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The products that you’ll be responsible for, drive much of the reporting and analysis … We’ve found that there are similar opportunities for people-to-person knowledge exchange with data discovery. In doing so, we were able to gain a better understanding of our users intent within the context of data discovery, and use this understanding to drive product development. In addition to viewing your podcast analytics in Anchor, you can now also access your podcast's stats directly on Spotify. However, in some cases, data scientists found it difficult to find the right person to talk to about a particular topic. Within the context of data discovery, a data scientist with low-intent has a broad set of goals and might not be able to identify exactly what it is they’re looking for. To kick things off, we spent time conducting user research to learn more about our users, their needs, and their specific pain points regarding data discovery. An example of an entirely data-driven decision would be our choice of a music recommendation algorithm that powers Spotify Radio. David Green: In terms of an example, have you got an example of a project where you've used people data or insights from analytics, to help either solve a business challenge at Spotify or maybe help to improve employee experience, or maybe both? For more complex operations, we have Luigi at our disposal, governing a zoo of Python, Pig and other animals which can be made to talk to any storage systems, run machine learning algorithms and even provide daily reports. If you have yet to set up your Spotify … Most data is user-centric and allows us to provide music … Other Spotify group companies: We will share your personal data with other Spotify group companies to carry out our daily business operations and to enable us to maintain and provide the Spotify … We found there were a few issues with this approach. If data discovery is time-consuming, it significantly increases the time it takes to produce insights, which … By understanding the user’s intent, enabling knowledge exchange through people, and by helping people get started with a dataset they’ve discovered, we’ve been able to significantly improve the data discovery experience for data scientists at Spotify. to Lexikon to better represent the landscape of insights production. So all this sounds… complicated. She has become your new genre guide. So… we needed a transactional email system. I also participated in a hackathon where I developed a Spotify App code-named Genderify that tapped into our massive data-set to determine exactly how “manly” a playlist is. Data scientists in a high-intent mode of discovery were often looking for one of these top used datasets that met their needs. In this case, a user can search for “track uri”, navigate to the “track_uri” schema field page, see the top BigQuery tables that contain the schema field, and navigate to the dataset page. However, months after the initial launch, we surveyed the insights community and learned that data scientists still reported data discovery as a major pain point, reporting significant time spent on finding the right dataset. To enable Spotifiers to make faster, smarter decisions, we’ve developed a suite of internal products to accelerate the production and consumption of insights. So, you go to the artist page on Spotify where you can check out the most popular tracks across different albums, read an artist bio, check out playlists where people tend to discover the artist, and explore similar artists. First, we focused on the search ranking algorithm. Using these features on the artist page after your first listen allows you to truly discover and build a connection with the artist. If you’re interested in helping us tackle similar problems or you’re a data scientist that’s looking to work at a company where producing impactful insights is becoming easier every day, visit the Join the Band page to view open roles. Your data is updated approximately every day. Compare to last visit See how your personal ranking changes over … After making these changes, we now see that 20% of monthly active users navigate to BigQuery tables through personalized recommendations on the homepage. Decisions that cannot be made by data alone are meticulously tracked and fed back into the system so future decisions can be based off of it. - Spotify Library to get access to Spotify platform music data - Seaborn and matplotlib for data visualization - Pandas and numpy for data analysis - Sklearn to build the Machine Learning model. After working at Spotify for only a few months, I was talking about term weighting and signing up for internal courses on the R programming language. Imagine you’re starting to explore the genre of jazz. Sounds robotic, but humans cannot be trusted so it’s cool. You’ve just had a high-intent discovery! owning a dashboard) rather than insights consumption (e.g. Get a detailed audio analysis for a single track identified by its unique Spotify ID. So you pull up Spotify on your phone, search for the track, and play it (on repeat). A data scientist with high-intent has a specific set of goals and can likely articulate exactly what they’re looking for. View your most