Automated data analysis and text narratives
Autolysis is a domain-agnostic solution that solves pattern-based behavioral problems. At its core, Autolysis uses Groupmeans – a statistical technique that is designed to identify the impact of groups over metrics or vice-versa. An open source version with a friendly license is hosted at https://github.com/gramener/autolysis and a web interface is available upon request. Text narratives that augment the statistical analysis forms a key aspect of interpreting results in a business context. I will discuss the challenges in setting up the technology stack and building a narrative engine using templates and retaining necessary context.
What do these questions have in common?
1) Which of my channels is fetching me the most viewership? – Media
2) Which segment of customer will most likely want to buy expensive watches? – Retail
3) How is the customer demographics impacting the loan repayment? – Banks
All the questions fall under a similar pattern of questions: what is the impact of a metric on a group or vice-versa. Autolysis is a domain-agnostic solution Gramener built to solve pattern-based behavioral problems and where minimal human intervention is necessary to interpret the analytical results.
I will discuss the technology stack setup behind Autolysis web version which is built using Gramex – a Gramener data and web server which is stacked on python – and web sockets.
Text narratives which retain insights, context, statistical relevance and augment the data analysis are an important aspect of explaining results. I will discuss the challenges involved in automating data analysis and smart text narration.
No hardware/machines required
I am interested in how automation impacts our daily lives. I work at Gramener as data scientist post my PhD at the Virginia Commonwealth University and a short post-doc at the University of Wisconsin-Madison.
I am interested in civic technology, automated learning, mapping for humanity, data journalism.