Habits That Drive Data Career Success

A few years ago, breaking into data science meant learning Python, machine learning, and building a solid portfolio. That’s still important—but the job market is shifting, and many people are focusing on the wrong things. Companies are no longer just looking for "SQL experts" or "deep learning specialists." They want problem solvers who understand data, business, and execution. Companies are prioritizing practical, real-world data skills over advanced modeling. The ability to clean, analyze, and communicate insights is often more valuable than knowing how to fine-tune a neural network. AI is exciting, but many businesses still struggle with basic data infrastructure, and that's why companies need professionals who can: - Work with real, messy data instead of perfect Kaggle datasets. - Build dashboards and reports that drive actual decisions. - Explain findings to leadership in clear, non-technical language. Hybrid Roles Are on the Rise - The lines between data analyst, data scientist, and analytics engineer are blurring. Many companies expect data scientists to: + Know SQL and database management. + Understand cloud platforms and deployment. + Work closely with product teams, not just focus on models. What Should You Focus On to Stay Competitive? 1. Master SQL and Data Manipulation – Almost every data job requires it. 2. Strengthen Your Business Acumen – Companies care about insights, not just models. 3. Improve Your Communication Skills – If leadership doesn’t understand your findings, they won’t act on them. 4. Work on Real-World Projects – Hiring managers want to see impact, not just academic exercises. The best data professionals aren’t just great at coding—they understand how to use data to solve real business problems. If you’re learning data science today, ask yourself: Are you focusing on what hiring managers actually need, or just chasing what looks impressive on paper?

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?

If you're building a data career, mastering the art of measurement planning can be one of the most effective ways to differentiate yourself from your peers. Companies need people who are thinking about this every time they launch a new initiative. If you can develop strong skills here, it can be your ticket to getting involved earlier on, in more projects, and to becoming seen as a true strategic partner in your organization. Here's what you should focus on... 1. Think Business First -> Resist the urge to dive straight into the data. -> Understand how critical this project is to the business. -> Ask what the key goals for the initiative are. -> What are the most important questions you'll answer? 2. Know Your Audience -> Who is driving the project? Is this the primary audience? -> What are the goals and incentives of key stakeholders? -> What data can you provide that will help them? -> What types of info may inspire them to take action? 3. Define the Key Performance Indicators (KPIs) -> For the goals identified, translate them to metrics -> Prioritize metrics based on importance to stakeholders -> Go a layer deeper, and think about KPI driving levers -> How do you picture optimizing the businesses KPIs? 4. Identify the Data Sources You'll Need -> Where will you get each data point you need? -> Who owns or manages each existing data source? -> Are the data sources available real-time? -> Are there gaps in existing data? How do you fill them? -> How can you automate or streamline reporting? If you can follow this framework, you should be able to break down any project and build a measurement plan that will help your organization identify goals, understand outcomes, and optimize performance to drive the business to new heights. We've got a free guide that goes deeper on this, called 'How to Build a Measurement' plan. CHECK IT OUT: --> https://bit.ly/3eaXGmq @ Data Pros - what else would you add here? #data #analytics #businessintelligence #measurement #planningforsuccess

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