3 Key Differences Between Data Analysis And Data Modeling

Data is the new currency in today’s digital world and enterprises are looking to find new ways to increase the value of their information assets. Here, we are discussing the differences between data analysis and data modeling. Business intelligence has become an integral part of modern-day information management. Corporations are employing processes and technical solutions to convert raw data into valuable and meaningful information. This helps them in gaining valuable insight into different aspects of their business and drives their success. However, the emergence of new practices has also led to some confusion. Many organizations are wondering whether they need data analysis in their business intelligence programs or not. They feel that since they are hiring data management consulting companies for developing data models, there is not that great a need for analysis. They need to understand that both the practices are distinct and are important in their own right. The following are the three main points explaining the difference between both the fields.

1. Data Analysis Involves The Evaluation Of An Organization’s Data Assets

Data analysis involves the evaluation of the information assets of an organization to discover helpful insight which can aid the decision-making process. Like any other data-related activity, this also starts with a definite objective. All the processes or tools that are used in the analysis are chosen based on their relevance for meeting the end goal. Many organizations also try to find the answer to a question with the exercise. All the information that can help in finding the relevant solution is then collected from various sources. This leads to the collection of raw data in a variety of formats. Data analysts cleanse and aggregate all these items and then consolidate them into a format that will be compatible with the technical tools. Once the disparate items have been merged and aggregated, they are fed to analyst tools for exploration. The solutions try to find the answer to the question with which the entire process started by identifying patterns in the information. Finally, all the trends and patterns are then assessed by users to draw conclusions. These assumptions are then used for making future projections and defining a plan of action to resolve the issue. Data analysis requires not only technical proficiency but also an in-depth understanding of the business context of the information assets. After all, the technical processes are being conducted to extract some valuable business gains. In brief, data analysis is a comprehensive assessment of the data elements of an enterprise to drive business growth.

2. Data Modeling Is an Evaluation Of An Organization’s Data Management

In order to understand the difference between data analysis and data modeling, you will need to know the definition of the latter. It can be said that data modeling involves the evaluation of an organization's data management framework. The practice involves understanding how an organization is collecting, storing, and handling information. It maps all the locations where the assets are residing. It also visualizes the relationships between various elements and how they fit in with their business context. The process starts with the identification of business requirements that need to be fulfilled. Then information assets are identified which will be needed for the purpose. Then it is decided how that data will be used to accomplish the goal. Visualization is done by creating entity relationship diagrams that visually depict the link between the main business concepts through data. A data dictionary is created which documents the items that are needed for implementing the necessary function. Another important exercise in the process is to create a data map. This helps in visually explaining how elements will move from one system to the other while executing a procedure. It also helps in understanding how various systems will be connected to each other through common elements. Mapping will also be helpful in resolving potential issues. In short, data modeling is the creation of a model that defines how an enterprise must handle its information assets to derive maximum value out of them.

3. Data Modeling Can Be Dependent On Data Analysis

This final point will be helpful in understanding the difference between the two business intelligence practices. Data modeling, in some aspects, can be dependent on data analysis. Let’s say while creating a model you link two different fields with each other. You will have to understand the composition of the elements in both sections to know whether mapping will be seamless or not. Data analysis enters the picture at this stage. Evaluation of the elements will provide details about their composition. This will help complete the modeling process in an efficient manner.

Conclusion

Data analysis and data modeling are both necessary practices required for running an efficient business intelligence program. While the former provides an effective way of assessing your information assets, the latter helps create a framework that enables efficient data management. It is important that you understand the distinct nature of two and the value they bring to your program.