It will not be wrong if we say data is the new electricity. We are living in the time that's called the age of the fourth industrial revolution. This era is all about Big Data and Artificial Intelligence. It is all because of the extensive amount of data that resulted in the development of tons of new and smart technologies. According to research, around 2.5 exabytes of data is being produced every day. There are a lot of organizations with their businesses centered on data. These organizations are working with the data and pulling out some fruitful outcomes for other organizations.
Data is creating magic in the market. In almost every aspect of the business, data has shown how powerful it can be. Organizations are making their marketing strategies based on all their data, and it is turning out to be the best for them. Let's say, the more you have the data, the better it would turn out for you. Because as many customer experiences and feedbacks you would have, it would help you in the betterment of the product, and this way, you would capture even bigger consumer markets.
But for making all this possible, we need to process the data we have. The amount of data we have is enormous right now, and we cannot just pull out the outcome from it. We need some kind of process to make this data worth it for us. That's where data science comes in.
Data Science
The process or art of cultivating a large amount of data in a way that gives some meaningful output is called data science. In this process, we can use various tools, machine learning principles, and algorithms. In other words, data science is a concept of tackling an enormous amount of data, it could be cleansing, preparation, or data analysis. And the person who performs this task is a data scientist. To extract the meaningful results, he lets the data go through all the machine learning and predictive analysis process. To learn more, you can always take our training program on data science.
Structuring the Data Science Dream Team
As with a lot of other things in life, data science is a team sport, and building a team is not as simple as it looks. You need all the knowledge and skills of statistics, mathematics, data extraction, data management, and a lot of other things. recently, data science has been playing the role of catalyst for innovation in the business. By using data and finding out about insights, let us do accurate predictions, and this is what is required to have a competitive edge over the competitors. Structuring the dream team of data science starts with some basic concepts. Let's have a look at them first.
- Developing New Areas of Expertise
The first thing you need to do is to upskill the IT team you already have and ask them to manage the data modeling process. Teams can us Machine learning as a service (MLaas) concepts to tackle datasets. Or the other way, there can be one Data specialist, working with the existing IT team. In this way, data-specific tasks will be executed by a specialist, and all other tasks will be controlled by the IT team.
- Skills Diversity
The most important factor that guarantees an efficient data science team is the wide range of skills on tap. All the team members making sure to play their part with their strengths is enough. If someone is a great program, then the other one is excellent with the data interpretation. For instance, if both of them work together, they will finish the work of hours in half of the time.
- Attributes that Count
When it comes to hiring members for a data science team, there are some qualities other than the data skill that count the most. Being able to communicate and work in a team environment is an important thing. The team should be sorted in a way that everyone gets to do what they are best at. From the interpretation of data to programming to present that in front of the clients, everything has its value. All of this will make more sense if you learn data science by taking some training online.
Who is the Part of the Team?
There is only one rule when it comes to a team there is no I in it. The first thing is first so, let's focus on building a team. Let's see who'll be doing what, to extract those fruitful results out of the data.
- Data Engineer
Data engineers build pipelines of data to make it accessible for data scientists to work with it. Responsibilities of data engineers include data ingestion, data processing, and data storage. A data engineer is a vital part of the team, as there is no data science without the right data.
- Data Scientist
After the work of data engineers, data scientists have access to the right data. Now they are ones to dig all the critical insights out of that data. For that, they use statistical methods and algorithms.
- Business Analyst
A business analyst is a person who takes all the insights and results taken out by data scientist and make the business strategy. It is usually a person who knows the business inside out.
- Machine Learning Engineer
Machine learning engineers it is an emerging role in the data science team. Well, deployment of the models created by data scientists is the responsibility of a machine learning engineer.
- Software Developer
The last member of the team is a software developer. They are responsible for developing applications by using the data and the data-models. Applications that can be applied to the production and can be used by non-tech people. As technology is on the rise, they have a lot of tools to make their work easier.
If you want to build a data science dream team, that’s pretty much all you need to know. By applying all of these steps, you can up your game in the business world. This team can do pretty much all when it comes to data. From finding out the problems to application development and deployment, all can be done by this data science team. That's all from us. But if you have any questions about your dream data science team, feel free to contact our experts at any time.