In an age in which commercial quantitative tools are both powerful and affordable, quantitative analysis is often limited by data considerations. An increase in the ease and speed with which data can be incorporated can enhance productivity and lead to better research. In this presentation we review some typical quantitative data storage requirements, and some possible solutions. We illustrate an approach developed at OMAM which uses a novel relational database schema to provide outstanding performance. Mike Servent is Head of Quantitative & Modelling Systems at Old Mutual Asset Managers, UK. His team forms part of the Quantitative Strategies Group at OMAM which runs Global Equity Market-Neutral and Long-only funds with approx $ 2.5 billion AUM. The team provides quantitative platforms including statistical packages, optimisers, databases and reporting systems, as well as OMAM's proprietary quant models. Previously Mike has worked at MSCI Barra and at BITA Risk.