Consumer-focused innovation around location intelligence, LI, has improved the way we live. With the apps and maps on my phone, I can easily arrange to meet friends for dinner at the hot new restaurant and find the best way to get there during rush hour. I can find the closest urgent care when one of the kids gets sick while on vacation. Most of us couldn’t imagine life without these types of location-based apps.
These popular, easy-to-use consumer tools couldn’t help but provide inspiration for the enterprise and therefore this type of innovation is finding its way into the growing business intelligence, BI, market – worldwide a $14.4 billion industry in 2013 according to Gartner. For example, retail stores are using real-time consumer location data wherever possible to better target shoppers in their area with promotions.
But so far, location information hasn’t yet proven to be a useful tool for businesses with field workers at remote job sites, a situation that seems to naturally lend itself to capturing and sharing in-place observations. For the myriad of businesses that manage geographically dispersed projects like construction, energy, utilities, agriculture, public safety and natural resources, the benefits seem obvious with the technology literally at their fingertips.
One BI scenario which would benefit from the infusion real-time location information is police departments that are already using layered maps to identify crime patterns across varying geographies. They create maps with past crime statistics to predict what and where crimes are most likely to occur in the future. At first glance, it’s a smart, thoughtful approach to crime fighting. But why not take it a step further to bring real-time information into the Big Data picture? Why not turn field resources and citizens into sensors that can show you what they’re encountering in the course of their surveillance and daily lives?
Using past experience to crunch static location data to predict the future is strategic but it doesn’t improve the velocity of ground truth data, or help make real-time decisions with real-time observations. To do that, another evolution in LI needs to take place to fully benefit business – and it can be done by treating field workers themselves as business sensors.
In the police department example, traditional, predictive BI analytics doesn’t take advantage of the reality of what’s happening on the streets at a given moment. Over time, a large number of observations are recorded, filtered, compiled and analyzed. LI, in the traditional sense, doesn’t allow for observations to be simply shared and turned into tasks. The result is that intelligence is less timely and accurate, and collaboration is slow and cumbersome.
Using a more commercial business example, when energy companies explore remote areas for new oil and gas wells, traditional BI helps them use data to model areas of interest. When they start producing, traditional geographic information systems, GIS, may provide details on the location of pipeline that must be managed. But neither helps ground teams and decision makers share the same view of remote operations so they can make better decisions faster. What if a well or pipeline inspection form could be shared with a click on a smart phone? What if that inspection note showed corrosion that could be automatically assigned to a field crew as a maintenance task? Today, it takes months to get that done, via painful paper processes, leading to costly downtime, damages and repairs.
Field teams understand the remote location better than predictive models. It’s time to put their observations to use, with a tool as simple as their own smartphone. Or better yet, it’s time to leverage both traditional, predictive BI with the immediate observations of the most valuable business sensor, our workers.
Mike Gundling is Vice President of Product Management for TerraGo, a pioneer of geospatial collaboration software and makers of TerraGo Edge, an open collaboration solution for mobile workflows that immediately syncs field data with headquarters.