AI Use Cases

GenAI Accelerator

Source data migra­tion with auto­ma­tion of the source data connection

Insur­ance

Brief his­tory






As an inde­pend­ent com­pany under pub­lic law, Suva is the most import­ant pro­vider of com­puls­ory acci­dent insur­ance in Switzer­land. It insures around 2 mil­lion employ­ees against occu­pa­tional acci­dents, occu­pa­tional ill­nesses and non-occu­pa­tional accidents.

Seit mehr als 10 Jahren unter­hält die Suva ein stetig wach­sendes Data Ware­house als Datendreh­scheibe für den internen und externen Informationsbedarf.


“For more than ten years, synvert has been a competent partner that has supported us in all areas of BI development. From business analysis and the development of complex ETL routes to reporting, synvert's consultants take on all the work. Thanks to their excellent know-how and good social skills, the project goals can be achieved at all times. The collaboration is so easy and good that we can concentrate on the work at all times. The collaboration is a real success!”


Reto Christen (Suva)

Bereichsleiter VTI (Competence Center Data Warehouse)


Goals & challenges


After more than 20 years, a new claims and premium system had to be introduced. This changeover from the self-developed, Cobol-based insurance system to an industry solution extended for Suva (AdCubum Syrius) fundamentally changed the data foundation for the DWH. The DWH had to be adapted to the new requirements with the following objectives:


  • Migra­tion of all data due to the replace­ment of the old sys­tem with a new, oper­a­tional system
  • Trans­fer of the entire database
  • Auto­ma­tion of the source data connection
  • Max­imum pos­sible fur­ther use of the exist­ing core DWH layer
  • Con­sist­ent data quality


Require­ments


The following extended requirements were placed on the newly created DWH, which went beyond the basic functionalities and necessitated a comprehensive expansion of the architecture as well as a far-reaching adaptation to the needs of the specialist departments and the company as a whole. These requirements reflect the constantly growing need for efficient data processing, flexible data analysis and prompt provision of information in order to meet the dynamic requirements of the company:


  • Intro­duc­tion of an addi­tional BI tool
  • Intro­duc­tion of an addi­tional sys­tem for oper­a­tional report­ing to be imple­men­ted with BI technologies
  • Con­ver­sion from monthly load to daily load

Key points


As not all legacy data was transferred to the new source system, a separate archiving solution was implemented as part of the data warehouse (DWH). This archive now functions as an additional data source and enables a complete view of Suva's entire data history since 1918. A central component of the project was the use of a new BI technology (OBIEE) to improve the analysis and reporting functionalities. In addition, the loading cycle was changed from a monthly to a daily load in order to provide the operational reports in particular with up-to-date data on a daily basis. This led to an optimisation of loading times and an increased need for automation. The implementation was under great time pressure, as the DWH had to be adapted to the new source data structures at the same time as the new operational system was introduced. Timely completion was therefore essential to ensure a smooth transition and continuous data availability.



Archi­tec­ture


In order to cope with the rapidly increasing number of source objects, a generic approach was used to process mutations from the source system. New tables can now be registered using entries in metadata tables. A PL/SQL framework (GenDel) provides the deltas of the data to be processed using database views. Informatica PowerCenter is still used for the ETL processes. The consolidated anchor modeling of the core DWH introduced by synvert in previous projects provided the ideal basis for reusing existing structures and also ensured the flexibility of extensibility.

An additional BI tool, ORACLE Business Intelligence Enterprise Edition (OBIEE), was added to the existing SAS infrastructure. With ORACLE as the strategic database, the strengths in the area of Online Analytical Processing (OLAP) can be utilized. OBIEE is also used as a front end for operational reporting.

Gen­er­ated bene­fits



  • By using GenDel, the source data con­nec­tion can be auto­mated to the highest degree up to the DWQ layer close to the source
  • By switch­ing to a daily char­ging cycle, data can be made avail­able more quickly
  • In addi­tion to plan­ning eval­u­ations, require­ments for oper­a­tional eval­u­ations can also be and data inter­faces can be cre­ated with high cadence from the DWH
  • Strength­en­ing the data ware­house as a cent­ral data hub within Suva


Ser­vices accom­plished by synvert 



  • GenDel imple­ment­a­tion for auto­mat­ing source con­nec­tion: instead of indi­vidual ETL pro­cesses, new source tables can now be con­nec­ted much more effi­ciently and easily
  • Ana­lysis and detailed design of entire data areas in the DWH (e.g. costs, premi­ums and payroll totals)
  • Exe­cu­tion of the entire pro­ject cycle for oper­a­tional report­ing: from require­ments ana­lysis and design to imple­ment­a­tion and test­ing of the sys­tem and reports.

Deliv­ery / conclusion


The conversion of the DWH was implemented on schedule. Thanks to the neutral core DWH modeling, which was already based on technical logic in earlier project phases, a large part of the existing data model could continue to be used - despite the complete conversion to a new source system - and the importance of the DWH within Suva was further strengthened. By using GenDel, Suva meets the growing demand for automation of ETL processes and has a flexible and freely configurable interface for the simple connection of additional source data

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