By Cornellius Yudha Wijaya, Data Scientist | Lecturer | Biologist | Writer | Podcaster.

Photo by bruce mars on Unsplash.

As a Data Scientist, understanding your data is a must. No advanced algorithm or fancy machine learning model could replace someone’s competency in analysing data. It means you should be a Data Literate to be a great data scientist.

Why do you need to understand your data? Businesses deal with changes every day, and the resulting uncertainty, through analytics. The daily changes and uncertainty nature push business to act faster. A successful business would be they who could move quickly enough but not reckless enough. Here, Data Scientist studies the situation, go and find the root cause analysis, assess alternatives, and implement a solution. The quicker business can assemble enough data to do an investigation, the faster they can make a reasonable decision. This is why to become a great data scientist; we need to understand data. It also means you need to be Data Literate.

What is Data Literate? According to Wikipedia, Data Literacy is the ability to read, work, analyse, and argue with data, so Data Literate means you have Data Literacy knowledge or at least act on one. Data Literacy also tells the capability to do a job involving data. There are many ways to measure literacy, of course. Although, here are my signs to see from people if they are Data Literate.

“Data Literacy is the ability to read, work, analyse, and argue with data”

1. You communicate your point well

Do you understand what things are shown to you and how you should communicate it to the others?. One sign that I see for people who are data literate is that they could deliver what their point well. No over-complicated explanation, just merely the critical point.

Albert Einstein has quoted,

If you can’t explain it simply, you don’t understand it well enough. — Albert Einstein

With understanding, I mean people could process the information they get; it does not mean that they need to understand everything at the moment, but at the very least, there is a point they could get from the information. When people don’t understand, they often put too much word in the explanation as a cover. Only when someone understands enough, they could turn what they are though into a simple reason. It is an excellent sign of data-literate if you could communicate your point well.

2. You not easily overwhelmed by information

Data is everywhere, and you could easily drown in it. Just take an example in the pandemic situation right now. We have a lot of information regarding COVID-19, from the disease information, economic suffering from the pandemic, to how to wash your hands. Many people I know are overwhelmed by the news and do not even know which one is true or not.

Information overwhelmed could cause you to ignore many important things and get distracted by the other. While working with the data, we always need to remember what is the business question and what information is essential. People who in their everyday life manage the information well have the talent to become a great Data Scientist.

3. You take action based on data

The importance of Data Literacy is that you could understand the data and take action based on the information. While there is so much information out there, you could process the data well in your head to take any action based on that.

For example, you want to buy a computer from an online store. You see, there are many choices out there, so what you do first is “What kind of Laptop I need?”; You could say it is for gaming purposes, then from there, you try to find on the internet which one is the best gaming laptop but within your budget. How you define best? You could try to set it by many good reviews, so you open many tech websites to see other people’s reviews. You were collecting the review and decided the best based on the data.

The above process is like defining the problem (gaming) and the scope (budget) -> collecting data (reviews) -> take action. You might think it’s a common thing to do for bought based on the review, but many people not even bother with collecting data.

In the end, while it takes domain knowledge and business insight to produce a reasonable action based on the data, if you had tried to incorporate data before taking action, then you are in the right way.

4. You are fast to act but not too fast

Business moves fast; it means the business decision needs to move even quicker. What makes things uncertain is when to act. Acting too soon when we have less data is certainly reckless, but waiting to have a lot of data may be too late. The decision of when to act is what Data Literacy needed. That is why the need to respond to change quickly is an essential mindset for Data Scientist.

Take an example in dating; you could say it’s reckless if you propose marriage to someone you just know for a few days, although waiting too long to make a move could mean that someone could be taken by the other. You might want to collect enough data about the other person to decide if you’re going to take a step further or not. By “enough,” it could be anything; it might be your values or just the physical characteristic. If I make it into a business analogy, you might say the “enough” part is the essential part of your business data that need to be fulfilled before you take action.

5. You are interested in data

While some people are so overwhelmed by the data, some also just ignore it bluntly. They did not care what happens in the world and not even try to catch up with the latest information. I was surprised during quarantine time that my friend does not even know the swab PCR test is needed to confirm if COVID-19 positively contracts someone or not. While I did not want to judge people’s way of life, I am just surprised.

It is as expected for people who are data literate would be interested in data. I did not mean that they need to start mining data everywhere, but, at the very least is like to be kept up-to-date with everything.

Also, some people I know who were data literate are always interested in the art of data visualisation. They interested in the data and continuously pursued how to present the data with the most elegant form. This is because they want the data could be communicated well, and everybody could take valuable information from the data.

6. You argue with data

When you look at the internet comments to look for argument, you could see many of them are mostly baseless comments or just strictly hoax. It is not much different from real-life; in my place, many people would argue only based on their feelings or what they think at the moment while a data literate people would base their argument with data, rather than a baseless object.

In the business environment, Data Scientist is the driver of the Data-Driven business. In a Data-Driven business, many elements need to be convinced so that your business Data-Driven (Yeah, Data-Driven business involved a lot of people who need to be convinced). For that to happen, Data Scientist needs to be Data Literate for building a sound argument.

Arguing with data is harder than we think because it takes an understanding of the data and communicates it well so the information would be received. It takes a big responsibility as well to argue with data as we need to clarify that the data we have is following the right criteria. I am myself love to argue with data when the occasion is present, but I would prefer silence if I did not have sufficient evidence.

7. You question things

In the world full of information, every day, you would be bombarded by new data. While many are correct, but some information is definitely false or just merely irrelevant. Data literate people are people who would questioning information they get; “Is it True or Not?”, “It is useful for me or not?” “Do I need this data?”, etc.

Data literate people would also be people who are hungry with information, which is why they keep questioning things to acquire new things. It might be something that they interested or just random idea suggested by search engines.

If we think it in the business environment, you might already finish your analysis and reach an answer, but is this answer true? Or you had built the most accurate machine learning model, but is this model really could be implemented in the real world? This is often the case when Data Scientists work, and things that you as Data Scientist should ask. Although, you still need to find that compromised point because the question could be endless after all.

Conclusion

I have presented my personal view of the sign that people are data literate, or at least have a talent for one. They communicate well, not overwhelmed by information, take action using data, argue with data, taking action fast enough but not reckless, interested in data, and questioning things.

Original. Reposted with permission.   Related:

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