Architectures are critical to improve organization efficiency (Weill & Ross, 2004). Consequently, IT managers must basically anticipate to provide without interruption enough IT capacity to enable business' operation plans and forecasts, in order to ensure the functioning continuity of manufacturing systems (Kloesterboer, 2011). Nowadays, IT architectures are composed of multiple connected and interdependent components. As a consequence, IT systems give raise to emergent phenomena, which often cannot be anticipated by only considering individual components (Mitleton-Kelly, 2011). To cope with such complexity, IT executives more and more rely on data analysis to manage the capacity of information systems (Gunther, 2007; Allspaw, 2008). IT management best practices, like ITIL framework, recommend setting up information systems, dedicated to facilitate the access to all historical data necessary to manage capacity (Lloyd & Rudd, 2007). Operationalizing such a data-driven IT capacity approach is often difficult, notably when confronted to large scale complex information systems. However, these complex systems are typically those requiring innovative approach for capacity management. Our presentation proposes original concepts to enable the constitution and exploitation of an IT capacity information system: 1) Relevant data are likely to be dispersed through disparate databases (Lloyd & Rudd, 2007). An aggregation and filtering is a prerequisite to any exploitation of these data. Fulfilling this requirement can present many problems. A solution, based on the effective use of metadata, will be introduced, to provide IT managers with all appropriate information. 2) When available, data need then to be properly exploited. IT managers have to handle some challenging topics: how to build predictive IT capacity models, using business activity data as inputs (Kloesterboer, 2011)? How to define automatic monitoring tools (Dugmore & Lacy, 2005) to detect abnormal system's behavior? Statistical methods overcoming such challenges will be proposed. Real cases will be used to bring these concepts to life.