INTERVIEW FOR TOOLBAR MAGAZINE

Today we have the pleasure of conversing with ángel Molina, a distinguished Data Analyst in AI certified in KNIME, who has a solid 15-year trajectory in the business world, with about 4 years directly in data analysis, data science, and lately, very much in artificial intelligence. His experience spans from implementing data analysis solutions to innovating in complex projects, facing and overcoming challenges with skill. Additionally, he has decided to share his vast knowledge and experience through a newsletter channel on LinkedIn, where he offers valuable insights and advice to other professionals in the sector. In this interview, we will explore his beginnings, the challenges he has faced and overcome, as well as the impact of his newsletter channel on the data analysis community. Welcome, ángel, and thank you for being with us!

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Beginnings

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Q: What led you to become interested in data analysis and how did you start in this field?

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A: First of all, the pleasure is mine, and it's a delight to be part of this month's issue, especially considering that it's a magazine I read every month and don't miss a single edition.

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Regarding your question, my interest in data analysis arose very naturally. It wasn't that I sought to get into data analysis to improve my career, but rather data analysis was the result of a long process of learning and continuous improvement in my work.

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I realised that informed decisions cannot be made without having analysed data, whether for personal use or to prepare reports. This understanding led me to train myself and seek the right tools to carry out this analysis effectively.

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In essence, my path to data analysis was driven by the need to improve processes in my work and make better decisions. It was a journey of discovery where training and practical application constantly fed into each other.

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Q: What skills do you consider essential for a data analyst in their early years of career?

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A: Obviously, as I usually say, you have to enjoy playing with numbers. Starting from that base, the love for data and its analysis, there are several skills that I consider essential:

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1. Analytical curiosity: It's fundamental to have a constant desire to explore data, ask questions, and seek patterns and insights.

2. Resilience: In an environment as changing as the current one, with new technologies emerging almost daily, it's crucial to keep an open mind and adapt to new situations. Being willing to keep learning always and use changes as opportunities to improve.

3. Effective communication: Having valuable information is useless if you don't know how to communicate it. It's essential to develop the ability to convey complex ideas clearly and convincingly, whether through numbers, graphs, reports, or presentations.

4. Critical thinking: The ability to evaluate information objectively, identify biases, and make evidence-based decisions is crucial in this field.

5. Technical skills: Mastering data analysis tools like KNIME, programming languages like Python or R, and visualisation platforms is indispensable.

6. Collaboration and leadership: Being willing to work in a team, lead projects when necessary, and knowing how to get the best out of each team member is fundamental for success in more advanced roles.

7. Problem-solving: The ability to tackle complex challenges, break them down into manageable parts, and find creative solutions is invaluable in data analysis.

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These skills, combined with a solid technical foundation and a commitment to continuous learning, are key to standing out and growing as a data analyst in the early years of one's career.

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Q: How did you get certified in KNIME and what motivated you to choose this tool in particular?

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A: I always say that, within a minimum of functionality, there isn't a data analysis or data science tool that is universally better than another. The crucial thing is to find the one that best adapts to your projects and with which you feel most comfortable.

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In my case, my path to KNIME was a natural evolution. I started working with advanced Excel, using Power Query and Power Pivot, then explored Python and Power BI. However, I ended up choosing KNIME for several reasons:

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1. Ease of use: I immediately felt comfortable with its interface and workflow.

2. Versatility: It's a tool that allows me to develop not only very advanced data analysis projects, but also data science and even artificial intelligence projects.

3. Accessibility: KNIME is a free platform, which makes it accessible to all professionals and companies, regardless of their budget.

4. Visual programming: I can perform complex analyses without needing to write a single line of code, which greatly speeds up the process.

5. Community: What really made me fall in love with KNIME was its community. The support, shared resources, and collaboration between users are invaluable.

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As for the certification, I obtained it through the official KNIME programme, which includes a series of courses and a final exam. I was motivated to get certified to validate my skills and delve even deeper into the tool's capabilities.

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This certification has not only improved my technical skills, but has also opened up new professional opportunities and allowed me to connect with other experts in the field.

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Q: Is there any person or event that has been key in your decision to pursue a career in data analysis?

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A: In my career in data analysis, both events and people have been crucial for my development and motivation.

