My Data Journey — by Darnell Dawson

5 min readMar 17, 2022

This article is for professionals who are curious about upskilling in data analytics and coding but need convincing. How are they going to fit into the competitive digital landscape?

Fear of AI Replacing my Profession

I am a compliance and risk professional who’s been hearing about the importance of data and the coming dominance of A.I. for at least half a decade.

Predictions that A.I. will upend long-held white-collar professions have always filled me with fear and intrigue. I recently resolved a fairly complex insurance issue over the phone by having a “conversation” with voice recognition technology. Interacting with this A.I.-driven voice stirred an acute sense of trepidation, that as a professional with a limited technical background, maybe I would have a difficult time adapting to new technologies. On top of that, I would not be able to compete with newly minted Computer Science graduates and Data Analysts.

Delving into Data with Formal Training

Nevertheless, I took opportunities to upskill. Initially, these were in-person evening classes in data analytics, covering the basics of Excel, SQL and Tableau. This was pre-pandemic, before online learning became widespread.

While I didn’t walk away from the course feeling like a data wizard, I gained a better sense of how to effectively use data visualization, how to tell stories with data, and how to run rudimentary SQL queries. This background knowledge in data fundamentals helped, but one large gap in my knowledge remained: I didn’t know anything about coding.

There were few things that intimidated me more than studying coding. I put off learning it for years, even though a highly successful and intelligent friend and mentor of mine strongly encouraged me to learn Python over seven years ago. While he was right, I didn’t take his advice at first, precisely because he was “intelligent” and highly successful: not only does this man have a computer science degree from a renowned university, he was instrumental in rolling out one of the most successful software operations of all time. I didn’t think I would be able to mimic anything he did given my liberal arts background.

Easing into Learning to Code During the Pandemic

Like many, I used work-from-home time to try out online courses for professional and personal development. I must have signed up for every course on Coursera and Udemy covering Data Science Basics, Intro to Python, and Data Analytics. After fits and starts, I found a few courses and books that gradually helped me gain an understanding of coding logic and how to combine coding and data analysis. A few weeks later, terms like string, Boolean, integer, tuple, and Panda started to make more sense. I understood how to combine them with other tools such as HTML and Excel to automate tasks and analyze trends.

Frustration is Part of the Process, but Persistence Pays off

For every new data type or variable I mastered in Python, I encountered an ample dose of frustration.

I felt the agony of typing commands and being met with errors and endless loops. I spent a whole evening working on a line of code that I thought should only take five minutes to execute. This led to visions of me throwing my laptop like the scene from “The Social Network,” where a spurned colleague hurls a laptop at an actor portraying Mark Zuckerberg.

But once I calmed down and patience prevailed, I was able to write a program that scraped a website to obtain price data, and yet another which allowed me to automatically populate data in Excel files. It wasn’t so much the output of these simple programs that engaged me but the fact that I was learning how to “speak to a computer.” I had been using computers for years without understanding their underlying logic. Nevertheless, the process of learning to speak to a computer caused some very real human frustration.

Working with Data Experts on Data-Driven Projects for Rapid Impact

Even a rudimentary understanding of Python and coding concepts can be helpful for client engagements. While I don’t bill myself as a Python expert, being able to articulate how data types, coding, testing, compliance, and internal controls interact has made it much easier to communicate effectively with different stakeholders. For example, when speaking to a client about an automated testing plan, I asked which types or data fields we were able to obtain, whether they were in string format, and if the data was compatible with the existing systems that the business owner used.

My questions were appreciated. I didn’t have to be the coding “expert.”

Thanks to my high-level knowledge, I was able to better collaborate with colleagues who have more advanced data manipulation skills but may not possess the domain knowledge regarding the particular risk we are aiming to mitigate or regulations we have to comply with. That’s where I fit in! It helped me realize that I’m not as replaceable as I once feared.

Speaking the Language of Technologists

It was empowering to understand coding logic and have more confidence when communicating with data and coding experts. I was better able to describe process flows in a manner that technologists and businesspeople alike can understand.

This is perhaps the highest value-add for a nontechnical professional in being data-proficient. Even if you don’t learn coding, understanding how data is organized and learning how to create and interpret data visualizations will enable you to make better decisions and better interact with more experienced data analysts and technologists. So, whether it’s being able to produce visualizations, automate tasks, or better interpret data, there are many ways of upskilling to augment one’s existing professional skills.

Many different types of “intelligence” will be needed in this new data-driven world! And while being able to manipulate and interpret data will become essential, combining one’s existing knowledge with data analytics allows for better insights and practical value. Thus, I encourage anyone who is hesitating to upskill their data abilities to take the first steps. Doing so will only help augment your current skillset, and rest assured: it’s unlikely you’ll be replaced by Artificial Intelligence anytime soon.

Darnell Dawson is a risk and compliance professional who’s held roles spanning Bank Examiner to Compliance Officer at the Federal Reserve Bank of New York, KPMG, Barclays, and Deutsche Bank before joining Arrayo. He has previously lived in China, is fluent in Mandarin Chinese and French, and speaks a smattering of Spanish. He enjoys studying foreign languages and Python in his spare time.




Arrayo empowers data-intensive businesses. Based in Boston and New York, Arrayo delivers services across FinTech, BioTech, and HighTech.