Data Analysis and Modeling: 4 Critical Differences

Generally speaking, Data Analytics and Modeling are closely-knit terms; hence miscued in widespread chitchats. And, when it comes to organizing data for analysis, solutions that help improve overall data quality also come in handy. Therefore, making appear the need to understand the vocabulary related to Data Analysis a prerequisite.

So, in this article, you will learn about Data Analysis and Data Modeling. We’ll also go through a few critical differences between Data Analysis and Data Modeling and why they are essential to make better use of data.

What is Data Analysis?

Data Analysis is a process of discovering, identifying, inspecting, cleaning, transforming, and modeling data to get valuable information that helps executives make better judgments. Many teams, groups, and organizations have different approaches to Data Analysis. Data Analysis helps reduce the risks involved in making decisions by providing essential information, statistics, and insights represented in charts, tables, images, graphs, etc.

Data Analysis plays a crucial role in today’s business world that helps make business decisions and choices that are more scientific, makes business operations more effective. It’s a technique to gain insights from data. Data Analysts have access to the organization’s data and use their technical and analytical skills to query, manipulate and make most of the data.

What is Data Modeling?

Data Modeling is the process of creating a simplified diagram of a system. It represents data and its flow using all the data elements it contains with the help of text and symbols. In Data Modeling, the data models provide the blueprint to design a new Database. It is responsible for creating a data model for an information system by applying certain formal techniques. 

Moreover, Data Modeling is used to analyze and identify data requirements essential for business processes. Professional data models work closely with business stakeholders and potential users of the information system. Data Modeling involves data represented as a flowchart that illustrates data entities, attributes, and the relationships between entities

Key Differences Between Data Analysis and Modeling

Now that you have a brief understanding of Data Analysis and Modeling. In this section, you will read about a few critical differences between Data Analysis and Data Modeling that will make you better understand both.

Generic Differences

Data Analysis helps in evaluating data which also includes generating and managing reports for business users, exploring data using queries, merging data from multiple sources, and understanding the data more clearly. It helps in making better-informed decisions based on stats and facts within data.

Data Modeling tells how organizations manage data. It involves different techniques such as ERD to explore the high-level concepts in data and identify how these concepts are related to other data in the system. Analysts need to analyze data to make data modeling decisions. That means Data Modeling can include some Data Analysis for making the right data models.

Data Analysis and Modeling Types

The various types of Data Analysis commonly employed in the worlds of technology and business are listed below:

• Diagnostic Analysis: Diagnostic Analysis answers questions using insights generated from data and statistical analysis to identify patterns in data. Business Analysts try to find patterns that existed in the past data and use the solutions to resolve the present challenges.
• Predictive Analysis: Predictive Analysis uses patterns discovered from old and present data to predict future events. Thus, it improves the accuracy.
• Prescriptive Data: It uses a mix of all the insights gained from other Data Analysis types and presents it in a simplified manner to provide a Prescriptive Analysis of the data. 
• Statistical Analysis: Statistical Analysis deals with data collection, analysis, modeling, interpretation, discovery, and presentation on dashboards. 

Data Modeling matures as business needs increase with an increase in data complexity over time. There are several types of Data Modeling listed below:

• Hierarchical Data Models: It represents the data’s one-to-many relationship in the tree data structure. Here, every record of the data has a single root that maps or points to one or many child tables. This Data Model is less efficient than the newly developed data model approaches. Though, it is still widely used in XML (Extensible Markup Language) and GISs (geographic information systems).
• Relational Data Models: Relational Data Modeling doesn’t need a detailed knowledge of the physical properties of the data storage that is being used. Here, the data segments are joined using tables that reduce the Database complexity.
• Entity-Relationship (ER) Data Models: It uses diagrams to represent the relationships between entities of the Database. Various ER Data Modeling tools are being used to create visually detailed maps to deliver Database design objectives.
• Object-Oriented Data Models: Object-oriented Data Modeling incorporates tables that also support complex data relationships. This approach is employed in multimedia and hypertext databases as well as other use cases.
• Dimensional Data Models: It is designed for optimizing data accessing speeds for Data Analysis in Data Warehouse. This Data Modeling approach is widely used across OLAP systems.

Data Analysis and Modeling Process

As the volume of data and its complexity increases, it’s also required to have an effective and efficient process that can be used to harness the value of data. The Data Analysis generally includes different phases listed below:

• Identify: It answers questions like what is the company trying to solve, What you need to measure, how you measure, etc.
• Collect: In this stage, the raw data is collected required to generate insights and answer your questions related to data. The data can be collected from multiple sources such as apps, Databases, etc. 
• Clean: In this the data is prepared for Data Analysis which involves, removing duplicates, identifying and handling anomalies separately, and standardizing the data into a format. 
• Analyze: In this stage of the Data Analysis process the data is manipulated using various Data Analysis tools and identifying new trends, correlations, outliers, etc to get some idea of the data. It also includes visualizing data to transform data into an easy-to-understand graphical format.
• Interpret: This is the last phase where the results of the analysis are measured for how well it answers the questions. What it concludes about the data, etc. 

Data Modeling evaluates data processing and storage in detail. Its techniques have different approaches that tell how data models are designed and how all the business requirements are satisfied by the Data models. The Data Modeling process follows some paths iteratively. The different stages of the Data Modeling process are listed below:

1. Identifying Entities: The first step of Data Modeling is to gain a better understanding of the dataset that is to be modeled. Each entity should be cohesive and logically discrete from all others.
2. Identifying the main properties of each entity: Every entity type can be differentiated from each other due to the presence of at least one unique property.
3. Identifying Relationships Among Entities: In this stage, it is defined how entities relate to each other and specify the nature of the relationships each entity has with the others. These relationships are generally documented using Unified Modeling Language (UML).
4. Mapping Attributes: In this stage, data is used to meet the business requirements and transform or relate as per the requirements. For this, many Data Modeling formats are in use.
5. Normalizing Data: In this stage, the keys are assigned and the degree of normalization is decided that will balance the need to reduce the redundancy. 
6. Validating the Model: It is an iterative process that needs to be continuously optimized and refined as per the changing business requirements.

Benefits of Data Analysis and Modeling

There are many similar benefits of Data Analysis and Modeling. A few benefits of Data Analysis are listed below: 

• Informed Decision Making: Data Analysis allows businesses to reduce financial loss and improve business decisions. With the help of Predictive Analysis, users can anticipate future business changes.
• Streamline Operations: Data Analysis enhances operational efficiency. The data collection and analysis of the supply chain can help in figuring out delays and bottlenecks of the business and assist in identifying where future events may occur.
• Enhance Security: Analyzing the organizational data for past data breaches allows users to understand the cause of data breaches and make the system more secure.

A few benefits of Data Modeling are listed below: 

• Increased Involvement: Data Modeling requires business inputs that encourage business users to collaborate with Data Management teams and business stakeholders, to ideally result in better systems.
• Efficient Database Design: Data Modeling provides a detailed blueprint to Database designers which streamlines the workflow and reduces the risk of design missteps that require revisions later in the process.
• Better Use of Data: Data Modeling allow companies to use their data more efficiently which leads to increase productivity and business performance.

Conclusion

In this article, you learned about what is Data Analysis and Modeling. You also went through some of the differences between Data Analysis and Modeling and how both are essential for making better use of data that helps in better business decision making.

Share your experience of learning about Data Analysis and Modeling in the comments section below!