listened tracks and artists and switch between 3 different time periods. Our Analytics Pipeline powers far more than satirical apps. One of these products is Lexikon, a library of data and insights that help employees find and understand the data and knowledge generated by members of our insights community. “show me queries on this table that reference this specific field”). Internally, everyone (not just engineers) has access to three tools: Dashboards, Data Warehouse, and Luigi. This shows the number of queries referencing the schema field and the number of unique people who have queried the schema field. Subscribe and listen to hear insights from business and industry leaders who share a passion for the power of data & analytics. … Analyzing Spotify Dataset. So the conclusion is to rely on data whenever possible. Matching data is compressed and periodically synced to HDFS. We’ve learned a lot since we first launched this product. Through its desktop site and mobile app, Spotify logs over 100 billion data points per day based on the activities of its 207 million active users around the world. So, we built a Lexikon Slack Bot to improve discussions about datasets. an overview of the most used schema fields in the table, and. We could clearly see that these emails were having a positive effect on user engagement. We’ve also seen a significant increase in engagement with the average number of sessions per MAU increasing from ~3 to ~9 since our initial launch. Within a few weeks we knew which email templates worked best and, more importantly, we could see the impact these email campaigns had on our users. The research and learnings from Spotify’ Insights community help make Spotify the best it can be. It’s rare that a single dataset will contain all of the information for which a data scientist is looking. Read writing about Data Analytics in Spotify Insights. In addition to using learnings from user surveys, feedback sessions, and exploratory analysis to drive product development, we also conducted research on knowledge management theory to better understand how we might adjust our approach (recommended reading: Knowledge Management in Organizations: a critical introduction by Hislop, Bosua, and Helms). In this role, you will help drive the roadmap and development of Spotify’s Ads ecosystem data and analytics products. Skiley.net is the most feature-rich Spotify analytics tool, with this site, you can track your … Let’s say you’re having a rough day and you want to listen to some music to lift your spirit. viewing a dashboard). This mode of discovery is particularly important for new employees or for people who are starting on a new project or team. Our People Analytics model is set up for tracking HR data and metrics for getting informed better and faster, for progressive thinking, planning, acting, and leading. This backend system for sending emails would simply log a message every time an email was sent with the fields (username, timestamp, email-campaign, campaign-version). Following this change, in user feedback sessions, data scientists reported that the search results not only seemed more relevant, but they were also more confident in the datasets they discovered because they were able to see the dataset they found was used widely by others across the company. find datasets that I might not be using, but I should know about. My experience at Spotify is a perfect example of how simple this is and shows how any engineer can make a meaningful impact. For instance, we have dashboards that show us user growth in particular regions, or user engagement, or even the number of emails we deliver. This will give you even more valuable insights into your episode performance, demographics, and more. Spotify is a digital music service that gives you access to millions of songs. Once this data made its way into HDFS, we had all the data we needed to determine the best performing email template for a campaign and we could track the effect a single email had on a user’s experience. find the top datasets that a team has used because I’m collaborating on a new project with them. Typically data is available in our Data Warehouse and Dashboards within 24 hours, but in some cases data is available within a few hours or even instantly through tools like Storm. Through user research, we learned that data scientists would often have a lot of questions about how to start using a dataset, which slowed down their ability to start using the dataset they just discovered. We believed that the crux of the problem was that we lacked a centralized catalog of these data and insights resources. In the first version of Lexikon, most traffic to BigQuery table pages was driven by search. For example, an example query might be out-dated because it included a join to a deprecated table. We do our best to base every decision, programmatic and managerial, on data and this extends into the culture. The typical data scientist at Spotify works with ~25-30 different datasets in a month. While this isn’t the most widely used feature, we’ve seen that it is consistently used by 15% of users who visit a dataset page. We see our different data … The only reason that’s possible is because Spotify now knows what to create—thanks to