My work


I deal with data!... often from different sources and in different formats; it is the most valuable asset a business has that is not recorded in the accounts

Large corporations have large volumes of data that sit behind their operational systems; generally called back office, but it orchistrates the way the business runs and operates

This needs to be modelled into a design that may be optimised for storage (a data warehouse) and thus reduncancy and duplicity of data is removed to minimise the space this takes up


Data needs to be mapped and consolidated so that a single common data model is produced and it is restructured to make sense to all users

Business rules can and are applied in order to map or conform to the actual business running, giving a single version of the truth that is consistant and accurate

The solution needs to be able to grow with the business, so technical restrictions need to be removed during the modelling process


Once data has been transformed and stored, further consolidation can take place into star or snowflake schemas which are optimised for reporting output to the business

Constructed of facts and dimensions, even conformed dimensions at times, this can be logged and traced through the database and scheduled to ensure even data flow

Best practices need to be adopted so that processes can be run many times with the same results, thus giving an idempotent process which needs little or no manual management


From the new consolidated and centralised data, reports and managed queries can be produced to supply the business with the data it needs

This allows decisions to be based on fact rather than fiction, using statistical analysis to understand their market and progress before commitment

It is also gives the best single outlet for detailled deconstruction of historic events and trends, demonstrating market conditions or business initiatives success or failure

Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger

Mitigates the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long running, analysis queries in transaction processing databases

Improve data quality, by providing uniform codes and descriptions, flagging or even fixing bad data, presenting the organisations data in a consistent manner

Maintain data history, even if the source transaction systems do not and provide a single common data model for all data of interest regardless of the data's source

Restructure the data so that it makes sense to the business users as well as delivering excellent query performance, even for complex analytic queries, without impacting the operational systems

Cross-source data enrichment such as Customer Relationship Management benefitting from detailed transaction analysis adding to customer profile data