Narrator helps your data team implement a single table that covers every interaction customers have with your business (opened emails, paid invoices, new support tickets, etc...). This provides the foundation for a comprehensive self-service experience for your company. Anyone can answer their own data questions without needing SQL and generate comprehensive analyses instantly to ensure they make the most informed decisions.
An activity stream is a table centered around your core customer that makes self-service analytics possible. It's based on the Activity Schema data modeling approach. In Narrator, it's a 10 column, time-series table with all of your customer activities. As customers engage with your company (purchase products, call support, churn, visit the website), more activities are added to the activity stream. You can think of the activity stream as a timeline of all interactions that all customers have had with your company.
Important note, the customer can be any entity that's core to your business, can be a client stream, company stream, vehicle stream, etc…
Narrator uses activity transformations (simple < 25-line SQL queries that you define) to add activities to your activity stream. The activity transformation becomes the definition that will be used to define a customer concept (activity) across your company.
Activities represent the important actions that your customer does (Completed Order, Viewed Page, etc). Everything in Narrator is built using activities. They each have a customer identifier (typically email address), a timestamp of when it occurred, and a set of feature columns that are specific to each activity.
Once you've built your activity stream, it can be reassembled into any table you need for reporting, data science, or analysis. Narrator is SO powerful because any dataset can be created with this single data model. To make this possible, Narrator uses an innovative new-approach to joins called Relationships.
Because Activity Schema is a standard, any query can be automatically generated using the Narrator platform. Dataset was built to handle the complex SQL logic required to query it (ie. self joins, relational joins, etc), so your queries are always accurate, optimized for performance, and easy to read!
Check out the dataset question bank for examples of the types of data tables that can be generated using your activity stream.
Using the analyze button, you can understand how any feature is influencing any KPI from any dataset built in Narrator. It will assemble a comprehensive analysis that provides concrete recommendations alongside a story to tell you why your KPIs are influenced by each feature.
Read more about the Analyze Button.
Create a data-driven culture
Users are empowered to ask any question and get immediate answers
Users are able to test hypotheses and have confidence in answers
Save time spent on data
Maintenance and modifications are simple - enabling you to quickly adjust to changes in the market, business, or technology stack.
Single source of truth / Universal definitions
All tables are derived from the same centralized source (the activity stream). Once you define a concept (activity), that definition can be used by everyone at your company. No more re-defining what it means to "churn" each time you have a new question related to churn.
If you make a change to the definition, those changes will automatically cascade to all datasets using it without having to change the data structure.
Bridge systems without foreign keys
Foreign keys don't always exist so we built a model that joins on customer, time, and occurrence. By relating activities by customer and time, you can always bridge systems without relying on a foreign key. (ex. What was the last webpage someone visited before they submitted a support ticket, but only if it happened within 30 minutes?)
The activity stream takes advantage of your existing data warehouse because it's a long columnar table. This is what warehouses were built for.
One layer of dependency
One table, one dependency, so one layer of logic to trace the source of any piece of information. All datasets are derived from the same source. No more web of dependencies to manage.
A universal data model means analyses can be templatized and shared
Because your data is in a standardized format, it becomes easy to share and reproduce analytical approaches (see: Analyze Button), enabling data scientists and analysts to share and iterate on their methods in a hassle-free way.
Queries are standardized and optimized for performance
The Dataset tool was built to handle complicated queries that you'll need to work with an activity stream table (self joins, relational joins, window functions, etc). The SQL it generates is easy to read, always accurate, and optimized for performance.
Narrator provides a transformation layer to build your activity stream (built and validated by your data team), as well as a dedicated tool for self-service analytics (for everyone in your organization)!
Updated 9 months ago