Data masking versus data creation in finance and banking

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"We decided to go with Grid-Tools because they offered more functionality and capability than the other solutions we investigated. We were also impressed with the speed and performance of the tool and chose it because it offered us the ability to generate data as well as mask and de-identify it."

Project Manager, Large Multinational Bank

"The uniqueness of Datamaker is that the tool is not only capable of data masking and database subsetting, but it generates test data and manages and controls what is in the test environment. It even goes a step further and actually helps to improve test processes through complete, end-to-end test data management."

Edwin van Vliet, Test Data Manager for large Dutch Bank, Yacht Consultancy

Provisioning data for banking and financial systems: data masking or data creation?

Data in the banking sector is highly vulnerable to a number of threats including; malicious or negligent insiders, cyber crime, and outsourcing sensitive records to third parties amongst others. Most organisations have various security measures in place to help guard against these in production environments. However, this is not the case in testing environments, where the data is even more vulnerable. Current orthodox suggests that masking the test data is the solution to this problem. However, for many reasons, data masking is no longer adequate for testing and development initiatives in banking.

The data collected on an individual by a financial organisation can be incredibly detailed and contain many potentially revealing interconnections. For example, Employee A is a developer who needs to provision test data that changes over time from a pool of 1000 employees, so starts with his boss, Employee B. We know that Employee B is a) a man, b) has had a recent 10% in his monthly direct debit, and c) this happened in the last month. From a) we can halve our pool, b) takes the possible employees to 120 and c) drops it to 27. We can then trace to when the company pay rise was paid and we can pinpoint our man through nothing more than the innocent curiosity of a colleague!

As such examples demonstrate, the complexity of data held in the banking sector offers too many potential identifiers to safely mask the data. It can end up being a time-consuming, costly project that doesn’t even secure the necessary compliance. In addition to this, full copies of production data take up lots of space, leading to increased infrastructure costs, as well as longer testing cycles. There is also a danger that the referential integrity of the data is compromised, producing ineffective tests.

Synthetic data creation can solve many of these problems. Tool-created data is entirely synthetic, so no production data ever enters the testing environment. This is the only way to ensure full compliance with current industry regulations! Synthetic data is also uniquely configurable to the requirements of your project; using data profiling, Grid-Tools is able to construct a picture of how your data is related and then create new data with all the characteristics of the production data. By using various coverage techniques, synthetic data can also be created to ensure all data combinations automatically exist. The result is low volume, relational, highly-enriched data; perfect for high-quality testing without leaving you storing inefficient bulky copies of the production data.

This synthetic data is stored in a central data repository, making it easily accessible and allowing for efficient, data-centric testing solutions. It also means that the created data can be re-used for test upgrades, later versions and migrations to other projects. For more information on how Datamaker™ can work for you, please follow the link to our Datamaker™ sub-site or contact us.

Improving Efficiency in Data Management

The information held in the financial sector often needs to be accessed by a range of people. It is therefore imperative that it is quick and easy to find. However, generating data from a tool like Datamaker™ can greatly improve the efficiency of your data management techniques:

  • Generating data from scratch means it can be matched to your specific requirements
  • Maintaining referential and relational integrity shortens test and development cycles
  • Less, but richer test data increases the range of testing scenarios and reduces disk usage
  • Automated data creation reduces chances of manual error, leading to greater compliance
  • Central data repository provides one view and editing point for all the generated test data
  • Re-usable data that can be stored for future tests, migrations and upgrade projects
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