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Case study: TIER-1 INSURANCE

Becoming Agile with Velotix

A tier-1 insurance company needed help to gain control of its data. As its data ecosystem expanded rapidly and access requests increased, the data team became overwhelmed. It turned to Velotix to create a more efficient data workflow to help meet company needs.

Speeding Up

Detecting & Preventing
Dark Data

Scaling Data

About the Company

This leading insurance company offers many products, including Liability, Financial, Property, Global Specialty, Personal, and Accident and Health insurance. 

As a global company operating in dozens of locations, it relies on various databases, including Oracle, SQL Server, Cloudera, and MongoDB. Over the years, this tier-1 insurance company created processes for managing data access based on active directory groups & RBAC (role-based access control). Although these processes worked, they were difficult and time-consuming, preventing internal changes like shifting to the Cloud or updating regulations.

The Challenge

As data use exploded, the company lost control of its data. Introducing a new data lake technology expanded the company’s data storage capacity, adding a new set of projects for the company’s employees. Employees created replicas of critical tables to complete these projects, including sensitive data preventing the company from tracking, governing, or managing its data. 

This proliferation of dark data—unapproved and ungoverned data subsets—posed a significant risk to compliance and data governance. Without access or knowledge of what kind of data exists within the company, data owners could not enforce policy rules, mask data from unauthorized viewers, or deliver meaningful insights to their business counterparts. Overall, the company’s vast data infrastructure included 1000+ database instances, from pulling and requesting data to logging interactions, with several petabytes of data.

Before the datalake, the company used ServiceNow tickets to manage data requests and approvals. Data owners in each division defined the permissions in the databases where requests were approved. In parallel, the enterprise hired Data Analysts for strategic decision-making, highlighting the immense value of data assets and putting pressure on the data team to grant access faster. 

However, the data owners couldn’t meet demand fast enough as they needed to process each request upfront, which was time-consuming and costly. 

The Solution 

Velotix’s Data Lineage helped the data owners trace data back to its origins. With Data Discovery, Classification, and AI Policy Database, the company automatically organizes its data and applies the appropriate policy rules. 

Using an automated workflow for data requests and speeding up response time with the help of AI Recommendations helps the company remain policy-compliant and confident its data is secure through every transformation. 

As a result, this tier-1 insurance company gained real-time visibility over unregulated datasets, defeated dark data, and consolidated data governance with Velotix—finally allowing fast data access at scale.

Securing Data Access at Scale

  • Data Lineage & Dark Data Detection
    Velotix identifies the presence of dark data, and then, using Data Lineage, the data team discovers and tracks where the duplicated datasets originate from. This process ensures a clean and compliant access framework that remains policy-compliant.
  • Velotix AI Recommendations
    For every access request, the data team receives AI Recommendations based on historical behavior to help approve or deny access faster.
  • Fast Data Access at Scale
    Velotix automates the data team’s access workflow and enables managing every request from one place while automating policy compliance through an AI Policy Database. Now, the company remains secure as they scale. 


By implementing Velotix, the tier-1 insurance company was able to: 

  • Reduce unregulated and unmonitored datasets by 35% within the first month of implementation
  • Policy breaches reduced by 45%
  • Approvals expedited by 20% due to reduction in manual work
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