Doni Surya Putra— RevoU Instructor | Head of Data at KitaBisa

Doni's Profile

Doni Surya Putra, or usually goes by Doni, is an experienced Data Analyst with six years of experience in the data field. Currently, Doni is working as the Head of Data at KitaBisa (a donation and fundraising site for initiatives, campaigns, and social programs) as the Head of Data.

He enjoys working with numbers and loves all Big Data related topics such as Python, SQL, and data warehouse.

His responsibilities at KitaBisa include:

• Managing the data engineer and BI analyst team.
• Supporting the management with analysis.
• Initiating data project and ensuring that the project is a success.

Doni started his career as a Data Analyst back in 2016. He began at Kudo (now known as GrabKiosk- Kudo is a platform that provides opportunities to millions of Indonesian to grow their current business through technology) as a Lead Data Analyst for almost two years.

After that, he got promoted to Business Intelligence Manager for more than a year.

If he has to look back to his first moment in his career, he didn't have anyone that could give him a complete view of the data industry. He mostly did trial and error for everything when he started, which took a lot of time and energy.

Through his experience, Doni believes that it is critical to have someone who can guide you regarding your career's most efficient and effective path.

His experience working with data has given him many valuable insights that he would like to share as Revou’s Data Analytics instructor.

Join the program to learn more from Doni!

More on Doni

Questions & Answers

#1 Can you explain to us what your typical day looks like in your current role at KitaBisa?

My day consist of 3 kinds of work:

• Meeting
• Technical work
• Strategic thinking time

There are some days that meetings take a whole time, but there are some days that I focus on the technical work.

The meeting consisted of a lot of alignment and discussion on how we solve the problem in KitaBisa using data.

Most technical work is quality checking, and the rest is doing some analysis on high-level metrics.

And at the end of the day, I booked some time to think about the data team's strategic initiative and solve some non-technical problems.

#2 Biggest myths and misconceptions about Data Analytics?

There are two biggest myths in data analytics that I know of:

• Data analytic = decision
• Data analytic = ML (Machine Learning)

The first one is that if we are working in data analytics, we are responsible for making the business/product decision.

That is so wrong; we, as data analysts, have the job of giving more inspiration to our stakeholders. We are not going to decide anything, and it is up to them if our work is inspiring enough for them to decide.

The second one is that many people think that because we work in data analytics, we use an advanced machine learning or statistics model to help the business.

That expectation is what makes a lot of new aspiring data analysts frustrated. The truth is, as long as we as data analysts can give inspiration to our stakeholders, the tools or way we do things is not always using cool stuff like that.

Sometimes we can use a combination of Excel and communication to become great data analysts.

#3 Your proudest professional achievement at KitaBisa?

My proudest achievement is when our data product is rolled out to production, which results from the effort of the data team, the product team, and the engineering team.

Working on a product that we believe can help our user is the best thing to do.

#4 Things you wish you could have learned earlier in your career?

When I started my career, the worst thing is I didn't have anyone that could give me a complete view of the data industry. I mostly did trial and error for everything when I started, which took a lot of time and energy. So it is critical to have someone that can guide you on the most efficient and effective path of your career.

#5 Books / Online resources you follow to remain on top of the most important industry trends and keep improving in your field (blogs, podcasts, newsletters)?

For analytics:

• Analytic Engineering Roundup
• Cassie Kozyrkov

For life:

• The Subtle Art of Not Giving a F*** by Mark Manson
• Principles by Ray Dalio

#6 Your tips for someone who is interested in starting his career in Data Analytics (aside from applying to RevoU :)?

Don’t be overwhelmed with data analytics knowledge. It is a wide job. So start small, try everything, and focus on fundamentals.

The great senior analysts that I know have great fundamentals on data analysis and focusing on polishing that.

#7 The fundamental skills a Data Analyst is expected to have?

There are two fundamentals that I think are needed in every Data Analyst:

• Communication
• Critical thinking

It won't be very worthy if you have a great insight that you already gathered from the data, but you can't communicate it to the right person.

Also, there is a lot of information that you can get from the data. You can see it from many different points of view, so it is essential to think critically about everything as a data analyst.

#8 What distinguishes a good Data Analyst from a great one?

Can they give inspiration to the stakeholder? That is the one question that distinguishes the bad, good, and great data analysts.

No matter what tools or methods analysts can use, the job is done as long as it can inspire stakeholders.

Learn from Doni and other great instructors by applying to RevoU Data Analytics program

Looking to kickstart your career in Data Analytics but don’t know where to start? Apply to RevoU 13-weeks Data Analytics Program

How RevoU works:

✓ Live daily interactive online classes for 13 weeks (7–9pm WIB)

✓ Learn from the best instructors in the industry (such as Doni)

✓ Personalised career coaching with 1:1 mentorship sessions

✓ If you are looking for a job and don’t get one at the end of the Program, the entire course is FREE