The Data Analytics Family: Understanding the Roles and Responsibilities of Each Member

Data analytics can be a complex and multifaceted field, with many different tasks and responsibilities involved in the process of collecting, cleaning, visualizing, modeling, and interpreting data.

To help understand and navigate this complexity, it can be useful to think of data analytics as a family, with each aspect of the field playing a specific role. In this article, we will explore the different “members” of the data analytics family, including the “parent” of data collection, the “older sibling” of data cleaning, the “middle child” of data visualization, the “youngest child” of data modeling, and the “family elder” of data interpretation. We will also discuss the role of the “pet,” which can be thought of as something that adds a unique perspective or capability to the family. By thinking of data analytics as a family, we can gain a better understanding of the relationships and dependencies between different tasks and responsibilities within the field.

Here are some examples of how different aspects of data analytics could be thought of as different family members:

• Data collection: This could be thought of as the “parent” of the family, as it is responsible for gathering and organizing the raw data that is used for analysis.
• Data cleaning: This could be thought of as the “older sibling,” as it is responsible for ensuring that the data is organized and ready for analysis.
• Data visualization: This could be thought of as the “middle child,” as it is responsible for presenting the data in an easily understandable way.
• Data modeling: This could be thought of as the “youngest child,” as it involves using the cleaned and organized data to make predictions and forecasts.
• Data interpretation: This could be thought of as the “family elder,” as it involves using all of the above elements to draw conclusions and make informed decisions based on the data.
• Specialized data processes: This could be a process that is not essential to the core tasks of data analytics, but that adds a unique capability or makes certain tasks more efficient. For example, a process that involves using natural language processing techniques to extract structured data from unstructured text documents could be considered a “pet.” This process could be used to supplement or complement more traditional data cleaning techniques, and could allow data professionals to work with a wider range of data sources.