Dan: My Data Analyst Career Journey

Hi Dan, thank you so much for taking the time. Please introduce yourself to our site’s readers.

Hi, I’m Dan from the UK. I’m a Data Analytics Consultant with The Information Lab, where we help companies make sense of their data by providing solutions on short, medium, and long-term projects across all kinds of industries. In October 2023, I completed their Data School programme, where I underwent a 4-month training period, learning data tools like Tableau, Alteryx, and Power BI, as well as SQL. During this period, I also worked with a variety of clients on short-term projects in industries such as fashion, travel, beauty, sport, and finance.

At The Information Lab, I work with some of the industry's experts in data visualisation and data preparation, which is a great support to have so early in my data analyst journey. What I love most about being a data analyst is that I get to be both analytical and creative, using a broad mix of soft skills and technical skills on a variety of projects. Working for a data analytics consultancy also means I’ve had great exposure to a variety of industries and worked on projects across many areas rather than being limited to specific areas of data analytics. This keeps things interesting and helps to shape my future as I consider the industries I want to work in going forward.

I’ve now been with The Information Lab for 11 months, but two years ago, I didn’t even know the role of “Data Analyst” existed. I’m glad I’ve found my way into this industry, and I see a lifelong career in this space, so it’s great to share my journey and some tips with others via your blog.

Can you share with us how you entered the data analytics industry, and tell us about your first data analyst role?

I graduated from university with a degree in sports science in 2020 and a master’s degree in exercise physiology in 2021. The plan was to go into sport, but COVID put a stop to all opportunities, so I had to pivot. I had no idea what I wanted to do but after a long search, I found a graduate scheme at a genomics research institute where I had the opportunity to rotate around different teams. The company was in a health-focused industry, so it felt like a good enough option after my degree.

In my first month there, I was supporting a team that was consolidating data from hundreds of spreadsheets and visualising that data in Tableau, which was completely new to me at the time. I sat with the data analyst, Mike, who talked me through his process of gathering and cleaning the data before loading it into Tableau for reporting. The Python aspects went straight over my head, but Tableau’s easy drag-and-drop functionality to build charts blew me away.

My manager booked me onto an internal Tableau training course, as I was going to be using the tool myself. This training course happened to be led by Matt Francis, a respected member of the Tableau community and Tableau Zen Master (which was the previous title before Tableau Visionary). After that session, I was hooked, and I found myself spending the next 18 months of my graduate scheme trying to find ways to introduce Tableau to other teams and practise my data skills. Naturally, I didn’t know everything and often got stuck, so I spent some evenings and weekends googling “how to do ‘xyz’ in Tableau” and other data-related questions, which kickstarted my bug for self-development in data.

During that time, I developed dashboards for several teams, either by volunteering to build something to help a team as a side-project or simply building a dashboard anyway and presenting it to the stakeholders as, “Hey, I see you’ve been using this data. Here’s what you can do with it.” The feedback was great, and my passion for data analysis began. Towards the end of the programme, I concluded that I wanted to get into data full-time, which was when I came across The Information Lab and their Data School Programme.

What is it that you're personally finding most exciting about being a data analyst?

What excites me most about being a data analyst is the problem-solving aspect. Each project starts with a challenge or a desired outcome, and it’s up to me and or our team to navigate through various stages to find the best solution. This involves everything from scoping the project to gathering, cleaning, and analysing the data, and finally visualising and reporting the findings. At every step, it’s an ongoing problem-solving process, where I work with different stakeholders to piece together the puzzle. The real reward comes at the end when I get to present a solution. When you deliver a piece of work that you can see being used, that saves someone time or money or makes better decisions, it’s satisfying to know that my work has made a tangible impact.

You seem to have had a very diverse experience, tell us more about working on projects across different industries (travel, transport, fashion, beauty, sport and banking). Could you tell us a bit more about those experiences, and share how what you do drives insights and business decisions?

Consulting for clients across various industries early in my career has been hugely insightful because it gives you a good understanding of how these different companies use their data. Some companies are quite mature in their data journey, with centralised data teams using all the latest tools, whereas others are just starting out with small teams or even a one-person band doing a bit of everything. You may even find that across an organisation, data literacy varies significantly between teams, which opens the door for plenty of opportunities for early-career data analysts to seasoned experts to make an impact.

