One of the most important software engineering processes is data modeling. The process of creating data models for information systems, data modeling is used by data engineers, data architects, database administrators, software developers, and other information technology (IT) professionals to communicate connections between data structures, create databases, and implement applications.
Data modeling can be a complex process. Read this guide to learn more about data modeling and how it works. We'll cover what is data modeling with examples, data modeling types, data modeling techniques, modeling methodologies, and the benefits and potential challenges of data modeling. By the end of this guide, you'll know how to hire the right team of data scientists and analysts.
What is Data Modeling?
Data or database modeling involves creating a data model — a visual representation of an entire information system or parts of it.
Data analysts, scientists, and other data science professionals regularly use data models to create databases, manage data for processing, populate data warehouses, and create applications that let users access information in meaningful ways. They also use data modeling to ensure regulatory compliance and consistency in naming, default values, and semantics.
A successful data model should answer the following questions:
- What kind of information does my company store?
- Where does the information come from? Where will it go?
- How do we structure our data? What data formats do we use?
- What are our processes? What is our business context?
What is the Purpose of Data Modeling?
The purpose of data modeling is to create consistent, high-quality structured data for running enterprise applications and achieving consistent results. It also helps you identify:
- Business concepts
- The possible relationships between various pieces of data
- Queries that can be run against that information
- Business requirements and processes
Types of Data Modeling
There are three types of data modeling: physical models, logical models, and conceptual models.
This type of model describes how the data system will be implemented through an actual database management system (DBMS). As a result, physical models are the least abstract type of data model.
Physical data models have finalized designs that can be implemented as part of a relational database — databases that organize data into columns and rows that form tables. They also contain relationships between tables that show the nullability and cardinality of relationships. Additionally, they:
- Offer database abstraction
- Provide database schemata — abstract designs that represent a database's data storage — for how the data will be physically stored in a database. A good example of a schema is a star schema, a database organizational structure that uses one or more fact tables to develop dimensional data marts and data warehouses.
- Help you visualize the database structure by replicating database constraints, column keys, triggers, indexes, and other relational DBMS (RDBMS) features
- Have columns with exact lengths assigned, data types, and default values
Developers and database administrators typically create physical models to:
- Implement databases
- Define how databases will be used or implemented
Logical data models (LDMs) are more abstract than physical models and provide more detail about the relationships and concepts between data elements. They exist independent of the physical database that outlines how the data will be implemented.
Data science professionals use logical models to create visual understandings of data attributes, entities, relationships, and keys. The purpose of creating a logical data model is to create a technical map of underlying data structures and data.
Also known as domain models, conceptual models are the most abstract data model type. They provide a big-picture view of what the data system contains, which business rules are involved, and how it will be managed.
Conceptual data models are created independently from hardware specifications like location and software specifications like DBMS technology and vendor. The purpose of conceptual models is to gather initial project requirements, track performance measures, and represent data as users see it in the "real world." Conceptual models do not identify the physical characteristics or processing flow of data.
Data architects and business stakeholders are usually responsible for creating conceptual data models. The aim of these data models is to define, organize, and set the scope of various business rules and concepts.
Data Modeling Techniques
Data modeling has rapidly evolved in the past few decades. Accordingly, there are many data modeling techniques and models, such as:
The network model is a flexible way to represent objects and their relationships. When viewed as a graph where relationship types and nodes are arcs, its schema is not restricted to being a lattice or hierarchy. In other words, it allows each record to have multiple child and parent records, creating a generalized graph structure.
The main argument in favor of network data models was that they allowed more natural relationship modeling between entities. As a result, network data models were adopted by the CODASYL Data Base Task Group in 1969 and went through a major update in 1971. Several network database systems were also popular on mainframes and minicomputers in the 1970s. However, they were soon replaced by relational databases in the 1980s.
Also known as a one-to-many database, the hierarchical model is a data model where lower levels are subordinated under a hierarchy of increasingly higher-level units. The result is a tree-like structure where each child can only have one parent, and each parent record can have one or more child records. Applications and users must traverse the tree starting from the root node to fetch data from a hierarchical model.
