Never before have we seen enterprises adapt and transform as rapidly as they have since the arrival of COVID-19. In the private sector, these decisions have come relatively easily (even if the execution is hard): meet the customer where they are, expand infrastructure to meet the ballooning digital demand, and enable legions of employees to work remotely. It’s simply a matter of good business.
Sometimes perceived as slow to adopt digital transformation, how can government entities become agile champions of the cloud and cloud analytics? To explore this topic, I spoke with Steve Bennett, Ph.D., formerly with the U.S. Department of Homeland Security where he led teams working in biological surveillance, and currently Director, Global Government Practice, SAS.
Daniel: Steve, based on your experience working in the U.S. Department of Homeland Security, what did you see as some of the main challenges the government faces with adopting cloud computing and leveraging the potential of cloud analytics?
Steve: When I worked at the U.S. Department of Homeland Security, we were using analytics to understand new health threats, and we had two challenges that were a constant headache. One was how we store data and remain compliant. The other was managing a large collection of tools—one to enable visualization and dashboards, another for optimization, and yet another for machine learning and predictive analytics.
Daniel: Storing data in a compliant manner and then being able to manage a set of analytics tools are two challenges that I have seen as well. People may not think of a government or a city council as having a large amount of data to store and analyze. However, government Internet of Things (IoT) estates are quite large. Think of light poles, luminaires, air quality sensors, water meters, and water quality management systems. All of these are connected and generating a huge amount of data. For the government to digitally transform, government analysts will need to make sense out of all that data on a large scale. They will need to see patterns, and eventually make predictions. How do you recommend government teams get started?
Steve: With the vast amount of data generated by government IoT projects and the performance requirements, cloud computing makes sense. Cloud computing provides the ability to scale elastically and on-demand, it supports policies, technologies, and controls that strengthen security, and it eliminates the capital expense of buying hardware and software.
In terms of analytic tools, teams should evaluate how tools model data, the process used for extract transform load (ETL), and the simplicity of the user interface. They should make sure the analytics tools democratize the analysis process. All types of users (business, engineering, data science, and IT) should be able to access, explore, visualize, and transform data into insights.
Daniel: Can you give an example of how cloud-based analytics has been applied in the government?
Steve: Let’s look at a solution that was implemented by SAS and Microsoft for the Town of Cary, North Carolina, USA. During storm events, Cary had no visibility into nearby river levels or how quickly the water was rising. Traditionally, the town relied on citizens to alert them of floods through phone calls, text messages, and other means. The town staff processed these requests manually dispatching public work personnel to erect barriers and close roads.
A key requirement for the Town of Cary was that their new flood prediction system needed to integrate with existing business systems. These included using the SAS Visual Analytics dashboard integrated with ArcGIS for real-time visualization, Salesforce for alerts, automated notifications, and work orders, and data sharing for regional partner response systems.
The Town of Cary installed water level sensors at various points along the Walnut Creek stream basin and rain gauges at several Town of Cary owned facilities. Data on stormwater levels were transferred to the Azure cloud over an LTE wireless connection.
Azure IoT Hub was used to provision, authenticate, and manage the two-way communication to the sensors. SAS Analytics for IoT combined streaming data from sensors and gauges with weather data for real-time scoring, dashboarding, and historical reporting. SAS Visual Analytics provided an interactive dashboard, reports, business intelligence, and analytics. The dashboard integrated with ESRI ArcGIS for additional geographic analysis and data visualization.
With the end-to-end IoT solution, town staff can now visualize flooding events in real-time. Stormwater personnel receive notifications and can generate work orders automatically. The data is also shared with regional partners.
“The Azure IoT platform has been a critical piece of our technology ecosystem and accelerates our ability to scale.” —Terry Yates, Smart City Strategist, Town of Cary
These predictive analytics applications have immense effects on city budgets, and more importantly, human lives—but they wouldn’t be possible without the scale of the cloud.
“We’re still connecting some of the dots, but we’re already seeing real benefits in the automation of formerly manual processes. Previously, we might get a call from a citizen, which would cause us to dispatch public works or emergency services depending on the type of flooding. Now the data triggers alerts that automatically notify stormwater personnel, who can react and address the flooded areas. It’s much more efficient and could ultimately save lives.” —Nicole Raimundo, Chief Information Officer, Town of Cary
Daniel: That’s an excellent example. How do you see cloud computing and analytics playing a role as governments address climate change?
Steve: As governments are compelled by climate change to make commitments around sustainability, carbon management, transportation, or emergency response, the importance of a connected data system that can drive predictive insights becomes clear. Natural disasters are increasing at unprecedented rates and can cost local government tens or even hundreds of millions to recover.
Daniel: Thanks, Steve. How would you like to close our discussion today?
Steve: Governments have a wide range of motivations for adopting analytics in the cloud. Analytics help governments identify fraud and help police become more efficient in investigations. And in the healthcare space, we’re using analytics to find anomalies in public health, figure out how to optimize hospital bed usage, and manage a supply chain for personal protective equipment in the wake of COVID-19.
The possibilities of powerful analytics in government are truly exciting. I recommend that public institutions embrace the cloud quickly to drive tangible results.
From connecting and drawing insight out of city-wide IoT footprints to reversing entire tax systems in a matter of days, only the cloud can meet the new demands placed on the government. It’s simply a matter of good government.
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