The ROI – PolarLake RDD and Enterprise Application Integration (EAI)
John Randles
This paper compares the experience of using PolarLake RDD in a real world project against the customer’s estimates to do the same project that were based on traditional EAI technologies. All measures are based on the IT implementation effort and do not take into account consequential business benefits such as higher STP rates, operational efficiencies etc.
Also it does not take into account the possibly exponentially higher build costs required when each individual line of business is responsible for the integration of their own pieces of Reference Data from a generic message to all systems.
In order to understand the metrics presented in this report it is important to understand the scale of the undertaking involved. The table below is a summary of the complexity measures that drove effort involved in the implementation.
| Complexity Measure | Number |
|---|---|
| Number of downstream systems | 3 |
| Number of asset types | 10 |
| Number of issue components per DSS | 12 |
| Number of issuer components per DSS | 3 |
| Number of potential integration maps: (10x3x12)+(3x3) | 369 |
| Average number of business rules per mapping | 25 |
| Total number of possible business rules | 9,225 |
| Variations within asset class, geography, market, language, etc | Potentially significant, but not factored into above calculation |
| Operations: insert, update (selective), delete, refresh | Potentially significant, but not factored into above calculation |
| Transactionality, cardinality, dependencies, sequencing, exceptions management | Potentially significant, but not factored into above calculation |
To receive a copy of this paper, please email info@polarlake.com.
2010