Synthetic Data Finance - Santander Case
Rainer Weickert
IT & Sales Enthusiast ?? | Former Airbus Aerospace Engineer | ???? | Iberian Peninsula Explorer ???? | Talks about: AI, Tech, Science, History & Languages
“We have been using Daphne for 3 years in the central database of the company. Our system normalizes data from dozens of source systems into one common enterprise data model, to feed various internal and external reports or subsequent engines on a daily basis. A detailed and holistic testing on realistic data covering the full scope of the production data in the UAT environment is thus indispensable for our daily work.”
- Julian K., Data Management Business Architect
When developing new products, companies often need large and rich datasets to test functionality. Yet, this often requires obtaining data from a productive environment or developing it manually- both of which could lead to an original security breach or pointless application in the end. The use of datasets containing sensitive information under GDPR is restricted. Therefore, companies must take measures to protect the data from leaks and theft and prevent misuse.
Throughout the entire software development process, from analysts to developers to testers, everyone needs consistent data in order to do their job correctly. And it falls on the Chief Data Officer to make sure that not only is the data protected from breaches, but that its usage is being audited as well. DAPHNE? was created to solve a problem that development teams face the most: wasting time and energy on creating, integrating, and maintaining data sets for new environments and applications. With DAPHNE?, there is no need for manual intervention or tracing; the process is automated from start to finish. This also minimizes operational risk.
The Santander Consumer Bank is the largest car financing bank in Germany. However, being hindered by success, they were unable to keep up with data generation and exploitation. This was because their business cases were either too complex or too large for testing teams to handle while keeping their customers sensitive data safe. Being the leader in your industry means always being on the cutting-edge of innovation and exploiting any competitive advantages you may have. This includes developing new applications and software to maintain that advantage. However, their former testing environment was not realistic enough and did not provide accurate results. They needed something that would more accurately reflect their portfolio for proper results going forward. In addition, the sensitive nature of the data meant that it had to be masked to protect people’s sensible information. To complicate things further, the bank needed a solution quickly.
Daphne allowed us a simple to use and easy to maintain solution to have a full mimic of the production data including all characteristics available for testing. In particular the possibility to mask sensitive data while keeping referential integrity across multiple tables/columns directly, allows to verify all transformations on “harmless” data with no loss in functionality. With Daphne we saw a significant decline in issues related to incomplete or inconsistent test data.
- Julian K., Data Management Business Architect
From a BO point of view, […] I think that this tool will help to all of the business users to make proper testing, with proper data and this will help us a lot having extensive testing in PRE avoiding “surprises” in PRO.
- Javier F., Business Application Owner
In addition, each user action is saved in audit logs and data access always requires authorization. The combination of DAPHNE? modules like the advanced masking rules where the user can generate valid credit card numbers or human-readable names together with the user role concept makes DAPHNE an ideal synthetic data governance tool. Plus, it all adheres to GDPR so you wont have to worry about any fines or reputation damage from a breach.
DAPHNE? has surpassed the technological limits, offering compatibility with the most common database technologies on the market. DAPHNE? is the only solution in the market that maintains referential integrity in applications that share different technologies such as Oracle, SAS, IBM - DB2, Hadoop, MySQL, Postgres SQL.
If you like to find out more:
Rainer Adrian Weickert - Senior Account Executive
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Co-Founder at BIT Technologies GmbH | Business Intelligence and Big Data
1 年Generating synthetic data can help create efficiencies and benefits across your organization! Great post!