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  • FIMA: Modernizing Data Quality & Governance. Unlocking Performance & Reducing Risk

    This paper will evaluate how financial services companies are managing the challenges posed by data quality management. By analyzing which data types and data characteristics businesses are struggling with, we will uncover the true business costs associated with data quality. We will also gauge how data governance programs are maturing and how they are being measured. Finally, we will asses how data is being managed within financial institutions.

  • Upgrade to Oracle E-Business Suite R12 While Controlling the Impact of Data Growth

    Written expressly for application owners responsible for the overall Oracle E-Business Suite and the R12 upgrade project, this white paper discusses: The causes and effects of accelerated data growth in an R12 upgrade;common ways to handle data growth; best practices, such as lean data management, to assist in controlling data growth; how to automate lean data management with Informatica® Application Information Lifecycle Management™ (ILM) software

  • Data Quality: Improving the Value of Your Data

    High quality data allows greater confidence in analytic systems and decreases the time spent reconciling data. It enables a more uniform version of the truth, allowing stakeholders the ability to identify and implement necessary changes. This in turn causes companies to cut costs and increase ROI.

  • Great Data – The Key to Building and Sustaining Customer Relationships

    In an ideal world, marketers would leverage their data to identify, segment, and target customers and prospects when and where they are most likely to respond, and with the best possible messaging to drive engagement, response, and sales conversations. Marketers don’t live in that ideal world. Garter finds that 82% of CMOs don’t feel prepared to deal with large sets of data. How can data enrichment help marketers get to that ideal world/overcome these challenges? See how to put data to work for you, the key challenges you need to understand, and how to solve these challenges with a strategy that combines data infrastructure and the content of your data itself.

  • Data Quality Management: Beyond the Basics

    Not all data is equal. While all data may be important, it is critical to evaluate and prioritize data based on quality, usability and total value to your business mission, goals and objectives.