As organizations are discovering the value of data-driven decision-making, more and more companies are looking to adopt a data science practice. With that, the demand for data scientists is on the rise. However, if an organization aims to quickly scale their data science practice, it is not reasonable to expect data scientists to hold all the responsibilities of the practice. It is certainly possible to have data scientists doing data engineering on a small scale, but it’s not a viable strategy. Ultimately, it leads to teams underutilizing Big Data and inhibits them from producing better insights.
Data engineers are an equally critical resource for an effective data science practice. Having a data science practice that consists of both data engineers and data scientists allows each to maximize their efforts in their area of focus while benefiting from the collaborative work of the other. This leads to a scalable practice that is able to fully leverage data for the best insights.
Because these roles can overlap, they are often confused for one another or lumped into one role. Therefore, in order to eradicate any ambiguity of data science team roles, understanding data engineers and data scientists and the role each plays in a data science practice is important to ensure success.
Data engineers prepare and transform data to allow for data scientists to focus on data analysis. In an organization, data engineers lay a strong foundation for the data science practice.
The role of a data engineer may vary depending on how advanced their data science practice is, but certain fundamental functions remain the same.
Data engineers are responsible for building data infrastructure and creating data pipelines. A data pipeline is a series of data processing elements connected together that moves data from one system to another. The life cycle of the data pipeline consists of data ingestion, data processing, data storage, and information access.
The data engineer’s creation of data pipelines is critical for data scientists, as analysis can only begin when the data becomes available. It becomes particularly important to have data engineers do this work as a practice grows because the complexity of data infrastructure preparation increases as both the number of data sources and the requirements for analysis increase.
Data engineers are also responsible for cleansing the data. When there is a need to refine and process the data in order to turn it into information, data engineers are responsible for data munging and data wrangling.
Ultimately, the objective of data engineers is to prepare the data so that it is manageable and ready to be used by the data scientists.
Before the data engineering role was created, data scientists cleaned and prepared data and then performed the analysis and identified insights. However, as the amount of data and data sources has increased, it has become more complex, time-consuming, and nearly impossible for data scientists to do it all, let alone do it well.
Today, a data scientist focuses on performing advanced analysis on the data that has been prepared by data engineers. Data scientists are responsible for working with both structured and unstructured data to analyze big datasets to meet business needs. This includes producing insights to predict trends, mitigate risk, forecast behaviors, etc.
In order to gain such insights, data scientists are responsible for the following:
- Designing various predicting or classifying machine learning models
- Performing statistical analysis on the data made available by data engineers
- Presenting insights through understandable visualizations to business users
The ultimate objective of a data scientist is to provide an organization with data-driven insights to solve business challenges and achieve various business goals.
DATA SCIENCE PRACTICE
An organization aiming to grow their data science practice will want to have data engineers and data scientists who work together while focusing on their individual role in the data science process.
While there are some overlapping skills, that doesn’t mean the roles are one in the same. For instance, data scientists and data engineers are both considered programmers, but data engineers are far more skilled in programming and data scientists are more skilled at data analytics. Data engineers don’t need to have as advanced analytical skills, they simply need to have enough knowledge to understand the requirements of each data science project so that they can properly prepare the data for the data scientists to analyze. For this reason, it is crucial that there is a clear understanding of each role’s responsibilities as well as strong communication between a team’s data engineers and data scientists.
To achieve an effective data science practice that has the ability to quickly scale, the organization will need effective collaborative work between highly skilled data engineers and data scientists.