The Power of Python for Data Management (or How You’ve Been Doing Data Management All Along Without Even Realizing It)

Are you using Airflow or Pandas? Great! You've contributed to better data management at your organization.

The breakthrough of AI has reignited focus on high-quality data and effective data governance (not that scary as it sounds!) and management practices. AI needs fit-for-purpose data to reach its potential, and we already have powerful toolkit — like Airflow, Pandas, Matplotlib/Seaborn, or Great Expectations — to optimize workflows and ensure data quality.

No specific skills, knowledge or experience skills required.

Are you using Airflow or Pandas? Great! You've been contributing to better data management at your organization.

Now, let’s take one step back. Data governance and management professionals have eagerly awaited this moment. The breakthrough of AI has reignited the focus on high-quality data and effective data governance (it's not as scary as it sounds!) and management practices and tools. Although these disciplines aren't new, they've finally emerged from the shadow of big data, data engineering, cloud engineering and other things we’ve been focused on lately.

Ultimately, AI needs fit-for-purpose data to reach its full potential. How do we obtain reliable and accessible data?

Fortunately, we already have powerful tools in our tech stack to enhance our data management practices. Let's explore how Python-based solutions and libraries — specifically Airflow and Pandas, along with Matplotlib/Seaborn for visualization or Great Expectations for data quality checks, and other — can optimize our workflows and ensure data quality.

By utilizing these tools, you are instrumental in driving your organization toward its AI goals and better data management. Even if you’ve never framed your work that way before.

P.S. And when it comes to definitions, data governance isn’t as scary as it sounds. The most useful definition I've encountered is that data governance is really about “preventing people from doing stupid stuff with data” (Charlotte Ledoux, LinkedIn post feed). And good data management in this light, would be actually doing smart stuff with data.