After spending over a year recruiting Analysts, Engineers, and Governance Managers in the world of data, I felt fairly confident that I had a good sense of what it takes to be a Data Analytics Consultant.
Now, stepping into the role myself as a fresh face and starting Week 1 at The Information Lab, I’m already building on what I thought I knew. What I write below will seem obvious to people already embedded in data, but I’m quite excited to note my current understanding and how it changes as I go through the next four months of training.
What Does a Data Analytics Consultant Do?
I see the role as three main parts: 1) working with data (the technical stuff), 2) helping people make smarter decisions, and 3) applying consulting skills. When breaking that down, there are some key elements that I commonly hear- elements that make a great Data Analytics Consultant:
1) Working With Data
- Combining data from different sources to create usable datasets
- Cleaning and prepping messy data so it makes sense
- Moving data between systems and tweaking it along the way (ETL/ELT)
- Keeping databases organised and easy to access
- Analysing and creating visuals that make data easy to understand and act on
2) Helping People Make Smarter Decisions
Solving problems by spotting inefficiencies and fixing them
- Streamlining operations and processes to make things run smoother
- Using data to guide strategic decisions and find opportunities
- Looking ahead by forecasting trends and aligning them with goals
3) The Consulting Side
- Figuring out what stakeholders really need and why it matters
- Building trust by showing you know your stuff
- Communicating insights in a way that makes sense and can actually be used
- Collaborating with stakeholders to guide their decisions effectively
Tools of the Trade
Some of the key tools that I expect myself to be learning:
- Tableau: great for building dashboards and making data visual
- Alteryx Designer: for cleaning, prepping, and automating data workflows
- Power BI: another tool for visualisation and reporting
- SQL: handy for digging into databases and working with large datasets
- Snowflake and AWS: for managing and storing data in the cloud
I’ve also heard buzzwords like Data Engineering being thrown around, and I’m curious to learn more on those—but one step at a time. For now, let’s focus on nailing the basics.
Looking Ahead
These are just my first impressions. There’s a lot to learn, and I know my understanding will grow as I go deeper into training and start working on projects. I’ll check back in a month to see how far I’ve come!