Created in the 1960s by IBM, the hierarchical model was one of the first database models to receive wide acceptance, mostly due to its ability to tie one part of data to another. Like the network model, it lost popularity when the relational model came out. That's because the relational model is much more flexible.
The relational model (RM) represents databases as a collection of relations.
The primary storage unit in the relational model, a relation is a table of values where every row is a collection of related data values and denotes a real-world relationship or entity. Each record is stored in a table in a row known as a tuple, while attributes of the data are defined in columns known as fields.
Examples of well-known RMs include:
- Microsoft Access
- SQL Server
- Informix Dynamic Server
- Oracle Rdb
The relational database started with E. F. Codd's groundbreaking 1970 paper, A Relational Model of Data for Large Shared Data Banks, which established that data should be independent of any storage system or hardware and provided for automatic navigation. Simply put, Codd believed that data should be stored in tables and that relationships should exist between the different datasets or tables.
Most modern databases and database systems are based on the relational system. However, an increasing number of teams and companies have started adopting non-relational databases, which don't use the tabular schema of columns and rows found in relational databases. Developers and other IT professionals often use non-relational databases to organize large quantities of diverse and complex data.
The entity-relationship (ER) model is a high-level data model that establishes a conceptual design for databases. Essentially, it's a flowchart that shows how entities — such as objects, people, and concepts — relate to each other in a system. Most ER models use defined sets of symbols — such as diamonds, rectangles, ovals, and connecting lines — to demonstrate the relationship between entities, attributes, and relationships.
In many ways, ER models resemble data structure diagrams (DSDs), which focus on the relationship of elements in entities rather than relationships between entities. ER models are also regularly used with data flow diagrams (DFDs), which provide information about the inputs and outputs of each entity.
IT professionals typically use entity-relationship models for the following:
- Database design and troubleshooting: Developers, data architects, and data engineers use ER diagrams to model and design relational databases' business rules and logic. They also use it to determine requirements for projects and model certain database(s).
- Business information systems: These formal diagrams are used to create or analyze relational databases for business processes. They are great for simplifying processes, improving results, and uncovering information more efficiently and effectively.
- Research: Many researchers use ER models to create useful databases for analyzing historical and current data.
- Education: Educational providers like schools and colleges store use entity-relationship models to store educational information for later retrieval.
The dimensional model (DM) is a data structure technique optimized for storing data in a data warehouse. A data warehouse is a system for reporting and analyzing data that is a vital component of business intelligence (BI).
The purpose of DMs is to optimize the database for faster data retrieval. Accordingly, DMs are designed to read, analyze, and summarize numeric data like balances, values, weights, and counts in a data warehouse.
DMs contain several important elements, including:
- Facts: These are the metrics, facts, or measurements from your business process. An example would be a yearly sales number.
- Dimensions: These provide the context for a business process event. In other words, they establish the who, what, and where of a fact.
- Attributes: These are the characteristics of the dimensions in DMs.
- Fact tables: These are primary tables in DMs. They contain foreign keys to dimension tables and measurements or facts. Foreign keys are columns or sets of columns in a table with values that correspond to the primary key's values in another table.
- Dimension tables: These tables contain dimensions of facts and are joined to fact tables through foreign keys.
Finally, object-oriented models (OOMs) visualize concepts, data, processes, ideas, and combinations of these as capsules called objects. Objects support several interfaces through which they communicate with other objects. Programmers often use them to encapsulate things so they can use them in other parts of the model or program.
There are three types of OOMs:
- Class model: This model displays all of the classes present in the system. It also displays the behavior and attributes associated with the objects.
- State model: This model describes the aspects of objects that are concerned with operation sequencing and time.
- Interaction model: This model is used to show different interactions between objects and how objects collaborate to achieve system behavior. Programmers typically show the interaction model via use case, activity, and sequence diagrams.
