OneDPO

Customer Case Studies - Up to100x Better

Companies big and small protect their data better using OneDPO

100x - simpler, faster, less cost

Estimated time to analyze 1 TB of data with 250 tables for data breach risks, audit user access, analyze activities for privacy risks, and opportunities to reduce the risks

Using generic tools and scripts With OneDPO
  • Data scanning – Data inventory, scan for PI/PII
~2 weeks 2-3 hrs
  • Audit user access privileges
 ~1 week <1 hr
  • Analyze activities on the data
  • Audit activity logs to find inactive users
3-4 weeks 1-2 hrs
  • Find stale data
2-3 weeks <1 hr
  • Sort through the data assets to find risks
  • Prioritize the riskiest data assets users and activities
 3-4 weeks <1 hr
~500 hours 5-6 hours

Draining the data sea - Reducing risks & cutting costs in banking

The head of data platforms of a large bank engaged OneDPO, expressing special concern about the division’s data warehouse, which had grown to billions of rows of data but without an equal growth in security and risk management controls. OneDPO gave unprecedented visibility into the data risks facing the division, with granularity down to the individual user and database table level. 

Modernizing Data Infrastructure - reducing breach risks

Moving large volumes of data from data warehouses to a data lake is usually a gnarly process. So before a data migration, the CISO of a large U.S. university was concerned about security risks in the cloud.  If a data breach occurred, thousands of student data records and valuable intellectual property would be exposed. The school could face penalties from governments at home and overseas. So, his priority was to get a migration solution that provided safe, private storage and reduced the risk of data breaches and theft.

Reduced privacy risks by 10x

OneDPO saved several months of work that the privacy team would have spent in analyzing and discovering data protection (potential GDPR) issues across one million files and tables. Our platform identified sensitive data. Our insight that 99% of the data was never used helped the customer reduce their data and associated risks tenfold.

Discovered breach risks

Our solution helped a Silicon Valley consumer technology company with over 100M users. They were expecting to find ~20 data tables, but our solution found 5700 tables across their data lake. The data analytics team was surprised to see 92% of the data was stale that no one in the company was using it. Many of the stale data tables had personally identifiable information, posing a significant risk.

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