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TDWI Upside - Where Data Means Business

IT and Enterprise Data Management


Timeline of an Agile Data Environment: A Detailed View (Part 3 of 3)

In the last part of our series, we examine the detailed activities of an analytics team before, during, and after a sprint. You can compare your team’s work flow to the activities list in this article to see opportunities to become even more agile.

3 Keys to Building an Agile, DevOps-Powered Analytics Environment (Part 2 of 3)

Working with data requires adaptation of many traditional agile techniques. How do we put agile practices and DevOps tools to use for analytics and data science projects?

Best Practices for Building an Agile Analytics Development Environment (Part 1 of 3)

Creating an agile analytics development environment is about much more than just tools. It is a combination of culture, practices, and tools that enable high productivity, high data quality, and maximum business value.

The Death of Hadoop?

Is Hadoop dead? Not so fast. Plan on supporting multiple environments for some time to come.

Data Digest: Untrustworthy Data, Valuable DBAs, Blockchain Applications

Why some organizations don’t trust their data, how DBAs fit into the modern data-driven enterprise, and how blockchain is changing supply chains.

Data Digest: Improving Data Management, Selecting Self-Service Technology, Effective Data Quality

How data management impacts customer experience, factors for choosing self-service architecture, and improving data quality and risk management.

Models in Times of Uncertainty

Data models seem to represent the real world, but a model can be misleading if the relationship between truth and information is not understood.

Modern Metadata Management

Tool automation and intelligence are replacing manual technical tasks with immediate business results.

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