This is an article for those who graduated or about to graduate from a Data Analytics program. Those who can be honest enough with themselves to admit that they are not the most technically talented people in the room but who are still hoping to find a job as analysts. I am hoping there are more data professionals out there that can relate to this. This is my story.
As I transition from a Data Analytics Certificate program into the workforce, I have struggled to find where my skills, background and experience fit into today's challenging labor market. After almost 3 years as a part-time student, I have now completed most of the program. The variety of courses has allowed me to find where my strengths, my interests and my passions lay. And because every story has its challenges, I also realized where my struggles and weaknesses shyly hid. As I don't come from a technical background, I felt challenged with most of the courses that were heavy on the technical side. For me, R, Phyton, and some of the more advance SQL functions did not come as natural. To put it in a blunt way, those lessons were actually painful, and the learning curve was steep. The learning curve is still steep and will continue to be steep. It takes me twice as much effort and time as it would a technical talented person.
Of course, I am familiar with coding and programming languages. As a professional aspiring to work in the data space, I have to, and so do you. I can write queries, and so should you. Now, when it comes to more complex code, I would probably be able to understand it, however: Do I see myself writing this type of code? Absolutely not! Am I ready to apply Machine Learning or Artificial Intelligence concepts? Absolutely not! At least not in the near future.
As I scrolled through my LinkedIn and realized the highly skilled technical talent in my network, I found myself doubting if I even stood a chance.
I was not being pessimistic. I had hard evidence in front of my eyes, or my screen, I should more appropriately say. I was just being realistic. As realistic as I could be. It was clear that my strengths didn't lay in code syntax or algorithms. If you relate to my story, I guess the question revolving around your head is…Can I still succeed as a data professional? The short answer is YES. Here is how (at least the HOW that my mentor shared with me)!
Of course, I knew I could bring together my soft skills, my professional education, and my
academic skills as Data Analyst. I knew all of this before even talking to my mentor. However, what she did, was to add more layers and perspective to my job search. I had allowed myself to fall into the trap of believing that I could only apply to Data Analytics jobs. After all, I wasn't becoming one for nothing right? What she made me realize is that I could also call myself a developer. It didn't make sense, a developer without necessarily being a "programmer”? What did I miss? Now, that was new and news to me. In the past decades, the common understanding, or at least, my understanding of a developer was "a person who wrote computer programs".
So, every time I spotted the word developer, I automatically skipped the job posting. I would do what Donabel Santos, author, BCIT instructor and Tableau Expert, and Andrew Drinkwater from Plaid Analytics describe as "self-elimination" in the Data Storytelling course from LightsOnData. To be honest, I wouldn't even read the description of the role. Has this happened to you too?
I would intrinsically associate the word developer with highly skilled technical programmer who writes gazillion lines of complicated code.
My conclusion is that this happens because the Data Analytics field has expanded so widely that even those of us with one foot in the data space are struggling to find the roles we fit in. My mentor actually confirmed this. She went through the same thing when she was job hunting. The advantage that I, and hopefully you have now, is that with this information in hand, we can do so much more. It finally clicks. If you design dashboards, you fall under the developer category. If you build reports, you also fall under the developer category. Even if you are using visualization tools such as Tableau or Power BI to design and build solutions, you are still a developer in the analytics context. It makes total sense, doesn't it? I know this might sound very obvious for a lot
of you. However, it was not that obvious to me. And perhaps it is not that obvious if you look it up in Wikipedia either. As of today, Wikipedia lists three categories under developer:
- software developer,
- video game developer, and
- web developer.
With this information, how is one supposed to get to the conclusion that a Business Intelligence Developer is, potentially a BA who develops data solutions? Maybe there are some smarter readers out there. And maybe I am not as smart as I thought. Anyways, I had my aha! moment thanks to my mentor.
By applying to DA positions only, my mentor pointed out, you are probably missing out on your best chances.
Specially, since candidates competing for those positions have a lot to offer. Does a list of ten different packages and programming languages sound familiar to you? That wish list is more common than not, right? So, note to yourself: Next time you are job hunting, take some risks, challenge yourself and include Business Intelligence Analyst and Business Intelligence Developer in your job applications, or at least read the full posting, so at least you don't self-eliminate by the position name.
To wrap it up, professionals who use visualization tools such as Tableau and Power BI can still be referred to as developers. This is because we can "develop" solutions in these platforms. This might sound "simple" as opposed to the complexity of being a developer. However, give yourself some (or a lot of credit!) because we know there is a lot of work behind building a meaningful and effective BI solution. Yes, these tools, make the job "easier", but the developing piece comes down to being able to efficiently use what the software offers. So, for those of you out there who are just starting in this beautiful, yet challenging analytics path: You can grow into a data related career as a developer without necessarily being the most talented one…technically speaking (no pun intended!).
My last piece of advice is: “If your brain thinks algorithms and programming languages are more complicated than fun, then leverage your skills and excel at everything else” - LLMM
Be top-notch at gathering requirements, understanding the business logic, building queries, modeling data, mining data, discovering trends and patterns, and above all, become the best at communicating insights. Oh please! work on those communication skills as much as you can. I know you know. It makes a difference. So the short answer to I don't write code, should I still consider myself a developer? Absolutely yes!
Finally, Thank you to all of those who have been instrumental in my professional journey, to my coach, and my mentors. Thank you to those who have inspired me along the way, my instructors and industry leaders who have answered my technical and non-technical questions. And of course, to those who encouraged me to resume my professional career in the data space, whatever the role I take "looks" like. Thank you LightsOnData for allowing me to share my experience with your community. Thank you BCIT for providing an education with such high standards and top notch instructors. Thank you BCIT Career Services staff for finding me such a great mentor. Yes! mentors do make a big difference!