Trends in Data Analysis Careers

📊 The World Economic Forum's Future of Jobs Report 2025 validates what many of us in tech have been experiencing: Data and AI are not just part of our future - they're driving it. 🚀 Looking at the fastest-growing jobs by 2030, I'm struck by how data-centric the list is: - Big Data Specialists (#1) - AI/ML Specialists (#3) - Data Warehousing Specialists (#6) - Data Analysts/Scientists (#11) 💫 For those of us already in data careers, this report is more than just validation - it's a signal of expanding opportunities. As data and AI become central to every industry, we're not just growing in numbers; we're growing in impact. The breadth of data roles on this list suggests we'll see more specialized paths emerge, more leadership opportunities develop, and greater potential to shape how organizations and industries transform through data. 🎯 What's particularly exciting is that there are multiple entry points into these growing fields. For those early in their journey, starting as a Data Analyst provides an excellent foundation - you'll develop core skills that overlap with Data Science, Data Engineering, and AI Engineering roles. 💡 But here's what many don't realize: If you're currently in a role on the "declining" list, you likely already have valuable transferable skills for a data career. Take accounting clerks, for example: Attention to detail → Data quality and validation Financial reporting → Data visualization and reporting Process documentation → Data pipeline documentation Industry knowledge → Domain expertise in financial data 🌟 This industry expertise is invaluable. An accounting clerk transitioning to a data analyst role in finance brings contextual understanding that's impossible to teach. The same applies across industries - your current domain knowledge could be your unique advantage in a data role. 🔮 Whether you're looking to pivot into data, advance your existing data career, or expand your impact in the field, the WEF report makes one thing clear: the future is data-driven, and there's room for everyone to grow and make their mark. 💭 What's your take on these job projections? Are you considering a transition into data or exploring new specializations within the field? Let's discuss in the comments! #DataCareers #FutureOfWork #DataEngineer #AIEngineer #DataScience #AI #WEF2025

Back in 2020, being a Data Analyst often meant being a generalist — handling everything from reporting to modeling, sometimes even engineering tasks. But fast forward to 2025, and the landscape looks very different. Now, we’re seeing a growing demand for specialized roles like: ✔️ Product Analyst ✔️ Marketing Analyst ✔️ Risk Analyst ✔️ Power BI Developer ✔️ Healthcare Analyst …and many more. This shift reflects the increasing complexity of data challenges and the need for deeper domain expertise. As someone navigating the data field, I find it both exciting and essential to keep sharpening skills in specific areas while staying curious about the bigger picture. 💡 Tip: Whether you’re just starting out or already in the field — focus on a niche, but learn to collaborate across roles. That’s where real impact happens. 👉 Which of these roles are you working toward or exploring? I’d love to hear your path. #DataAnalytics #CareerGrowth #DataAnalyst2025 #PowerBI #SQL #ProductAnalytics #Specialization #LinkedInLearning

Starting the year with the analytics trends shaping my work, sparking conversations with teams, and quietly changing what it means to be “good at data.” 2 shifts stand out: 1. The analyst role is changing - analysts are becoming curators of context. Systems struggle with context and meaning. And meaning lives in the work most teams underinvest in: definitions, semantics, lineage, thresholds, and guardrails. Analysts have always owned this layer: 🔹 What “active” means, what “customer” means. 🔹 What the expected threshold for alerts is. 🔹 What’s a valid baseline. 🔹 When a metric movement is real vs. noise. The difference now is that it’s no longer just analysts relying on this context. The data team, the broader organization, and automated systems depend on it. The job becomes less about reporting and more about making sure systems don’t make the wrong decision with the right-looking data. 2. The nature of analytics tooling is shifting from explaining and optimizing to powering decisions. You can see this in how products are evolving: 🔹 Notebooks have become home of unified workflows: code, visualizations, commentary, app sharing, and collaboration. 🔹 Nextgen Sheets are now warehouse-native, governed, and programmable. 🔹 IDEs are merging with BI tools, allowing analysts to write code and visualize results instantly. 🔹 BI is moving from static dashboards to dynamic, conversational, and reasoning reports. 2026 will be about building trusted context and decision systems that both humans and machines can rely on. Finally, that’s where analytics becomes foundational 📈 📊 .

