Data-driven regulation: How can technology support effective decision-making?

In a data-driven world, status quo approaches to regulation aren’t cutting it anymore. So how can governments capitalize on data-based technology that has dominated the private sector for years? 

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The digital government model of the future — one in which regulators save billions of dollars in time and resources because they have leveraged sophisticated technologies in their day-to-day work — is, in many ways, already here. Certain types of data-driven technology, according to some reports, could save the U.S. government between $3.4 billion and $41.1 billion a year. It begs the question: how can a simple transition create such massive benefits for regulators the world over?   

Leveraging the benefits of data-driven technology

Between cloud-based technology, automation, artificial intelligence, and other tools, a regulatory body that uses data-driven processes has ever-expanding potential. According to Dr. Sheila Marchant-Short, CEO and Registrar of the College of Registered Nurses of Prince Edward Island, registration and renewal account for roughly 85% of the workload and resources expended by regulators. 

“One of the major regulatory challenges made worse by the pandemic is the demand for faster application processes to deal with workforce planning by governments and employers,” says Dr. Marchant-Short. “Although this is not entirely the work of the regulator, there is a demand on the regulator to contribute to the solution.” 

A report from Deloitte estimates that automating employee tasks in the public sector could save the U.S. government between 96.7 million and 1.2 billion hours in labor every year. The report also suggests that process automation and AI utilization combined could save between $3.3 billion and $41.1 billion a year. A regulator that takes advantage of the latest developments in data-driven tech can slash costs simply by implementing tools that already exist. These tools can include:  

Predictive analytics

As its name suggests, data-driven technology can only exist with substantial data collection. This can include, for example, paper files and machine-readable data, the latter of which can be created from physical documents either through manual data entry or data-driven software that scans documents and automatically processes them. Once the machine has its foundational data source, it can begin to collect and analyze the material at hand, leading to the development of standard analytics, which capture basic patterns and trends within the data. 

Using standard analytics, administrators can provide in-line analytics, which often appear in front-facing modules within the overall software user interface. In-line analytics give users the power to analyze and search data on their own terms. This could mean, for example, giving a licensee access to analytics regarding their continuing education, indicating what paths for professional development are most viable. On top of these basic analytics, a data-driven system could also perform predictive modeling, which analyzes trends and attempts to give users an idea of what future developments in the data will look like. 

Once a data-driven system has created a predictive model, it can analyze it, compare it to actual developments in data collection as time goes on, and use all the information gathered to attain new knowledge and make itself more accurate and efficient. This process can help regulators cut expenses and save staff countless hours of tedious analysis, allowing them to handle their workload more effectively. 

Artificial Intelligence

Artificial Intelligence (AI) refers to the use of computer technology to simulate human learning and decision-making processes. John McCarthy, the Dartmouth University professor who coined the term in 1956, defined AI as an attempt to make computers “use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” At the core of any form of AI is a collection of data, which provides a foundation upon which the AI system can work and grow itself. 

AI influences many facets of day-to-day life for most U.S. citizens. Between language processing, speech simulation, pattern formation, predictive analysis, cognitive recognition, and other applications, the potential of AI to transform daily processes grows with every passing year. Because an AI system is rooted in data, the passage of time only allows it to amass more material as it deepens its understanding of the work at hand and enhances its own performance.  

Regulators can leverage AI by using it to provide targeted continuing education analytics for licensees to most efficiently further their professional development. AI can also aid with form processing, as visual processing tools can free up staff from manually entering the data contained in paper forms. Regulators can also run AI tools on complaints databases to determine areas of weakness in their industry and adapt their approach in kind. 

Regulating AI

The U.S. government does not currently regulate AI, but as the technology continues to affect more public and private sector processes, regulators have sent a clear message that it is only a matter of time before they do. When adopting this technology, companies should craft policies and procedures across the board to create a compliance-by-design program that promotes AI innovation but also ensures transparency and comprehensibility of systems. Companies should also audit and review their usage regularly and document these processes to comply with regulators who may seek further information. 

Cloud-based technology

Cloud-based technology refers to computing methods that allow users of a private network to store files, host servers, build databases, run applications, and access many other internet services in a decentralized system. Because the technology does not involve storing files on a local hard drive, cloud computing (from an adequate vendor) offers a degree of security, scalability, and flexibility that makes it an attractive choice for businesses, individuals, and regulators. 

Before COVID-19, government adoption of cloud technology was largely uncommon. The pandemic, however, forced the hands of many regulators, who found themselves seeking more flexible solutions to adapt to an explosion in data volume, a surge in demand for services, and a sudden shift to work-from-home in many industries. A separate study conducted by Deloitte found that regulators increasingly invested in cloud technology throughout the pandemic to grapple with this new reality. 

Budget data from the study also shows that as the amount of cloud technology spending increased, variability in spending between different government sectors decreased. The amount of federal defense cloud spending, civilian cloud spending, and healthcare cloud spending have continued to approach a point of convergence as time goes on. State-level regulators have also invested in cloud technology, like in California, where the number of state cloud contracts in 2021 climbed from 129 to 158.   


Data-driven automation, a process reliant on AI technology, offers regulators the opportunity to further streamline their day-to-day work. With traditional processes costing hours of inputting, processing, and communicating about data, automation presents an opportunity for regulators to free up their staff so they can handle tasks more immediately pertinent to the public interest, or tasks that require advanced levels of human judgment. 

In the field of professional licensing, the benefits of automation are easy to imagine — by automating the process of applying for and renewing licenses, regulators not only save time on data collection and entry, but also save money on paper, ink, postage, and other costs that can have negative implications for regulatory budgets. And by using online portals for these new processes, regulators can provide licensees with a secure 24-hour interface displaying their license status and other pertinent information, not to mention the public having access to real-time, reliable information about licensees. 


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