Case Studies

80% Faster Data Categorization of Disparate Datasets

Devron achieves 80 percent faster data categorization of disparate datasets for the public sector.

About the Client

The United States Intelligence Community is a group of separate United States government intelligence agencies and subordinate organizations that work both separately and collectively to conduct intelligence activities which support the foreign policy and national security interests of the United States.

Use Case

Data Categorization

Industry

Government

Before

Delayed insight & complex risk driver identification process
Expensive data engineering overhead
High cost for risk misalignment

After

Automated categorization
< 4 hours to categorize 2TB of data
Accelerated time to insight

The Objective

The Challenge

The US Intelligence community has mountains of data they need to filter and categorize to identify insights about national intelligence targets. The data is located in different private cloud environments across the globe. It’s unstructured, consisting of text, PDF, images, and videos, and cannot be moved due to its sensitive nature.

Historically, local analysts would manually sort through the data to organize it into different national security topics. This process was very laborious and time- consuming. On average, it would take one analyst six weeks to categorize 1GB of data. In total, the intelligence community has petabytes of data. Even if a thousand analysts were tasked with categorization, it would take them over a hundred years.

Data Challenges

Disparate Data Sources

Heterogeneous Schema

Sensitive Data (PII)

Immovable Data

Data Description

Unstructured Data

Various Data Formats

Petabytes of Data

Devron Solution

Intelligence community machine learning

Enter Devron—a federated machine learning platform designed specifically to unlock disparate, heterogeneous datasets such as this for advanced analytics. Because Devron offers access to data in situ, their data science team was able to automate the categorization process, deploying a supervised machine learning algorithm to each private cloud that categorized the data in a fraction of the time.

Devron Platform for sensitive intelligence data

By design, Devron natively keeps the source information private, never sharing the raw data—only model learnings are sent back to the global model. This inherent privacy enabled more analysts within the intelligence community to harness the data for insights instead of the small few with high enough security clearances to see the raw data.

Client Results

With Devron, the US intelligence community categorized 2TB of data in less than 4 hours, improving their speed by nearly 80%. As a result, they accelerated their time to insight, uncovering new learnings about national intelligence targets faster.

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80% Faster Data Categorization of Disparate Datasets

Devron Benefits

Devron enables data science teams to realize enhanced outcomes, improved data science agility, and reduced overhead—all without moving or exposing data.

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