With the Centers for Disease Control and Prevention (CDC) recently forming the Center for Forecasting and Outbreak Analytics to advance public health decision-making, I am once again struck by data parallels between our pandemic environment and shortly after 9/11.

Before discussing those similarities, I commend the CDC for its leadership in establishing the new Center, which Director Rochelle P. Walensky called “the country’s first government-wide public health forecasting center.” As a longtime data and information management professional, I’m excited about the Center’s mission of using data to predict, connect and inform. By developing a comprehensive and disciplined data collection and management methodology, then broadly disseminating the resulting information, the Center will move the U.S. a long way down the road to a better public health infrastructure.

Using data to anticipate threats

My premise about the likeness between today’s COVID-19 pandemic and post-9/11 – and with the benefit of hindsight – acknowledges that both crises resulted from a lack of foresight and the ability to use data to anticipate threats. Shortly after 9/11, the media began lamenting the failure of the intelligence community to predict and prevent the tragedy. We’re now seeing comparable criticism of public health authorities for not protecting us more effectively from the coronavirus. Consider these data points from the post 9/11 era – all of which resonate with current pandemic discussions:

  • Federal and state agencies lack coordination: In 2005, Richard Posner wrote a paper called, “Remaking Domestic Intelligence,” which he turned into a book of the same name. One of his main contentions was that issues leading to 9/11 were systemic, and an incremental approach to change was not going to work. While threat indicators were available well before 9/11, systemic and organizational impediments prevented experts from “connecting the dots.”
  • Open-source data is a viable starting point for threat-related summaries: In the 2005 timeframe, the relatively new Department of Homeland Security (DHS) was criticized for taking too long to create an inventory of the country’s critical infrastructure. The pundits then pounced on the agency when a graduate student published a purportedly comprehensive list based on publicly available data.
  • Systematic national information gathering is essential to averting crises: After 9/11, there was greater awareness that information must be collected, managed and shared in a systematic fashion, so that government agencies could turn data into information and form actionable insights. One example: Establishing state and local “fusion centers” supported by DHS enhanced threat information sharing and analysis.

A time for hope

While one might want to throw up their hands in exasperation and wonder why we seem to have the same discussion after each crisis, there is much to be hopeful about. Putting domestic and international political challenges aside since those are outside my area of expertise. I live in the world of data and analytics, and in this realm, much has changed and improved, for example:

  • Advancements in managing data now versus post-9/11: The concept of “big data” did not widely exist in the mid-aughts and was primarily limited to the intelligence community and academia. Today, big data is well established and rapidly maturing. More packaged systems are available and the larger technology players are making it easier to integrate an organization’s big data into its overall ecosystem.
  • We know how to share data: I would even go so far as to say we know how to share information, which I think of as data in context (what geeks call data plus metadata). Google, Amazon, Facebook and others have demonstrated the power of data that’s systematically organized and operationally applied. While the term “digital transformation” is overused, it is happening everywhere. Digitally mature companies have market capitalizations greater than counterparts that have not taken the leap[1].
  • We manage information better: It’s easy to say that data as an IT discipline is well established, however, data as a management discipline is less mature. The issue here is that data management by itself is not a game changer, but a systematic and focused information management approach drives critical business and mission decisions by enabling data to flow across boundaries, between applications and amongst different users. This ensures that wherever data goes, it is understandable within the context it was created and can be methodically operationalized at scale.

Going forward

I’m also hopeful that over the next few years our national public health community will evaluate what went right and wrong during the pandemic – and identify how to fix things going forward. That outcome mirrors Resigility’s commitment to help clients build custom enterprise data management capabilities and the operational strength to achieve high-impact results and true change.

Jonathan Adams

Director of Data & Information Services, Resigility

[1] There is not much debate on this topic anymore. However, in the 2012-2015 timeframe when big data and analytics were favored buzzwords, there were many studies. One book that pulls it all together is Leading Digital, by George Westerman, Didier Bonnet and Andrew McAfee.