Season 2: Episode #4 | Top Skills for Data Analysts in 2025
🚀 The role of data analysts is evolving fast—are you keeping up? In this episode, Vadym and Ievgen dive into the essential skills that every data analyst needs to thrive in 2025.
🔍 What you’ll learn in this episode:
1️⃣ The must-have technical skills, from SQL to data modeling and visualization.
2️⃣ How soft skills like strategic thinking and communication set top analysts apart.
3️⃣ The emerging trends shaping the future of data analytics, including AI and econometrics.
Podcast listing
Vadym: Hello, everyone, and welcome back to The Data Crunch Podcast! I’m Vadym, your host, joined as always by our expert in all things data, Ievgen.
Today, we’re discussing the top skills data analysts need to thrive in 2025. Data analytics is more than just a job title – it plays a crucial role in shaping how businesses operate and more importantly, how they grow revenue.
Ievgen, it feels like the demand for skilled data analysts is only increasing. Why is this role so vital right now?
Ievgen: Great topic, Vadym, great topic to discuss. And a great question as well. Actually it’s so funny as we’re recording this episode on Tuesday, the second day of the biggest analytics conference in Europe - SUPERWEEK.
Basically, Companies are generating more data than ever before,
Every day the amount of data is growing, the amount of data that companies collect.
However I haven’t ever seen the businesses that grow revenue with the same speed.
So without skilled analysts, without the right frameworks to work with data to make sense of that data, that data collected basically has no value.
Analysts, skilled data analysts, digital analysts, bridge the gap between raw data that’s being collected and answers to business questions that help organizations make informed decisions, stay competitive and grow faster.
Vadym: That makes sense. So, let’s dive into the skills analysts need to stay ahead. Where should we start?
Ievgen: Let’s begin with the technical skills – these are the foundation of any data analyst’s toolkit. The three must-haves are SQL, spreadsheets and some sort of data management skills, data engineering skills.
Vadym: SQL is something we hear about a lot in this field. Why is it so important?
Ievgen: SQL is the language of databases. It’s the data language. If you want to speak to data - you need to know SQL. Whether you’re pulling sales data, customer feedback, or operational metrics, from some sort of databases, data warehouses where you store that data - chances are you’re using SQL to access it. It’s a core skill, core language that every data analyst needs, and it’s often part of technical screenings during the job interviews. In 90% of cases.
Vadym: That makes sense – SQL is about accessing the data. Once you have it, though, you need to analyze it, right?
Ievgen: Exactly. So SQL is used to access the data, to manipulate data, to transform it, to extract it. As I said - to speak to data.
But then - you got the data, you have to do something meaningful, right?
I would say start with Excel or Google Sheets next.
Like traditional BI tools for data visualization are great, but you can learn that later on. Business folks actually love reports in spreadsheets. Because they will be able to manipulate the data, massage the data, play with it.
They already know some sort of VLOOKUPs or ARRAYFORMULAs.
Plus, they all know how to build a simple chart.
We’ve specifically created a tool within our Reports Extension that transforms any table with data into a dashboard with pie charts, line charts, time series things, cohort reports and a pivot.
Like the easiest way to check out the tabular data is to run our free extension on top.
Vadym: Great. Then there’s data engineering you’ve said, right? What does that involve?
Ievgen: Let’s say data management. It’s a big topic. It is all about organizing and storing data effectively. Think about how much data a company generates every day… It’s not enough to have like tons of spreadsheets. The data needs to be stored securely, but accessible, plus it should be structured for analysis.
So this all start with data collection.
Like taking your data from varios platforms: facebook ads, google analytics, quickbooks, stripe, sales crm, any tool into some sort of storage.
At least spreadsheets, but better to use a datawarehouse like snowflake or google bigquery. It’s just a great investment going forward.
Vadym: It sounds like these skills are the building blocks for any data analyst. What’s the next step once you have the data is in place?
Ievgen: After you’ve collected the data, the next step is preparation.
This involves cleaning the data, making sure it’s reliable. organizing that data so to say. Then you need to build what we call - the data model - basically mapping the data into objects with attributes, thats are easier to understand and analyze…
We at OWOX are launching those templates with indistry specific data models that anyone could use.
Vadym: Why is this step so critical? Why data preparation, data modeling is important?
Ievgen: Imagine trying to build a report with messy or incomplete data - it leads to errors and unreliable insights.
Or when you get the data, but you don’t know how to join then.
Like website visitors and clients. What’s the key there? Even if you have 2 tables ready, how to map them correctly?
That’s what a data analyst does, basically building those connections, relationships, and the data model allows to ensure, that all of the future reports are going to be built by the same rules.