The "Data Analyst" role has come under heavy duress in the last decade challenged by at least three waves of market forces - but the underlying job-to-be-done is as important than ever. As businesses collect more data and strive to quantify and understand the complex relationships between inputs and outputs, it is curious to observe the constant battering of the role of the "data analyst". 1) The First Wave: "Democratizing Data" The first and still active wave that challenged the role of a data analyst is the concept of "democratizing data." The idea is simple—give everyone in the business access to data and allow them to make decisions without relying on a dedicated analyst. However, as many organizations discover, this is far easier said than done. Data access doesn’t necessarily equate to good analysis or interpretation. The overly simplistic nature of today’s tools mask the inherent complexity of analyzing data. So, instead of empowering the entire organization to be analytically rigorous, it leads to a proliferation of isolated dashboards and reports—most of which don’t connect the dots or provide the insights that the business truly needs. 2) The Second Wave: "The Rise of the Analytics Engineer" Next came the rise of the "analytics engineer." With data growing in volume and complexity, the importance of engineers who build and maintain data pipelines became more easily justified than the role of the analyst, who must sift through this data, explore complex relationships, and derive insights. I saw first hand analysts and data scientists transition to data or analytics engineers. It also helped that from a pure career perspective, engineering ladders had well-defined paths. This wave also (accidentally) poured gasoline on the concept of “democratizing data” by making it a lot easier to create more data assets. 3) The Third Wave: "AI Will Replace Analysts" And now, the third and perhaps most disruptive wave—the "AI wave". Many predict that AI-powered tools will render human analysts obsolete, claiming that AI can perform data analysis faster, more accurately, and at scale. While I’m bullish about the role AI can play in this domain, I remain skeptical about its wholesale replacement of human analysts. The underlying skills involved here lean towards artificial general intelligence (AGI), but we are simply not there yet. So, while I can’t predict how long the traditional data analyst or data scientist role will thrive, or what the optimal percentage of data professionals will be in an organization, I can say with confidence that the underlying needs of the role will never go away. Businesses will always need individuals empowered with the right tools, who can understand metrics and drivers, spot trends and anomalies, and guide decision-making with data-driven insights.

The data analyst role you know is changing. 2026 will demand more. Gartner predicts that 80% of analytics tasks will be automated. I coach career changers into $100K+ data careers, here's what I see coming 👇🏽 The "pull a report and send it over" analyst? That's gone. AI handles those tasks in seconds now. The analyst who only knows SQL and Excel? They'll struggle. Companies expect more. Here are my 5 predictions for data analytics in 2026: 𝟭. 𝗔𝗜 𝗳𝗹𝘂𝗲𝗻𝗰𝘆 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗻𝗼𝗻-𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲 You won't compete with AI. You'll compete with analysts who USE AI. Prompt engineering, AI-assisted analysis, automated workflows. Learn them or get left behind. 𝟮. 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 𝗯𝗲𝗮𝘁𝘀 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘀𝗸𝗶𝗹𝗹𝘀 Anyone can pull numbers. Few can make executives care. The analysts who translate data into decisions will run the room. 𝟯. 𝗧𝗵𝗲 "𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗔𝗻𝗮𝗹𝘆𝘀𝘁" 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘁𝗵𝗲 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 SQL + Python + Visualization + Communication. Not "nice to have." Expected. One-trick analysts will struggle to compete. 𝟰. 𝗥𝗲𝗺𝗼𝘁𝗲 𝗿𝗼𝗹𝗲𝘀 𝗴𝗲𝘁 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 Companies figured out they can hire globally. Your competition isn't local anymore. Stand out or blend in. 𝟱. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝗰𝘂𝗺𝗲𝗻 > 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗱𝗲𝗽𝘁𝗵 Knowing the business matters more than knowing every Python library. The best analysts understand revenue, margins, and what keeps the CEO up at night. Here's the truth: The bar is rising. But for those who adapt? The opportunities are bigger than ever. I've watched career changers land $100K+ roles by focusing on what actually matters. Not degrees. Not certifications. Skills that solve problems. Which prediction hits hardest for you? Drop a number below. Let's talk about it.

