Data analysis and data modeling are two essential skills for information technology professionals who work with data. But what do they mean and how are they related? In this article, you will learn the basics of data analysis and data modeling, and how they can help you design, manage, and improve data systems.
Data analysis is the process of examining, manipulating, and transforming data to extract insights, patterns, and trends. Data analysis can be done for various purposes, such as exploring data, testing hypotheses, solving problems, or making decisions. Data analysis can involve different techniques and tools, such as statistics, machine learning, data visualization, or programming languages like Python or R. Data analysis can also be applied to different types of data, such as structured, unstructured, or semi-structured data.
In my experience one of the most time and resource intensive portions of data analytics projects is getting the data. It can be a challenge to find the right data, and then to clean or structure it (modeling mentioned next) but this phase of the project will take the bulk of the time. The actual analysis and reporting off the data is probably only going to be less than 50% of the entire project time.
Data modeling is the process of creating a representation of data and its relationships, usually in the form of diagrams, schemas, or rules. Data modeling can be done for various purposes, such as designing data structures, ensuring data quality, enforcing data integrity, or facilitating data communication. Data modeling can involve different levels and approaches, such as conceptual, logical, or physical data modeling, or relational, hierarchical, or network data modeling. Data modeling can also be applied to different types of data, such as relational, non-relational, or multidimensional data.
It's important to note this referencing to modeling is not about predictive modeling. While that can be done in the process of data analysis or after the data has been modeled into a schema in a database. The actual creation of predictive models from data is a different task all together.
Data analysis and data modeling are related in several ways. First, data analysis depends on data modeling, as data modeling provides the foundation and framework for data analysis. Data modeling defines the data sources, attributes, entities, and relationships that data analysis can use to perform queries, calculations, or transformations. Data modeling also ensures that the data is accurate, consistent, and reliable for data analysis. Second, data analysis informs data modeling, as data analysis can reveal the needs, requirements, and challenges of data modeling. Data analysis can help identify the data characteristics, patterns, and anomalies that data modeling can use to design, modify, or optimize data structures. Data analysis can also help evaluate the performance, efficiency, and effectiveness of data modeling.
Data analysis and data modeling can be incredibly beneficial for information technology professionals who work with data. These techniques can help improve data quality by detecting and correcting errors, inconsistencies, or redundancies. Additionally, they can enhance data understanding by describing and explaining data features, behaviors, and outcomes. Moreover, they can support data decision-making by providing data evidence, insights, and recommendations. Finally, they can increase data value by generating data knowledge, innovation, and impact.
In order to perform data analysis and data modeling effectively and efficiently, it is important to define the data objectives and scope precisely. The data techniques and tools chosen should match the data objectives, scope, and type in order to affect the quality and speed of the process. Additionally, documentation of the steps and results should be thorough and systematic in order to ensure data reproducibility, traceability, and communication. Lastly, validating and verifying the outputs and assumptions regularly is essential for ensuring data reliability, validity, and accuracy.
Data analysis and data modeling can be applied to different data scenarios to illustrate their use. For example, in an e-commerce company, data modeling can create a logical data model that defines customer entities, attributes, and relationships. Data analysis can then use SQL queries to perform data manipulation and extraction. Similarly, for a marketing campaign, data modeling can create a non-relational data model that captures the social media posts, metadata, and interactions. Data analysis can use Python libraries for data cleaning and transformation, as well as data visualization tools to display data distributions, trends, or correlations.