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As for events, one that marked a before and after was the KNIME Summit in 2021. In the midst of the pandemic, when we still couldn't meet in person due to restrictions, I attended this virtual event and was deeply impressed. The closeness, human quality, and simplicity of all the KNIME colleagues impacted me enormously. This summit was a turning point that drove me to keep learning and specialising in this tool.

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On the other hand, there are people who have been fundamental in my trajectory, and I would like to take this opportunity to publicly thank them for their continued support, both in my professional career and on a personal level:

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1. From KNIME, I must mention Roberto Cadili and Rosaria Silipo. Their guidance and support have been invaluable in my growth as a data analysis professional.

2. However, if I had to highlight someone as my mentor, that would undoubtedly be the Doctor, professor (as I like to call him) and founding partner of IQuartil, Don Ignacio Perez. His influence on my career has been (and is) profound and lasting.

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These experiences and people have been fundamental not only in my decision to pursue a career in data analysis, but also in my continuous growth in this field. They have inspired, challenged, and supported me every step of the way, shaping not only my professional trajectory but also my approach to and passion for data analysis.

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Professional Experience

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Q: With 15 years of experience, how have you seen the field of data analysis evolve?

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A: In the last 15 years, the field of data analysis has experienced a radical transformation. The evolution has been constant and accelerated, marked by several significant milestones:

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1. Big Data: The exponential increase in the amount of available data has completely changed the scale and scope of our work.

2. Cloud Computing: The widespread adoption of cloud solutions has democratised access to powerful computational resources, allowing for more complex and larger-scale analyses.

3. Machine Learning and AI: The rise of machine learning techniques and artificial intelligence has taken our analytical capabilities to a new level, allowing for more accurate predictions and more sophisticated pattern discoveries.

4. Tools and platforms: The evolution of tools like Python, R, and platforms like KNIME has made advanced analysis more accessible to a greater number of professionals.

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However, the most drastic and recent change has undoubtedly been the emergence of ChatGPT and advances in generative artificial intelligence. This has posed both a challenge and an enormous opportunity in the field of data analysis:

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- Challenge because it forces us to constantly stay updated and rethink many of our traditional processes and methods.

- Opportunity because every day we can automate more processes and more easily, freeing up time for higher value-added tasks.

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This evolution is leading us towards a paradigm where data analysis becomes more accessible and powerful, but also more complex and with greater ethical implications. As professionals, we must be prepared to adapt our skills, take advantage of these new tools, and, at the same time, maintain a critical and ethical approach in our work.

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Q: What has been the most challenging project you've worked on and what did you learn from that experience?

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A: One of the most challenging projects I've worked on, and in which I'm currently immersed, involves the application of cutting-edge technologies in the field of laboratory analysis. Although I can't reveal specific details due to confidentiality, I can share some general aspects that make it so challenging and enriching:

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The project focuses on predicting the results of substance analysis, using an innovative combination of technologies:

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1. Local Artificial Intelligence: We're implementing AI locally using KNIME in conjunction with GPT4ALL. This allows us to harness the power of large language models without compromising data security and confidentiality.

2. Traditional Machine Learning: We complement the AI approach with more conventional machine learning techniques, allowing us to leverage the best of both worlds.

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The main challenges and learnings from this project include:

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- Technology integration: Combining local AI with traditional ML techniques has required a creative approach and a deep understanding of both technologies.

- Handling sensitive data: Working with highly confidential laboratory information has forced us to implement rigorous security and privacy protocols.

- Precision and reliability: In a laboratory environment, accuracy is crucial. We've had to constantly fine-tune our models to ensure reliable results.

- Scalability: Designing a solution that can handle large volumes of data and adapt to different types of analysis has been a significant challenge.

- Interpretability: Ensuring that the results of our models are interpretable and explainable for laboratory professionals has been fundamental.

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This project has taught me the importance of maintaining a balance between technological innovation and practical applicability. It has also reinforced my conviction that the future of data analysis lies in the intelligent combination of different technologies and approaches.

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Furthermore, it has highlighted the growing importance of ethics and responsibility in handling sensitive data, especially when applying advanced AI technologies.

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Q: How do you handle the pressure of delivering accurate and useful analyses in a competitive business environment?

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A: Pressure is inevitable in a competitive business environment, but I've learned to handle it constructively:

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1. I see it as an opportunity to improve and bring out the best in myself and my team.

