Aren’t all good data scientists’ great data scientists? As much as we’d like to think good data scientists to be great data scientists, it isn’t. No doubt good data scientists are a rarity. But great ones are even more difficult to find. It is a bigger challenge like finding a needle in a haystack.
Data science industry is a thriving industry. Multiple organizations have started implementing data science practices for better future predictions. Moving to a data scientist role comes with an effective data science practice. Lack of expert talent is not what the industries are after today. The data science job market has changed exponentially. Building machine learning models were once an exclusive skill that only a few expert data scientists could develop.
It is not the same scenario today.
Nowadays tech professionals having basic coding skills can train themselves in building a simple keras or scikit-learn models. The data science hype barely slowed down. And because data science tools have become easier to use, becoming a data science professional is no biggie today.
However, the expectations of what data scientist should come up with have changed. Several companies have now realized that training a machine learning model is only a minute part of what it takes to become successful in the data science industry.
Those looking to become great data scientist must understand this – the difference between a good data scientist and a great data scientist is like differentiating between lightning and a lightning bug.
Here are some of the best traits of great data scientists: –
1. Explore and collect valuable data
Does it sound to cliché? It does. It is said your analysis is only as good as your data. What does this imply? If the organization does not have access to good data, then irrespective of how great your machine learning models are, the research obtained from these models will only be as good as the data set provided.
It is preferred to have a lot of innovation done while applying existing methods to newer types of data, rather than using new machine learning models.
For instance, imagine you’re working in a small company with a small project to handle. Chances are there are huge opportunities for collecting data that nobody has taken advantage before. Based on the level of the company, the other team may lack the skillset that is required to store data for analysis or there might be a probability they might be losing valuable data the company is not aware of.
Being a part of the business team will give you an idea of where the organization stands in terms of what data the company already has, and how it has been used. At such instances, the data science professional should be able to ask questions such as: –
- What are the company’s data, industry data, and customer data we’re not collecting?
- What is the kind of data we’re collecting but not using?
Critically, these experts must stay close with the business teams. Once you’ve done this you’ll have a clearer picture of prioritizing things that are the most important.
2. Stay close with business teams
Being the only “data” person available ensure you do not isolate yourself. The first and foremost step for you is to work closely with the business team of the organization. Doing so will give you a better idea of understanding the company’s priorities. They are the best people that can provide useful information. Being in a smaller company gives you the liberty to iterate on ideas quickly. And perhaps you can focus on making the biggest impact.
3. Attention to decision-making
Working with different teams might get challenging if you’re unable to pay attention. It is important for you to know how, when, and by whom important decisions come to pass in companies. Who makes the decision on the marketing strategy, how strategy is implemented. Are these strategies evaluated every month or every quarter? Whether this marketing strategy comes from the director of marketing or someone else.
Knowing such questions will help you build a stronger impact on the company.
4. Know your competitors
Analyzing competitors is a blind spot data science experts must keep an eye for. It’s unlikely for you to get access to the internal of your competitors, but there are public data that can be looked upon to if needed. From these data, you might be able to analyze the strategy used and find the weakness in the competitor to exploit. Digging deep into the competitor data gives you a clearer understanding of the business context align with the mission of the company. In return, this will benefit you in the long run as a great data scientist.
5. Encouraging a data-driven culture
Being in a small data team can be difficult. However, you can demonstrate your skills by showing them how using data could benefit their team. Helping transform company goals into data metrics that can be easily measured with data. It is likely possible you’re going to be the best person to take up directives and transform them into measurable metrics.
6. Strong focus on upskilling
Data science industry is changing quickly. If you’re looking to keep your organization at the peak, then you need to sharpen your skills. To stay future-ready, you need to keep up the pace with new tools and technologies. Data scientists need to embrace change and need to recognize their expertise are based on when a prediction rests on positive insights.
A good data scientist may be able to help find the relationship from a humongous amount of data. But a great data scientist helps develop newer insight into the larger world. Over the years, the birth of dozens of data analyst, data science professional, and good statisticians have grown immensely. And maybe a few great ones.
Irrespective of the industry you work in, chances are there will always be leaders or people with incomparable skills that separates you from the normal lot. How often do you find leaders or professionals with skills beyond comparison? It is but a rarity.
