As more and more companies start to use data-related applications to manage their huge assets of data, the concepts of data modeling and analytics are becoming increasingly important. While they typically rely on one each, they are two very distinct concepts.
Companies use data analysis to clean, transform, and model their sets of data, whereas they use data modeling to map out and visualize the process with which they collect and store their data. For data-driven businesses, both concepts are invaluable, and it should be clear to data-driven businesses how, exactly, they are interdependent. Let’s dive into the details behind these two concepts and explore some use cases to better understand them.
Defining Data Modeling
Data modeling typically starts with a data model. Businesses need to use a data model to analyze the way components of their enterprise data relate to one another. Businesses may use a data model to analyze different aspects such as data storage and data generation, often with specific tools that can indicate how enterprise data has impacted a business’ series of software systems.
With data modeling, businesses can define the way in which they present their final data to an end-user. It also defines the process of how enterprise data can flow on a step-by-step basis. Data modeling is essential when it comes to creating IT systems that a business relies on to maintain its continuity of operations.
Organizations should employ data modelers to define their data models who have experience in data modeling as well as analytics, and they should be experts in working with the different types of data models. These types of data models, such as conceptual data models, logical data models, and physical data models, allow data modelers to define the most critical aspects of business data, focus on different parameters of enterprise data, and develop blueprints for the flow of enterprise data.
It may help for data modelers to simplify their process of data analysis with no-code data pipelines. These pipelines are particularly helpful for businesses focusing on business automation, such as those interfacing with the process flows between decentralized autonomous organizations (DAOs). The processes behind DAOs, for example, do not require human handling and thus can benefit from a data model that defines each DAO process that occurs independently from human input. No-code data pipelines are usually totally automated, which means they can deliver data to a source in real time without running the risk of losing data along the way.
Businesses use the process of analytics to transform, process, and arrange their sets of raw data with the intent of obtaining greater insights into their business-critical decisions.
Through the process of analytics, businesses can better understand how to measure the data-driven decisions they make in order to mitigate the risks of making data-driven decisions in the first place. For instance, analytics experts can use the process of analytics to convert raw data into digestible pieces of information such as charts and tables that let non-technical stakeholders visually glean insights from their enterprise data.
Analytics has become much more popular in recent years due to its ability to allow company leaders to better understand their customer audience while cutting their losses when making data-driven decisions around their target customers. With analytics, businesses and their marketing teams can fine-tune their advertising campaigns and lead more effective strategies for managing their reputation because of how well they can mitigate risks that come with making data-driven decisions.
Analytics has been making rapid advances and is currently largely automated thanks to different algorithms. Given its recent advances, analytics experts should be familiar with the two main types of qualitative data analysis: descriptive analysis and inferential analysis, which help analytics experts locate patterns in data and define correlations between different sets of data respectively. There is also qualitative data analysis, which stands in stark contrast to quantitative analysis in that it deals with non-qualitative data such as video, images, and audio.
Now that we have a grasp on the definitions of analytics and data modeling, let’s take a quick look at some use cases.
Data Modeling and Analytics Use Cases
Arguably the most important use case that relates to data modeling and analytics is being able to generate high-quality applications that aren’t prone to running into errors. When businesses attempt to develop applications without data modeling and analytics, they usually end up writing code without a proper structure, which quickly turns code into an inscrutable mess. With data modeling and analytics, though, the quality of the applications that businesses develop becomes much higher, and applications become easier to update and modify.
The process of requirement analysis also becomes more viable with the help of data modeling. With data modeling as well as analytics, data experts have an easier time documenting requirements for application development and can ultimately create a product that is more sophisticated and better able to meet customer requirements.
The time it takes to develop a high-quality application is also significantly shortened with the help of data modeling. A proper data model that exists before application development begins can help cut down on the time it takes to gather requirements and prevent ad-hoc planning. Data models also facilitate more seamless changes to an application farther along the development lifecycle since data experts can simply add new requirements to their model on an as-needed basis.
Businesses that want to become more data-driven should pay close attention to and embrace data modeling and analytics. These concepts make it possible to represent and plan different data flows while obtaining important insights that can drive a larger number of data-driven business decisions. Keep in mind that the process of data modeling relies on data analysis for data experts to arrive at a blueprint that can map out the different steps of a specific flow of data.