Data Cleansing

Our modules have been built such that when the data is loaded, it automatically identifies any missing or incorrect elements, yet still allows the data to be loaded and reported on. This process not only allows the data to be cleansed at source, it also provides a financial value to the data that has not been correctly assigned. Users can prioritise effort on the data corrections that have the most impact.

If you are not confident in data quality, consider using the tool for a month or two before formal ‘go-live’. Spending the time to improve data up front means that issues can be addressed quickly to provide powerful analytics and reporting capability from Day 1.

Introducing Dimensions

Core settlement platforms typically provide AUM or financial extracts with two to three dimensions. For example, a marker representing the investment manager or department; and a chart of account showing the nature of income or cost.

The power of IBM Planning Analytics is the multi-dimensionality that it can bring under a controlled structure. Our AUM solution accelerator allows users to slice and dice data across 16 dimensions. Imagine how powerful it could be if you can structure enterprise wide AUM, Inflows/Outflows and financials by 16 consistent dimensions.

Introducing Hierarchies

Each dimension is structured in a data hierarchy. At the start of your journey these hierarchies will be designed by you to reflect your business yet can be easily changed as the business changes.

Combine two data sources into a single dimension and the same hierarchy, and your data starts to become structured.

For example, your core system has 30+ rate cards and your secondary platform has five rate cards. Roll up each one into Fee Only, Commission & Fee and you immediately have a data structure for Rate Cards that is consistently applied across your platforms.