Data modeling

Preparing for the Future of Big Data Modeling

Data modeling is the process of defining data structures and relationships between data elements. It also includes documenting what data items are needed to support business processes, what types of information they contain, how these items relate to each other, and their security requirements.

Data models can be used for many purposes including:

  • Quality assurance (QA) of both structured and unstructured data sources.
  • Understanding the scope of a project.
  • Making decisions on which systems or databases will house particular types of information.
  • Managing long-term system evolution.
  • Developing interfaces with other applications.
  • Designing application programming interfaces (APIs).
  • Creating reports based on historical data usage patterns.

Data modeling is often used to design databases that are deployed by an enterprise; data models can also be imported into a database management system (DBMS) and then further modified for use.

Data modeling components

Data modeling involves the study of:

  • Entities, which typically represent objects within the scope of concern of the business process being modeled.
  • Relationships among those entities and attributes associated with each entity and relationship.

The main steps in constructing a data model include identifying or clarifying all relevant concepts, expressing these as unambiguous symbols called “objects” representing real-world phenomena, specifying their interrelationships such as object types (semantic classification), defining constraints on them such as cardinality restrictions (for example whether one person may have only one spouse at any given time), determining logical relationships among them (such as an “is-a” relationship between, say, customers and their accounts), expressing these relationships in the data model.

Trends in Data Modeling

Data Modeling Trends will continue as long as business and technical analysts have an appreciation for how to best utilize information in order to solve business problems. This field is the basis of much that the United States and the rest of the world do on a daily basis. It is essential that data-modeling tools are updated as new technology emerges and is applied to solve problems in business. As new applications and techniques are discovered, the data modeling tools must be adaptable enough to facilitate the transformations.

Structure is everything

One of the data modeling trends that has been prominent for many years is the use of structured data. The primary purpose of using structured data is to assist in decision-making by providing a map of changes over time. Business and technical investors benefit greatly from Data Modeling as it allows them to visualize complex data patterns in an intuitive, easily understandable manner. Over the next decade, data modeling will have to do even more, with less, for better results.

Graph databases

In order to make data modeling more pertinent to business intelligence experts, business rules will also need to change. Currently, business rules exist to describe how data is stored and retrieved from a database. However, new business intelligence data models are forcing these rules to evolve. According to one data modeling trend, all business intelligence data modeling should essentially be graph databases. Graph databases allow users to quickly navigate large amounts of data in a format that makes sense to them. As data is processed, the resulting information is much more relevant to the users.

Speed and accuracy

Another data modeling trend is related to the generation of data models that are faster and more accurate. Today’s computers are fast, with an emphasis on speed as opposed to quality. To make data modeling even more relevant to today’s business environment, developers focus on creating models that can process data at a rate of light speed, as opposed to the traditional slow paces of traditional data modeling. This focus on speed has created what is called “parallel processing”. With parallel processing, information is processed almost instantly, leading to less wasted time.

Data modeling modern trends
data modeling trends

Data modeling trends also point to models that will allow multiple devices to run a program, as long as each device has access to the same data. Depending on what device is used, this could be data from an iPod or smartphone to a tablet. Although this technology will not be available in all models, it is a clear sign that mobile devices will continue to play a large role in data collecting and processing capabilities.


In order to be able to store and retrieve data, modern applications will require data structures and formats. Currently, there are two data structure formats available, namely text files and databases. Text data models are more widely used because they are simple to manage and update. In contrast, data structures such as databases and XML documents are more complex and harder to manage. When data is managed in a database or a text file, managers can access the data using a standard web browser.

Data modeling tools and analytics

Data modeling trends also point to the use of data models that will help data analytics function more effectively. Today, analytics is often confused with data modeling. Analytics is an umbrella term that includes many different types of data models, including business intelligence (BI), social data modeling, and business intelligence (BI) models. Business intelligence (BI) models are designed to analyze large sets of data sets and interpret the patterns that emerge.

Data models will continue to evolve as managers develop greater understandings of business problems and the ability to solve them. Data models are an essential part of data automation. Data automation will continue to impact all facets of data management. Trends in data modeling suggest that data modeling is not set in stone. However, data modeling trends point to a continued focus on efficiency, adaptability, and interoperability in data models.