SoundCloud, I love you, but you’re terrible

I finally started using SoundCloud for a new jazz/electro project called Fynix. I casually used it in the past under my own name, in order to share WIP tracks, or just odd stuff that didn’t fit on bandcamp. But I never used it seriously until recently. Now I am using it every day, and trying to connect with other artists. I am remixing one track a week, listening to everything on The Upload, and liking/commenting as much as I can.

SoundCloud is the best social network for musicians right now. But it still has a terrible identity crisis. Most of the services seem to be aimed at listeners, or aimed at nobody in particular.

So in this post, I’m going to vent about SoundCloud. It’s a good platform, but with a few changes it could be great.

1. I am an artist. Stop treating me like a listener.

Is it really that difficult for you to recognize that I am a musician, and not a listener? I’ve uploaded 15 tracks. It seems like a pretty simple conditional check to me. So why is my home feed cluttered up with reposts? Why can’t I easily find the new tracks by my friends?

This is the core underlying problem with SoundCloud. It has two distinct types of users, and yet it treats all users the same.

2. Your “Who to Follow” recommendations suck. They REALLY suck.

I’ve basically stopped checking “Who to Follow” even though I want to connect with as many musicians as possible. The recommendations seem arbitrary and just plain stupid.

The main problem is that, as a musician, I want to follow other musicians. I want to follow people who will interact with me, and who will promote my work as much as I promote theirs. Yet, the “Who to Follow” list is full of seemingly random people.

Is this person from the same city as me? No. Do they follow lots of people / will they follow back? No. Are they working in a genre similar to mine? No. Do they like and comment on lots of tracks? No.

So why the heck would I want to follow them?

3. Where are my friends latest tracks?

This last one is just infuriating. When I log in, I want to see the latest tracks posted by my friends. So I go to my homescreen, and it is pure luck if I can find something posted by someone I actually talk to on SoundCloud. It’s all reposts. Even if I unfollow all the huge repost accounts, I am stuck looking at reposts by my friends, rather than their new tracks.

Okay, so let’s click the dropdown and go to the list of users I am “following”. Are they sorted by recent activity? No. They are sorted by the order in which I followed them. To find out if they have new tracks, I must click on them individually and check their profiles. Because that is really practical.

Okay, so maybe there’s a playlist of my friends tracks on the Discover page? Nope. It’s all a random collection of garbage.

As far as I can tell, there is no way for me to listen to my friends’ recent tracks. This discourages real interactions.

Ultimately, the problem is data, and intelligence. SoundCloud has none.

You could blame design for these problems. The website shows a lack of direction, as if committees are leading the product in lots of different directions. SoundCloud seems to want to focus on listeners, to compete in the same space as Spotify.

But even if that’s the case, it should be trivial to see that I don’t use the website like a regular listener. I use it like a musician. I want to connect and interact with other musicians.

And this is such a trivial data/analytics problem that I can only think that they aren’t led by data at all. Maybe this is just what I see because I lead our data team, but it seems apparent to me that data is either not used, or used poorly in all these features.

For instance, shouldn’t the “Who to Follow” list be based on who I have followed in the past? I’ve followed lots of people who make jazz/electro music, yet no jazz/electro artists are in my “Who to Follow” list. I follow people who like and comment on my tracks, yet I am told to follow people who follow 12 people and have never posted a comment.

The most disappointing thing is that none of this is hard.

4. Oh yeah, and your browser detection sucks.

When I am browsing your site on my tablet, I do not want to use the app. I do not want your very limited mobile site. I just want the regular site (and yes, I know I can get it with a few extra clicks, but it should be the default).

Tips for Managing Joins in Looker

Looker is a fantastic product. It really makes data and visualizations much more manageable. The main goal of Looker is to allow people who aren’t data analysts to do some basic data analysis. To some extent, it achieves this, but there are limits to how far this can go. Ultimately, Looker is a big graphical user interface for writing SQL and generating charts. Under-the-hood, it’s programmable by data engineers, but it’s limited by the fact that non-technical users are using it.

The major design challenge for Looker is joins. A data engineer writes the joins into what Looker calls “explores”. Explores are rules for how data can be explored, but ultimately just a container for joins. When someone creates a new chart, they start by selecting an explore, and thus selecting the joins that will be used in the chart.

They pick the join from a dropdown under the word “Explore”. This is the main design bottleneck. Such a UI encourages users to have only a limited number of joins that can fit in the vertical resolution of the screen. This means limiting the number of explores, and hence limiting the ways tables are joined. This encourages using pre-existing joins for new charts.

This creates two problems.

  1. A non-technical user will not understand the implication of choosing an explore. They may not see that the explore they chose limits how the data can be analyzed. In fact, a non-savvy user may pick the wrong explore entirely, and create a chart that is entirely wrong.
  2. The joins may evolve over time. A programmer might change a join for a new chart, and this may make old charts incorrect.

The problem is that SQL joins are fundamentally interpretations of the data. Unless a join occurs on id fields AND is a one-to-one relationship, then a join interprets the data in some way.

So how can you limit the negative impact of re-using joins?

1. Encourage simple charts

Encourage your teammates to make charts as simple as possible. If possible, a chart should show a single quantity as it changes over a single dimension. This should eliminate or minimize the use of joins in the chart, thus making it far more future-proof.

2. Give explores long, verbose names

Make explore names as descriptive as possible. Try to communicate the choice that a user is making when they choose an explore. For instance, you might name one explore “Products Today” and another one “Product Events Over Time”. These names might indicate that the first explore looks at the products table, but the second explore shows events relating to products joined with a time dimension.

One of the mistakes I made while first starting out with Looker is naming the explores with single word names. I now see that short names create maintenance nightmares. Before assessing the problems with a given chart, I need to know which explore the maker chose for it, and because the names were selected so poorly, the choice was often incorrect.

I hope these ideas help you find a path to a maintainable data project. To be honest, I have a lot of digging-out to do!