Data Mesh is a decentralized approach to data architecture that has recently increased in popularity. As opposed to being centralized, data ownership is handed to the domain or business function that understands the structure, purpose, and value of the data best. With this ownership comes the responsibility of each team to make sure that the data is clean, free of errors, and accessible.
Topics: Trends/News
This post is an expansion on an excellent piece titled “Data Cleaning IS Analysis, Not Grunt Work” by Randy Au, here in the context of marketing analytics. In his article, Randy talks about an often undervalued aspect of any analytics journey: data cleaning. In most literature on this topic, data cleaning is seen as a necessary but menial step required to enable more valuable analysis down the line. What this fails to capture is that every decision that is made around data through the pipeline affects the type of analysis, and results, you will be able to do in later stages – whether it is mining for patterns in the data or refining your queries. In this blog post, we’ll expand on this topic in the context of marketing data, and how you best set yourself up for success in your analysis of data in later stages.
