At 90 Seconds, our team’s main responsibility is to lead and build data analytics capabilities across the organization. To ensure that we can fulfill this duty, we have to build up the data team, with clear structure, responsibilities, and directions.
One of the first tasks we set ourselves to was to define the goal of the team, the different functions, and how we all fit together.
The team goal
Regardless of where you sit within the data team, you have one goal: to make positive impact with data.
This can be achieved through:
- Democratizing data: to build a strong data infrastructure, a data warehouse, and a business intelligence platform that embeds trust and accessibility into data
- Advanced data analytics: insights, machine learning, artificial intelligence, and data automation deliverables that are baked into our core products
The three data tracks
Note: There are also two different paths that one can chose to follow: either individual contributor or manager track
Business Intelligence: responsible for defining and building KPIs, functional reports, insights, and recommendations to the business. They are heavily embedded into each business function very early on. Strong SQL, visualization, story-telling, analytical mind, and stakeholder management are essential.
Data Engineering: responsible for the whole data infrastructure, data pipelines, automation, and integrations between different platform. Strong SQL, Python, big data infrastructure and engineering are must-have skills.
Machine Learning: responsible for machine learning products: predictive modeling, recommendation engines, and so on. Strong SQL, Python, Machine Learning design and implementation are needed.
The three tracks, although separated, share a single goal as well as a lot of similar skills and responsibilities. As one progress through each track, he or she will have to master skills from the other tracks to a certain level, along with leadership and management experience.
Here are the two fundamental characteristics of the team: technical capabilities and genuine curiosity .
Everyone in the team has to be exceptionally strong at what they do. No excuse. New hires, promotions, and rewards are mainly based on their technical capabilities, demonstrated through their deliverables. Without a strong foundation in technical, achieving great work with efficiency would be difficult.
Curiosity motivates each of our team members to constantly dig deeper, find insights, and come up with creative way exploring our data. A data person is like a detective in a way, with data being the subject of the investigation. I personally like this analogy from Joma Tech’s interview with a Quant researcher a lot.
(and why we don’t have a Data Scientist)
“How do I become a data scientist?”
— this is one of the most frequently asked questions to people who work in the data analytics field these days. It is such a big hype that makes people move from one function to another, within a company or to different company despite how painful the process is for every single person involved. Not only that, people want to jump into the data field by any means necessary so that they can eventually become a Data Scientist because it is The Sexiest Job of the 21st Century. At the same time, some companies start giving out Data Scientist titles in order to attract people at even lower cost.
So, why don’t we have a data scientist?
To be fair, we just don’t have that title – simply because it is too general. It’s like all members in our Engineering team are engineers, but they don’t really have Engineer title, but instead: iOs, Android, Back-End, Front-End, Full-Stack, or DevOps Engineer.
We don’t want to motivate our team members by giving them vague titles, with unclear responsibilities, but rather, a clear career progression, through personal growth and impact. We can’t be successful if our team members aren’t motivated to focus on what they do best.
A push for clarity
At the end of the day, a successful team will always need to have clear structure, direction, and responsibilities defined.
There is, however, no single formula that can be applied to all organizations. It’s pretty much boiled down to the main product, the core values, the size, and the direction of the company. Investing more time early on into planning, defining, and aligning these core definitions will help the team go a long way.
If you want to contribute to the next generation of amazing products at 90 Seconds, let us know!