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Difference Between Data Modeling and Data Analysis

  Data analysis is using the statistical knowledge to interpret data, while data modelers rely more on logic to make connections between data sets. Nowadays, we are seeing an increase in data analysis skills in business analysis jobs. 

What is Data Analysis?

Data analysis is a technique to gain insight into an organization’s data. these analysts rely on mathematical algorithms to find trends. Data on sales, spending, and even customer information is all collected by these professionals. Data analysts use Excel, SQL, and Google Analytics to organize information.

They then write reports based on their findings, reports that help companies make pivotal decisions regarding how to better meet customer needs or improve areas of workflow to make the employees more efficient.

A data analyst might have the following responsibilities:

– create and analyze important reports to help the business make better decisions.

– merge data from multiple data sources together, as part of data mining, so it can be analyzed and reported on.

 – Developing visuals of their findings, such as charts and graphs, to include in reports

– run queries on existing data sources to evaluate analytics and analyze trends.

– Working with software developers to create the algorithms

– Removing irrelevant data from the database.

What is Data Modelling?

Data modeling is a set of tools and techniques used to understand and analyze how an organization should collect, update, and store data. It is a critical skill for the business analyst who is involved with discovering, analyzing, and specifying changes to how software systems create and maintain information.

Using programs, such as Erwin, data modelers create maps that logically show how parts of the data are related. They do this by making connections between company departments and integrate all the separate systems they may use.

Job responsibilities of a data modeler include:

– They create a conceptual-level data dictionary to communicate data requirements that are important to business stakeholders.

– They create an entity relationship diagram to visualize relationships between key business concepts.

– They create a data map to resolve potential data issues for a data migration or integration project.

– Developing clear procedures for the flow of data between departments

– Securing the data by implementing encryption or firewall procedures

– Standardizing the abbreviations used across the platform.

Data Modeling Can Require Some Data Analysis

Data modeling requires a little bit of data analysis. In order to say this field is going to map to this field in a systems integration project, you probably need to look at the data and understand how the data is put together.

This is why Data modeling requiring concepts or technical skills like SQL because if you know SQL and can query the database, it’s a little bit easier to be able to research that information and figure it out for yourself using a little bit of data analysis to inform your data models.