How to position yourself for breaking into data analysis (with a non-technical background)
Learn how to position yourself if you want to transition into data analysis and you are coming from a non-technical background (e.g. marketing).
- Don’t focus only on technical skills
- Develop a personal brand and pitch yourself as somebody who understands both worlds: business and data
- Have a portfolio of brands to showcase your brands
- Play your strengths during interviewing and demonstrate how you understand the business and can provide value with data
Those who want to transition into data analysis from a non-technical or traditional data professional background such as marketing are often held back by one concern: “Can I ever compete against those with a technical skillset?”
This answer is: Yes!
And you even have some advantages compared to those with a purely technical background.
How to position yourself
Let’s talk about how you can position yourself to be outstanding. But before that let’s clear up one misconception when we talk about breaking into data analysis.
A common misconception about breaking into data analysis
I can’t recount the times I saw somebody post the question of what they should do to transition into data analysis and all the answers were a variation of “learn technical skills x, y, and z”.
No wonder those without a technical background are concerned that they will never be able to compete for a data analysis related role.
However, the truth is the best technical skills don’t matter if you can’t translate your data analysis into business value, i.e. business decisions.
There needs to be a mindset shift to stop over-emphasizing technical skills and focusing more on the outcome. Nobody cares in the end how you got the insights as long as it makes the business money or cuts costs.
Yes, some technical skills are essential but having business acumen and knowing how to use them to provide actual value for the business is at least as important.
This mindset change will be the basis for the following and how to position yourself for breaking into data analysis with a non-technical background.
Develop a personal brand: The best of both worlds
With the above emphasis on outcome and providing value you have an advantage compared to those with a purely technical background: Your domain knowledge.
You know the ins and outs of the business. You know what drives it, what challenges it has, and which business decision could be based on data insights.
Somebody with a purely technical background might have excellent skills in math, stats, modeling, or CS but they will never have the in-depth business acumen.
Make use of this knowledge. And this is the one thing I recommend to people who want to transition from another area of the business (e.g. marketing) to data analysis: Build a story around yourself where you position yourself as someone who knows both worlds – the business side as well as the data side.
You are the translator of business challenges into data analysis projects. You translate data into business value and business (marketing) decisions to help the business grow.
To strengthen this link between the business world and the data world, have a good data analysis framework in place. It’s a process that will help you link business challenges and objectives with the right data and data analysis methods to ensure your data insights will be actionable.
Build a project portfolio to show your brand
So what’s the best way to build that “best-of-both-worlds” brand? Build a portfolio of data analysis side projects.
One of the biggest assets for me when I broke into data analysis was that I built a portfolio of analytical and data analysis projects during my time as a marketer. Whenever there was a chance to apply those skills I did. Soon I had a little personal brand as the go-to “data guy” among my peers.
This will help you internally if you decide to apply for another internal data analysis role but externally as well. You will build confidence in those data analysis skills you might have built through courses and books by applying them to real-life use cases and creating actual business values. And the projects will help you in the application process for external roles as you’ll see in the section below.
Going through the data analysis application process
Going through the above guide and applying it will help you tremendously when applying for any data analysis related role. For many analytical roles you will have a two-stage application process:
First, there will be a screening stage by HR to see if you are a general fit and have relevant experiences. This is where your portfolio of relevant analytical projects (which you should definitely mention on your CV) will already help you a lot.
Second, the stage with the actual interviews often conducted by your future team manager and members. This will be a mix between simple technical questions (e.g. what is a left join in SQL) or cases (e.g. you have to analyze a dataset with a given tool) and STAR questions (more on that below). Both have the goal of evaluating your skillset and more importantly what unique value you bring to the team and what makes you stand out from others.
And we discussed that value already a lot: You are the one who combines the best of both worlds – the domain knowledge as well as the data analytics skills. This will be your storyline for the whole interviewing process and how you’ll position yourself throughout by putting the right projects on your resume to get through the screening process but also during the interview when answering questions.
So let’s have a look at how you do that. While it’s relatively easy to prepare for the technical questions (by taking courses, learning the technical skills, etc.) the STAR format can be quite unfamiliar for many but offer good opportunities to position yourself.
Disclaimer: I am mostly speaking here from my experience at a tech company. However, I believe the general process is relatively similar in other industries.
The STAR interviewing method
Obviously part of the interview process will be technical questions to test your knowledge, e.g. in tools and coding abilities. However, a major part will also be about past experiences and how you would react in certain situations.
A common method to ask those questions is the STAR interviewing method. According to Wikipedia the situation, task, action, result (STAR) format is defined as follows:
- Situation: The interviewer wants you to present a recent challenging situation in which you found yourself.
- Task: What were you required to achieve? The interviewer will be looking to see what you were trying to achieve from the situation.
- Action: What did you do? The interviewer will be looking for information on what you did, why you did it, and what the alternatives were.
- Results: What was the outcome of your actions? What did you achieve through your actions? Did you meet your objectives? What did you learn from this experience? Have you used this learning since?
Let’s go through an example on this:
A question by the interviewer could be something like (Situation): “Tell me about a time when you had to use data to make a marketing decision”. Your Answer: “Our company was trying to become more profitable and we weren’t sure what to focus on to improve”.
After you described the general situation the follow-up question would then probably be about what you had to do in more detail (Task). The answer to this could be something like: “I had to analyze advertising channels to find those to focus on.
The action part would then be about what you did, e.g. what tools and techniques you used to analyze the advertising data.
And lastly, the result is about your impact and outcome. So what did you achieve with your analysis, e.g. “Based on the analysis we focused on one advertising channel in particular and improved ROI by +46%”.
The STAR interviewing technique allows the interviewer not only to examine what skills an interviewee possess and how they approach problems but also how value-driven your data analysis is.
Your time to shine! It is not a coincidence that the above-mentioned data analysis framework is quite similar to the STAR format as they are both very result-oriented. If you followed that framework in the past you are set up exceptionally well to answer these kinds of questions. This is your chance to show that you truly are the best of both worlds – you know what data insight is needed to really have an impact by driving a business decision and you know what kind of data and data analysis technique is needed to get to that data insight.
How to use your project portfolio
By now it should be clear that having a portfolio of data projects is extremely helpful to answer STAR questions. They give you tons of options and examples to answer those kinds of questions showing off what you have accomplished in the past. And if you followed a data analysis framework for your projects it’s incredibly easy to use them as examples as the format is so similar and your projects are so impact-oriented. Again, highlighting how business value-focused you are by combining a practitioner’s experience with the data world.
Excursion: A pro tip for acing interviews
I know an upcoming interview can be very intimidating, especially since you don’t know which questions are going to be asked. Well, it doesn’t have to be! You can prepare incredibly well if you go to something like Glassdor and go through all the interview experience reports there for the potential position and company you want to apply for. Each of those reports has usually at least one interview question they were asked.
Collect them and write down how you would answer those questions, e.g. with portfolio projects you would use as examples.
Even if those questions don’t come up during the interview, similar will probably come and you’ll have some answers to choose from.
Summing up: how to competing against others with a more technical background
When I first transitioned into data analysis I was facing a similar challenge: I never thought I would be able to compete against those with a more traditional professional data background. I had a marketing background and was competing against peers who were coming from computer science, math, stats, or something similar. But I made sure to point out my strengths: my in-depth business acumen. A lot of data guys emphasize on their strong technical skills and try to compete there but neglect their understanding of the business. I on the other hand pitched myself as somebody who combines the best of both worlds which led to me getting the job in the end as I was able to show how I would provide actual value and impact to the business.