While efforts to build a data dictionary are often undertaken out of a zeal for documentation that we would normally applaud, in practice data dictionaries and data catalogs end up being a large maintenance burden for little actual value, and tend to very quickly become out of date. Instead of investing in building out traditional data dictionaries, we recommend a few different approaches for achieving the same goals in ways that are less burdensome to maintain and better serve the original objectives as well.

Data warehouses are not just for business intelligence (BI) anymore. You can maximize the value of your data engineering, data science, and analytics work by investing in building out a multi-use data-platform that serves business users, Analysts, Statisticians, and intelligent applications. In my last post, data-dies-in-darkness, I described how you can improve your organization’s data quality by exposing more data to more people. You can stretch this idea even farther by expanding the stakeholders of your data warehouse to include intelligent applications.