What is a Data Analyst? Interview with Analytics Manager at Cho Tot

Data Analysts give a voice to data. They conduct deep dive analytics to provide insights for corporate decisions and plans.

In this interview with Ms. Nguyen Thi Thuy Hanh, Analytics Manager at Cho Tot, we explore:

• What is a Data Analyst and their specific tasks.
• The similarities and differences between a Business Analyst and a Data Analyst.
• 3 biggest lessons learnt.

Read the Vietnamese version here.

Biography: After graduating with a masters degree in Economics from France in 2008, Ms. Nguyen returned to Vietnam and has worked at Techcombank since 2009.

After five years, she was promoted from Analytics Executive to Associate Manager of the Analytics and Customer Intelligence Centre in the Retail Department.

In 2014, she became the Business Analytics Lead at Military Bank (MB). From 2015 to now, Hanh has worked as the Analytics Manager at Cho Tot.

Data Analytics sounds complicated and dry. Why did you choose this career path?

There is an old Vietnamese saying, “it’s not you who chooses your job, but the job that chooses you.” I think this is so true for me (smile).

I believe the luckiest moment of my life is when I started my very first job in the Data sector right after graduation. And the longer I am at it, the more I think it was the best path for my career.

Working in the Analytics area, for me, feels like you are doing your final exam. And you have to find the answers no matter the cost. Though, sometimes, time runs out before you can find the right answer. (smile).

Needless to say, your findings and recommendations are based on data, and might have a big impact, positive or negative, depending on the accuracy of the work you’ve done.

For example, a business wants to increase their sales revenue and needs analytics to inform their approach.

After analyzing the data, you find that they could increase their revenue by cutting certain low performance salesmen, and you make this recommendation to the business.

However, this kind of recommendation needs to be highly accurate as the effect is huge, you might see your heart race until the revenue reports catch up with your forecasts.

This kind of challenge makes me excited to work in Analytics.

In addition, data is becoming more and more important to the world. We can see broad shifts in focus to Artificial Intelligence (AI). And the rapid growth in this area might be good motivation to consider moving your career path in this direction.

Do you think that Data Analyst and Business Analyst are the same role?

Clearly Data Analyst (DA) and Business Analyst (BA) aren’t the same job.

If you worked at a large company, such as a bank, you would see a lot of BA in the IT department, where they working 100% as a BA – a bridge between business users and developers. Their job is “translating” the language of business users to coders, and vice versa.

A DA is someone who builds reporting systems, finding valuable insights from data to give the suitable recommendations and solutions to businesses. A DA is like a painter with data as their canvas.

Could you explain more about similarities/differences between these two roles?

1. Differences:

When business people come to a BA, mostly they already know what they want. They only need the BA to interpret and refine their needs in a way the coders will understand.

For example, when I was working as a BA for MB, my main responsibility was to explain to the project team all the measurements, dimensions and data units, or the calculation of data aggregations from a fixed set of reports. (Though, sometimes, I also improve the reports if needed).

In contrast, business users often come to a DA with questions, or to confirm an intuition. The DA will use data to answer these, or to give insights and recommendations which businesses can use to plan their future strategy.

It is also common that DA actively comes to business users to provide valuable insights that they have found during their work.

As a DA, I have to be responsible for designing a reporting system and deep dive analysis for daily operations, and the strategy planning work of business users.

2. Similarities:

Both of BA and DA interact with, and need to understand the business and data systems.

In fact, there is a lot of blurred lines between these two roles, especially in start-up environments, where you could find yourself working as a BA and a DA at the same time every day.

For example, both of BA and DA usually have to face this type of question: “The revenue of my team seems to have decreased recently, how can I monitor it?”, or “I want to check sales performance of each member of my team. Could I have a daily report on our system to do that?”

In these cases, the BA and DA will have the same solution/recommendation for business users.

Is Analytics Manager the next step for a Data Analyst in their career path?

Depending on the market or the industry you are working in, there are lots of different jobs in data area.

In Vietnam, to my knowledge, most people working in data field are working as a Data Engineer, Data Architect, Database Administrator, Data Analyst or Business Analyst.

But in the USA, you could find much more varied roles such as Data Visualization Engineer, Statistician, Data Scientist, and so on.

And, most Data Analysts whom I have met in Vietnam seem to want to become a Data Scientist. Perhaps the reason is that “Data Scientist” is a trendy title now, and they are promoted as rock-stars in this field, thanks to the media (laugh).

But, personally, I think all jobs are equally important.

After working with some Data Scientist colleagues of mine in Vietnam and outside the country, I tend to think, more or less, it is the same as the work performed by people whom we call “Data Analysts”. The biggest difference is just the percentage of time contributing to different tasks.

DA usually spend more time in deep dive analysis and predictive models.

Also, from my point of view, I do not think Analytics Manager is the next step in the career ladder, which all DA should aim to.  As with all other managing positions, when become an Analytics Manager, it means that you are now required to manage and give direction to the team.  You still need to understand the area to give direction, but it requires a skill set you might not have developed before.

In my specific case, I took this managing position because maybe I am not as good as my colleagues at improving my technical skills and foundation. (laugh)

What are main responsibilities of Analytics Manager?

• Provide actionable and strategic insight for all decision makers.
• Ensure the reporting and data analyst system running with high efficiency and accuracy.

A normal working day of Analytics Manager?

