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Casos

Unified Risk System

septiembre 11, 2020 by Bluetab

Unified Risk System

Within financial institutions, the Data Units or Information Systems areas develop reports and scorecards to facilitate user areas’ work. Once the reports are in production, a security and governance framework needs to be established that defines the permissions for access to them.

In this context, Bluetab is developing the information access model, determining the criteria for controlling access to the data:

  • barra-bluetab Access restriction. Establishing which users can access which reports (e.g. the Risks User can access the RDA Scorecard).
  • barra-bluetab Data restriction. Indicating, within a report, what data can be accessed (e.g. an office manager can only view the data for their office).

In this context, Bluetab is developing the security model for the reports of a leading Spanish financial institution by implementing two types of security levels, and enabling this to provide coverage to the various group entities:

  • barra-bluetab Access Security: managed through Catalogue Groups, as a method of organisation to which users can be assigned while giving access to the various scorecard elements. Therefore, these groups will allow a user or users to access a report, or not. Users are associated with a tool catalogue group with access to a particular set of reports.
  • barra-bluetab Data Security: managed through Application Roles, as a feature that enables control over what data a user or users assigned to the role can or cannot query. Therefore, these roles will allow, or not, a user to view detailed information on data at the customer or office level. However, users will always be able to view aggregated information.

This project has been carried out within the institution’s technological stack, implementing the security model in OBIEE.

SUCCESS STORIES

Publicado en: Casos Etiquetado como: data-fabric

Unified Risk System

septiembre 11, 2020 by Bluetab

Unified Risk System

The scenario of banking concentration in Europe and Spain, with the resulting mergers and acquisitions, has forced financial institutions to revisit their risk metrics systems. In this context, Bluetab has been involved with one of the leading banks in the Spanish market, producing a report from the entity’s Unified Risk System. The project arose due to the need to redefine the current information system associated with the new risk metrics, allowing both the integration of information from Spain and other from geographic areas and the inclusion of this information and its adaptation to the new information architecture defined for the future.

The project scope includes both gathering of requirements and prior technical-functional analysis to lead to the design and construction of a data mart that provisions the reports required by the Risks Department as applicable to the new unified system defined. 

The extraction, transformation and loading processes provisioned in the database are performed in DataStage, with the construction of scorecards for reporting and validation of the information in OBIEE.

SUCCESS STORIES

Publicado en: Casos Etiquetado como: data-fabric

Information system exploitation and reporting

septiembre 11, 2020 by Bluetab

Information system exploitation and reporting

One of the leading Financial Institutions in the Spanish domestic market decided to perform a split of the services of its new information platform with the aim of ensuring technical-functional understanding of the various layers, as well as to ensure the level of efficiency and demand on suppliers. The latter point refers to demanding service level agreements that are measured by daily levels according to an internal ticketing process.

Within the organised services structure, we are responsible for the platform outputs, which involves developing all the information system operation and reporting projects. Our team designs the logical model, building the data extraction and transformation processes through the DataStage (IBM) product and generates the scorecards.

Within the service, user areas’ projects are addressed, mainly Risks, Commercial and Finance, as well as common services such as the administration of on-premises and cloud tools, attention to incidents and centre of excellence (or expert support).

Under this schema, the generation of reports and scorecards is “industrialised” following a well-defined working methodology that enables simplification of maintenance, speeding up of developments and delivery of a homogeneous product to the end user that conforms to the style guides defined by the institutions.

The main technologies applied for reporting are MicroStrategy and WebFOCUS; IBM DataStage and Db2 Warehouse for the processing and storage part.

SUCCESS STORIES

Publicado en: Casos Etiquetado como: data-fabric

Energy Expansion Activity Reporting

septiembre 11, 2020 by Bluetab

Energy Expansion Activity Reporting

Balancing customers and expansion activity is a necessary exercise to enable understanding of the actual situation of assets, as well as to match commercial activity with the actual situation of the supply points.


Bluetab has developed operational reporting for one of Spain’s largest energy companies, enabling daily tracking of its expansion activity, grouping of the various types of customers based on the situation of the contracted services and also allowing the specific customer detail to be reached.

Project scope includes functional analysis, infrastructure deployment in AWS, data modelling, development, and preparation of reports in Power BI.

SUCCESS STORIES

Publicado en: Casos Etiquetado como: data-fabric

Utilities sector Data Lake

septiembre 11, 2020 by Bluetab

Utilities sector Data Lake

Within the digital transformation process, the option was taken to build a Data Lake to serve various departments in a cross-cutting manner.

Bluetab has developed a Data Lake in AWS for a leading Spanish energy companies, which gets its data from the on-premises commercial systems, from database and file sources, through continual, daily ingestion processes.

The project scope includes functional analysis, deployment of infrastructure in AWS and development of data consolidation processes in Data Lake using fully scalable technologies.

SUCCESS STORIES

Publicado en: Casos Etiquetado como: data-fabric

Big Data platform security model

septiembre 10, 2020 by Bluetab

Big Data platform security model

It is about the definition and implementation of a global model for one of the main Spanish Financial Institutions, which guarantees the security of access to the data according to the regulations that apply in each area of ​​the different entities of the group, in a homogeneous way throughout the business group. This implies the identification of sensitive data and diagnosing its physical location, trying to limit its access only to those people considered necessary and as such with access privileges to them.

For the purpose of defining the appropriate access criteria, the following types of data are determined:

  • barra-bluetab Personal keys that should only be known by the user
  • barra-bluetab Data that allows or enables fraud on its own
  • barra-bluetab Sensitive data due to the nature of being able to have an impact on the privacy of the person, and on their fundamental rights, greater protection is necessary
  • barra-bluetab Data that allows to uniquely identify a person or legal entity outside the Entity
  • barra-bluetab Information that identifies the employee in the organization
  • barra-bluetab Data for internal use, which in general are considered as the other areas of data not collected in the above

To cover the established criteria, different levels of security have been determined for:

  • barra-bluetab Objects without personal identification fields (internal use only)
  • barra-bluetab Objects with personal identification fields treated (tokenized)
  • barra-bluetab Objects with untreated personal identification fields (clear)
  • barra-bluetab Restricted access objects.

The following tools are available to facilitate individual implementation by project area: automatic labeling of existing fields and registration of new ones based on recursive neural networks, HDFS table classifier, compactor of physical files in the tables, tokenization based on the information schemes provided and a catalog of entities as a common repository in which table names are linked with their path within the HDFS.

SUCCESS STORIES

Publicado en: Casos Etiquetado como: data-strategy

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