Laying the Groundwork
CTP combined agency-level experience design with cloud adoption best practices to design and develop a solution that delivers great customer experience , and a cloud deployment that optimizes performance and cost .
To get started , CTP led an Experience Design ( XD ) Discovery Workshop with key stakeholders — including the CEO and executives with industry experience — to unlock key goals and problems the media firm was facing . CTP identified the industry personas – “ power players ,” “ industry veterans ,” “ industry front-runners ” and “ program directors ” – who each had different motivations and different experiences leveraging analytics and technology . We led interviews and user testing with the key personas and shadowed them to better understand their days and lives . The common thread was that personas tended to use data inconsistently , usually as a secondary tool to validate assumptions . What they needed was a way to easily visualize data regarding a song ’ s performance against other key indicators and overlapping audience profiles .
CTP moved on to a prototyping phase . The team collected rows of data , using Google Sheets and JSON , to scope out a model to visualize song trajectory . They explored several visualizations to display the data , and created a UI , leveraging geomaps , bar graphs and trend lines . This led to an evolved workflow used to benchmark artist and song lifecycles .
CTP discovered that the biggest challenge was a lack of “ clean data ,” since it all was not created equally . For example , market boundaries and time windows for sales were not defined consistently across retail , broadcast and digital channels . To extrapolate cross-platform insights , CTP looked at ways to re-architect existing data strategies . Engineers helped the client scale a data “ pool ” as a precursor to building standard metrics their teams could rely on .
In parallel , using the existing Excel worksheets to develop a re-imagined application user experience , CTP created a secondary mechanism validating the data with industry veterans . This would help train their data models , reduce complexity and build intelligence over time . As a result , the team scaled back and simplified the UI to provide the right data at the right time to the right person – in the appropriate format and medium or channel .
The Music Analytics Dashboard application follows a traditional three-tiered architecture with a presentation layer , a business layer and a data layer . The presentation layer is an AWS Elastic Beanstalk application , running node . js and built using React framework components . The business layer is more in line with the current trends of serverless architecture – written using Amazon API Gateway and AWS Lambda , along with custom authorizers , and integration with Azure Active Directory via their OAuth interface . The data layer is a multi-availability zone AWS RDS instance using the PostgreSQL engine due to the relational nature of the data .
The serverless ETL pipeline is a MapReduce pipeline built using serverless components instead of Hadoop . Key features include : abstracted data ingestion that connects to FTP , S3 , Google Cloud Storage , SQL databases , SOAP services , Windows network shares and HTTP data sources ; Node . js modules for all things ETL ; and async streaming between different data protocols .
The Amazon CloudSearch component provides the application with an autocomplete search bar , as well as a full search results page that includes fuzzy matching .
10 | THE DOPPLER | SUMMER 2018