The great thing about data is that you can specialise in a specific area or be a generalist working across different sectors. What I’ve learned is that the way you work and the processes you follow are similar between projects. I’ve worked for a train company on learning and development data, in sport on people resourcing, and on the typical e-commerce sales project. In all of these experiences, you rely on the same core skills despite each project having different requirements. Subject matter knowledge does help, of course, but that’s something you can pick up along the way as you’re always working closely with a subject matter expert.

Driving insights and business decisions through data can come in all shapes and sizes. I recall one particular project where a team was consolidating sales figures from stores around the world each week. The data would come in via each store’s Excel spreadsheet, and someone was manually copying and pasting these into a master sheet to complete some basic analysis. This would take someone the best part of an entire Monday to do. Our solution automated the entire process, consolidating the data, cleaning it, and visualising it in reports in seconds. This not only saved a day's worth of work but it also minimised errors. The additional analysis provided deeper insight into customer demographics and customer buying behaviour which to be used by teams like buying to improve their product offering for each store.

In another piece of work, I supported a company on a supply chain project. They wanted to better understand where their shipments were in the supply chain so they could utilise their warehouse space more effectively and avoid costs from renting additional space. We pulled in data from across the supply chain process, such as which products had left the manufacturing facility, which shipments were being held in port, and which were in transit. By doing so, we could forecast when shipments would reach the warehouse and in what volumes, enabling better space utilisation.These are totally different examples but fundamentally the steps were the same. Gather requirements, find the data, clean the data, conduct analysis and report findings via visualisations for end-users.

What would you say are the must have data analyst skills to thrive in today’s environment? How did you start improving your skills as a data analyst? What are you currently doing to keep up with the ever changing data analytics industry?

There are a few key skills you need to focus on. Problem-solving is a big one. The job is all about tackling challenges, whether it’s figuring out why something looks off in the data or finding the best way to present complex insights. You need to approach every problem with a mindset aimed at finding effective solutions and making sure the data answers the right questions as clearly as possible.

Attention to detail is crucial as well. Big decisions are often based on the data we work with, so it’s vital that everything is spot on. When something doesn’t add up, you’ve got to dig deep to figure out what went wrong and why. This careful attention ensures that the insights you provide are accurate and trustworthy.

Curiosity is another must-have. The field of data analysis is always changing, with new tools and techniques popping up all the time. Being curious and eager to learn about these new developments keeps you ahead of the curve and helps you apply the latest methods to your work.

Finally, while not exactly a skill, asking for help is an important behaviour. Don’t be afraid to reach out to colleagues with more experience or knowledge. You don’t need to know everything yourself; knowing how to leverage the expertise around you is what really counts. This is something I still struggle with as everything can be a learning opportunity and I often want to go off and find a solution myself. However, that can take time and time is not always on your side on projects so it can be quicker to ask others for support and do the learning of what and why later on.

Now I’m working with business data 9-5, it’s refreshing to switch to sport data analysis in the evening and weekends to push my skill development in a fun way. You’ll feel less friction and find it easier to put the time and energy into something that interests you. I often get involved in the Tableau and Alteryx community by engaging in community projects and challenges. I’m also a big F1 fan so I’ve challenged myself to create 24 data viz projects this season, one each race weekend which has been good fun and has been good practice for developing at speed, testing out ideas and tackling different data structures and sources. You can see more here if you’re into F1 too.

Can you share what factors are most important to you in a data-focused career and why? Do you have a vision of what you'd like your career growth to look like?

Coming from a sports background, I would love to eventually circle back into the sports industry. Perhaps with a sports team or in sports journalism. However, what I’m beginning to realise is that it’s not just about the industry I work in; more importantly, it's about being part of a team that supports my growth and professional development. For me, the most critical factors in a data-focused career are engaging work, continuous learning opportunities, and being in an environment that encourages development.

Data analysis is such a broad field and closely breaches into data engineering, data science, AI, product management and more so there’s plenty of paths to take as my curiosity grows. That said, what I really enjoy most is the data visualisation aspect of data analysis. I love how accessible it is, how it’s often the front-facing part of a project where you can directly see the impact of your work. There's something incredibly rewarding about creating tools and visualisations that others use to improve their decision making and strategy.

Something that a lot of people are wondering and asking about - What recommendations would you give to someone who is looking to join the data industry and get their first full-time data analyst position?

