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erwin recently hosted the second in its six-part webinar series on the practice of data governance and how to proactively deal with its complexities. Led by Frank Pörschmann of iDIGMA GmbH, an IT industry veteran and data governance strategist, the second webinar focused on “ The Value of Data Governance & How to Quantify It.”.
We provide actionable advice around how organizations, and ultimately the builders of data and analytic apps, are adjusting to meet these changes. Key to all this is data, and those organizations that are data-driven have been on the leading edge of these changes. Using data today to build tomorrow’s workforce.
And while cloud-native architecture is paramount to drive the future of analytics apps, AI is also a critical component in order to reduce manual, repetitive steps during data prep and give business users the ability to gain new insights from which they can take action. Best-of-Breed Open Source Technologies. AI Exploration.
DBB builds a budget based on key business objectives, baseline assumptions about external drivers, and a results-driven approach to internal businessdrivers. For example, consider a ski resort business in which early-season and late-season business are especially dependent on weather conditions.
By leveraging data analysis to solve high-value business problems, they will become more efficient. This is in contrast to traditional BI, which extracts insight from data outside of the app. that gathers data from many sources. These systems are designed for people whose primary job is data analysis.
Identifying Key BusinessDrivers. The DBB process begins with identifying the variables that have the greatest impact on overall business performance. DBB builds a budget based on key business objectives, baseline assumptions about external drivers, and a results-driven approach to internal businessdrivers.
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