Scrum 🔗
For the last 2 years, I have discovered and followed the Scrum framework in the Data Analytics Tribe. I know there are Pro’s and Con’s opinions in the Data Science area regarding it, but I must confess that for me it was a good opportunity to better auto-organise, know each other and move from a reactive to a proactive setup.
How Scrum adventure started? 🔗
In the pandemic year, 2020, we had a programme initiated by our Agile Coach Lead to enhance the Scrum internal team and to complete all spots with resources, even part-time as I was. I took the challenge with open-minded and having in background a pressure of high workload exploded due to pandemic and the origin of my team - digital, the digital team :D .
Now each week, this bunch of passionate scrum, potential, servant leaders meet and discovered the SCRUM Guide with our lead help.
As you see, this is very empiric and simple thing. You bring a bunch of people, you set an objective and then you meet each week with the aim … well, our aim was to lead a squad and pass the Scrum Master exam.
Each week we discovered new topics like Daily Scrum or Retrospective or other. After few meetings, we started to receive small homeworks, a task of discovery and soon we started to prepare together for the SM exam. Pretty cool!
What about Data Science? 🔗
I’ve been in this universe for a while and I know that each team and industry has different behaviours, tools, methods and so on. On a topic everyone should agree on is that Data Science definition hadn’t got a consensus, therefore lots of people continue to consider it a Buzzword! I do agree, because lately you become interesting if you do some ML Ops, before it was Python, even before it was Big Data and Hadoop and so on.
To summarize, the main ideas on data science is:
- you need a problem
- you need some data
- you need tools & methods
- and the aim is to generate insights and actions.
The way we use it in our team is Data in Action.
Top mantras for DA teams 🔗
- Checks daily the data & figures associated with it
- Tests hypothesis to generate insights
- Treats you from analysis paralysis
- Curiosity
- Works on transforming data in relevant viz ( together with requester, not against or for someone)
- Choose one solution (to work on at a time), not more
- Fights for transparency
- Fights for bringing the right people in the project discussions
- Watch on the market and (continuously) build on new skills in the team
Is Scrum like a special thing? 🔗
No! Scrum is a framework just as other used in DS, CRISP-DM, SEMMA, A-B-C or others. You can follow it and discover things in which it excels like the way it’s using empiricism and helps people auto-organised in teams. It helps on building trust and team spirit. It helps in identifying issues faster.
In my experience, results starts after some time, there is no recipe for fast success, however it can help you to assess if things are not worth your time through the Fast Fail approach.