A day in the life of a Data Lab Analyst

Data Analyst, Leanne Finn, shares why data should be part of your L&D day-to-day.

Leanne Finn Learning Insights Analyst

A day in the life of a Data Lab Analyst

“Why would I need to deal with data as an L&D professional?” Data isn’t about establishing a team of data science excellence… It’s a cultural thing. And it’s not limited to L&D. In 2021, everyone in an organization needs an understanding of basic data literacy; think, speak, read, and write data. That’s why The Content Club has a load of learning resources all about data to get you and your teams started.

You may not be a number cruncher or predictive modeler. But if you have the skills to ask the right questions, stay curious and be the facilitator who brings the experts together, then you’re already on top of the game and your value goes beyond learning content and training.

So, this article is all about those experts, and yep you guessed it, that’s me! An analyst in the Data Lab whose job it is to work with clients, just like you, to strategise, analyse and tell human stories from data. Wondering what that looks like for you? Well, here’s how a typical data analyst day goes…

9.00am: My day always starts with a steaming hot mug of coffee (or maybe three) and a quick catch-up with the rest of the team. We start the day off together in the Data Lab and take the first 15 minutes of our day to run through current projects, task blockers, and any ideas we have.

We’ve got an exciting day ahead today as we’re hosting a Data Dash workshop this afternoon; a series of mind mapping sessions where we look at the value, proposition, and design of all employee suggested data goals and refine them down until we have a clear strategy of measuring a data goal in the end.

The team has also been briefed on a new data request that has just landed, for an exciting award submission for ‘Best Use of Social and Collaborative Learning Technologies.

9.15am - We plan to work on the awards data first and each split out a couple of tickets to action each. The headphones go on and I start scripting. Luckily, the client wants to report on very similar insights that we are already exploring (The teams and department's most popular pieces of content and how their learners socially interact with them). I get into a good flow state, jot down a couple of ideas that spring to mind whilst working on the project, and get my work ready to be reviewed (always important to have someone else review any work you are doing!) by 9.50am.

9.55am - PING! I’ve got a pop-up on my google chat. Support has sent a query regarding the REST APIs and need me to help a client that is having trouble mapping fields being returned back from the REST API endpoint. I investigate the fields the client raised and test to make sure there are no issues when I pull them. I then send a quick message back to support with some supporting info to help.

10.15am: An email pops into my inbox and I jump on a quick call to discuss a reporting question with a Data Lab client. We always love speaking to our customers in person, it’s quicker, easier and quite frankly… We love a chat! After clarifying some of the field name references reported on, we’re happy and back on track. We’re always available whenever a client calls!

10.35am: I’ve been working on a good project this week which has been all about pulling custom results on user engagement and completions on learning pathways. I’m about 90% complete on this request now, so it won’t be long until this is finished off and with my team to QA.

12.35pm: LUNCH!! Little information is probably needed here but I managed to squeeze in a quick run, shower, scoff a sandwich and top up on coffee. Important. 

13.35pm: I jump on a call with my teammates before the Data Lab workshop this afternoon to set up the board (we have digital sticky notes aplenty) and discuss notes from the client’s pre-work document. This is a brief we provide and tailor for each client to share their current needs, concerns, objectives and requirements. Then we break the workshops up into a series of sessions based on this. 

Today our first session will focus on mind-mapping and exploring our data goals as individuals, as well as a business; for example this could be highlighting personal learning goals, barriers to learning, wishlists and collating the different areas of interest that the team have highlighted. Session two will focus on refining these goals down collectively as a team. The best part is that the team then has an anonymised vote of the million-dollar metric to vote on at the end. 

No client session is the same, so we are excited to see what we will uncover with the clients today and what ideas everyone will bring to the table. There’s a good range of different users of the platform and company stakeholders from the c-suite to commercial directors, so it should be a good one.

14.00pm: PING! The first friendly but slightly apprehensive face appears on screen, welcomed by lots of other smiley faces. We ease the nerves of the client and explain how the session will run. We set the clients off with a simple task (to make sure everyone is happy with the tools and that the users are anonymised) and start collaborating.

The first task we set is mind mapping. Everyone is asked to add as many sticky notes about their role, organisation, L&D goals, and how data impacts them on a daily basis. The clients quickly get to work and our Data Lab team organises the results into similar categories.

After that is done, we focus on the relationships between what has been listed and how this impacts them within THRIVE, categorise these results again and refine them into specific goals.

By the time we end the call, we have a chatty, smiling, group of data lovers! It’s been insightful as some topics covered were unknown by other members of the teams so it has helped to highlight other useful areas to address. We sign off the call and I quickly write up all my notes from the session.

17.00pm - I wrap the day up with planning out what I am going to do tomorrow! I check my kanban board, make a note of any tickets to action and check my emails again for the end of the day. 

And that’s a day in the life of someone working to make you look good and even more importantly make L&D indispensable through data that tells human stories!

Want to get started? Take a look at the Data Lab and check out our free planning resource, helping you ask the right questions you need to map, action and measure your learning data strategy in six simple steps.

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