top of page
Search

Google Data Analytics Professional Certificate | 2 Ask Questions to Make Data-Driven Decisions

Writer: goldenstateservicesj
goldenstateservicesj
Aug 4, 2023
5 min read

tl:dr

  • I’m currently completing the Google Data Analytics Professional Certificate in an effort to sidestep careers to become a Data Analyst

  • I just wanted to reflect on some specific things that surprised me, made me think or something I can relate back to volleyball analysis.  They include:

    • Asking effective questions to define the problem and confirm stakeholder’s expectations is harder than I thought.  Coming up with SMART questions is not easy.  

    • Knowing the difference between deliverables, milestones and timeline in a scope of work (SOW) was confusing at first.  Isn’t the timeline just a detailed version of milestones and isn’t deliverables just the things you do on a timeline?  After looking at a strong example of a SOW, I now have a clearer idea of what they are. 

    • If I am going to be a data analyst, I’m probably going to be a part of a bigger team.  This is the opposite of being a volleyball analyst, where I am usually on my own doing everything.  This is something I will need to adjust to.  

Reflecting on Course 2 | Ask Questions to Make Data-Driven Decisions

I am currently completing the Google Data Analytics Professional Certificate in an effort to sidestep careers and become a Data Analyst.  It has eight courses and I wanted to reflect on each before moving on to the next course.  See the links below as I update them.  

This blog post isn’t a full recap of the course.  I just wanted to reflect on specific things that surprised me, made me think or something I can relate back to volleyball analysis.

Asking Effective Questions is Harder than I Thought

The first stage of the data analysis process is to ask questions to define the problem and confirm stakeholder expectations.  It’s a simple concept but I learnt there’s so much more to it.  So I learnt we need to ask SMART questions, as described below.

Specific questions are simple, significant, and focused on a single topic or a few closely related ideas. Measurable questions can be quantified and assessed.Action-oriented questions encourage change.  Relevant questions matter, are important, and have significance to the problem you’re trying to solve.Time-bound questions specify the time to be studied.

So rather than asking: are kids getting enough exercise these days?  You should ask what percentage of kids achieve the recommended 60 minutes of physical activity at least five days a week?  This is because the first question is vague but the second question is specific and measurable.  

Again, this sounds simple enough but when I was tasked to think of my own SMART questions to ask a person who uses data, it was harder than I thought.  I spent a good deal of time on this task of figuring out what questions I wanted to ask, what the person may want to know, and modifying the question so it was a SMART question.  It was a great task because I learn better by doing.  It reminded me of the time when I first learnt to use DataVolley and you really needed to clock up the hours to get better at it.  

Knowing the Difference Between Deliverables, Milestones and Timeline in a Scope of Work Was Confusing at First

I learnt that a scope of work (SOW) is an agreed-upon outline of the work you’re going to perform on a project.  We do it because it keeps everyone involved (you, your team, stakeholders, sponsors, and contributors) on the same page.  I also learnt that SOWs normally have four foundational pieces of content:four foundational pieces of content:

  • Deliverables – What work is being done, and what things are being created as a result of this project?

  • Milestones – What are the major milestones for progress in your project?

  • Timeline – The timeline is a way of mapping expectations for how long each step of the process should take.

  • Reports – Good SOWs also set boundaries for how and when you’ll give status updates to stakeholders.

I’m not going to lie but this confused me.  Isn’t the timeline just a detailed version of milestones?  And isn’t deliverables just the things you do on a timeline? 

When I had to write up my own SOW using a template, I didn’t know what to put for deliverables, milestones and timelines.  In the end, I just wrote down a timeline of tasks needed to complete the data analysis project (my idea was to gather customer feedback for a business).  The course then gave a strong example of a SOW.  It was great because it did make me understand better what was meant by these terms.  Deliverables, to me, are things that the stakeholders can take home with them.  These could be my recommendations for actions, charts, reports, etc.  These aren’t things like analysing the data as they are not going to take that home with them.  The milestones are pretty much the bullet points of what you are going to do, e.g. create a feedback form, collect data, analyse data, share data.  The timeline still contains the mini-tasks, with deadlines, needed to complete each milestone.  With this knowledge, I actually went back and rewrote the whole SOW using the strong example as a basis.  

You’re a Part of a Bigger Team

A good chunk of the course was working effectively with stakeholders, email etiquette, in-person/ virtual meeting do’s and don’ts, and conflict resolution.  I did think it was a bit weird that I was learning about this as it has little to do with data analysis.  It’s more of a general workplace thing.  However, this got me realising that if I am going to be a data analyst, I am probably going to be a part of a bigger team.  There’s the executive team (e.g. VP, Chief Marketing Officer), the customer-facing team (e.g. customer service people), and the data science team (e.g. other data analysts, data scientists, and data engineers).  The course is run by Google so I imagine the course presenter, an analyst at Google, was one amongst thousands at Google.

This is the opposite of being a volleyball analyst, as I am usually on my own collecting and analysing the data.  I do work with coaches and players but that’s it.  There’s no executive team or customer-facing team.  The data science team is just me.  This is something I will need to adjust to.  

Final Thoughts

This course was harder than the first course.  I wrote 17 pages of notes compared to 10 pages in the first course.  I liked the two tasks of thinking of my own SMART questions and writing up my own SOW.  These are the types of tasks that help the knowledge stick (learn by doing).

Charting My Pages of Notes

Since this is a data analytics course, I thought I should chart something.  I went with the number of pages of notes I typed up.  It’s a proxy indication of things I didn’t know.  

 
 
 

Comments

bottom of page