The proactive data analyst's handbook

Your business stakeholder wants you to be proactive. Your manager can't stop using this word during performance evaluations. You want to get promoted sooner.

Why did I write this handbook?

It does take much for you to go from good to great. Being a sound technical or competent data analyst will not get you over the line. At the end of the day, it's about your intent, how you add value and impact beyond your team.

I'm pretty sure you may have come across this post.

These things don't need any expertise in data & analytics.

I'm going one step deeper in this handbook - what you can do to stand out in each of these different scenarios at work.

Who is it meant for?

While the title it says it's for data analysts, there is a lot of it that can be used across competencies.

Therefore, if you are an early-career professional or even a first time manager, you might find something useful here.

Onboarding

1. Document your understanding of the business model. Build this for yourself - create a flowchart, gather the key documentation and the dashboards. Treat this as a running document until you feel you have everything covered.
2. Create a flowchart of the data stack, if none exists.
3. Offer to be a new joiner's onboarding buddy. By expediting the onboarding process of the team member, everyone wins.
4. If there is no onboarding documentation, volunteer to build one for the team.

Managing up

1. Meetings:
  Always be prepared - if possible share the agenda ahead.
  If your manager cancels the meeting (not reschedule, mind you), then take the initiative to propose a new time. Signal the intent that this is a important meeting for you.
  Check with your manager if you can connect with the skip level manager.
2. Updates:
3. Feedback:

1. Review it to augment your own learning. You can use the See / think / wonder framework
  1. I see that [the following are the main observations / they have used X approach]
  2. I think [I can add to that / the reason is / instead, I think / I disagree that]
  3. I wonder [what the outcome would if another approach was taken / how I can apply this in my context]
2. Share it with a business stakeholder or peer who will find it useful
3. Make a note of the datasets used for your own future reference

When an analysis or case study is shared with you

Don't just consume it - take action!

When you schedule a meeting to explain your analysis

1. Share the content ahead. [How to structure your presentation?]
2. Invite questions on the content ahead of the meeting - so you can run a more efficient meeting
3. Use the questions as inputs to build an FAQ section for your analysis

1. Review it to augment your own learning. You can use the See / think / wonder framework
  1. I see that [the following are the main observations / they have used X approach]
  2. I think [I can add to that / the reason is / instead, I think / I disagree that]
  3. I wonder [what the outcome would if another approach was taken / how I can apply this in my context]
2. Share it with a business stakeholder or peer who will find it useful
3. Make a note of the datasets used for your own future reference

When an analysis or case study is shared with you

Don't just consume it - take action!

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