Blog

In this companion piece to a previous post on emerging tech and the methodology mindset, the author helps you consider how to acquire a personal diversity of thought and skill.

This article advocates for your organization to adopt a methodology mindset to orient around the problem and begin with the first principles of a solution.

From data strategy and AI readiness to GenAI use cases, read the snapshot of roundtable discussion takeaways at IIA’s 2024 Analytics Symposium.

Read this article for a rare example of experience design and data architect teams collaborating to gain a deeper understanding of the end user, and ultimately better data platform.

Viewing data governance as a tactical decision layer can help us understand the interplay between data strategy and data management. Ensure your strategic goals are translated into actionable operations.

For some business problems, the optimal solution is likely a combination of predictive and generative AI. This article explores when both technologies shine and use cases for a hybrid approach.

Building on our four-part series on the machine learning lifecycle, this article captures another data scientist’s take on the six critical steps in creating good data science products.

In our final piece in this series, we explore the scoping stage, where you define the project’s goals, assess feasibility, and determine the resources required to complete it successfully.

In Part 3 of “Machine Learning Lifecycle,” the author explores the nuts and bolts of data collection, from defining data sets to data splits.