MMS • RSS
Article originally posted on Data Science Central. Visit Data Science Central
It is very easy to assume that Data Scientists are highly technical people. Ones who invest all their intellect into interpreting data, concluding smart insights, and offering strategies accordingly. However, this assumption is quite quick to make and presents an incomplete image of any Data Scientist.
Due to such assumptions, many people, even recruiters, only focus on the hard skills of Data Scientists. In reality, a good Data Scientist also possesses the right combination of hard and soft skills just like any other professions.
Furthermore, we live in an evolved industrial environment now. In today’s era, professionals including Data Scientists can’t survive without the use of soft skills. Gone are the days when machines were under the explicit control of humans. In those days, professionals required a deep kind of technical knowledge to keep the machines running. Today, most machines are self-learning; adapting to existing patterns. This requires Data Scientists to go beyond their hard skills. They require a unique creativity that allows them to work with machines rather on them.
Importance of Soft Skills for Data Scientists
A part of the hard skills assumption is kind of right. Data Scientists do require a greater level of productivity and experience than our traditional engineers. But without a comprehensive set of soft skills, a Data Scientist can’t execute their knowledge to a good extent. The machines that Data Scientists work with are programmed to cater to the needs of the users. Instead of producing tangible outcomes from the machines, we are deriving decisions. To make this possible, Data Scientists need skills to understand the needs of the user so they can manage the right decisions. Therefore, soft skills are significant to become a successful Data Scientist.
5 Soft Skills for a Data Scientist
Following, want to discuss 5 soft skills that Data Scientist must possess:
1. Understanding the Business
As mentioned earlier, a Data Scientist must be able to understand the user needs. Therefore, when Data Scientist work for a certain business, they should have a deep understating of what the business’s customers need and demand. For this purpose, they also need to understand the business.
Smart analytics are deeply associated with what the business needs. The Data Scientists need to know the strengths and weaknesses of the business in order to determine where it stands and is headed towards. This way the data insights can be converted into smart analysis and decisions to overcome the gaps.
The business world’s competition also affects the way Data Scientists work. Therefore, knowing the position of the business within this race for glory is also significant. It requires them to keep up to date with the changing trends and which ones can be leveraged to boost the company’s position. It should not be ignored that Data Science can play a major role in achieving a competitive edge in any given industry.
2. Translating the Tech Language
One of the roles Data Scientists play is being the middlemen between the tech and non-tech people of a company. Their job is two way. They must understand the demands of the non-tech workers and determine what technologies are required to meet them. On the other hand, they have to take the IT department’s results and translate them to the non-tech people so they know how the given technologies can help them.
3. Syncing the Business with Technology
Once again it is important to emphasize the skill for understanding the business. With this understanding comes the know-how of the company’s needs for succeeding in the industry. This ensures that Data Scientists can determine the complementing technologies.
Data analysis itself may not evolve much with time but newer technologies to accomplish the analysis keep erupting. A skilled Data Scientist will be able to keep up with changing trends and use the technologies for making smarter analysis for the business; in a way that provides the competitive edge it needs.
4. Giving Data Analytics a Perspective
Those who understand the business side of the industry only can often be solely result driven. Their focus on how those results can actually be achieved is not so sharp. This is why they hire Data Scientists and other professionals who can collectively provide the perspective the business-minded people might lack.
Data does not always reveal a story that the higher-ups want to listen to. In such cases, it is the job of the Data Scientist to explain the more technical sides of the story in an understandable manner. For this purpose, people skills come in handy because it is important to convince the company to move in the right direction; one the data suggests and will lead to a productive result.
5. Curiosity for Discovery
The thing about data is that it does not always tell a single thing. It can be interpreted in multiple ways. Only a skilled Data Scientist will be able to make more than one conclusion. They also require the curiosity that drives them to discover more from any givens set of data.
Data Scientists should have the soft skill of being creative; thinking outside the box. This allows them to often discover patterns and solutions that others might miss. Hence, their creativity can provide the differentiation factor a business needs to compete in a given industry.
In conclusion, Data Scientists are not just assets in terms of technical knowledge but in terms of allowing that knowledge to be understandable as well. In today’s world, a lot of machines can compensate for the hard skills but in the end, the machines are still just physical assets. No matter how smart and advanced you make them, they will never bring the people skills to the table.
Hiring your next Data Scientist, try finding an individual that balances the hard skills with soft skills. A combination of human and business understanding that allows the synchronization between technological and human assets of the company. Complementing soft skills to hard skills ensure long lasting business success.