What does Alteryx do?

Data is messy and disorganized: the need for ETL

If you’ve been reading Technically for a while, you’ll be familiar with the fundamental fact that the data you need is usually not the data you have. Imagine you’re part of a marketing team, and you want some insight into how one of your recent campaigns is doing. Easy, right? Not so fast. The data you need is all over the place, physically:

• Form signups and whitepaper downloads from HubSpot
• Web traffic data collected via Segment in your data warehouse
• Paid search data from Google in AdWords
• …the list goes on

So first, you’ll need to get all of this data into one place so you can analyze it together. But before then, you’ve got another issue: the data is dirty, and needs to be cleaned before you can use it. A few examples:

• You need to filter out whitepaper downloads that came from your teammates
• Web traffic data is erroneously overstated for last July because of a bug
• Your campaigns in AdWords are poorly organized and named
• …the list goes on

None of this cleaning is difficult but it is necessary and it is time consuming. And this phenomenon extends well beyond just the marketing team – pretty much every team’s data is all over the place and needs some TLC. 

ETL – which stands for extract, transform, load – is the process of remedying all of the above.