Manual data prep is one of the most common challenges data analysts face in almost every industry or project. Fixing or automating data can turn into a large and risky task, taking much longer than expected, which is why automation is oftentimes cut from a project or flagged to do later. In this blog, we’ll provide some insight as to why manual data practices can bring unnecessary risk to your projects and how to overcome those obstacles.
While every analyst knows that automation practices lead to better data quality, more accurate reporting, and ultimately the ability for them to focus on more analysis, starting the process to fix or automate data usually brings to light all the dirty laundry that has been tossed aside over the years.
So if the manual processes are working “well enough” and you have great analysts on the job, then the next thought is often “automation can wait.” But this thought process needs to change, and here’s why:
But the good news is that most, if not all, of these potential risks can be mitigated by automating your data processes as much as possible.
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