How To Be a Good Data Analyst Without Good Data

Musings on data quality

Megan Bowers
5 min readMar 10, 2023

“This is wrong.” — key stakeholder

I dreaded hearing that phrase (and variations of it) when showing dashboards to project stakeholders. I would have a lot of anxiety around data analytics projects in the beginning because I didn’t want to fail. (I’m not great at handling failure— still working on that.)

But once I had more experience as an analyst, my perspective started to change. I wasn’t a bad analyst; rather, I was given bad data.

What can you do?

Photo by Jon Tyson on Unsplash

In this article, I will walk through some of the strategies I employed when the data quality for a project was… less than ideal.

A quick note before I start: a lot of the time, the data is out of your control! There could be business decisions that were made years ago that have negatively impacted the data quality at your organization. IT reductions, acquisitions, failed ERP transitions, etc. Long-term, sustainable change in data quality needs to be an initiative from leaders higher up than analysts.

But, at the end of the day, you often have to work toward a deliverable anyway. So, how do you make progress?

Automate data cleaning tasks



Megan Bowers

Sr. Content Manager @ Alteryx. I mostly write about data science and career advice. Occasionally I’m funny. Find me on LinkedIn!