For years, most data processing has happened in a centralized location, such as a data center or the cloud. These “towers” have traditionally been the only locations capable of providing the processing power needed to generate actionable intelligence serving the employees and citizens in satellite offices, or the “trenches.”
Every day, government employees within these trenches — local motor vehicles departments, town halls, zoning administrations and other locations — perform personalized transactions with local citizens, and each location is unique. For example, where one DMV office might process an inordinate number of trailer registrations, the DMV in the next town over might have fewer of those and more title transfers.
The problem is that the processed data in the tower is no different to anyone accessing it, while none of the offices are the same. As far as the data is concerned, offices might have the same staff, same functionality and same levels of service. The information that comes from the tower is not customized for each location, which leaves the human staff to interpret and translate to provide a locally contextualized set of options that citizens can understand.
This process can be made more efficient by bringing some data processing out of the tower and into the trenches at the edge of the network, making citizen interactions more personalized and pleasant.
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Agencies Benefit from Data Personalized for the Citizen Experience
It’s a refrain that we’ve all probably heard at least once before: “This system wasn’t built for me.” Employees are simply not getting what they need out of the computer systems they are using.
Part of the issue might be that the recommendations employees receive from the tower are too generic and are meant to serve the interests of employees and citizens at multiple locations. Let’s continue with the DMV example. Information sent from the tower to the trench might not account for a coastal DMV location that requires more specific information on boating licenses than an office in the rural plains would.
The generic nature of the output requires employees to do more work. Agency employees must interpret the output and personalize it to a citizen’s request. In some instances, the information they receive may be so far out of context that it leads the employee to make the wrong recommendation.
LEARN MORE: How state and local agencies are automating data analysis.
Edge Computing Takes Workloads to the Customer Service Desk
Embedding intelligence by running artificial intelligence and machine learning (AI/ML) applications closer to the network’s edge can help increase employee performance while simultaneously delivering faster and more personalized results for an optimal citizen experience.
This isn’t a new concept. Many cities have already invested time and resources in building out their edge environments. Why not apply the same approach to local government offices, which already have more stable and resilient connectivity with the necessary security functionality and most likely a fair amount of processing capabilities? Take advantage of the IT infrastructure these offices have in place and use it to analyze and process smaller yet no less important workloads right at the point of citizen interactions.
Larger data sets being processed in the tower still inform these edge-hosted AI/ML workloads, but they will not be completely reliant on the data sets. Rather, it can be the other way around: Information analyzed and processed in the trenches can be sent back to the tower via a feedback loop. The more information the tower receives, the more accurate and tailored its recommendations will be in the future.
It is also important to ensure that the delivery mechanisms run smoothly and without the need for human intervention. Fortunately, government IT professionals use automation technologies to easily automate workflows, processing, system configuration and more. Automation can also help complement IT security strategies and provide greater resiliency and faster processing at the edge.
Edge Computing Meets Platform Expertise
To be clear, while multiple industries are successfully implementing edge-run AI/ML applications, it’s not easy, cheap or instantaneous. Local governments will need to set aside money for both the technology and the people to make this happen, especially if there is no current edge computing infrastructure in place.
Edge computing is where data management meets hardware and platform expertise. Those are typically two very different skill sets, and as such, they require teams to integrate resources and cross-train. Investing in both skill sets will be important and might mean adding more staff or, at minimum, working with a partner that can manage both.
Whatever the case, moving data processing from the tower to the trench should not be an all-or-nothing undertaking. Government agencies should begin by carefully assessing their existing technologies and needs, planning for necessary investments and perhaps running a pilot or two — a “crawl, walk, run” approach.
Once the edge infrastructure is up and running, agencies are likely to discover that relying on the tower is not as necessary as it used to be. Employees will receive more customized information faster since they won’t have to wait for output from a central data center. Most importantly, citizens will get the personalized and efficient service that local governments are committed to providing.