data. For example, an employee who queries/owns datasets, views/owns dashboards, authors research reports, and/or runs A/B test experiments related to the given keyword will be returned in the list of results. With the first iteration of Lexikon, we used the knowledge management strategy of codification, which is based on the objectivist perspective of knowledge. Since launching these new entity pages, we’ve seen that they’ve proven to be a critical pathway for discovery, with 44% of Lexikon’s monthly active users visiting these types of pages. Powerful stuff. find a relevant dataset located in a particular BigQuery project, find a dataset that my colleague has used of which I can’t remember the name, and/or. So what do we do with all this data? In the first version of Lexikon, we introduced example queries that allowed data producers to submit example queries to give data scientists an idea of how they might use the available dataset. datasets)— as well as discover knowledge generated through past research and analysis. at Spotify, resulting in more research and insights being produced across the company. Get more. So, we built a feature on a BigQuery table page that allows the user to see tables that are most commonly joined with the given dataset. Without big data, Spotify would not have turned out the way it did and with a growing user base only more data will be generated in the future. We will share your personal data for activities such as statistical analysis and academic study but only in a pseudonymised format. This was especially true for new employees who hadn’t yet built personal connections with members of the insights community. Once you’ve determined that you’ve found the right dataset, it can be quite daunting to try to understand all of the available fields and determine which ones are actually relevant. Since making these improvements to the data discovery experience in Lexikon we see that adoption of Lexikon amongst data scientists has increased from 75% to 95%, putting it in the top 5 tools used by data scientists. Python is beautifully complemented by Pandas when it comes to data analysis. Spotify is all the music you’ll ever need. This gives users the opportunity to see a variety of up-to-date queries that use the dataset, and the ability to search for specific queries on the dataset (e.g. engineers, data-savvy product managers, etc.). Then, Spotify also offers a data tool called Spotify Analytics, designed specifically for labels that want to track performance of all their artists on Spotify, providing a functionality to Spotify for Artists, but … Most of our recurring data is added to our analytics pipeline by a set of daemons that constantly parse the syslog on production machines looking for messages we have defined along with the associated data for each message. Lexikon’s user base has organically grown from ~550 to ~870 monthly active users as it has proven to be useful to data consumers in non-insights specialist roles (e.g. We learned through data analysis that although we have tens of thousands of datasets on BigQuery, the majority of consumption occurred on a relatively small share of top datasets. Most data is user-centric and allows us to provide music recommendations, choose the next song you hear on radio and many other things. Spotify’s technology leaders point to the particular importance of BigQuery, the Google Cloud data analysis tool, as well as Pub/Sub, for faster software application development. In addition to basic metadata about the schema fields, we included consumption statistics at the schema field level. Since launching this feature, we’ve seen that 25% of users who visit a dataset page use the queries feature. Similar to artist discovery, one of the most critical steps in data discovery is the final step—starting to use the dataset you’ve discovered. In this blog post, we want to share the story of how we iterated on Lexikon to better support data discovery. Exploring the Spotify API with R: A tutorial for beginners, by a beginner, Mia Smith. I took this project on as an opportunity to learn Python. Newsletter emailaddress. It’s likely the case that they’ll need to join a dataset with others in order to answer the question they have. Through user research, we learned that data scientists who failed to discover the data they were looking for would often fall back to finding an expert in the insights community on a given topic and connecting with them in person or online. For example, a data scientist might be looking for the best dataset to use that contains a track’s URI track_uri. recommendations for datasets you haven’t used, but might find useful. Andrew Maher is a Product Manager for Spotify’s Insights Platform Product Area. Shortly after joining Spotify, we decided as a company that we wanted to send users emails telling them if their friends joined and if new songs were added to a playlist they subscribed to. You want to hear more and learn about the artist. find popular datasets used widely across the company, find datasets that are relevant to the work my team is doing, and/or. You’ve just had a low-intent discovery experience! In 2016, as we started migrating to the Google Cloud Platform, we saw an explosion of dataset creation in