The Best Managers for Data Roles
Or any roles since all roles are now data roles, right?! Plus announcing the launch of an exciting new section on using AI tools!
Hi everyone! 👋
Welcome to the new subscribers reading this newsletter!
A couple of announcements before we jump into the main content about data managers.
We hosted a webinar last night on ‘How to Design for Data-Heavy Products’. It is now available on our YouTube channel in case you’d like to check it out. It was super informative and can be applied to data-heavy presentations as well that many of us in data roles tackle on a daily basis.
The cheat sheet to download tips from the presentation can be accessed here
As you know, using AI tools in our jobs or work is becoming an imperative. Shopify CEO Tobi Lutke sent a now leaked memo to all employees that they need to learn to use AI tools to save time and gain productivity in their roles. He called it a ‘baseline expectation’. More companies are now following suit.
So, how do you join this exciting trend and upskill with AI tools? Which ones can and should you use in your role? How do they work?
We will soon have the answers for you. We are launching a new section in this newsletter called ‘How-to AI’.
We will introduce one new AI tool per week.
Examples include video meetings summarization tools, creating and automating presentation slides, creating visuals in seconds, extracting key details from documents and so on.
For each tool, we will show step-by-step how it can be used for a basic use case as well as tips, tricks and other information.
Think of it as actionable documentation for AI tools that you can use immediately in your role!
The first How-to AI article will release in the first full week in May!
Stay tuned! More details coming soon.
Now onto the discussion about data managers!
Anyone who has worked in any organization, large or small, knows that your manager is a key player in your work life and success, at a given point in time.
While it is true for any role (and as the sub-heading claims that all roles are now data roles) data roles might require a different kind of TLC from managers.
That’s what this article is about -
the unique leadership needs of data roles and
the qualities in the best managers for these roles
It is from the perspective of employees in data roles and their expectations about their managers. Thus, it is entirely one-sided, I admit.
I will write a follow up from the managerial perspective in the near future. I’ve been on both sides. It is only fair.
For now, let’s give the one side its spotlight.
📊 What is unique about data roles
On the surface, not much. But dig deeper and there are some stand outs. These matter more if you are early in your career.
Here are some unique pressures faced by those in data roles. This isn’t a complete list by any means.
Theory is even more different from reality than in some other fields. Applying theory then becomes a bit of a judgement call. For example, as students of statistics, we tend to obsess over p-values and error metrics and confidence intervals and so on. The obsession is necessary. But, there are also trade-offs to be made once you start thinking about the implications of those indicators for revenue generation or profits. The more experience you have working on those problems, the easier it gets to tackle them. It is usually helpful to get a ‘second’ opinion, preferably from someone with more experience such as your manager.
Soft skills such as the ability to communicate the results of a complex model matter more than you think! You can have the most sophisticated analysis done and ready to share. But, if you aren’t able to break down the technical bits into simple, non-technical language that all stakeholders can understand, you’re the only one who knows how cool your work is! Becoming good at this skill takes lots of practice and someone to try those skills on who can help you finesse it before you get on the big stage. Managers can help here.
If you are new to your role or organization, you may need to use new tools and need considerable onboarding support. In some cases, training is beneficial if your manager is willing to give you the additional time and resources to upskill.
It can be intimidating to work with numbers that drive a business. Having your manager to go to for ensuring that you are on the right track without getting penalized for it is important.
To hear more on these from employees themselves, keep reading!
First, some background.
Instead of writing this piece based on personal experience alone, I decided to survey and interview people in relevant roles.
I posted a survey through LinkedIn which received responses from folks representing different industries - consumer goods, footwear, apparel, utility, transportation, technology, media and entertainment.
I also interviewed another dozen or so people 1:1 where I was able to probe further by asking deeper follow up questions.
I promised them anonymity. I am keeping the promise.
If AI can help, why not use it!
