A guide to learning data analysis skills
This is the introductory post for an in-depth series on what data analysis skills to learn as a marketer.
- Don’t focus solely on technical skills, but on translating data into business value.
- Learn tool-agnostic skills.
- Have a baseline proficiency in spreadsheets, basic stats, and basic data viz. Also, learn how to connect data analysis with business decisions.
- If you want to further go down the data analysis route add SQL, dashboarding, and data storytelling to this.
- Want to become an expert data analyst? Learn Python and ETL tools.
Not sure which data analysis skills to learn? You are not alone.
A question I frequently see and get is what skills you should learn as a marketer when you want to get started with data analysis. Especially when you don’t have a technical background in math, statistics, or computer science and want to transition into data analysis from another role it’s hard to understand what skills you should learn.
Usually, when this question gets asked in a forum the answers mention seemingly random skills without any clear guidance on what is important and in which order you should learn the skills. Furthermore, there is too much emphasis on technical skills incl. buzzwords such as SQL, Python, and so on.
So, wouldn’t it be great if there would be a clear guide or framework showing the exact learning path you should follow when it comes to data analysis? Detailing what you should know when it comes to the different skills? Specifically for a marketing context?
Below is a learning framework that gives you clear guidance on what skills to learn, how in-depth, and in which order when you want to get started with data analysis.
The framework is especially useful for those who don’t have a technical background or want to transition from another field (e.g. if you are a marketer). It gives a clear learning path and can be adjusted depending on your current skill level and how deep you want to go when it comes to data analysis skills – without wasting time on nonessential things. It’s a system that you can use to teach yourself data analysis in only a few months by focusing on the right things.
Two misconceptions when it comes to data analysis skills
Before we dive into the framework and talk about what data analysis skills you should learn exactly there are usually two misconceptions that come up among the answers when somebody asks what data analysis skills they should learn:
First, the recommendations focus too much on technical skills
E.g. “learn SQL, Python, R, etc.”
However, the best technical skills don’t matter if you can’t translate your data analysis into business value, i.e. business decisions. You did your analysis in Google Sheets but saved the company hundreds of thousands in costs? Well, congrats! Nobody (and I mean nobody) will ask how the analysis was done.
And this is also your biggest strength as somebody with a marketing/business background: You have strong business acumen. You know what matters for the business and what provides value. Play that strength by positioning yourself as somebody in-between the data and the business world. That’s how you can compete easily against those with a purely technical background, by providing business value (that’s what I did when I transitioned). And that’s also why I focus so much on teaching a data analysis framework that gives you a structured framework for translating data into business outcomes.
Second, the recommended skills focus on specific tools
E.g. “learn Tableau, Google Analytics, etc.”
Yes, those are probably some of the most used tools out there. However, chances are that at every job you will have at least some tools for website analytics, dashboards, or whatever that are not mainstream.
So rather than learning the ins-and-outs of a specific tool (also on the job, you can still always Google any challenge you encounter…) learn agnostic skills which you can transfer to different tools.
For example, instead of knowing every detailed feature in Tableau, rather learn what makes a good dashboard, how it should be structured, and how it provides value to the audience (relating directly back to what I wrote above). Rather treat Tableau as a vehicle to learn (and show) those things than learning it for its own sake.
This makes you a lot more flexible when it comes to looking for new job opportunities and you don’t risk getting stuck when a new market leader among the tools emerges.
The Data Analysis Skill Pyramid
The Data Analysis Skill Pyramid is a framework to give you a clear learning path on what to learn when it comes to data analysis – without wasting time on nonessential things. It will help you to decide which data analysis skills to prioritize depending on your goals: No matter if you are a marketer who wants a future-proof skill to work more data-driven and to stand out among peers or if you want to transition into data analysis as a career path.
All the skills in the pyramid are ordered along two axes:
The horizontal axis shows you if a skill is rather a soft skill or a technical skill. For some skills, there won’t be a clear distinction between those two as they provide communication tools. However, in general, the more to the right skill is placed the more it is a soft skill, and the more to the left it is a technical skill.
The vertical axis gives some guidance on how much scale and quality you will be able to provide with the respective data analysis skills. Scale means in this context automating things, reaching larger audiences, and being able to work with larger data sets. Quality means that you can access more data and do more sophisticated analysis which you couldn’t with lower-level skills. More on that down below.
The pyramid is divided into three stages representing beginner, intermediate, and advanced data analysis skills respectively. Below gives you a rough overview of what each stage and skill stands for.
Step 1: One-off analysis (beginner skills)
This stage is all about doing simpler to intermediate one-off analysis without a lot of automation. We’ll start in the lower right corner as that is a skill you already possess as a marketer.
