I was asked on Threads, as a former Head of Marketing Analytics, what advice would I give to those who are contemplating pivoting into data analytics & data science.
As someone who's been in the field since 2013, and who's been managing, mentoring, and coaching others in the data field for the past 8+ 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, or Data Science Association (that I co-founded), can be your starting point.
If you are a current or experienced data & analytics practitioner, what other advice would you give to those who are thinking about pivoting into these fields?
