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Data teams struggle in many areas including a quick and iterative approach for converting an analytics/algorithm into a production-ready reusable and integrated software....
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The current model of point-to-point data exchange internally and externally is simply not secure, fast, flexible, or consistent. This prevents individuals and...
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Data management has distinct phasing. Fitting all phases inside a single data warehouse environment creates an unmanageable monster. Instead, think of creating a...
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Models represent the real world and make it more understandable. Data models play the same role and are a powerful communication tool for the IT professional and business...
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Self-serve analytics is not simply transitioning IT-driven analytics to the business. Organizations are struggling to understand what's involved in the analytics...
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The amount of data within organizations is growing at an exponential rate, creating a need for organizations to adopt a formal approach to governing their data. However,...
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The key to landscaping your data environment lies in ensuring foundational disciplines are optimized with a recognition for the interdependency among the various disciplines.
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For AI to be able to answer questions or discover new relationships, it needs an underlining architecture that not only describes the data AI will operate on but also...
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Use this blueprint to understand what AI really means in practice and to get started with your AI explorations to harness its transformative power.
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Data platform selection should be based on common best practices and, at the same time, be optimized for the organization’s specific needs and goals and support an...
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