In the light of big data and real-time business intelligence discussions, one thing becomes increasingly clear: data latency is expensive, old-fashioned and not competitive. As things speed up, new technologies are needed that can cope with increasingly challenging demands. This is especially true for the financial industry where time really is money.
In-memory analytics has great potential to become the philosopher’s stone in this issue. The concept is simple: traditional BI queries data stored on physical disks whereas in-memory analytics uses data and queries located in the server’s RAM, making query results available near time. While this concept is not new, it is far from standard in the finance industry. Yet.
As pioneer banks are taking their first steps into IMC (in-memory computing) – such as Germany-based Dekabank or Swedish Avanza bank – we will be likely to see a fundamental technological turnover in bank’s BI in the near future, triggered by falling costs and increasing capacity of RAM as this article describes very well.
The advantages for banks are obvious: Dekabank’s use of Quartet FS is only one example how in-memory computing boosts performance through high-speed risk analysis combined with trading positions, which allows for faster reaction and near time alerting. Rapid fraud detection and credit card reporting are other benefits to name only a few.
As IMC gains ground, the heavyweights of the IT industry come up with their solutions. Quartet FS, Oracle TimesTen, SAS High-Performance Data Mining, SAP HANA or IBM DB2 with BLU Acceleration are some examples. We at MyPrivateBanking Research are looking forward to see how fast the finance industry will be able to adopt this promising technology.