10 things I wished I knew when I began my data career (Part I)

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Summary (TL;DR)

This post is inspired by a final presentation for an in-person Data Analyst class I taught. The class was aimed at adults transitioning to a new career in Data Analysis and Data Science.

There are so many classes that teach the technical skills to become an analyst, but there is very little content to help you grow your career in this field.

These are the top 10 tips I wish someone told me when I began my career as a Data Analyst (in no particular order):

  1. Most of your work involves cleaning up after someone else.
  2. People will ask for reports that may not help answer their actual question.
  3. Offer your technical skills to everyone, not just your manager.
  4. Always look out for new opportunities.
  5. Iterate and share your analysis. Be agile.
  6. Data is power. Watch out for people who want to use it to manipulate the narrative.
  7. Create decks that a 10-year old can understand. Keep the details in the appendix.
  8. Know the context of the business as much as possible.
  9. Deliver bad/critical news to stakeholders in private first.
  10. During your presentations, guide people to reach the same conclusions you have.

Most of your work involves cleaning up after someone else

As an Associate or Junior Analyst, most of your time involves cleaning data and running reports.

In your data career, you will always have to deal with dirty data that some other department or group will give you; this is inevitable.

However, always look for opportunities within your organization to enforce good data collection practices. Ask yourself these questions whenever you’re cleaning up data:

  1. Why is the data in this dirty format to begin with?
  2. Is there any possible automation we can create to reduce the amount of data cleanup further down the data pipeline?
  3. What do other data analysts in the organization do to clean up this data?

Understanding these data cleaning efficiencies can help make your entire organization more efficient, while simultaneously accelerating your career.

People will ask for reports that may not help answer their actual question

As an Associate Analyst, I did whatever people asked me to do; this typically involves creating ad-hoc reports.

After a year of creating ad-hoc reports, I realized the process was inefficient because people, who are not intimate with the data, will often request data they think may help answer their question, but in the end,does not.

People would then come back to me and request different data. Their question was finally answered after rounds of ad-hoc reporting.

This process was insanely inefficient and I could have saved so much time if I had asked them this simple question:

Interesting, may I know the context of this data request to better assist you?

As the data expert, if you know the context, you can quickly determine the exact report you need to answer their question.

Furthermore, you will suddenly become much more valuable to your organization as a data steward who can advise people.

Be the data steward by always asking for more context from the data request. You will bring more value to your organization and accelerate your career.

Offer your technical skills to everyone, not just your manager

I wish I had known about this tip earlier in my career as a Data Analyst.

One of the quickest ways to expose yourself to the greater organization and to grow your influence as a data steward is to volunteer your skills to other people in your organization.

As you start off in your career, know that you are one of the few people who can make magic happen with data and technology for everyone else in the organization.

Go out and make allies within your organization by offering your technical skills to their projects.

You will build your influence by exposing yourself to the right people, therefore increasing your chances of working on high-impact projects, which can propel your career.

Additional Tip

If you happen to work in a company that has Executive Administrators, they can be your best friends because they can expose you to the key players in the organization.

They know what is happening because they manage the executives’ schedules and see the agendas and decks that are being passed around.

Find opportunities to help these administrators and you will build great allies.

Always look out for new opportunities

This tip is similar to the previous tip.

As you build your reputation and data stewardship, you will have built enough trust within the organization for people to offer you new opportunities.

Never turn down the opportunity to learn something new or work with other people from different functions. I guarantee you will gain new skills and influence in your organization.

If you feel apprehensive or nervous about stepping into a project you may not feel qualified for, that is normal. The best way to be competent at something is to just do it and learn as you go.

It’s okay that you don’t know everything — own it and focus on figuring it out. Ask your colleagues and boss for direction if you need it.

Make sure to tell them what you’re doing is a stretch-goal and they will do everything they can to direct and support you.

As a knowledge worker, you’re getting paid to figure things out; not to know everything.

Iterate and share your analysis. Be agile.

I’ve wasted so much time early in my career when I started because I did not bother to consult with my colleagues throughout my analysis.

Knowledge work should never be in isolation — this includes data analysis as well. Be proactive and share your work as much as possible with your manager and colleagues during your analysis.

Data analysis is about discovery and iteration; share your discoveries with your team often enough so they can see whether you’re working in the right direction.

Be agile and ready to reorient your analysis whenever you receive rapid feedback from your team.

Doing so will allow you to save time while involving your team in the process, enabling you to receive multiple perspectives on a problem, which will lead to better solutions.

Continue on to Part 2 here.

Written on December 28, 2019