With the hype of Big data, different job roles have become a craze, and not to mention, data scientists and data engineers are two of them. To get value from Big data, you need both roles. However, only having a capable team of data scientists or data engineers on hand is not enough. To realize the actual business value, you have to make sure your engineers and data scientists work in concert with one another. But what are the gaps between data scientists and engineers so that you need to bridge the gap between the two?

Gaps between data scientists and engineers

The job of a data scientist is most innovative. They extract new thoughts from the data ingested by a company. On the other hand, a data engineer mostly works on that data on production. They build off the ideas generated by the data scientists and create a sustainable view of data. Data scientists decipher, manipulate and merchandise on data to get positive business outcomes. Data mining and statistical analysis are some of the tasks that data scientists perform on collected data.

While data scientists generate the resilient and secured model, data engineers make the systems fast and more functional. So, we can say, both the data scientists and data engineers have different day-to-day concerns.

This leads to the question that beyond the role difference, how can you extract the best business value by utilizing the two teams? For this, you need to dedicate resources and time to perfecting engineering and data science relations. Here are the few ways that can help in frictionless business success.

Related post – How much programming language is required for data scientists?

How to bridge the gaps between data scientists and data engineers?

Training

To work together seamlessly, two teams must understand their basic operations. The same applies here. The data scientists and data engineers must understand each other’s terminologies and start to speak in the same language. Cross-training is a wise decision to perform this. Divide the data scientists and engineers into groups and encourage them to knowledge sharing. For data scientists, it helps to learn them – 

• Coding patterns
• Code writing in a more organized way
• Understanding tech stack and infrastructure trade-offs

Similarly, for data engineers, it makes a scope to learn 

• Data science related terminologies
• Data modeling
• Innovative thinking to leverage more effective production results.

When both sides are in sync, it generates a more efficient software development process.

More clean code, more higher value

When data scientists and data engineers speak in the same language, it is easy to handle the code more tactically. You can be more focused on cleaning the code. When the data scientists start work on the data, they use more iterative and experimental models to train the data. This creates a chaotic situation for data engineers as this creates a fluid environment for them. If the code from the prototyping phase is passed to the data engineers, it can create a roadblock. As a result, the model falls short in terms of scalability, stability, and speed.

So, the data scientists and engineers must be aligned on various parameters like coding standards, security standards, and data access patterns. This creates a framework for the data scientists within the ecosystem. This, as a whole, allow them to overcome the challenges they usually face.

Productize the clean code

There is a need for an environment where both data scientists and data engineers can lean on their strengths. This can be done by maximizing the value from clean code. For this, a centralized location is required to store the documented and curated features. This is a data management layer that can feed curated data to machine learning algorithms. Here consistency of models is essential. This not only increases the stability of algorithms but also improves the team’s overall efficiency.

Final verdict

The proliferation of Big data and Machine learning has opened up new opportunities and challenges along the way. In the first phase of Big data, we have seen it took time to realize it, but there were no efficiencies out of it. In the second phase, data scientists and data engineers extract the value out of the big data. Hence, working with these two teams should be seamless and organized. Once they are in sync, your customers will get the best value out of business.