packing house. That kind of pre-exposure affects product freshness because fruit that is picked and left unrefrigerated for long periods of time will spoil sooner. Despite varying exposures to heat, all of these strawberries shipped out as a single lot and were labeled by the producer with the same “best by” or “harvested on” dates.

Now, with the help of sensors placed on pallets, food producers, transporters, distribution centers, and retailers have full visibility of produce shipments and also of the condition of the produce. A geospatial layer of data can be superimposed on this real-time data so that if a critical alert situation for potential spoilage arises, managers know exactly where it is and how best to intervene.

Implementing Real-Time Geospatial Technology

As more businesses expect their geospatial data to be dynamic, this changes the focus for geospatial specialists in several ways.

Data Management and the Cloud

The world generates 2.5 quintillion bytes of data a day, and 90 percent of the total worldwide data was created in the past two years. Not all of this data is geospatial-related, but when it is, storage needs can be extreme. Companies have two choices for their geospatial data—either invest in processors and storage to house all of this data on-premises, or look to the cloud. The easy choice is to move data to the cloud because storage is cheaper. However, companies must now consider whether that data, if needed in real-time, will be able to give real-time results if there’s any difficulty with communications latency or accessing the cloud. It might become necessary to invest in faster communication links with the cloud to meet real-time data needs.

Infusion into the Business and Business Analytics

In many cases, GIS departments have been silos of geospatial information that have devoted their time to mapping static information and then distributing it. To incorporate real-time, fluid geospatial information, geospatial specialists will need to adjust the way they do work. Part of this change will require geospatial specialists to get more directly involved with the end business, working with other business managers to determine what real-time geospatial information will be needed to perform the desired analytics so critical questions for the business can be addressed.

Citizen Geospatial Specialists

As geospatial technology expands into new areas of business like real-time reporting, more business users will want to interact with this information and use it. However, they will not necessarily have formal training in geospatial engineering. Geospatial product vendors are already aware of this. To accommodate these new “citizen” geospatial practitioners, vendors are simplifying their geospatial tools for non-expert use. Geospatial engineering departments will need to assume a more active role by providing support and assistance to non-geospatial business users as questions arise. 

Managing Change

What organizations moving from static to real-time geospatial information applications already know is that the move requires change—both in IT and in business operations. A good way to approach this move is to do it one application at a time. In this way, change can more slowly be introduced into the organization. This gives employees a better opportunity to adjust to change and develop confidence.

Handling Digital Data Overload

The introduction of Internet of Things (IoT) devices like sensors and cameras will further add to the troves of data that organizations are already collecting. And geospatial applications will be impacted by this. Business users will begin to demand new layers of information to be superimposed over the geodata as new data sources become available. 

This is where an interdisciplinary data team makes sense. A group of business users, IT specialists, and geospatial professionals can meet to decide which new data sources should be admitted into a geospatial system for purposes of integration and aggregation. This is a systematic approach that gets buy-in and cooperation from everyone. It also ensures that the data you add to your geospatial platform is supported by key stakeholders throughout the company, verified as quality data and protected for information security.