3 Reasons Why Data Scientists and Data Engineers Should be Best Friends
And how to ensure a smooth collaboration
Data scientists and data engineers have a lot in common!
They both work with data, but their roles are different. Data scientists use data to understand and solve problems, while data engineers use data to build and maintain systems. They need to work together to learn from each other and create synergies. This blog post will explain why they should be best friends!
“The best teamwork comes from people who are working independently toward one goal in unison.” - James Cash Penney
First, what is the role of a data scientist?
Data scientists and Data analysts use data to understand and solve problems. They gather data, clean it up, analyze it, visualize it, and make predictions based on their collected data.
Data scientists are usually very skilled at programming languages like Python or R and can write code to aggregate datasets, create models, run analyses and build data visualization.
They will analyze data and find insights to help businesses make better decisions based on what they see in the data collected by companies over time. Data scientists can build models that predict future outcomes using machine learning techniques or deliver analysis to fuel strategy!
And what is the role of a data engineer?
Data engineers use data to build and maintain data ecosystems, fuel a data-driven strategy, support analytics capabilities. Data engineers are data management experts who focus on data infrastructure and architecture. They implement complex data systems for data storage, processing, real-time data streams to facilitate analytics processes.
They will, for example, take care of:
- Building data pipelines to transform raw data into usable KPIs.
- Building and developing analytics capabilities with reliable data pipelines.
- Maintaining scalable data infrastructure capable of computing a high volume of data in real-time.
- Designing and implementing data models for data analysis purposes.
How do they work together?
You might have seen that although the job descriptions are different, we talk about data and analysis in both jobs.
Data science and data engineering are complementary in the data ecosystem.
They work together because if data scientists have no data, they can’t do their job! So data engineers build data pipelines that allow data to be collected, cleaned up, and sent for analysis by a data scientist or analytics team.
And as you may have seen in the Data Engineer description, there is also an overlap with what a Data Scientist does. They are also creating information visualization that will be used directly by the business users. This is an example of where both teams would need each other’s help!
They must work together because one cannot exist without the other; they rely on each other heavily to make sure everything works smoothly within their data ecosystem!
Examples of data science and data engineering teams working together:
- Reasons 1: Data scientists can help data engineers understand the data they need by defining business questions and goal metrics, which will be used to build pipelines, clean datasets, and ultimately dashboards. By having a close collaboration, Data scientists will be sure to have the data they need, and Data engineers will ensure that the data will deliver value.
- Reason 2: Data engineers can provide data scientists with clean datasets they use in their models or analyses so that data scientists don’t have to spend time data cleaning, which can be a very time-consuming process.
Let’s not undervalue this one; a data scientist’s job is 70% cleaning and aggregating data. So an excellent data engineering team can save hundreds of hours of a Data Scientist.
Data preparation accounts for about 80% of the work of data scientists — CrowdFlower survey
- Reason 3: Knowledge exchange! Data scientists can give insights on data engineering projects like the architecture of a data warehouse and how to improve it so that data will be easier for them (data scientists) to analyze in the future! Data engineers can also bridge data scientists and business users by using data science techniques to answer business questions.
What are some benefits of working together?
- Increased data quality: When data is groomed and organized correctly by data engineers, it makes data analysis much easier for data scientists, which leads to more accurate insights and predictions.
- Increased efficiency: Data pipelines are designed to be reliable and efficient so that data can be processed quickly without any errors. This allows data scientists to spend more time analyzing data and less time troubleshooting data issues.
- Increased collaboration: By working together, data scientists and data engineers can learn from each other’s expertise, which leads to a better understanding of the data ecosystem as well as faster problem-solving.
- Improved data analytics: The combined knowledge of data science and data engineering allows for more data-driven decisions, which can lead to better data products and services in the long run.
Ultimately, they can create powerful synergies when these two roles work together. Data scientists can learn from data engineers how to use big data technologies effectively. Data engineers can learn from data scientists how to use machine learning algorithms to improve system performance. Together, they can make sure that all of the company’s data is collected, stored, and analyzed efficiently. This will allow them to make better and faster decisions.
What are some challenges of working together?
- Data scientists might not understand or not be aware of some data limitations, which could make it difficult for them to design efficient models and analyses.
- Miscommunication: The biggest challenge of working together can be communication problems. Data scientists and data engineers must be clear about their data needs so that data pipelines can be designed accordingly.
- Incompatibility: Data scientists might need data in a different format than data engineers, which could lead to issues if they cannot communicate effectively with each other about how the data should flow through their respective systems!
What can be done to have a better collaboration?
- Data Scientists should provide as much detail as possible when requesting the data engineering team. This will help data engineers understand the data and the business question that needs to be answered.
- Data Scientists can use data engineers’ expertise to analyze data: data engineers are experts in manipulating data and they can help data scientists understand data limitations or data problems by providing a deep understanding of what data is available and how it can be used.
- Data Scientists can use data engineers’ help with data visualization. Data scientists are not always experts in data visualization, so they can rely on data engineers to help with creating charts and graphs that will effectively communicate data insights to business users.
- Data Scientists should be clear about which data is needed and how often it should be updated. This will help data engineers design data pipelines that are efficient and reliable.
- Data scientists can make the life of the Data engineer easier by prioritizing their requests being clear about data formats and data needs. There is an infinite amount of data that can be loaded or created. Data scientists should be able to prioritize their requests.
Conclusion
In conclusion, data scientists and data engineers should work together to create a data-driven culture in their organization. By doing so, they can:
- improve the efficiency of data processing, analytics, and decision-making.
- Save hours of working time from each other by simplifying their life.
- Learn from each other’s expertise, which will lead to better data products and services.
However, some challenges need to be overcome in order for this collaboration to be effective. These include communication problems and clear requirements. But with a bit of effort, these challenges can be overcome!
Data scientists and data engineers should be best friends! By collaborating, data scientists can learn how to use big data technologies effectively from data engineers. Data engineers can also learn from data scientists to directly provide insight to the business teams.
Together, they can make sure that all of the company’s data is collected, stored, and analyzed efficiently. This will allow them to make better decisions faster!
In conclusion, data scientists and data engineers should definitely be best friends! They have a lot in common and can create powerful synergies when they work together.
Valentin Mucke
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