Key Habits of Successful Data Analysts

Best way to stand out early in your data career? Think like a business owner 💡 👉 Talk to stakeholders to understand their motivations 👉 Build domain knowledge to learn the nuances of the business 👉 Clearly articulate how your analysis ties to specific goals or KPIs 👉 Draft a measurement plan before you even touch the data Early in my career all I wanted to do was build fancy reports and dashboards, but as soon as I started thinking this way everything changed. Not only did I start earning respect and recognition from management, but I began to actually see (and measure) the impact of my work. This was probably the single biggest catalyst in my career growth and development as an analyst. So to all the seasoned pros out there, what other advice would you give to help an analyst accelerate their career?

Harsh truth for data pros: You won’t move up just by getting better at tools. You have to change how you think. Here are 6 mindset shifts That quietly transform analyst careers: ⸻ 1/ Overthinking kills clarity → You don’t need the perfect chart. You need a useful one. The best insight is the one they actually use. ⸻ 2/ Vagueness kills accuracy → If the request is unclear, the output will be too. Don’t just accept ambiguity — push for clarity. ⸻ 3/ Over-design kills insight → Flashy visuals don’t fix fuzzy thinking. Good analysts make data make sense — not just look good. ⸻ 4/ Overpolishing kills delivery → You tweak, revise, reword… Meanwhile, the moment passes. Done > perfect. Impact needs a deadline. ⸻ 5/ Assumptions kill collaboration → “They probably meant X.” “They won’t get it.” Ask. Align. Communicate. Don’t build in a silo. ⸻ 6/ Inaction kills trust → That follow-up you didn’t send? The insight you never shared? The teammate you left waiting? Follow-through builds reputations. Delay erodes them. ⸻ These don’t show up in your resume. But they shape how you’re seen - and how far you go. Credit to César Solís who inspired this post.

The most challenging transition from "breaking into" a data career to "growing" your data career is your relationship with technical skills. Getting into data requires much investment in growing your technical skills and showing proficiency. The harsh truth is that these technical skills are just the bare minimum. While it's essential to upskill and improve your technical understanding, this alone won't get you promoted. What gets you promoted is applying your technical skills to business problems and getting buy-in to implement them. The key phrase here is "buy-in to implement," and this is where you NEED to become proficient in soft skills and selling internally to your peers and leadership. It's why I spend so much time talking to stakeholders across the business to understand the pains they experience and how data can support their respective business goals. It's why I spend so much time scoping problems and their impact. It's why I spend so much time bringing my stakeholder along the building process so they feel it's their project as well. Stop focusing on data itself, and instead focus on what data can do for your stakeholders and watch your career trajectory accelerate. #data #ai

Navigating New Data Analyst Challenges... As a new data analyst you will be faced with common challenges.Think of each hurdle is an opportunity for growth, and by mastering these, you'll set yourself up for success. 💎 Excel's Limitations💎 As much as I love Excel... Diversify your arsenal with advanced tools like Python, SQL or Powerbi for a more robust analytical experience and visual experience. Excel is just one piece of the puzzle, not the entire picture. 💎Art of Data Curation💎 Choose data wisely! The temptation to accumulate mountains of data is real, but finesse lies in cherry-picking only the most relevant. Avoid analysis paralysis; be a discerning curator of information. 💎Elegant Visualization💎 Craft compelling visualizations that captivate, not confuse. Avoid creating chaotic graphs that resemble spaghetti. Simplify your visuals... remember, less complexity often equals more impact. 💎Causal Does not = Correlation💎 Distinguish correlation from causation. Mere coincidence of data trends doesn't imply causality. Employ sound statistical techniques to establish causation, avoiding hasty assumptions 💎Honesty in Analysis💎 Maintain data integrity and transparency. Refrain from molding data to fit preconceived narratives. Trust in your insights is paramount... let the data speak its truth. 💎Plain Language💎 Effective communication is vital. Translate your findings into plain language for universal understanding. Avoid cryptic jargon that alienates non-analysts. 💎Collaboration💎 Foster collaboration...data analysis is a team effort. Seek insights from experts, engage colleagues in discussions, and learn from your peers. Together, you can orchestrate success. As a data analyst with over a decade of experience I still struggle with some of these... A chart that I think tells an amazing story but is too complicated... An analysis that is sound but so technical my end user has no idea the impact of the program... Remember working the in world of data is a journey where you are constantly learning... #dataanalytics #dailylearning #dataanalyst #careeradvancement

How do we mature in a Data Science career? There are different ways to move forward, but sooner or later, you realize 3 key things: 1. Soft skills matter more than coding skills If I were starting over at my first job, I wish I had honed on my soft skills more than my coding skills. When you are good at math and can code in 4 languages, arrogance comes easy. Watch out, because it can get you hired, but not promoted. How we connect with others is important to career growth. 2. Clean slides are important to your models This. Great models get lost in bad slides. If you can't communicate your results simply, or show your equations on slides, you may look smart, but that's about it. Businesses care about simplicity and simple charts that prove why you are right, and is important to the success of hours/weeks of work. 3. Listen to your stakeholders This took a while and still WIP. But I don't start just working on an analysis until if I've been able to understand the stakeholder's pain point and determine if a solution is needed. Its important to let people speak on what they need, in discovery, in more discovery, and especially in presentations. At the end of the day, its their decision to make whether they want to use your analysis for anything. Make room to listen. #datascience #learning

