The world’s leading publication for data science, AI, and ML professionals.

3 Animated Movie Quotes to Follow when Establishing a Data and Analytics Team

Kung Fu Panda quotes for the future Data and Analytics leaders

Photo by Ian Schneider on Unsplash
Photo by Ian Schneider on Unsplash

Before we start, I want to share a quote I can personally relate to:

" I have no special talent. I am only passionately curious." ― Albert Einstein

How it all started

Through my ten years of experience, I wore different hats – I was a researcher, BI consultant, Data Engineer and a mix of Data Scientist and Data Analyst. I can’t call myself an expert in any area, but one thing is clear – my passion is data, and my motto is simple: "You give me data, and I give you insights".

And then, at one point in my career, I got an offer to establish the Data and Analytics team from a small group of people working on the "data tasks".

My advantage was that I had a starting point of already two present colleagues in the company and defined data architecture with a modern data tool stack.

My disadvantage was that I was never on a leading function before. Not to mention, I was overwhelmed with all the organizational tasks and catching up with the processes related to team establishment.

Long story short, this is how my leadership journey began.

After more than one year of being a Data and Analytics lead, I will share the most beneficial personal tips and tricks for Building a team…by using life-valuable quotes from the Kung Fu Panda animated film [1]. 🙂

Kung Fu Panda’s quotes aligned with tips and tricks on building a team


"There is no secret ingredient." ― Mr Ping

Indeed, there is no secret ingredient when it comes to building a Data and Analytics team. The most important tips from my experience are: getting the support in this process and selecting the "great assets" for the team.

Let’s start elaborating on these two parts:

First, remember you are not alone on this path.

Reach out to your supervisors (C-level) and your peers (TL-level). Although you are familiar with the application process, you are now sitting at the other side of the table. The most valuable part is support from your supervisors/peers who have gone through the same process dozens of times already. They can share their own tips and tricks on what to pay attention to in the selection process.

However, keep in mind you can adapt the hiring process and select the people you think would be a great asset to the Data and Analytics team.

Second, select the "great assets" for your team.

This step is tricky as the final decision is yours, and sometimes you need to rely on your gut instinct. So my advice would be: when you are building a team, don’t hire people who only have preferences towards one area, e.g., purely data engineering or data science.

Instead, search for the Data Practitioners first, i.e. people who don’t have an issue handling the tasks in distinct areas. Now, let’s get the things clear: this is not feasible in every organization. In other words, this depends on the tool stack you are using and the complexity of the use cases you need to handle.

Finally, hiring people who are eager to resolve hybrid data problems and love to learn about distinct areas is the biggest asset for making a business impact.


"I can control when the fruit will fall. And I can control where to plant the seeds." ― Master Shifu

With all the new leadership tasks to handle, don’t forget to create, share and maintain a clear Data and Analytics vision aligned with the organizational vision.

Connecting the vision with the business impact will grow awareness among your supervisors/peers. As a result, they will better understand how analytical insights can ease their decision-making process and generate higher business value.

On the other hand, sharing a vision with the team members will help them understand the organizational development strategy and show how vital their work and contribution are.

Remember, you are planting the seeds on both sides.

Simple tips you can follow in this process to make sure the goals will be met:

  1. Create a clear roadmap with all the use cases to be handled.
  2. Use agile methodology to split the use cases into stories/tasks and share ownership of the tasks among the team members.
  3. Do frequent checkups on the delivered work and conduct retro meetings to ensure your team is on track with the "big picture".

"Teaching Kung Fu is an art that takes years to master." ― Master Shifu

The expectations within your organization towards the Data and Analytics team will rise over time.

In this process, you will need to acquire the new tools, learn new libraries or even coding languages and develop methodologies for resolving complex analytical use cases. Therefore, continuous education and knowledge sharing are essential in Data and Analytics team.

Tips and tricks which you can use to master the new requirements in the team:

  1. Promote internal knowledge sharing and exchanging ideas among the team members and other technical teams.
  2. Offer external knowledge sharing opportunities by utilizing e-learning platforms and consulting.
  3. Stress the importance of the research and business understanding before starting any new use case/task.

Finding the "Chi"

Photo by Sid Balachandran on Unsplash
Photo by Sid Balachandran on Unsplash

Finally, I can share a one-year summary of the most relevant organizational accomplishments in the Data and Analytics team:

1From Data Practitioners, the team members are now covering specific roles: Data Engineers, Data Analysts, and Data Scientists. Respectively, the tasks are being adapted to each role.

2 Data and Analytics roadmap is being adjusted quarterly and aligned with the organizational vision. In this way, the team members are informed about future tasks and have a clear idea of the prioritized use cases.

3 The internal knowledge sharing process between the roles is done weekly at the retro meetings. However, the support between the team members, regardless of their role, is also present daily.

4 The team members use external knowledge growth opportunities by utilizing the DataCamp and LinkedIn Learning platforms. In addition, when handling more challenging tasks, the team is getting support from consultancy companies.

5 Last but not least: the Data and Analytics team has become the team that connects the IT and business teams and fosters a stronger data culture.


References:

[1] Osborne, Mark, and John Stevenson. 2008. Kung Fu Panda. United States: DreamWorks Animation.


Credit where credit’s due:

  • Establishing Data and Analytics team wouldn’t be possible without the support of the leaders surrounding me. Thank you, Florian, Christian, Stephan and Christoph, for your help on this journey.
  • The "Chi" lies in the people that create the team. At the moment of writing this article, the Data and Analytics team consists of the following team members: Nataša, Lukas, Rafael, Milan and Stefan.

Related Articles