The Data Dilemma: Why Rushing Extracts Can Be a Recipe for Disaster
Hi, this is James with an issue of the talk data to me, lol Newsletter. In every issue, I cover topics related to data, & analytics through the lens of a data engineer. If you're into data engineering, architecture, algorithms, infrastructure, and dashboards, then subscribe here. Please connect with me via my LinkedIn here.
I often find myself in the uncomfortable position of being asked to drop everything and perform last-minute data extracts for leadership, with the expectation that I can deliver the results within an impossibly short time frame. This scenario has left me questioning if there’s a standard method I’m missing.
The Pressure and Doubt
Recently, during a conversation with a colleague, they recalled a meeting: one of the C-level executives bluntly stated, "If you're doing the extract properly, I don't see why you need to keep looking at it before you send it over." Some of her team members nodded in agreement. My colleague started to think that maybe they’re overcomplicating things or that there’s a simpler way to do this stuff.
The Reality of Data Extraction
Data extraction is not just a matter of clicking a button. It involves experimentation, checking for accuracy, and ensuring the data is reliable. Here are some key points that can often be overlooked in the heat of the moment:
Data Quality: Raw data often contains missing values, inaccurate data points, and inconsistencies. A thorough QA step is essential to ensure the data is correct and usable.
Complexity: If the data requires filtering, computation, or aggregation, it cannot be rushed. Each step adds complexity and the potential for errors.
Communication: There is often a disconnect between what leadership thinks is required and what actually needs to be done. Explaining the process and the risks involved can help manage expectations.
My Advice
Cover Your Bases: Specify the time constraints and the limitations of what can be achieved within that timeframe. Indicate that the requirements are probably not well-expressed and would require iteration. This approach helps in managing expectations and highlighting the risks involved.
Automate Where Possible: If the requests are similar, consider creating scripts or automated tools to generate the output quickly. However, always include a QA step to ensure the data's accuracy.
Educate and Demonstrate: Sometimes, it’s necessary to demonstrate the complexity of the task to the executives. Show them the SQL queries, ERD diagrams, and the steps involved in extracting and validating the data. This can help them understand why it can’t be done in 30 minutes.
Stand Firm on Quality: Emphasize that quality is not negotiable. If the data is not properly checked, it could lead to misleading results and poor decision-making. It’s better to take the time to do it right than to risk sending incorrect data.
Set Clear Expectations: Establish a clear QA/QC process and discuss who is responsible for what. This can include setting up an SLA (Service Level Agreement) for custom extract requests and outlining the error-checking responsibilities.
Moving Forward
Here are some strategies I would advise implementing to handle such requests more effectively:
Explain the Process: Be upfront about the challenges and the time required to ensure data quality. Explain that rushing the process can lead to errors and unreliable data.
Automate and Document: Where possible, automate repetitive tasks and document the processes thoroughly. This not only saves time but also provides a clear record of how the data was extracted.
Communicate Risks: Clearly communicate the risks associated with rushing the data extraction process. If the executives still insist on a quick turnaround, make sure they understand the potential consequences.
Seek Support: If necessary, involve my manager or other team members to help negotiate the timelines and expectations. It’s crucial to have a united front in explaining the importance of data quality.
Are there any useful strategies you can recommend that help improve your data extraction processes? If so, mention them in the comments below.