Data modeling is a process for conceptualizing the relationships between different types of information in an organization. Data models help users across disciplines store and interact with data more effectively for a variety of use cases.
► This is an introduction to how data modeling can increase the efficiency of IT landscapes.
The formalized practice of data modeling has occurred since the 1960s and has grown steadily in importance ever since. Today it is commonly utilized by IT professionals to identify the requirements necessary for handling data for the purposes of better supporting the business objects of an organization. As such, data modeling has become an integral part of maintaining IT landscape and ensuring an efficient means of storing and analyzing data.
When creating a data model to represent the infrastructure of an information system, it is important to make the models logical and easily understandable for those requiring insights on data objects in relation to their business needs. Just like how when moving into a new house, individuals tend to map out where to place furniture, electronics, etc., data modeling minimizes the difficulty to adapting to new environments and simplifies decision-making in complex situations. Overall, the process of data modeling entails defining the attributes of all data objects and connecting the relationships between the different types of information that need to be stored. This map, or diagram, helps IT professionals understand what key data needs to be stored and easily retrieved.
Data models are a representation of data objects and the relationships between those objects. This visual guide helps when performing data governance and creating data policies. Data modeling is a way to help organizations become more data-driven.
Source: LeanIX GmbH
IT professionals design data model structures based on the actual ways in which IT entities, personnel, and business capabilities interface with one another. Such objects become the main categories, or boxes, in the model. These objects are all interconnected, and the connections (or relationships) between the items are used to visualize data and guide policies on governing this data. Using solutions from LeanIX as an example, objects in an enterprise architecture data model can include Provider, IT Component, Application, Interface, Tech Stack, Project, Data Object, and User Group.
Data models reflect data that are absolutely essential for a business's continuing operation. Users can take advantage of having this information neatly structured to identify technical and functional overlap, foster business intelligence, and optimize how data are organized. Controlling how and where data interacts in either a server or cloud environment is crucial for implementing systems that equally benefit business and IT professionals. Further, a data model can be used to validate the technical and functional benefits of current and future data objects while also revealing if databases are correctly represented. If data is not accurately represented, there is a greater likelihood of false outputs from analytics reports and miscalculated strategic decisions.
A data model's structure helps align databases across the physical, conceptual, and logical levels. Thanks to easy-to-understand representations of the underlying data, it is particularly helpful for developers when creating physical database (e.g., missing or redundant data can be easily spotted to save time for developers). Though it easy to become overwhelmed by manual documentation efforts when outlining a data model, the efforts are invaluable when upgrading infrastructure.
Organizations can benefit from three specific types of data models depending on the information needing to be delivered. The three different types of data models are conceptual, logical, and physical.
Conceptual data models: Conceptual models reflect high-level and static business structures. In most cases, they are only generalized representations highlighting which business objects are involved in an information system.
Logical data models: Logical data models focus on data attributes, IT entity types, and relationships between the entities. A logical data model is useful for understanding the nature and compositions of data but not its actual implementation.
Physical data models: Physical data modes cover aspects related to the design and implementation of databases. These cover the structure of databases, including all relational databases and objects.
For theorizing new solutions and efficiently organizing rules, a conceptual model should be employed. This model is commonly used by data architects and stakeholders. Physical and logical data models, on the other hand, are useful for expressing how structures should be executed. Logical data models are commonly used by business analysts and data architects to help develop a database management system, a technical map of structures and rules for the model. Physical data models are typically employed by developers and database analysts to show the execution of a structure with the use of a database management system.
Choosing the correct type of data model for an organization rests on knowing the specific needs of a business. However, significant attention must be placed on the variety of stakeholder preferences involved in building a working data model. Data science professionals, for example, are likely to want models offering full visual views — the likes of which provided with physical and logical data models. Conversely, business representatives interested more in outcomes rather than technical details are likely to select a conceptual data model.
There are benefits to using data models of all types. The first of which is being able to reliably ensure that data objects in an IT landscape are correctly represented. This information can then be utilized to define connections between primary and foreign keys, tables, and procedures. A data model can then be used to build a physical database if sufficiently detailed. Data models can also be leveraged to communicate to business stakeholders throughout organizations and for locating accurate sources of data to auto-fill the model.
Unfortunately, there are also some challenges when using data models. In order to effectively create a data model, the creator should have a firm understanding of the characteristics of the data that is already physically stored. A data model is also a system that can result in complex application development, thereby making these process difficult to manage as well. Further, all changes to the data model, both large and small, require developers to modify the entire application system.
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There are three fundamental data modeling techniques: Entity Relationship Diagrams (ERDs), Unified Modeling Language Diagrams (UMLs), and Data Dictionaries.
ERD: An ERD is the default technique for data modeling and works especially well when modeling tabular data. This technique involves making graphical representations of data objects alongside their attributes and relationships. ERDs are very useful when designing traditional and Excel-based databases. They are also ideal for securing clear visuals of database schemas, along with top-level data.
UML: UMLs encompass a series of notations for designing and modeling information structures. Used by many as a general-purpose software notation, UMLs reflect either the behavior or structure of data objects and employ different diagram types for doing so. One of these diagrams is a class diagram, which relates to defining the classes, methods, and attributes of databases.
Data dictionaries: Data dictionaries are based on a tabular definition of data assets. This is a grouping of tables and data sets with an accompanying list of attributes and columns. Other optional sections of a data dictionary are item descriptions, additional constraints, and relationships between columns and tables.
Data modeling is essential for standardizing organizational assets and optimizing information systems. Though the practice has occurred in various forms for many years, its importance has grown exponentially in the present era of DevOps. The process of data modeling helps IT professionals define data requirements to support the business objects of an organization. To learn more about data modeling with and at LeanIX, here is information on our flexible data model.
This poster leverages examples of visual data objects to enable you to map the data objects of your organozation.
Whether you are in the banking industry, insurance industry, automotive, or logistics; this generic data object template is the perfect starting point.
We have included tips and best practices on how to get started with the modeling of your data objects to get a complete overview of your IT landscape.
What are the advantages of data modeling?