If you have a marketing/business background you already have some strong business acumen. So you can already put a checkmark behind that skill. This is incredibly valuable already, as this is often about the experience and it usually takes some time in the industry to get it. Only make sure you have a data analysis framework in place to translate your business questions into data analysis tasks.
A Data Analysis Framework
This is the process that connects the business acumen with the other more technical skills. It helps you connect business and marketing objectives with the appropriate data analysis. Again, the best technical skillset is useless if you don’t know how to apply it in the context of business and marketing objectives. Having a strong framework and process there will make it easy for you to pick the right data and data analysis method.
It’s essential for giving any data analysis value – especially in a marketing context where it ensures that you can translate your data into the next steps for the business.
Basic Data Visualization
Nobody likes to look at boring tables. You’ll have to share your data analysis with other stakeholders. Know what makes a good chart and what helps your audience. You can simply use Google Sheets or Excel as a vehicle to provide those charts.
Applied Stats & Modeling
This is what most people think about when referring to data analysis: applying statistics and models to business data to translate it into actionable insights. You don’t need a Ph.D. in math or stats. Just have a good understanding. Again, applicability > in-depth theoretical knowledge
Be very proficient in spreadsheets (again, doesn’t matter if Excel or Google Sheets as they are so similar). Still, a lot of analysis tasks can be done in simple spreadsheets. It’s quick and easy and everybody understands them or at least can work with them. Nothing to undervalue in a business environment.
That’s it for the first stage. One important side note: Don’t undervalue the skills from this stage.
You can already have a tremendous impact by knowing them. In the end, it doesn’t matter what technologies or techniques you use, the only thing that matters is the value your analysis creates for the business. You saved your business $100k by doing some marketing channel optimization analysis in a spreadsheet? Great! Believe me, nobody will care if you used Google Sheets for it…
Step 2: Improving Impact (intermediate skills)
This stage is about working more independently, autonomously, and having more impact.
SQL will help you a lot to get data autonomously and the developers will thank you for not taking up their time anymore. Also, it will help you to be more flexible with the kind of data sources you can use and you can start automating first things. Btw. It is a lot easier to learn than you would think. I use it daily in my job.
Again, it doesn’t matter if you are using something more advanced like Tableau or simpler such as Google Data Studio. Learn what makes a dashboard understandable and actionable to reach an audience with your data analysis at scale. Be careful here. Don’t just learn how to throw data at dashboard viewers (aka data dumps) but how to have an impact and provide actionable insights with dashboards.
This is an important one in marketing. If you want to have an impact you have to convince other people. Nothing better for this than an awesome presentation or pitch. This is a catalysator for the insights you generate with more technical skills. Again, you can have a tremendous impact no matter the technology you use if your outcome is directly linked to business objectives. Data well presented in a well-structured and action-oriented slide deck provides you with the leverage for this.
Step 3: Automation (advanced skills)
The last stage deals with automating things and doing more complex and sophisticated analyses.
If you want to learn some coding to do data analysis you’ll probably have to decide between Python and R. Personally I would lean towards Python as it is more flexible and useful outside of the data world. Knowing how to code will help you to automate and to do much more advanced analysis where other tools such as spreadsheets would break due to scale.
ETL tools refer to tools that extract, transform, load data from one or more data sources into an output that represents the data differently from the source or in a different context than the source. A typical output would be for example a dashboard. So this is mostly about automating things and building more complex data pipelines.
So, what should you learn as a marketer?
If you are completely new to data analysis start at the bottom. Master stage one to be able to autonomously do one-off analysis. If you want to become a better data (analysis) driven marketer you should make sure that you can check-off all the skills listed in that stage. As mentioned above you should already have some decent business acumen. Continue with learning a data analysis framework to have a bullet-proof process to make your analysis valuable and to translate your analysis into business decisions. Afterward continue with basic data viz, applied stats, and spreadsheets. Ideally, you learn those at the same time and in a marketing context to make sure you don’t only learn theoretical concepts but know how to apply them in practical use cases right away. Again you learn everything as a means to an end to provide business value rather than for its own sake.
Only after you have mastered stage one continue on your learning path towards stage two. As I believe data storytelling is so important as it is a catalysator and leverage for everything else you do when it comes to the more technical aspects of data analysis I recommend starting with that one before continuing with SQL.
Are the skills of stage three useful for a marketer? Yes, of course. However, are they necessary? Probably not…
The skills from stage three have some diminishing returns when considering investing time there. Consider those skills either when you want to become a data analyst or later on in your learning path if you want to specialize or dive deeper into data analysis. Either way, if you are just starting don’t make learning Python your priority for now.
The next parts of this series go more in-depth with the individual skills in each stage. As such we will start with stage one in the next part and I’ll detail what exactly you should know when it comes to beginner data analysis skills to get the fundamentals in that stage covered.