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This is an interesting and fun way to showcase not only Qlik Sense's Associative Difference and visualization features, but actually shows you a sample flow from problem to solution that we can all relate to; finding that next music track / artist to listen to.
I am always learning new things. When it comes to learning about Qlik Sense, or any software for that matter I am a big proponent of using "something" (scenarios, data and examples) that has interest to me so I can relate to it better. My data and example choice is using video game data, but I digress.
As you may know, I have been working with Qlik Solution Architect @TomiKomo - Tomi Komolafe - to share his stories and examples of “things” he encounters in the field when visiting customers and prospects. (You can see some of his solutions and experiences here.)
Well this time, Tomi wanted to share something fun. Similar to how you have seen me present many aspects of the product using video game or star wars related data, I paused and asked Tomi – “What speaks to you? What are one of your passions?” – he said: “Music, my friends and I love listening to music.” After further discussion – the idea of using Spotify data arose as Tomi and friends had Spotify accounts. BUT this has been done a few times already I am sure, and this time around we didn’t just want to present the data we wanted to show how Qlik Sense analysis in conjunction with the features of the Associative Difference can help solve a particular problem Tomi was having – “What songs or other artists out there am I missing?”
Is it possible that by using Qlik Sense Tomi can correlate data from his own and friend’s Spotify listening habits and find out what songs and artists he may not be listening to? – Watch this awesome video of what he discovered!
Take it away Tomi!
Tomi's Outline
Let's Begin
Have you ever thought to yourself, how come there’s isn’t a Qlik Spotify app that just tells me what songs I should listen to? Well, now, you are at least thinking about it and there is such an app.
I should set up the scene so I don’t come across as some Qlik nut who turns every moment into a “lets see if Qlik can solve it” scenario. Some of my closest friends live in New Jersey (I’m in DC, 4 hrs away) and during the pandemic, I was only able to visit them sparingly. Every so often, we would be grilling in the backyard or working out in our make-shift gym, playing music in the communal ANKER Bluetooth speaker. Some of the songs I heard, I really liked and I would’ve never heard them in my sphere of Spotify influence. Other songs were from artists I love but I hadn’t heard that particular song.
I cornered these 5 friends to get access to their Spotify song history (No friends were hurt in the development of this app). What you will discover are the myriad of ways we can analyze this data to bubble up interesting insights and trends. Fortunately, there’s a rich amount of data in the Spotify API anyone can access.
Here are some of the cool questions I will be answering using this data in this and subsequent videos:
Many more questions also keep bubbling up. What’s pretty cool is how some of the techniques used here can be applied to everyday business decisions. For example, which product should be paired with another product to improve margins based on supplier activity?
You should wait till the end of the video to see another cool surprise. It involves an extension that @joe_warbington , previously at Qlik, created.
Maybe I am a Qlik nut who turns everything into a Let’s see if Qlik can solve this…
I am always learning new things. When it comes to learning about Qlik Sense, or any software for that matter I am a big proponent of using "something" (scenarios, data and examples) that has interest to me so I can relate to it better. My data and example choice is using video game data, but I digress.As you may know, I have been working with Qlik Solution Architect @TomiKomo - Tomi Komolafe - to share his stories and examples of “things” he encounters in the field when visiting customers and prospects. (You can see some of his solutions and experiences here.)
Well this time, Tomi wanted to share something fun. Similar to how you have seen me present many aspects of the product using video game or star wars related data, I paused and asked Tomi – “What speaks to you? What are one of your passions?” – he said: “Music, my friends and I love listening to music.” After further discussion – the idea of using Spotify data arose as Tomi and friends had Spotify accounts. BUT this has been done a few times already I am sure, and this time around we didn’t just want to present the data we wanted to show how Qlik Sense analysis in conjunction with the features of the Associative Difference can help solve a particular problem Tomi was having – “What songs or other artists out there am I missing?”
Is it possible that by using Qlik Sense Tomi can correlate data from his own and friend’s Spotify listening habits and find out what songs and artists he may not be listening to? – Watch this awesome video of what he discovered!Take it away Tomi!
Tomi's Outline
- My friends and I listen to Spotify
- Use Qlik’s REST API to access our Spotify statistics
- Use Qlik’s powerful Set Analysis and Associative feature to find songs I should check out
- Play these songs right from my Dashboard to see if I like them
Let's Begin
Have you ever thought to yourself, how come there’s isn’t a Qlik Spotify app that just tells me what songs I should listen to? Well, now, you are at least thinking about it and there is such an app.
I should set up the scene so I don’t come across as some Qlik nut who turns every moment into a “lets see if Qlik can solve it” scenario. Some of my closest friends live in New Jersey (I’m in DC, 4 hrs away) and during the pandemic, I was only able to visit them sparingly. Every so often, we would be grilling in the backyard or working out in our make-shift gym, playing music in the communal ANKER Bluetooth speaker. Some of the songs I heard, I really liked and I would’ve never heard them in my sphere of Spotify influence. Other songs were from artists I love but I hadn’t heard that particular song.
I cornered these 5 friends to get access to their Spotify song history (No friends were hurt in the development of this app). What you will discover are the myriad of ways we can analyze this data to bubble up interesting insights and trends. Fortunately, there’s a rich amount of data in the Spotify API anyone can access.
Here are some of the cool questions I will be answering using this data in this and subsequent videos:
- Which friends are more like some other friend(s)?
Yes, I stand by the statement that our song choices say a LOT about us. - Which songs are guaranteed to be hits for me based on my friends tastes?
- This one is pretty cool - If I were an advertising company, which strategies would I use to penetrate each of our attention domains?
(Coming in another video release) - Did the pandemic have a significant effect on our song choices and which friend was affected the most?
(Coming in another video release)
Many more questions also keep bubbling up. What’s pretty cool is how some of the techniques used here can be applied to everyday business decisions. For example, which product should be paired with another product to improve margins based on supplier activity?
You should wait till the end of the video to see another cool surprise. It involves an extension that @joe_warbington , previously at Qlik, created.
Maybe I am a Qlik nut who turns everything into a Let’s see if Qlik can solve this…
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