Data Modeling Methodologies
Data modeling doesn't just have a variety of model types. It also has three modeling methodologies: bottom-up, top-down, and a combination of both.
In the bottom-up model, IT professionals specify individual system parts in detail. They link the parts to create larger components until they create a complete system.
In the top-down model, IT professionals create a system overview without delving into details for any part of it. They then refine each part, defining each section until the specifications are detailed enough to validate the model.
Combination of Both
If bottom-up and top-down don't fit your projects or team, you can use both modeling methodologies. IT professionals can do this by training a fragment-based segmentation algorithm that simultaneously takes into account both top-down and bottom-up cues.
Data modeling is vital to software projects. It allows for a thorough understanding of what your database should look like and how you can build an application or software on top of it.
Here's a breakdown of data modeling's main benefits:
Faster and More Cost-Effective Software and Application Development
Data modeling has a large impact on the time and cost to create a new application. Without a data model, your team would have to spend a lot of time gathering requirements from users and manually coding the database structure. They would also have to update the code and the database, which can be expensive and time-consuming, especially when you need to make multiple changes.
Going through a large stack of resumes and portfolios can be daunting, especially when you're already up to your nose in projects.
Fortunately, data modeling techniques can help you locate the right hire in minutes. All you have to do is hire data science professionals to build and analyze data models. You can then identify the most promising applicants in your talent pipeline. Data-driven recruitment can also help you:
- Unearth hiring issues: For instance, you can review your application form conversion rates to see if you need to redesign your LinkedIn profile or modify your questions. You can also look at applicant demographics to see if you are unconsciously discriminating against marginalized groups.
- Increase efficiency and productivity: A data-driven recruitment process allows you to keep tabs on productivity metrics, such as time-to-hire, source of hire, candidate experience scores, and the number of emails your hiring team exchanges with candidates before hiring them. You can then use these metrics to spot bottlenecks and identify potential recruiting process improvements, such as referral programs.
- Determine important ratios: Data-driven recruitment gives you access to important ratios like recruitment yield ratios, which can show you how many candidates you need to interview before making a single hire. If you need to interview hundreds of candidates before making a single hire, consider partnering with third-party solutions called talent marketplaces, like Revelo. Reliable and efficient, talent marketplaces use an algorithm to match you with the best talent for your project or team.
Early Detection of Data Errors and Issues
Data modeling can also help development and marketing teams spot errors early in the SDLC. That's because data models create an accurate view of how users interact with an app or software, down to details like how often they visit certain pages and what errors they encounter.
Without a data modeling solution, your team may not discover data errors and issues until the process is running. For instance, they may not realize there is a data issue until a customer makes a purchase via your app and receives a "bad data" error message.
Although data modeling provides many benefits, it also presents challenges, including:
- Difficulty of learning: Data modeling tools are hard to master. Even those with data science backgrounds may not have the experience and knowledge to successfully handle them. Additionally, users may find certain concepts difficult to grasp. For example, they may not know what is the most important consideration in data modeling. They may also fail to understand what is data modeling in SQL.
- Limited talent pool: Many developers have limited knowledge and experience with data modeling. As a result, you should specifically hire experienced data architects and engineers for data modeling. This can be hard to do, especially when your connections and hiring budget are limited.
- Not knowing where to start: Data modeling can be challenging to perform without proper documentation. You also need the right team and systems architecture to perform data modeling. For instance, if you only have a data analyst and you don't have the budget to hire a data engineer or architect, creating accurate and helpful data models will be an uphill battle.
Hire the Right Development Team
Data modeling offers many advantages, including faster and more cost-effective software development, higher-quality applications, and early detection of data errors and issues.
If you're interested in hiring a data modeling team, consider joining Revelo. As Latin America's premier tech talent marketplace, we offer access to 300,000 MAMAA-caliber IT professionals, including data analysts, data scientists, data architects, data engineers, and software developers. We have rigorously pre-vetted our talent on their English proficiency and soft and technical skills, so you don't have to.
Fill in this form today to get started on hiring the right team.