Welcome to 2026. The role of the junior data analyst is dead. If your plan this year is to learn Python or get better at Excel, you are preparing for a job that no longer exists. Technical execution is no longer a competitive advantage. AI has won the race for high-structure, low-creativity tasks. Your value is now defined by your ability to direct the AI. Stop competing with the machine on the how (the code). Start mastering the why (the context). Your 2026 AI goals: Goal 1: Delegate The Mundane Stop acting as a data cleaner. It is a waste of your cognitive abilities. Direct AI to write surgical Python or R scripts. You do not write the code; you audit it as the Lead Engineer. Goal 2: Look For A Fight Confirmation bias is the silent killer of analytics. Stop asking AI for insights and start asking for a fight. Use it to attack your original ideas and expose your blind spots before they reach the presentation. Goal 3: Survive The Murder Board Great stories fail because of weak defenses. Never present until you have prepped with AI. Force the machine to simulate your most cynical stakeholders to stress-test your logic and your narrative. The analyst who wins this year is not the one who writes the best code. It is the one who tells the best story. 2026 is here. You have your goals. Now do the work. #DataAnalytics #AI2026 #DataStorytelling #CareerStrategy #FutureOfWork Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Are you contemplating to pivot into data analytics & data science field? As someone who has been in the field since 2013, and who's been mentoring and coaching others in the data field for the past 7 years, here are my thoughts: 𝐓𝐢𝐦𝐞-𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐠𝐢𝐯𝐞𝐧 𝐭𝐨𝐝𝐚𝐲’𝐬 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐭𝐞𝐜𝐡 𝐥𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞: 𝟏) 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬 𝐨𝐯𝐞𝐫 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐬𝐜𝐫𝐚𝐭𝐜𝐡 Instead of learning SQL or Python from scratch, focus on using AI tools to meet existing analysis needs. For example, master how to craft prompts to generate SQL or Python code, or use GenAI to build processes, streamline data workflows, and uncover insights faster. You can also harness LLMs to enhance your analysis and insights generation, rather than slowly building your portfolio through years of hands-on experience. Use LLMs to critique and refine your insights and recommendations, ensuring that what you propose aligns with business goals and stakeholder questions. 𝟐) 𝐓𝐚𝐫𝐠𝐞𝐭 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐞𝐬 𝐰𝐢𝐭𝐡 𝐠𝐫𝐨𝐰𝐭𝐡 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 Focus on industries with bright futures like GenAI, healthcare, cybersecurity, green energy, or mental health. These sectors are more likely to need data professionals to drive growth through analysis and insights. Do your research by searching for industry reports or talking to seasoned practitioners to identify promising industries. Reports or analyses published by organizations such as below can be your start, e.g. US Bureau of Labor Statistics, McKinsey Global Institute, World Bank, CB Insights, or Gartner. 𝐒𝐨𝐦𝐞 𝐭𝐢𝐦𝐞𝐥𝐞𝐬𝐬 𝐚𝐝𝐯𝐢𝐜𝐞: 𝟏) 𝐆𝐞𝐭 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐟𝐢𝐫𝐬𝐭, 𝐜𝐫𝐞𝐝𝐞𝐧𝐭𝐢𝐚𝐥𝐬 𝐚𝐧𝐝 𝐩𝐞𝐫𝐟𝐞𝐜𝐭𝐢𝐨𝐧 𝐥𝐚𝐭𝐞𝐫 Instead of pursuing yet another bootcamp or credential (though you do need baseline technical skills), start by volunteering, interning, or offering to help current practitioners with projects. Build a portfolio using open-source data, freelance on platforms like Fiverr or Upwork, and secure your first data job—even if it’s not a 100% match to your current criteria. The ideal industry or company will come later once you’re in the door. 𝟐) 𝐍𝐞𝐯𝐞𝐫 𝐬𝐭𝐨𝐩 𝐧𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠 Whether it’s validating a specific industry’s need for your skills, creating opportunities for referrals, or honing your pitch for future interviews, networking is critical for career transitions and building long-term influence in your field. Identify “hubs” of people or communities that can help you gain new opportunities. Communities such as Women in Big Data, Women in Data Science (WiDS) Worldwide, or Data Science Association (that I helped co-found), can be your starting point. If you've been contemplating or ready to make the switch, book a Discovery session (via my profile) as your first step! Let’s explore how I can help you in our 1:1 coaching space—where to focus, and what steps to take to launch your new career in data analytics.

2024 Singapore Data and AI Hiring Trends! What were some of the trends I saw in 2024 in Data and AI from a hiring perspective? ✨ Demand for data scientists that can engage with business stakeholders to understand their business problems and build solutions that can solve those problems ✨ Demand for data scientists that have experience with NLP and especially LLMs. A lot of this demand was driven by companies that were working on GenAI products and solutions. As this space is rapidly evolving, candidates that gain experience with LLMs in either a professional or personal capacity, will have an advantage ✨ Demand for the manager that is still hands on. Gone are the days when management meant mostly 'supervising' the team. Because AI is evolving so rapidly, those that are in a management role and are not hands on or are not able to provide technical leadership when needed, are putting themselves at risk. This applies especially to middle management roles but I am also seeing it a few cases for senior management roles as well although, this is mostly at tech firms/ well funded start ups for now. ✨ Demand cooled for pure play machine learning engineers. The rapid progress of GenAI has meant that companies that are hiring for AI talent are looking for folks that can do a lot more than just develop models. This trend is still in its early stages so it will be interesting to see how it plays out in 2025 and beyond ✨ Strong demand for data platform and MLOps engineers. Building/ tuning models is one thing but being able to put them into production, hosting and monitoring their performance is critical to generate ROI. This is where data platform and MLOps engineers come in. Many companies realised a bit late that they didn't have the right platforms in place, which is why we are seeing demand for this talent pool, which is small to begin with ✨ Focus on and demand for data and AI governance will continue to grow, especially in FS. With AI developing so rapidly, regulation will have to play catch up. AI governance is still relatively new so companies want to make sure they stay on the right side of compliance, which will drive demand. ✨ Career Opportunities were limited for both fresh graduates entering the workforce in 2024 and for candidates exploring C-level and C-1 opportunities. Whether this is a 2024 trend or extends into 2025 remains to be seen. Look out for my 2025 star gazing post next week! 😉 What were some of the trends that you saw in data and AI in 2024? In my next post, I will will flip over to the other side - What were data/ AI candidates looking for when considering new opportunities in 2024? #AIDatahiring

