The banks that choose Cedacri are accessing innovative and complete outsourcing services that reduce costs. Their full outsourcing solution enables banks to realize savings of up to 30%, thanks primarily to the economies of scale, through sharing technologies, systems, structures, resources, and skills among the Group’s various client banks.
Cedacri’s testing and training environment is created for each client bank and managed through unique and manual processes, making collaboration, and the sharing of best practice difficult for developers. The process to prepare each environment required high volumes of data extraction and reloading - costly in terms of both time and resources. This mostly manual process was prone to human error, presented a business risk, and made meeting their clients’ privacy legislation requirements difficult. And while data privacy, especially customer details, is sacrosanct in most industries, the Financial Services sector is perhaps burdened by more obligations than most.
Emanuele Scolozzi, Senior Test Data Manager, explains what Cedacri was looking for: “We wanted to improve the data quality in our test environment to enable better testing processes, while saving time and MIPS consumption. Centralizing the management process for different platforms and data types would help us create dedicated development teams.
We also looked to reduce our storage requirement for training and testing which would generate substantial cost savings and we were hoping to eliminate all manual activity in the data extraction and reloading process.”
Cedacri uses its outsourcing services to create testing and training environments using current data as a template and individual changes, such as customer codes, are applied for the testing environment. To comply with Italian and European directives, data used in the training and testing environments must be comparable to the live information, but not recognizably so. Data must retain its data integrity even when used across different platforms, with sensitive details such as name, company name, address, VAT, tax code, and email address anonymized through a masking process.
A number of features were tested during a successful Proof of Concept (POC). The builder component of Data Express enables an inventory of organizational data to be taken, collated and centrally stored in a single metadata repository, or knowledge base. This was important to Cedacri in helping adhere to strict, customer-driven requirements.
The data management solution runs on either z/OS or in a distributed environment, depending on the client’s IT estate, and can provide automated processes to populate the knowledge base with physical and logical client environment information.
Cedacri wanted an intuitive User Interface and the ability to analyze and provide statistics on the data contained in the knowledge base.
Masking transforms sensitive information to anonymous data. However, to be efficient and economical the process must be repeatable to enable live data to be regularly refreshed in the masked environment.
The masking solution must be based, through classification, on exit routines for which the source code must be provided. This enables Cedacri to customize the routines based on specific client and/or project requirements.
The masking process manages the relationship between the original value and the masked value. Cedacri needs enough flexibility to change the relationship whenever required by a client for security purposes. Consistency of data between applications is very important too, with the same data entity, the customer name, for example, masked in the same way across different data store types ie DB2, VSAM and SQL Server.
Scolozzi illustrates the need for flexible data access with an example: “The client can choose whether to directly access live data or a backup. For instance, when parsing metadata and extracting the data, the solution must be able to directly access the DB2 table or a backup. This change needs to be in the easy control of an operator, by updating a parameter, rather than creating a new process.”
Using the same, centralized approach to understand data relationships, Data Express can also substantially reduce the size of test data through subsetting. Cedacri can generate multiple data reduction models, or rules, which can be implemented when needed. Data Express also provides data extraction simulation to predict key performance indicators (KPIs) such as CPU time, elapsed time, number of records read, and number of records written. Scolozzi comments: “As a direct result of our Data Express use, data storage has been reduced by 87% of the production volume for each of the test environments we generate for our clients.”