Date: 13th November 2018 Location: Customer Experience Theatre Time: 11:50 - 12:20 Speaker: Charlotte Emms Organisation: seenit About: How do you get your colleagues interested in the power of data? Taking you through Seenit’s journey using Couchbase's NoSQL database to create a regular, fully automated update in an easily digestible format.

1. Enabling Data-Driven
Decisions with Automated
Insights
Charlotte Emms
Data Insight Analyst @ Seenit
charlotte@seenit.io

2. Charlotte Emms
Data Insight Analyst @ Seenit
- 4 years of experience in the Data Analytics field
- Joined Seenit’s Product team in Nov ‘17

3. Introducing Seenit
A video collaboration platform that enables
organisations to co-create authentic and
engaging content with their communities
of customers, superfans, or employees.

4. What is Seenit?

5. A Brief History of Seenit
Founded in
January
2014
Over 200k
uploads hit
4k+ monthly
active users
MVP used Couchbase
deployed on GCP
Still the same today!
Seenit Capture v2.0
released in April 2018

6. Our Couchbase Journey
• Seenit’s MVP in 2014 was powered by Couchbase 2.2
back when querying the data required MapReduce
techniques
• The introduction of N1QL transformed Seenit, allowing
us to develop new features rapidly and analyse event-
based audit data for product usage research
• N1QL allowed Seenit to easily understand this data by
hiring a Data Analyst proficient in SQL

7. Non-first (N1) Normal Form Query Language (QL)
• It is based on ANSI 92 SQL
• Its query engine is optimized for modern, highly parallel multi-core
execution
SQL-like Query Language
• Expressive, familiar, and feature-rich language for querying,
transforming, and manipulating JSON data
N1QL extends SQL to handle data that is:
• Nested: Contains nested objects, arrays
• Heterogeneous: Schema-optional, non-uniform
• Distributed: Partitioned across a cluster
What is N1QL?

8. ...this is not a scalable approach for repeatable work!
The Seenit Data Analysis Toolkit
Display the results using
Plotly - a personal
favourite in terms of data
display versatility and
clarity
Extract data for analysis
using N1QL queries
through the Couchbase
Python SDK
Manipulate the data in
Jupyter Notebooks with
Python, using analytics
libraries like Pandas and
Numpy

9. How can I share
this data with the
wider team?

10. “Let’s build a dashboard”
- every data analyst joining a
startup ever (probably)

11. Tell a different
story with the data

12. How to Communicate Platform
Usage and Make it Interesting
There was a need for something more tangible. Something that
can engage someone from any role in the business
For example:
How many
uploads in a
week?
How far was
our global
reach this
week?
Which were the
active projects
this week & who
was running them
Have mobile
users been
interacting with
the in-app-feed?

13. Seenit’s Regular, Fully Automated Platform Update
Important
overused pun

14. Three core sections
○ This week in numbers
○ This week in lists (what’s new, what’s active)
○ This week in fun facts, which includes the following:
■ most exotic upload location (furthest from the office)
■ most committed contributor (uploads to projects)
■ biggest crowd pleaser (likes from others)
The “Engaging” Part

15. The Technical Journey
Productionised
● Introduced more
thorough HTML
templating using Jinja2
● Scheduled in Jenkins to
run once a week
● Runs in Kubernetes by
default - also set up to
run in Docker
Initial Idea / PoC
● Off the back of a
conversation at stand-up
● Designed a process in
Python through a Jupyter
notebook
● Query data in Couchbase,
manipulating and returning
in tabular form for friendly
email display
● Sent using my own gmail
account
MVP
● Found and wrestled with a
nicer looking email template
● Introduced fun facts section
● Moved code to a formal
Python project (separate
scripts, classes, functions)
using an executable file to
trigger the email generation
● Sent using Sendgrid as we do
for generic email
notifications from the
platform

16. Next steps

17. Another idea

18. DataBot
A Slack Bot user to
deliver bespoke
analytics on request
• Coded in Python and uses the
RTM (Real Time Messaging)
Slack API
• Mention the name of the bot
user to get a response
• The bot uses the first word
after the direct mention to
decide the correct response -
no AI/ML training or NLP
methods needed
Image

19. Image

20. Things I learnt
- You don’t need a relational database
structure to build automated data
programs
- You also don’t need a BI tool to
communicate data insights (at first*)
- Try new things even if they don’t go to
plan
- Learn new skills from teammates
- Welcome feedback and keep iterating!

21. Questions

22. Thank you
charlotte@seenit.io