You didn’t pursue a career in healthcare informatics just to chase outdated job titles. The world is changing. So are the roles. If you're still searching with 2015 job titles, you’ll miss the 2030 opportunities. Here’s the truth: The next decade will belong to those who understand not just healthcare, but data, automation, and digital systems together. And Healthcare Informatics is at that intersection. Top Hiring Trends for Healthcare Informatics (2024–2025): According to [HIMSS & BLS 2024 projections]: Healthcare Data Analyst roles grew by 18% last year. Clinical Decision Support & AI roles are emerging in major health systems. EHR System Support & Optimization remains the most in-demand skill. Population Health & Value-Based Care roles up by 11% due to Medicaid reforms. Clinical Research Informatics is growing in pharma/biotech. 2025–2035: What Roles Will Dominate? If you’re planning for long-term success, focus on roles that blend: Data + Outcomes AI + Patient Safety Compliance + Digital Health Here are the future-proof titles to track (and skill up for): Next-Gen Healthcare Informatics Roles: Healthcare Data Scientist (Python, SQL, predictive analytics) Clinical AI Analyst (ML models for outcomes + risk prediction) Digital Health Program Manager (mHealth, RPM, app-based care) Value-Based Care Analyst (Population health metrics, QI dashboards) Health Data Governance Specialist (HIPAA, HITECH, compliance) Clinical Informatics Consultant (Epic/Cerner + workflow redesign) Health Equity Data Analyst (DEI metrics, SDoH data) Telehealth Informatics Coordinator (virtual care workflows + UX design) Top Skills to Focus on (2025 and beyond): SQL, Python/R for health data Power BI / Tableau for dashboarding Epic or Cerner EHR optimization Clinical workflow mapping & UI/UX HL7, FHIR, interoperability knowledge Privacy regulations (HIPAA, GDPR) AI/ML foundations for clinical contexts Job Hunting Tip: Don’t search by degree. Search by outcome. Try: “Remote Patient Monitoring + Analyst” | “Epic + Optimization” | “Public Health + Data” These combos will open new doors. Tag a classmate, I’ll help you decode job titles, keywords, and roles that actually work in 2025. We rise faster when we learn together 💙 #HealthInformatics #HealthcareAnalytics #PublicHealthCareers #EntryLevelJobs #InternationalStudent #HealthTech

Data Talent Insights: The East of England Candidate Market Right Now 2025 is shaping up to be a strong year for data talent in the East of England, with more businesses ramping up their data teams. But what does that mean for you, whether you’re a hiring manager or a job-seeking data professional? I’ve been talking to candidates and reviewing profiles, so here are some key trends you need to know: Most Active Data Titles: Data Engineer – 35% Data Scientist – 20% Data Analyst – 18% Data Lead – 10% Data Manager – 8% Business Intelligence Developer – 5% Data Consultant – 4% Most Prevalent Tech Stacks: Python – 48% SQL / T-SQL – 40% Power BI – 32% AWS – 29% Azure – 22% Google Cloud Platform (GCP) – 17% Tableau – 20% Snowflake – 8% ETL Tools (Talend, Apache Airflow, etc.) – 8% Key Takeaways: Python & SQL are still king and continue to dominate as essential skills. Power BI leads the charge in visualisation & reporting - companies want experts who can build dashboards and insightful reports. Cloud platforms like AWS and Azure remain dominant. Whilst the job market continues to recover, finding the right fit is still the priority, technical environment matters more than ever. Businesses want to make sure their hires will deliver from day one and candidates are looking for somewhere to challenge their existing skillset. What I’m seeing across the market is that while many candidates are open to opportunities, the majority aren’t actively searching. If you’re hiring, you will have activity from active job-seekers. Get in touch with me to tap into the pool of passive talent just waiting for the right conversation. #DataTalent #DataProfessionals #HiringInsights #DataEngineer #DataScience #DataAnalysis #TechHiring #EastofEngland #RecruitmentInsights #CambridgeTech