BigQuery. Spotify strives to be entirely data driven. The Audio Analysis endpoint provides low-level audio analysis for all of the tracks in the Spotify catalog. In the case of Lexikon, we initially believed that if data producers did a great job describing their datasets there would be little-to-no need for person-to-person knowledge exchange. How fantastic is that? Datasets lacked clear ownership or documentation making it difficult for data scientists to find them. At the heart of Spotify lives a massive and growing data-set. The insights community at Spotify was quite excited to have this new tool and it quickly became one of the most widely used tools amongst data scientists, with ~75% of data scientists using it regularly, and ~550 monthly active users. You strike up a conversation and learn that she is a jazz aficionado. Data scientists are often curious to see how a dataset is actually used in practice. You happen to notice that your coworker has a jazz album on Spotify pulled up on her desktop screen. Whether we’re considering a big shift in our product strategy or we’re making a relatively quick decision about which track to add to one of our editorially-programmed playlists, data provides a foundation for sound decision making. Welcome to podcast from Dun & Bradstreet — The Power of Data, powering decisions with data. But to make use of it is actually really easy. Questions we look to answer … Rather than fight this, we decided to embrace the idea by (1) mapping expertise within the insights community and (2) providing supplemental information in collaboration tools. For example, as a data scientist, I may want to: In order to satisfy the needs of low-intent data discovery, we revamped the homepage of Lexikon to serve personalized dataset recommendations to users. You had some broad goal to lift your mood and you didn’t have extremely strict requirements on what you wanted to listen to. Analytics at Spotify May 13, 2013 Published by Jason Palmer At the heart of Spotify lives a massive and growing data-set. So, you open up Spotify, browse some of the mood playlists, and put on the Mood Booster playlist. Exploratory Data Analysis is often the most essential step of any Data Science project as it provides a great deal of insight towards building further analytics. This mode of discovery is often more important to more tenured data scientists who may be familiar with some datasets, but may be looking for something they haven’t used before that meets a certain set of criteria. find a dataset that contains a specific schema field. Engineers can easily add data to our analytics pipeline by adding a new message to our log parser and simply logging information to syslog using the correct format. So, we abandoned the curated example query and instead allow users to search through all recent queries made on the given dataset. You’re walking down the street and hear a passing car blasting a great song you haven’t heard in a while. So, we adjusted our search algorithm to weight search results more heavily based on popularity. With the help of a few other engineers, we built a fairly simple system that had the ability to deliver a lot of emails and also provided a way for people to create new email templates and A/B test different versions of an email template. At Spotify, we believe strongly in data-informed decision making. More than half of them are free, … Listen to The Power of Data on Spotify. When it comes to people data we have collected all the relevant components in one team, to make sure all sources and analysis … An incredible amount of data is created every second of every day with huge potential value for businesses around the world. As we know Spotify … find a dataset related to a particular topic. Explore our Marketing Campaign Planning Toolkit Campaign of the Week: Spotify use their data analytics in a risky but elegant marketing campaign When it comes to data analytics and … Don’t have enough data? In early 2017, we released Lexikon, a library for data and insights, as the solution to this problem. More weight is given to actions related to insights production (e.g. Ek was sharing the detail to highlight the success of Spotify for Artists, the company’s analytics dashboard for musicians, which provides information such as playlist inclusion, streams by … We were able to significantly improve the data discovery experience by (1) gaining a better understanding of our users intent, (2) enabling knowledge exchange through people, and (3) helping users get started with a dataset they’ve discovered. Since launching the Lexikon Slack Bot, we’ve seen a sustained 25% increase in the number of Lexikon links shared on Slack per week. We were able to see if an email had any effect on your listening habits, your account status and so on. An insight is a conclusion drawn from data that can help influence decisions and drive change. Our team decided to focus on this specific issue by iterating on Lexikon, with the goal to improve the data discovery experience for data scientists and ultimately accelerate insights production. Second, of the example queries that were submitted, they often became outdated quickly given the ever-changing landscape of data. In addition to improving the search