Data analytics is filled with complexity. Anyone saying otherwise is selling products. Knowing the data sources, data sets, general lineage, and behavior of the numbers are table stakes for the average data consumer. We must know where our data comes from. Much like we need to know where our food comes from and how it's processed. Is it safe to consume?

Lately, I’ve been hearing many stories about early career folks with data analyst titles turning to ChatGBT for help because they don't know where to go with questions. ChatGBT should only be used when the output can be rigorously challenged, which can only happen if you have the foundational knowledge of how the output was generated. Here are some handy Do’s and Don’ts to keep in mind before turning to ChatGBT.

DO’s
Check Your Data First:
🔹After cleaning or preparing data, look at a few examples to ensure they match expectations.
🔹Count the records in the table and compare them to the source to avoid missing data.

Know When to Stop:
🔹Set an acceptable error rate and stop when it's reached.
🔹Estimate the value of additional analysis using the 80/20 rule to avoid overanalyzing.

Automate Tasks:
🔹Automate routine scripts and tasks with cron jobs or schedulers.
🔹Saves time, ensures consistency, and reduces the risk of errors.

Document Your Process:
🔹Keep thorough documentation of your analysis steps, transformations, and insights.
🔹Acts as a reference for future analysis and enhances transparency.

Collaborate and Seek Feedback:
🔹Engage with colleagues and stakeholders to validate findings.
🔹Seek feedback on methodology and interpretations for a more robust analysis.

DON’Ts
Don't Drown in Data:
🔸Define clear business objectives to prioritize and extract meaningful insights.
🔸Avoid getting lost in excessive data without a focused approach.

Don't Start Without a Plan:
🔸 Outline steps, methodologies, and tools before starting analysis.
🔸Test techniques on a small sample before scaling up.

Don't Work With Messy Data:
🔸Build a data warehouse for consistent and efficient data access.
🔸Ensure data consistency by creating new tables from existing ones.

Don't Overlook Data Governance:
🔸Establish data governance policies for quality, privacy, and security.
🔸Neglecting governance can lead to inaccurate insights and legal issues.

Don't Neglect Data Visualization:
🔸Use visualization tools to present data effectively.
🔸Visualize data through charts and graphs for clearer communication.

Following these do's and avoiding common pitfalls can enhance your data analysis's accuracy, reliability, and effectiveness. Stay focused, plan ahead, ensure data quality, and communicate findings clearly—and ChatGBT becomes more of the validation tool it was meant to be rather than a crutch to rely on.

How do you use ChatGBT to assist your efforts? Where does it help? or fall short?