How many of you have heard of this little thing we call artificial intelligence? Perhaps you're somewhat familiar with it. Now, how many of you feel confident in your ability to understand data, analytics, and AI and apply these concepts in your career and daily life? That might be a different question altogether. What if you feel out of place at the data and AI table? What if you don't know where to start? Does the thought of a future with AI cause you anxiety?
This blog post addresses the "Where do I begin?" question. Where can one start developing and learning data, analytics, and AI skills? Is it necessary to return to school and obtain a degree in these fields? Does it require a substantial investment? My answer is no; you don't need to return to school. Instead, I will share four actionable steps you can take to get started. They include the three Cs of data and AI literacy, plus a crucial activity we are all familiar with: studying. Let’s dive in.
The first C: Curiosity
They say curiosity killed the cat, but here, I suggest letting curiosity enhance your data, analytics, and AI skills. Asking questions is beneficial! We should all excel at inquiring. Are you unclear on what generative AI is? Ask. Unsure how to analyze a dashboard or dataset? Pose a question. Don't know what topics or skills to study? Just ask. Fostering curiosity, asking questions, and networking with knowledgeable individuals are all positive steps.
One call to action for curiosity is to find a process or other thing in your career or life that you wonder how data can help it. Then, ask the question: How can data help this out? Then, write it down.
The second C: Creativity
How does creativity assist someone in the realm of data and AI literacy? What can you create with code, algorithms, dashboards, visualizations, and your inner nerd? The people I talk to often haven't pursued formal education in data, analytics, and AI. It might not have inspired them or their chosen field of study. That’s perfectly fine. I don’t expect everyone to become a data scientist, nor do I want people to shy away from these areas. Bring your creativity into these spaces and use them to aid your learning journey.
The third C: Critical Thinking
What role does critical thinking play in data, analytics, and AI? It allows you to question and critically evaluate the data presented to you. It’s not necessary to accept data at face value. Why not spend an hour or two each week thinking about data, AI, or related issues? We shouldn’t immediately accept data without scrutiny. Asking questions and reflecting, even briefly, is essential. We should maintain a healthy skepticism towards data.
If you want to know how to incorporate critical thinking into your work, find 30 or 60 minutes on your calendar each week and time block it. Then, in that meeting, shut down your phone and computer, think about the problem, and take notes—fact-check later.
Study
One of the best ways to advance is by gaining knowledge—repeatedly studying topics that interest you. You don’t need to master everything at once. Pick an area in data, analytics, and AI that fascinates you and dive deep—read about it, follow thought leaders on LinkedIn, and network with professionals.
Exploring good books is an excellent learning method; I recommend my book Be Data Literate: the Data Literacy Skills Everyone Needs to Succeed and Effective Data Storytelling by Brent Dykes for data newbies.
Wrapping up
In conclusion, you don't need to return to school or invest heavily to get started in data, analytics, and AI. Begin with the three Cs and a commitment to study. As you progress, you might need to develop some technical skills but don’t let that intimidate you at the start. Just get going, and don’t hesitate. You don’t need to become overly technical immediately. If you need assistance, don’t hesitate to reach out!
My call to action is to find areas to ask more questions, find ways to be creative, schedule time to think critically, and finally, find a book or subject to study and do so. Write these things down and set a goal to get them done, with a structure around it to succeed.
This blog is part of my ongoing series for Fullstory, where I aim to demystify data and AI and make these fields accessible to you. Consider these posts as interconnected pieces of a larger puzzle, each building on the last. There's no need for intimidation or technical wizardry here; like any subject, mastering data and AI comes down to dedicated study and a curious mind. I encourage you to explore topics that pique your interest and delve deeper into them. My goal is to help you navigate these areas with ease and confidence, empowering you to leverage data and AI both in your career and daily life.