A normal working day for me starts with checking:

• Daily stats, to make sure the system providing information on performance to business users (management board, operation managers and so on) is exact and on time.
• Performance analysis, to see if there is any sudden change which may impact to the business.
• KPI forecast, to make sure we will achieve the month’s target.

After that, I will continue with some reports and analysis projects, and support data if needed.

After work, apart from personal agenda, I will try to read and learn about data, visualization, code, artificial intelligence and the technology industry.

Is there anything people often misunderstand about Data Analysts?

People outside the field might not really understand what we, as DA, are working on, so maybe they doubt we are working at all (laugh).

For those who are DA-wannabe, or who do some serious research on the job, they seem to expect a super exciting job with most of their time spent on complex predictive or clustering models, not to mention machine learning and AI.

However, it is more likely that DA will have to spend a lot of time working with raw data.

For example, mostly their time will go with collecting and cleaning data, defining the data sets, querying and aggregating the raw data into a form that can be built upon.

It seems a bit sad, right? But, if you really love working as a DA, you will eventually fall in love with it, I hope. Because, this type of work will help you to understand the whole data system, and how the data is structured. Or, if you dive deep in it, you might understand why the data is recorded in this format rather than the already aggregated format.

What are your advantages/disadvantages when moving from the banking industry to the online industry?

Moving from an old industry such as banking to a rapidly growing industry, as well as from an old corporate company to a start-up-at-heart company, to be honest, was quite challenging for me.

Working in a corporate environment is like working in a manufacturing chain, with lots of process and policies, which are built up time to time.

Working for a start-up company, you have to get used to the daily changes to the work  process and requirements.

In banking, data is quite clean and accurate as the nature of most products is fixed and the integrity of the data has always been vital to their function.

But with eCommerce, we try to understand the customers as much as we can, so each activity of users while browsing will need to be recorded. And in each stage, we focus on a different activity by users to improve their experience, so we might need to get familiar with a new set of data.

However, the nature of the work is quite similar in every company. Thanks to that, it’s easier for me to catch up with the new job after I understand the industry and business.

And I have to say that all the Data Engineers in our team are really focused on building up a standard to fit the user’s data system needs. It really helps to make my work more efficient.

The biggest mistake you have made so far?

I remember one of my managers once said “The only one who never makes mistakes, is one who never works”. Oh well, as I am a hard-working person (laugh), I did make a mistake, a big one.

When I was working as DA and BA at the same time for a quite big project, after completing the preparation work for data/methodology/calculation tools, at the very last stage, we needed one tool to upload customer’s information to our website. At that point, we had about 2 millions customers. However, during the discussion, we didn’t talk about the upload.

On the day we had to upload the information, I found out that the upload tool which IT built for us had a maximum capacity of 20k records per upload. Meaning we would have to upload 100 times, taking 1 hour each time, not including the time to transfer from data server to the upload tool.

In the end, I had no choice but ask my manager for help. And as we couldn’t delay deploying the project to our customers, all the team members had to suspend all their work to do manual uploading using that simple upload tool.

After that, the IT team had to upgrade that tool to a more advanced version, which could perform all that work in around 20 minutes.

My mistake slowed down the whole team’s work and we had to delay some other lower priority deadlines.

The lesson learnt?  Once you are a DA, you have to pay attention to every single detail.

Who inspires you in this career path?

Mr Muthukrishnan, Head of Retail Bank Finance Analytics of HSBC in India, who was my first mentor, and who always inspired me to work in Analytics.

Mr Muthukrishnan is a leader with great vision, who always has a solution/recommendation for every business situation.

Additionally, at that point of time, when all the team members were new, with a strong economics background but no knowledge in coding, he was the one who encouraged and gave us space to learn SQL/SAS, and the one who proved that nothing is impossible if we kept trying.

And I mentioned above, he’s the one who said “The only one who never makes mistakes, is one who never works”, which is, to me, a great encouragement to try new things, improve and become more innovative.

After nearly 10 years in this field, what are the 3 biggest lessons you learnt?

1. Be data driven

I read this book called Every Data, and I recommend it to all who are interested in data. In the book, the author demonstrates how we consume a huge amount of data everyday. For that, we should be data driven in every decision we make.

For example, if your boyfriend told you bad things about each of his five exes, a simple predictive model will tell you that, if you ever break up with him, you will be the 6th one he says bad things about.

However, it seems a lots of women consider themselves the outline-rs (laugh).

2. Be simple

As somebody working with data, I love complex and fancy things (smile).

However, as the old mantra goes, it’s really “the simpler, the better”.

For example, more than once, I approached my manager with a complex presentation which included tons of charts and correlational models. The one requirement he always gives me, is make it simple to the point that anyone can understand.

The most important thing is to deliver the message to the audience, not to show off your “behind the scene” work, or your advanced statistics or data analysis technique.

3. Don’t stop improving

The field and the industry are moving forwards constantly, which requires us to adapt. After surviving  a long time in this industry, I know that this is the most important lesson in my life, and one that I always have to follow.

Do you have any advice to young people who want to pursuit a Data Analyst career?

The Analytics jobs needs a lots of your patience and time, and not every finding, project or research that you do will have big impact on the business, please please please be patient and have lots of love in what you are doing. (smile)

If you have any questions, or want to discuss more about Analytics, please feel free to ping me at skype hanhntt313 or email hanhntt313@gmail.com. It’s my pleasure to share and learn from each other.