I brought all survey responses, quotes and interview content in one document. As someone who earned a research degree and have put it into practice ever since, I could have meticulously analyzed all the qualitative, i.e. words, content myself over hours or days. I did indeed do a detailed review myself first and then decided to use the magic of LLMs to analyze the rest.
Let me point out something super important here.
We should all be learning how to use AI to become more productive and efficient. However, it is extremely important to know how to complete that process successfully without the help of AI. That will allow you to competently review and spot any mistakes that AI might have made. Because those systems are not yet perfect and prone to errors.
In other words, learn and know how to change a flat tire yourself (metaphorically speaking) before letting AI generate the step-by-step process for you.
I chose the AI tool Claude developed by Anthropic.
Here’s why - I wanted to upload the aforementioned document on Claude or any other chatbot tool like ChatGPT and its cousins.
One of Claude’s selling points for me, for this particular use case, is that it does not save any information you give it or store any documents past that specific event. This was important to me to protect my interviewees’ responses.
Full disclosure - everything above this section was written by me, a human. Everything below was generated by Claude with some edits and commentary by me after doing checks and balances with the original content.
Some of these below can apply to any roles in companies and not only to data roles.
📉 What problems did data employees face for which they needed managerial support?
Communication challenges
Difficulty explaining technical concepts to non-technical audiences
Having to navigate complex data explanations with executives
Work-life balance struggles
Needing flexibility for personal life while maintaining productivity
Investing "long hours" for growth
Analytical challenges
Going down "data rabbit holes"
Learning how to properly question and understand data
Moving beyond analysis to actionable business insights
Career path limitations
Navigating the tradeoffs between leadership roles vs. technical roles
Financial investments required for professional growth
📈 What were the positive actions and helpful behaviors that managers demonstrated?
Encouraged ownership and autonomy
Trusted employees to take ownership of their work
Provided freedom to take risks and be creative
Allowed flexibility in execution while maintaining accountability
Remained accessible and supportive
Were available to guide through challenges
Acted as sounding boards for ideas
Supported work-life balance with flexibility
Provided resources and opportunities
Ensured access to necessary tools and training (PowerBI, Tableau, classes)
Brought team members "to the table when it counted"
Empowered employees to share their work and questions
Offered clear communication
Explained the greater impact of tasks
Set clear expectations
Communicated requirements effectively
Gave candid, constructive feedback
Didn't avoid difficult conversations
Provided specific, actionable feedback
Held employees accountable for high-quality work
Fostered skills development
Taught effective questioning techniques
Shared communication strategies for different audiences
Helped employees develop executive presence
Demonstrated inclusive leadership
Valued employees' contributions
Created collaborative environments
Showed investment in employees' work and growth
Celebrated achievements
Appreciated accomplishments along the way
Recognized good work
⭐How to be a good manager for data roles?
Here are some key principles -
Effective leadership balances autonomy with support
Successful managers trust employees to own their work while remaining available for guidance
The best leaders provide freedom for execution while maintaining accountability and clear expectations
Clear communication and feedback are crucial
Direct, candid feedback—even when difficult to hear—is essential for professional growth
Understanding the business impact of data work ("follow the money") helps focus efforts effectively
Technical professionals need specific types of support
Access to proper tools, training, and resources is fundamental
Understanding how to communicate technical concepts to different audiences is a critical skill
Learning to balance technical excellence with business value is essential
Career growth requires navigating workplace challenges
Women and other minorities can face additional obstacles that require management of perceptions
Finding the right career path (technical vs. leadership) involves understanding personal strengths and preferences
Inclusion and empowerment drive engagement
Being "brought to the table" and having one's contributions valued increases motivation
Collaborative environments where leaders are invested in the work produce better outcomes
Work-life balance and flexibility remain important
Supporting employees' need for flexibility contributes to overall effectiveness
Leaders who understand and support work-life balance earn greater loyalty
Final thoughts
As I mentioned in the beginning, what started off as a survey and interviews about managers of data roles eventually blended into insights about how great managers come across and contribute, regardless of the type of role. Because at the end of the day, all roles are people-roles.