Public Sector

Across the public sector, the amount of data that agencies collect continues to grow exponentially. The question is, how do we channel the information that comes from all of this data toward the most effective outcomes? The first step is to make sure that everyone within an agency has a data-driven mindset, learning how to use it to improve the customer experience and enhance efficiency. By harnessing the power of AI and automated machine learning, DataRobot gives agencies the insight they need to power their agency’s mission.

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Benefits of AI

It has been said that data is the new oil, and it’s easy to see why. Data is one of the most valuable resources that any government agency works with, so it’s important to understand both its power and its purpose. This is where artificial intelligence (AI) holds enormous potential, helping agencies to speed up their response times and take decisive action in a rapidly changing world. DataRobot’s automated machine learning platform combines predictive modelling expertise with the best practices of data science to deliver accurate and actionable predictions with full transparency and interpretability.

Reduce Risk

Reduce Risk

  • Insider Threat
  • Counterterrorism
  • Injury Prevention
Improve Efficiencies

Improve Efficiencies

  • Cybersecurity
  • Employee Attrition
  • Predictive Maintenance
  • Case Loads
Protect the citizen

Protect the citizen

  • Fraud Detection
  • Public Health & Safety
  • Medical Fraud

Public Sector Use Cases

Across the public sector, leaders are challenged every day to balance the public’s need for services with the directive to do more with less. Success at every agency hinges on Use Cases and the ability to deliver insights from data quickly. Providing success in this environment requires fast and accurate predictions.

Check out all use cases

Insider Threat

Federal entities have used DataRobot to gather historical data on information policy violations in order to proactively block any potential misuse, even down to the level of specific individuals. Internet usage, improper document handling, and computer hardware violations all fall under this purview.


Helping the Intelligence Community keep up with threat analysis is one of the key insights that DataRobot models can provide. Since speed is of the essence, models can be developed quickly, and hypotheses can be tested, deployed, and refined rapidly.

Injury Prevention

Data analysis on service members serves numerous purposes. It can use medical and fitness history to predict injury rates and highlight candidates who may have a lower chance of getting injured. Analysis can also monitor the effectiveness of new injury prevention techniques, helping to choose higher quality candidates during the selection process.


DataRobot’s speed allows analysts to build, deploy, and refresh models to predict incoming threats in real time. Datasets can use historical network threats and penetration data to help monitor intrusion vectors through server log data, application logs, and other information sources.

Employee Attrition

By looking at historical and categorical data on its employees, DataRobot can help predict future employee churn so that agencies can offset it with organizational changes that can fix the problem. Further analysis can also uncover future deficiencies in talent that managers can address to ensure optimal performance.

Predictive Maintenance

The goal of preventive maintenance is to utilize data from your equipment to develop predictive models in order to reduce costs, as well as wear and tear on equipment. DataRobot’s models can help your agency understand the optimal time to purchase spare parts and schedule maintenance.

Case Loads

Historical data from applicants’ SF86s can be used to develop predictive models to flag persons that require further investigation, fast tracking those more likely to pass the investigation. DataRobot’s automation of the process will greatly take the pressure off investigators and help them to streamline their caseload.

Fraud Detection

With DataRobot, analysts can build the right model in minutes rather than months, using the data from previous fraud cases to serve as the historical basis for future model prediction. Testing models, deploying them to production, and refreshing when new data becomes available is a straightforward and rapid process that helps analysts get the right information to combat fraud.

Public Health and Safety

DataRobot can use data from several public sources to help develop machine learning models. Historical data from Medicare, the National Provider Identifier (NPI) Database, and CDC datasets can be combined to derive an aggregate model of drugs prescribed by county and opioid deaths. By understanding how all drugs are prescribed at a county level, agencies can identify relationships between non-opioid based drugs and their effect on the opioid death rate nationally.

Medical Fraud

The perpetrators of medical fraud are sophisticated and their tactics evolve quickly, often making solutions obsolete just as quickly as they are found. This is where DataRobot’s automated machine learning can make a big difference, predicting threat vectors more quickly and reducing the widespread cost and time inefficiencies that come with hiring more staff and investigating every claim.

DateRobot in the News

In-Q-Tel: Strategic Partnership Demonstrates DataRobot’s Leadership in Automated Machine Learning

Mar 29, 2018
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FedScoop: Three ways machine learning can end the security clearance backlog

Jun 01, 2018
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Nextgov: DHS Funds Machine Learning Tool to Boost Other Countries’ Airport Security

Aug 20, 2018
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Nextgov: Machine Learning Could Help Chip Away at the Security Clearance Backlog

Sep 21, 2018
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