2. I focus on efficient planning and task prioritisation.

3. I maintain clear communication with the team and stakeholders.

4. I seek a work-life balance, keeping my family life orderly.

5. I practice sports regularly and lead a healthy life.

6. I keep learning constantly to face new challenges with confidence.

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The key is to use pressure as motivation to improve, without allowing it to negatively affect my well-being or that of my family in the long term.

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Q: Can you share an example of a project where you used KNIME in an innovative way?

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A: I'm in love with KNIME's geospatial extension, and although I can't go into specific details, I can share some brushstrokes of an innovative project I carried out:

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We developed a route optimisation system for a logistics company using KNIME. The project combined:

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1. Geospatial analysis to map efficient routes.

2. Machine learning algorithms to predict traffic and delivery times.

3. Real-time data integration from vehicle GPS and weather conditions.

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The innovative aspect was the combination of these technologies in a single workflow in KNIME, allowing real-time updates and interactive visualisation for dispatchers.

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Q: How do you integrate new technologies and methodologies into your daily work as a data analyst?

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A: I dedicate part of the day to staying updated on platforms like LinkedIn and X or reading magazines like Tool Bar. When I find an interesting technology or methodology, I analyse and study it to see if it can improve my work. If I'm convinced, I test it on small projects and, if it works well, I integrate it into my daily routine.

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Difficulties and Challenges

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Q: What has been the biggest obstacle you've faced in your career and how did you overcome it?

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A: The biggest challenge has been the emergence of AI. To overcome it, I stay constantly updated, studying daily and taking advantage of these disruptive changes to improve my skills and processes.

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Q: What advice would you give to someone who is facing difficulties in their career as a data analyst?

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A: Let me use a Spanish saying: "In troubled waters, fishermen gain." In our sector, this means that in times of change and difficulty, there are great opportunities. My advice is to stay up to date with new technologies and tools, and take advantage of these opportunities to grow and stand out.

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Q: Have you encountered specific challenges related to data interpretation and how do you handle them?

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A: Yes, one of the biggest challenges is understanding exactly what the client needs. To handle this, I make sure to know their requirements well and, if possible, I meet with them personally to delve into their needs. This allows me to better adapt my work and ensure that the direction is correct from the beginning.

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Q: How do you deal with resistance to change in organisations when proposing data-based solutions?

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A: Soft skills are key in these cases, especially in the AI era. Effective communication is crucial: I present changes positively, adapting the strategy to the client. For example, instead of directly criticising, I praise what is being done well and then suggest improvements. This way, resistance is reduced and acceptance of new proposals is facilitated.

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Newsletter Channel

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Q: What inspired you to start your newsletter channel "Data Science with KNIME" and what is its main objective?

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A: I created it to give something back to the KNIME community, which has helped me so much. I also love facing new challenges, and writing technical articles has been an exciting way to do it, as I had never written publicly before, much less such technical articles. The main objective is to share knowledge and foster learning in the community, with clear and simple language to democratise data science in Spanish.

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Q: How do you decide on the topics you address in your newsletter?

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A: I choose topics based on several motivations: sometimes to test new updates or extensions of KNIME, other times for pure pleasure, like when I write about sports. I also select topics that I consider interesting and useful for the community, feeling a moral obligation to share them.

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Q: What kind of feedback have you received from your subscribers and how has it influenced the content you produce?

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A: I must say that it has been one of the best decisions of my life as I have been able to learn a lot from my entire community.

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On the other hand, I receive very nice messages that fill me with energy and are an incredible energy boost which motivates and inspires me to continue creating quality content.

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Q: Can you share a success story related to your newsletter, either an impact on your readers or on your career?

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A: One of the most exciting stories is how the success of the newsletter encouraged me to explore new formats, such as videos on YouTube, Instagram or TikTok. I would never have taken that step without the support and affection of my subscribers. Their messages and the positive impact I've seen gave me the confidence to venture into these new channels.

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Q: What advice would you give to other data analysts who want to share their knowledge through a similar communication channel?

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A: Many colleagues ask me this, and my advice is simple: Start! Don't let fear stop you. At first, it's normal to feel uncertain, but over time you'll be able to define and improve your content. Also, I especially encourage the Hispanic American community to share their knowledge in Spanish. There is increasing demand for content in our language, and it can be just as viral and valuable as content in English.

Do you want to automate processes and don’t know how? Do you need consultancy and/or training in KNIME and Artificial Intelligence? Contact me through any of my social media channels:

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