Business Intelligence

Machine learning isn't a magic wand. It’s a tool that requires a strong data foundation. Here's how to build that foundation so you can get to machine learning.

What factors go into handling these data sources? How do we process them? How do we provide data visualizations based on those sources to meet business needs?

Most want to transform and modernize their data but don’t know how. Here's how to successfully guide your organization along its data modernization journey.

Everyone wants to become data-driven. But, before you start modernizing your data, make sure you watch out for these common data transformation pitfalls.

Self-Service analytics allows business users to start taking ownership of some of the work. This solves some businesses needs while creating other challenges.

Process modeling is very flexible, can help you better understand current processes, and can help you document desired future states of processes.

Metrics and KPIs are among the primary techniques for anything related to BI. If you can measure it, you can report on it. The key is picking the right ones.

While it may be tempting to choose only the newer data lake format, or the tried-and-true data warehouse, both have pros and cons that we explore in this blog.

As it applies to Business Intelligence, decision modeling can be used to build out the repeatable decision logic for what to show on reports and dashboards.

Data modeling discovers your scope, identifies gaps, and is easily understood. But it doesn’t dig into the details so it must be supplemented with other models.