We’ve asked our #DataImpactFellows to share a day in their life.
Here Anne Alarilla shares what a typical day for her is like.
I work as a data and research analyst in the Cancer Intelligence Team at Cancer Research UK. To prepare for this entry, I started looking at other “a day in the life of…” blog posts and quickly learned that there is no one typical day as a data or a research analyst.
Like the posts I read, my day to day really varies depending on the project that I am working on. Sometimes there are slow days where only you work on your own project and therefore your day is pretty much dictated by you.
But other times your project might include working with other people and therefore how you manage your time might be dictated by shared deadlines. There are also days where you must do both.
Generally, my day goes like this…
I arrive for work and make coffee and breakfast (always porridge for those interested). I check my emails to see if any urgent requests have come in and create my to do list for the week.
I usually have my weekly catch up with my manager where we talk through the tasks I finished last week, general updates on my projects and/or any deadlines and, of course, life in general.
On other occasions, I might be in another meeting with colleagues who are involved with my projects. These are times where I might have to manage expectations, but also an opportunity for me to discuss ideas and engage in problem solving.
After my catch up/meetings, I end up adding more tasks to my list but I’m also able to prioritise which ones needs to get done first.
I know you’re all excited to know…OneNote is my main organisation tool right now.
For the lunch break, I sometimes go to the gym.
Yes, I am very lucky to be able to go to the gym at lunch.
But other times I have lunch with colleagues where we talk about Disney Movies or who has the best lunch or weekend plans.
Some of the things I could be doing in my afternoon include:
- Data sourcing for an enquiry or for my project
- Cleaning data using Alteryx for my analysis or for database upload, so our team can access it quicker
- Checking other analysts’ work, this could be analysis or data cleaning work flows
- Analysing my data in R for my project
- Writing the report for my project where I outline what we did, how we did, and we found from it
- Consulting the principal statistician or other analysts about any troubleshooting issues I might have for my project or any one-off analytical tasks.
- Presenting my project either internally or externally
- Reading journal articles for journal clubs
Most of this is not going to make much sense unless you’re in analysis but basically, it really varies!
For those interested here is an in-depth summary of the transferrable skills I use day to day:
This is my favourite part of my role. For me, this is usually quantitative data analysis and can vary from simple descriptive of counts, proportions and percentage changes. Or to more extravagant inferential analysis of linear regressions to age period cohort models.
You see how I added some jargon in there to make this post appear more intelligent?
In simple terms this is where we look at patterns in the data which might be because of different factors and we try and find out which one is the most important.
In some analysis roles this can also be qualitative data analysis where you analyse interviews, transcripts from focus groups, a free text answer from a questionnaire and the list really is endless.
Part of the reason why I love analysis is because it enables us to answer questions with a higher degree of certainty. But within analysis there are also challenges along the way that needs to be solved. For example, not knowing where to obtain the data you need.
This is where UK Data Service comes to my rescue
Or if you obtain your data in a different format and it needs to be manipulated and prepped before analysis.
Another type of problem solving that I come across is when we are given a research question by another team and we have to find a way to answer it but using an evidence-based approach. So, problem solving is a constant factor in my role, but the magnitude of the challenge differs day to day.
This is probably similar in other roles but as a data and research analyst, I am constantly learning new methodologies, new analytical programmes to use, new data sources, about new research with real world impact, new teams to work with, new ways to create impact and disseminate our findings and the list goes on.
If you’re open to it, there is a fair amount of knowledge to gain.
I think this is a crucial part of my role, whether it be managing the expectations of my manager, other analysts, internal members of our organisation or members of the public.
Managing expectations includes communicating with your manager or other analysts about competing deadlines or possible delays in outputs which may be out of your control. It is important they are aware of possible interruptions that might end up disturbing their work plan.
Also analysing impactful data means that interpretations of the findings should managed accordingly. Therefore, sometimes we have to manage the way our research is communicated or interpreted internally and externally so that any conclusions made are accurate.
We are here to inform not persuade.
Finally, and probably most importantly, I have to manage my own expectations that the code or workflow I am running for my analysis, for example, will run smoothly and stay calm when it doesn’t.
Anne primarily explores smoking, overweight and obesity prevalence in the UK and individual nations of the UK with a particular focus on prevalence trends. Anne’s research uses data from Annual Population Survey, Opinions and Lifestyle Survey, Health Survey for England, Scottish Health Survey, Health Survey Northern Ireland, National Survey for Wales and Welsh Health Survey. She previously presented her work on “Smoking Prevalence Trends by Occupational Groups in England” at the UK Data Service Health Studies User Conference 2018.
Anne has created a tool to accumulate smoking, overweight and obesity prevalence statistics from individual nations’ health surveys in one central place.
Follow Anne on Twitter: @alarillaanne