What steps can you take to become a data analytics evangelist?

Here are some steps to become a data analytics evangelist: 1. Master storytelling & communication skills to explain analytics insights clearly 2. Identify ways data can drive innovation & strategic decisions in your organization 3. Create compelling data visualizations & demos to showcase insight 4. Evangelize data-driven decision making with stakeholders & leadership 5. Mentor others on building analytics skills & data literacy 6. Share practical examples of how analytics has driven value 7. Build enthusiasm for data by your enthusiasm 8. Be a role model for ethical, unbiased uses of data 9. Connect analytics to tangible business outcomes & value 10. Advocate for greater data accessibility & tools for stakeholders 11. Stay passionate

One thing I’ve found helpful is that to "learn from the best" in the context of becoming a data analytics evangelist, you should: 1. Identify influential figures in data analytics. 2. Follow their work through blogs, podcasts, books, and research. 3. Engage in data analytics communities and forums. 4. Attend conferences and workshops to learn from experts. 5. Take online courses and MOOCs from renowned institutions. 6. Network with professionals in the field. 7. Seek mentorship from experienced data analysts. 8. Continuously practice and apply what you've learned to your own projects.

Learn from Influencers: Identify and follow renowned data analysts, journalists, researchers, and educators. Engage with their content to gain insights into their methodologies, thought processes, and storytelling techniques. Join Data Communities: Immerse yourself in online and local data communities and forums. Participate in discussions, exchange ideas, and learn from the experiences of other data enthusiasts. Attend Data Events: Participate in data analytics conferences, workshops, and meetups to stay abreast of the latest trends, technologies, and best practices. Network with industry experts and seek mentorship from experienced practitioners.

“Show me the money” applies here as well, although with a twist “show me the results”. I have seen that you can become evangelist by demonstrating what data science can do for business

Tap into your expertise: Figure out what you're good at and what you're passionate about in the data analytics realm. That's where you'll shine as a teacher or mentor. Pick your teaching ground: Decide how you want to spread your data knowledge. You could start a blog, create online courses, or volunteer as a mentor for aspiring data analysts. Set clear goals: Help your students or mentees understand what they're working towards. Outline specific skills or knowledge they should gain by the end of your sessions. Organize your lessons: Don't just wing it! Plan out your teaching sessions with a clear structure. This will make the learning process more effective and enjoyable for everyone.

One thing I found helpful, is build, maintain, curate, and grow your One Big Table ( OBT ). most of the business questions can be answered before the end of the meeting. Key part in earning trust is by ensuring the quality of OBT is not compromised in day or form.

Build credibility: Establish yourself as a trusted expert in data analytics by showcasing your knowledge, skills, and experience. Share your insights through blog posts, presentations, or workshops. Speak with passion: Convey your enthusiasm for data analytics and its potential to bring about positive change. Your passion will be contagious and inspire others to see the value of data. Tailor your message: Consider your audience and tailor your message to their needs and interests. Use language they understand and focus on the specific benefits data can bring to their organization or situation. Use storytelling: Data can be dry and abstract, so use storytelling techniques to make it more engaging.

Proactively showcase what's possible with data and analytics on problems that matter to your stakeholders. They'll know their business well but won't have the same knowledge of data and analytics. They may not know the right questions to ask or how to engage effectively. As a data and analytics leader, proactively connecting my teams to the priorities of the organisation was transformative and successfully built engagement. I've seen great analytics professionals collaborate to understand the problem space, work out the right data sources and techniques to apply (which isn't always AI) to generate valued solutions for stakeholders. It's this strategic alignment that builds trust and momentum, and kick started a data-driven culture.

To become a data analytics evangelist, it's crucial to embrace experimentation and innovation. Encourage exploration: Cultivate an environment where data analysts feel encouraged to ask questions, explore new ideas, & experiment with approaches. This will lead to new insights & innovative solutions. Provide resources & support: Make sure data analysts have access to the resources they need to experiment, such as data, tools, & training. Offer support & guidance to help them overcome challenges & refine their ideas. Embrace failure: Recognize that experimentation often leads to failures. Create a culture where failure is seen as a learning opportunity, not a setback. Encourage analysts to learn from their mistakes & keep experimenting.

Recognize individual & team achievements: Publicly recognize & appreciate the contributions of data analysts who have made significant achievements. This could involve individual or team awards, recognition in company newsletters or meetings, or even verbal acknowledgements. Share success stories: Showcase the impact of data analytics by sharing success stories with the wider organization. Highlight how data-driven insights have led to improved decision-making, cost savings, or increased revenue. Organize team celebrations: Host team-building events or celebrations to commemorate significant milestones or successful projects. These events can foster camaraderie, strengthen relationships, and promote a sense of accomplishment.

In addition to the steps mentioned, remember that "collaboration" is key. Engage in cross-functional projects to demonstrate the value of data analytics in various departments. Emphasize the importance of data-driven decision-making in achieving broader organizational goals. Collaborate with colleagues to integrate data insights into their workflows, fostering a culture of data-driven collaboration across the organization.