Data preparation, transformation ensures your future reports are going to be accurate and credible. It might not be glamorous, but it’s a skill that separates good analysts from the great ones.
Vadym: That’s right, data preparation is crucial. And I guess once the data is ready, that’s when the analysis starts, right?
Ievgen: It depends. But typically, yes. It’s time to build a report. At this stage, analysts creates a data mart - a SQL Query to basically extract some data from the database, and when this is done - one of two things happens:
First, he just shares that report with a stakeholder and that’s it.
Or, an analyst starts looking for patterns, trends, and insights that can drive that business user to make decisions. So either share the report, the result of the analyst work, OR, doing the analysis part and shares findings with stakeholders.
Or basically both.
But look, technical skills alone aren’t enough for this part – you need a strong set of soft skills, too, ok?
Vadym: Let’s talk about soft skills. Why are they so important for data analysts?
Ievgen: Soft skills are what take analysis from good to great.
For example, being a keen observer, knowing how business works, what’s the business model, what the goals are, what are the questions that busines is or should be asking, this all helps analysts spot patterns and anomalies that others might miss.
So this extends beyond the numbers – it’s about understanding how the business operates and what are the growth opportunities. How to find them?
Vadym: So, observation, understanding business goals, those are the keys.
What else?
Ievgen: So yes, another critical skill is asking the right questions.
Before diving into data, you need to understand the problem you’re solving. So first comes the goal or the problem. Then the question to ask… Thoughtful, probing questions help align your work with business goals. Then you start thinking about the metrics you need to measure in order to get the answers to those questions. Following me, right? So Goals or Problems -> Questions that can help to reach those goals -> Then the Answers to these questions with the right metrics and dimensions.
Vadym: Oh yes. That makes sense. And what if the data needed isn’t readily available?
Ievgen: That’s where resourcefulness comes in. Great analysts know how to find the data. Which platform to get those data from. Or even how to start collecting that data. Because look. Analytics is not always real time right.
It takes time to build the system but then you still iterate, iterate and iterate. You might need to work with other teams to gather new information.
Vadym: Once you’ve found insights in the data, how do you ensure they have an impact?
Ievgen: Communicating insights is just as important as finding them.
So that’s where you need those soft skills. Even the most talented or skilled analyst loses his value, the value of the reports if they are poorly presented. Great analysts excel at turning complex findings into super simple, compelling stories.
Vadym: It reminds me of the quote from Albert Einstein: “Make things as simple as possible, but no simpler.” Ok, what makes for a good data story?
Ievgen: It’s about clarity and relevance. Use visuals like charts or pivot tables, no need to create something fancy, to clarify your points, and always tie your findings back to the business impact.
For example, explain how it affects revenue or customer retention or marketing performance instead of just showing a sales trend.
Vadym: I am curious in your opinion, Ievgen. Looking ahead, are there any emerging trends or skills analysts should focus on for 2025?
Ievgen: Definitely. One big trend is econometrics, especially for analysts in finance. Again. I’ve studied that in the university back in the days.
So it’s about using statistical models to forecast trends and make future-profed decisions.
Vadym: And what about AI and machine learning?
Ievgen: These are becoming increasingly relevant, even for data analysts. You don’t need to be an expert, but having a foundational understanding can give you a significant edge.
Vadym: So, the role of a data analyst is evolving to include more advanced techniques and collaboration.
Ievgen: Exactly. It’s no longer just about crunching numbers. Analysts are becoming key players in strategic decision-making within businesses, and the ability to collaborate with other teams or stakeholders is more important than ever for the company success.
Vadym: This has been an incredible conversation, Ievgen. So, let’s summarize the key takeaways for our listeners.
Ievgen: Sure! Here’s what to focus on as a data analyst in 2025:
- Master core technical skills like SQL, maybe Python, and data management.
- Embrace soft skills like observation, asking the right questions, and resourcefulness.
- Learn how to tell compelling stories using data, simple visualizations and clear communication.
- Keep an eye on trends like AI to just simplify your life and stay ahead.
Vadym: Thanks, Ievgen.
And for those looking to expand your skills as a data analyst, head over to the OWOX website at OWOX DOT COM and explore our tools for better reporting, data collection, analytics tactics, visualization techniques, and much more. Get started for free today and empower business users to make more data-backed decisions with your reports.
For our listeners, the future of data analytics is exciting, but it’s also competitive. Staying ahead means continuously learning and adapting.
Ievgen: Thanks for tuning in to The Data Crunch Podcast! Don’t forget to subscribe and share this episode with your network. See you next time!