I started working in data analytics 8 years ago. I’ve learned a lot in that time. Here’s one of the BIGGEST mistakes to avoid to be more effective in your role and advance your career. In the world of data analytics, getting bogged down with ad hoc, reactive requests is a common challenge. The key to transcending this reactive loop lies in probing the WHY behind each request. When a co-worker comes to you with a request, don’t just immediately get started working on it and then send them the solution once you finish. It’s important that you discover the real need. Often times business users come with specific data requests based on their limited understanding of what data can do. As an analyst, when someone asks for a particular data set, it's crucial to ask why they need it. Understanding their underlying motivation can reveal more about what they're trying to solve or understand. If you don’t do this you’ll end up wasting A LOT of your time and you won’t even provide them the best solution. Once you grasp the real question or problem, you're in a position to offer a more effective solution. For example, if someone asks for a specific data pull, by understanding their ultimate goal (e.g., understanding customer behavior, improving operational efficiency), you might suggest a better, more comprehensive way to look at the problem using data. Business users aren't typically data experts, and allowing them to dictate data solutions can lead to suboptimal outcomes. Instead, train them to approach you with problems, not preconceived solutions. This approach not only leads to better data-driven decisions but also educates users about the potential and limitations of data analysis. By understanding the true motivation behind data requests, you position yourself not just as a data analyst, but as a strategic partner in problem-solving. This approach allows you to leverage your expertise to provide more insightful, impactful data analysis, ultimately enhancing the decision-making process within the organization. Remember this next time you get a request! 🤝 Every Thursday I send out a free newsletter to 9,000+ data crunchers like you. The content varies each week but includes SQL tips, open data jobs, freelance gigs, datasets for portfolio projects, data memes to keep it fun, and any other useful info we find. Click the link in my profile page to sign up or you can go to thequery.jobs! #data #dataanalyst

🧵 Here are some of my tips for new grads starting in rotational or analyst programs this fall 1) Define success early by creating a working timeline with your manager on where key milestones and skills should be hitting a certain sufficiency level. This most likely will change over time, but it gives you a base to start from and a running document to track your growth 2) Learn acronyms & definitions by writing down notes from meetings and 1:1's. Don’t ask the same question twice 3) Set up a recurring career conversation call with your manager to discuss highlights, areas of improvement & tracking what it takes to get to the next level in the growth of your role & career. I advice having these conversations once every 6 weeks 4) Find a sponsor who can go to bat for you. Preferably on your team or org. This helps your impact be highlighted when you are not in the room 5) Aim to be an expert in 1-2 things rather than 4-5. Impact over activity #careeradvice

Don't let the means become the ends. As a data analyst, you're not directly making decisions about pricing strategy or how product develops. But you and your work are critical to helping your less technical team members do those exact things. So when you're writing SQL queries and playing around with visualizations, or creating dashboards, remember – don't get caught up in the minutiae of ticking metrics off of a checklist. 𝙒𝙝𝙮 are you finding that metric to begin with? Is there 𝙖𝙣𝙤𝙩𝙝𝙚𝙧 𝙬𝙖𝙮 you can visualize it more effectively? 𝙒𝙝𝙖𝙩 is that chart saying about how your customers buy now? By the way, when you get really good at answering questions like that last one – being able to articulate what the number indicates, then thinking of ways to adapt to it – your managers and teammates will love you. 😊

Data analysts aren't lone wolves. The faster you start connecting? The better you will compound your impact. And if you want to become a go-to analyst, you need a collaborative network of strong, meaningful relationships. So, don't be the data analyst who: – Doesn't care about stakeholder buy-in. – Sees business problems only from a data POV. – Never clarifies data questions with different teams. – Lives in a silo disconnected from others' perspectives. Instead, be the data analyst who: – Seeks context with different teams for business problems. – Asks questions to gain insights regarding data questions. – Is open to diverse perspectives that fill in gaps. – Values nurturing trust with stakeholders. Benefit: You'll increase your potential for career growth and impact. Invest in cross-departmental relationships over time. When you get the opportunity to lead a data project. You won't have to fight for support.

6 things to AVOID in your first data job... - prioritize complex queries when basic ones work just fine - use technical jargon that no one else understands - create obscure, complicated data visualizations - figure out how to do everything without help - memorize every Excel formula imaginable - master every data tool possible Instead, DO these 10 things: - ask smart, insightful questions - build relationships with stakeholders - become the go-to person in 1 or 2 tools - communicate insights clearly & concisely - use language that everyone can understand - get curious about your data and why it matters - continue to learn new techniques and approaches - learn the business and hone your domain knowledge - think about how your analysis will impact business goals - collaborate with people who have more experience than you A data analyst's job is about more than just the data. It's about how to use that data to make a difference.