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On the one hand, I can see how maintaining data quality and providing high quality information management and analysis are pitted against each other, but on the other hand, wouldn't you consider that both are different sides of the same coin? High quality information management and analysis both fundamentally rely on a data governance model that establishes the roles and accountabilities of the data management staff, especially in how they support the business clients' needs. However, the quality of the management and the analysis are ultimately dependent on the quality of the data sets that compose the resource that is the target of the business applications. In this respect, we again can rely on data governance -- this time focusing on data quality expectations, dimensions, inspection, control and issues management. The ability to integrate technology to support these activities is admirable, but tools (such as cleansing or even profiling) will take a back seat to the processes defined to exploit technology for information value.
My suggestion is to take a step backward and assess the organization's existing framework for data governance (whether it is explicit or implicit) and see how those processes impact both data quality management and information management. Perhaps there are protocols that can be unified because their ultimate objectives are the same, providing the basis for a streamlining of both activities and reducing the competing demands.
More on data quality management
Read Data quality management pitfalls: Three common mistakes to avoid
Listen to How to develop and maintain an enterprise data quality management strategy, with Larry English
Read Data quality management: Follow the doctor's orders
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