How cloud analytics can drive digital transformation in government

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.