Create a clear roadmap with all the use cases to be handled.Simple tips you can follow in this process to make sure the goals will be met: Remember, you are planting the seeds on both sides. 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. As a result, they will better understand how analytical insights can ease their decision-making process and generate higher business value. 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.Ĭonnecting the vision with the business impact will grow awareness among your supervisors/peers. And I can control where to plant the seeds.” In other words, this depends on the tool stack you are using and the complexity of the use cases you need to handle.įinally, 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. Now, let’s get the things clear: this is not feasible in every organization. people who don’t have an issue handling the tasks in distinct areas. Instead, search for the Data Practitioners first, i.e. 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. This step is tricky as the final decision is yours, and sometimes you need to rely on your gut instinct. Second, select the “great assets” for your team. 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. They can share their own tips and tricks on what to pay attention to in the selection process. The most valuable part is support from your supervisors/peers who have gone through the same process dozens of times already. Although you are familiar with the application process, you are now sitting at the other side of the table. Reach out to your supervisors (C-level) and your peers (TL-level). ![]() Let’s start elaborating on these two parts:įirst, remember you are not alone on this path. The most important tips from my experience are: getting the support in this process and selecting the “great assets” for the team. Indeed, there is no secret ingredient when it comes to building a Data and Analytics team. ![]() ![]() Long story short, this is how my leadership journey began.Īfter 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. Not to mention, I was overwhelmed with all the organizational tasks and catching up with the processes related to team establishment. ![]() My disadvantage was that I was never on a leading function before. 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. 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”.Īnd 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”. 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. Before we start, I want to share a quote I can personally relate to:
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