It’s a question I see regularly online. Data analysis is a field that is growing in popularity and is becoming hugely competitive. For some roles, you’ll be up against hundreds, if not thousands, of applications for entry-level positions. It’s a daunting feeling, but you have to be patient and persistent, anticipating that you might not get the role on your first try. If you truly want to be a data analyst, you’ll get back up, develop your skills and experience, iterate and improve your CV, and try again. So, that would be my first tip.

My second tip would be to tailor your CV and cover letter each time. I’ve sat on hiring teams before, and the CVs that get placed in the rejected pile are the ones that either don’t meet the essential requirements or are not clear enough. You need to make it as obvious as possible to the recruiter that you tick the boxes.

My third tip is around experience. When you’re just starting out, you probably don’t have much experience. Personal projects are great, and they are a way forward, but everyone else applying at an entry level will also have personal projects under their belt. The way you can stand out is by showing initiative with voluntary real-world projects. Get hold of some data, find some insights, and provide recommendations. For example, if you’re at university, reach out to societies to report on their demographics to drive diversity and inclusion. If you’re with a religious group, speak to your place of worship about reporting on their weekly attendances to forecast the food and beverages required for the service. If you follow amateur sports, gather data on local players to recommend teams with signing opportunities.If you’re already in the workplace but have little data experience, reach out to colleagues who work with data and offer to support them with side-of-desk tasks.

However, the key step that people often miss is the “so what.” After each bit of analysis, think about who benefits from it, what findings you discovered, and what these findings can lead to. That way, you can provide evidence that you understand the impact of your work and can communicate its value effectively.

Anything you'd like to highlight, or add? Something that is not specified above but you hear a lot?

Even though I’m already in a data analytics role, I still spend most evenings doing extra “stuff,” whether that’s on LinkedIn or working on personal projects. It’s not for everyone, but I genuinely enjoy it. I like having something to work towards. At some point, I’ll be looking for my next role, could face redundancy, or maybe even want to switch careers. When that time comes, having a solid portfolio of work and a broad network isn’t going to be my limiting factor.

Building a portfolio isn’t just for landing your first data role—it’s something you should carry with you throughout your career. Whether you’re aiming for a new job, a promotion, or just want to show the impact you’ve had in your current role, having that portfolio is key. And it’s not just about proving you’ve got the skills; it’s about quantifying your impact. Hiring teams don’t just want to see that you can do the job—they want to know how bringing you on board will benefit them. Being able to show the tangible results of your work is a great way to sell yourself.

An extra one: How do you see the increased availability of AI tools such as ChatGPT, Bard etc., impacting the typical data analyst role? Are you using AI tools to augment your thinking, analysis and overall work?

I use AI every day—it’s become a key part of my role and even my personal life. The sooner you get on board with AI, the better equipped you’ll be as a data analyst because of the variety of tasks it can help with. Whether it’s generating mock data, adding comments to code, or debugging a tricky calculation, AI tools have sped up my problem-solving process significantly.

There’s some criticism that relying on AI for certain tasks won’t help you develop in your role. I get where that’s coming from, but I don’t fully agree. With the right prompts, AI can be a great learning tool. Instead of just asking AI models to solve a problem, I’ll often ask it to walk me through how to solve it. That way, it’s more like having a colleague guide you through an issue rather than someone solving a problem for you.

Beyond hard skills, AI can also boost your soft skills. For instance, when you’re working with clients from different industries on short-term projects, it can be tough to get inside the mind of a stakeholder who’s been in the game for 30 years. What problems do they face? What are their priorities? AI tools like ChatGPT can give you a head start, even if you’ve never worked in that industry before. There are tons of benefits, and things are moving fast—but that’s what makes it exciting. It’s opening up new areas for development as a data analyst.

As for the fear that AI might take over jobs, especially in data, I’m not too concerned. While AI might take over some roles in certain industries, companies are generally slow to adopt new technologies. There are policies, regulations, and a lot of scepticism that can hold them back. So, most data analyst roles are safe, and there will be plenty of data jobs around for a long time. I wouldn’t stress about AI stealing all the data jobs anytime soon.

This has been great, thank you again, Dan. Where could people see more of your work, and connect with you?

You can reach out to me on LinkedIn and good luck in your data careers.