Data Analytics Skills Every Innovator Should Have

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?

I've interviewed over 100+ people for data consultant and analyst roles. Here are my prioritized top 5 skills I look for in quality candidates. --> Requirements Gathering <-- Data projects are tough. Not because they are technically complex but because stakeholders don't know what they don't know. Strong analysts are able to pull out the key pieces of information from non-data people. 'What are we trying to solve?' and 'Why is it important?' are underrated questions. I want analysts who can dig in so much they start to understand the relationship between data and the business better than the stakeholders do. That takes relentless curiosity. --> Communication <-- Pulling out useful requirements begins with smart questions asked in the right way at the right time. Being able to read a room, challenge assumptions, empathize with users, and clearly articulate interesting findings goes a long way in building trust. For non-data people, analytics feels like voodoo magic. Understanding how to communicate topics at the right level for an audience is a tough skill - you get there through strong written and verbal communication. --> Attention to Detail <-- In a world filled with AI LLMs and beautifully designed dashboards, it's easy to gloss over the details of data quality and accuracy. Most people won't even notice, honestly. I want to work with people who care so much about their craft that they refuse to put half-baked products into production. This means they are doing all the rigor of quality assurance when no one is watching - ensuring data is modeled, calculated, filtered, and displayed correctly. Be accountable to the outcome. --> Software Experience <-- It doesn't matter what reporting software you choose, I want to see competency during a live interview. Certifications may help get the interview, but I've also interviewed plenty of certified gurus who barely understand the core concepts of aggregations, order of filter operations, and design theory. I love doing live show-and-tell demos because you can uncover who is good at studying for certification exams and who actually understands the tools they claim to be experts in. --> SQL Experience <-- Non-F1000 organizations don't have large budgets for data teams to hire data preparers and data analysts. I want people who have some working experience to fish for data sets on their own - and that starts with SQL, the language of data. Depending on the level of seniority, my expectations are minimally having the ability to read and interpret SQL statements given to you because it shows an opportunity to start writing from scratch as part of a development plan. That's the top 5 skills I look for in data analysts - what did I miss?

The Data Analyst roadmap that helped 50+ of my clients Tier 1: Excel & SQL – Your bread and butter for handling data. Tier 2: Data Cleaning & EDA – Messy data = useless insights. Tier 3: Data Visualization & BI Tools (Tableau, Power BI) – Communicating insights clearly. Tier 4: Statistical Analysis & ML Basics – The deeper layer of understanding. Then, you need projects. Not just any projects, but ones that actually make an impact: - Find, clean, and analyze real-world data – No pre-cleaned Kaggle datasets. - Build dashboards that tell a story – Not just charts, but insights that drive decisions. - Solve real business problems – Show companies you understand their needs. - Create a compelling case study. Write about your process, results, and impact.- Record a video breakdown – Prove you can explain complex data in simple terms. - Target specific industries – Finance, healthcare, e-commerce, whatever excites you The market is tougher than ever. You could be the most skilled data analyst out there, But if you can’t communicate your value, you’ll be overlooked. Focus on: -> Networking & outreach – Talk to hiring managers and industry professionals. Cold applications aren’t enough. -> Building a personal brand – Share insights, create content, and let recruiters come to you. -> Positioning yourself as a problem-solver – Companies don’t just need analysts. They need people who drive business impact. Note: This is an example roadmap that you should customize for your own goals and needs. (Like I customize everything to my clients.) Analyze your situation and realize what skill or habit is missing from your process that is on this roadmap, then double down on that. Follow me, Jaret André and let’s land you your next data job!