rank, we also introduced new types of entities (e.g. Shout out to our current team (Ambrish Misra, Bastian Kuberek, Beverly Mah, David Lau, Erik Fox, and Nithya Muralidharan) and others who have contributed to Lexikon (Adam Bly, Aliza Aufrichtig, Colleen McClowry, David Riordan, Edward Lee, Luca Masud, Mark Koh, Molly Simon, Mindy Yuan, Niko Stahl, and Tianyu Wu). First, we ran into challenges encouraging data producers to share example queries for all datasets. links to view more information in Lexikon, request access, or open directly in BigQuery. This is how we collect people data and put it to work At Spotify, we take data very seriously and we try to make every decision data-informed. started migrating to the Google Cloud Platform, Knowledge Management in Organizations: a critical introduction. Katarina Berg: Yeah.For instance, there're a couple of things that we see with the data… Skiley.net. Listening is everything - Spotify In addition to the schema field page, we’ve added BigQuery Project, people, and team pages, which can serve as a similar stepping stone on the pathway to data discovery. 2.12K followers 4.4K … This feature gives Lexikon users a way to sort the list of available fields by usage to easily find the ones that are likely to be the most relevant. The hypothesis we wanted to test was that sending these emails would have a positive impact on user engagement and help more users to come back to using the app more often. The first release allowed users to search and browse available BigQuery tables (i.e. So, we developed the features Schema-field consumption statistics, Queries, and Tables commonly joined to address this last mile of discovery. New engineers at Spotify will notice that the culture has a way of engulfing you in a data-driven mindset. Data Warehouse is a more complex system that allows you to access our data-set directly. It was mostly a joke, but utilized listening data to provide an accurate statistical map of a playlist and displayed a result of 0-100, 100 representing an extreme edge case where a person registered as female had never listened to any tracks on your playlist. “experts”). You can’t get the song out of your head and need to listen to it immediately. Sentiment analysis of musical taste: a cross-European comparison, Paul Elvers. We are a company full of ambitious, highly intelligent, and highly opinionated people and yet as often as possible decisions are made using data. Spotify Audio Features. Using data about human behavior, relationships, and traits as the basis for making business decisions. However, in reality, while the first iteration of Lexikon reduced the need for person-to-person knowledge exchange in discovery contexts, there were still instances in which people found it useful to connect with others to find the right data. This data is very much still in use today. Dataflow, for real-time and historical data analysis… Make data the most important asset you have because it is the only reliable decision maker that can scale your company. At this time, we also drastically increased our hiring of insights specialists (data scientists, analysts, user researchers, etc.) The homepage provides users with a number of potentially relevant, algorithmically generated suggestions for datasets including: While we did experiment with more advanced methods for serving recommendations, including using natural language processing and topic modeling on the dataset metadata to provide content-based recommendations, we determined through user feedback that relatively simple heuristics leveraging data consumption statistics worked quite well. Dashboards provides an interface similar to Google Analytics and allows users to create their own custom screens containing data they are interested in from our pipeline. It allows us to recognize trends, discover bugs, and analyze the effect of an event on a user and the entire ecosystem. With Spotify’s option to export your personal data, and Google’s free, easy-to-use tool to visualize data called Google Data Studio, we’re going to show you just how to do that. Following the release of the first version of Lexikon, we found that data scientists continued to talk with each other about datasets in Slack. For comparison, more people report using Lexikon than BigQuery UI, Python, or Tableau at Spotify. Pretty much everything. Rather than discourage this discussion, we felt like we could help improve the person-to-person knowledge exchange by providing supplemental information. It was really nice to see how his taste of … Although Spotify approaches this process from a variety of angles, the overarching goal is to provide a music-listening experience that is unique to each user, and that will inspire them to continue listening and discovering new music that they will be engaged with we… However, research would often only have a localized impact in certain parts of the business, going unseen by others that might find it useful to influence their decision making. Datasets often contain dozens or even hundreds of schema fields. Our belief was that by making these types of entities more explorable, we would open up new pathways for data discovery. The Audio Analysis …

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