Thought Leaders From US Federal Government Share Thoughts On Unleashing The Power Of Advanced Analytics

In today’s data-driven world, the federal government is increasingly recognizing the transformative potential of data analytics in driving decision-making. Data is a critical asset, enabling government agencies to gain deep insights, identify trends, and better inform citizens.

Government agencies such as the US Centers for Disease Control and Prevention (CDC) and the National Institutes of Health (NIH) leverage advanced analytics to drive groundbreaking research initiatives, accelerate medical advancements, and monitor and respond to public health threats quicker and more effectively. Data analytics empowers these agencies to uncover patterns in data and make advancements in ways they could never do before.

However, while data analytics are providing clear benefits, the issue of how to gain increasing data while maintaining data privacy remains a critical consideration. As government agencies collect and analyze vast amounts of sensitive personal information, ensuring data privacy and maintaining public trust is of utmost importance. Striking a balance between transparency, which allows for greater public accountability, and protecting individual privacy is a complex challenge.

At the May 2023 GovFuture Forum event at George Mason University (GMU) in the Washington, DC region, government leaders Stacey Marovich (Lead Health Informatics Scientist, CDC’s National Institute for Occupational Safety and Health (NIOSH)), Dr. Susan Gregurick (Associate Director for Data Science and Director of the Office of Data Science Strategy, NIH), and industry veteran John Landers (Solutions Engineer, Public Sector Sales Engineering, Alteryx) shared their perspectives on the use of data and advanced analytics in government.

Key takeaways from the event and this panel discussion are detailed below.

Stacey Marovich (CDC):

“One of our biggest challenges is data harmonization. The way that public health works is that the data is collected at the local and state level. So it’s collected in many, many different ways. Every state, city, region kind of does things differently. So they are trying to harmonize and standardize the data at a national level, to be able to use it for research purposes. And of course, one of our biggest issues is just budget [as] we’re dependent on Congress for our budget.”

“We also are always looking to bring new resources on board and to stay ahead of new tools that are coming about. We try to modernize our tools and be able to use the data in more actionable ways with real-time data collection and the push for that is really where I’d like to see things go. Basically have NIOSH be used as far upstream as we can get it and have the data collected and coded in real time at the point of when the data is collected.”

Dr. Susan Gregurick (NIH):

“For us, one of the bigger challenges is looking at transparency and bias. If you’re using large language models, they hallucinate, which in the healthcare sector can be quite a substantial problem. And so for us, we’re really looking at not just large language models, but AI and ML in general, how we can create more ethical and more transparent and hopefully less biases in our data sets and in our algorithms. This for us is a really big challenge because of the widespread use of ChatGPT and Google Bard and other capabilities where people are asking questions that would relate to their health. And we want to make sure that the information that they get is accurate and that they understand where that information came from and understand the accuracy. So this is for us a super big challenge. And of course, we’re going to fund a whole lot of research in this area as we move forward.”

“I think what I’m most excited about is the integration of the large language models with the very specific domain-specific language models. What I’d love to see is that ability to take the information gathered from those more precise iterative language models and curate the data. We cannot manually curate data anymore. This is an old-fashioned way of thinking. We really have to utilize analytics to really curate the data, give it a measure of quantity and quality, and then feed that back into our algorithms for better assessment of bias and transparency.”

John Landers (Alteryx):

“There is a tension between those that collect data and hold it and those that want to use it for research. And I think that that tension is always gonna be built in, and in some cases, you really have to want to know what the answer is badly to actually get information. ”

“Artificial intelligence and the breakthrough of ChatGPT over the last couple of weeks has just been huge. Being able to try to upskill everybody to be able to work in a modern environment is extremely important. And I think there’s a lot of excitement around artificial intelligence and ChatGPT, even though most people probably don’t understand what that means and what the implications are. But it’s going to get people excited and there’s going to be a big push forward in this tech and in technology areas related to this conversation.”

Data analytics has become an indispensable tool in the federal government’s arsenal, empowering evidence-based decision-making, particularly within agencies like the CDC and NIH. The ability to harness data effectively enables these agencies to monitor public health, drive research, and make informed data-driven choices. However, the responsible use of data must be accompanied by a commitment to transparency and privacy protection. As the federal government continues to unleash the power of data analytics and look forward to the future, it must navigate this delicate balance to maximize the benefits data can provide while safeguarding individual privacy and maintaining public trust.

The full recording for the GovFuture Forum May 2023 event is available on the GovFuture site.