Primary Data Modeling Techniques

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Data Modeling Techniques

Data Modeling techniques are used to create a map or blueprint showing how an organization gathers and processes data. Data models help to define the logical structure for an organization’s data. Data Modeling techniques are necessary for businesses wanting to maximize and streamline their ability to analyze and understand data.

While developing the model, the data modeler will work with the business’s data and marketing team to determine the business’s needs.

It can take time to develop an effective Data Modeling program. However, from a big picture perspective, it is worth it, because it can save a significant amount of labor and money by finding errors before they occur.

Many modern data models include the use of automation and visual interfaces (this allows the user to directly manipulate graphic data on the screen).

Data Modeling begins with collecting information about the organization’s business requirements and how the data will be used. This information is used to establish a series of business rules, which are then translated into data structures and used to create a database design. Data Modeling techniques are often part of Data Strategy used to simplify data processing across all departments.

Ideally, a data model should be treated as a living document, which evolves along with the business’s changing needs.

A few years ago, several people predicted Data Modeling was going to fade away. With the rise of AI, machine learning, and self-service tools, it was thought Data Modeling would be unnecessary. But in reality, the opposite took place. It has become more necessary than ever. Without functional models, business analysts, database engineers, and data scientists would have no idea where their data comes from, and would consistently run into problems with Data Quality.

The Benefits of Using Data Modeling Techniques

Data Modeling techniques typically use standardized schemas to provide a consistent, uniform way of defining and organizing data resources throughout an organization. Failing to develop a functional data model can cause operational inefficiencies.

However, with proper Data Modeling techniques, businesses can gain a certain amount of operational resilience. The benefits of a well-designed data model include:

  • Reduced Costs: It is not unusual for organizations with poor Data Modeling techniques to decide they must redesign their data gathering process, due to increasing operational costs. However, if an organization develops a well-designed Data Modeling strategy early on, these additional costs never occur. Part of Data Modeling involves creating a system that defines how data is gathered. This reduces costs since the business’s needs are taken into account both before and while deploying Data Modeling techniques.
  • Enhanced Collaboration: Data Modeling promotes communication between business intelligence teams and software developers, promoting greater cooperation and fewer database development errors. The Data Modeling process should provide a common language, putting everyone on the same page.
  • Faster Time to Market: By deploying optimum Data Modeling practices, based on the needs of individual departments, businesses can reduce the amount of time needed to make their products and services available. The appropriate Data Modeling techniques can eliminate the bottlenecks organizations can experience when deploying a Data Strategy.
  • Data Quality: Many data research projects spend large amounts of time on data wrangling (transforming raw data into a usable format). An appropriate data model, however, requires business problems, or projects, be clearly defined, followed by an appropriate data gathering process. This has the effect of streamlining the entire data flow, and also enhances Data Quality.
  • Increased Consistency: The use of Data Modeling techniques help businesses to ensure data consistency, enabling data stored at different locations to be identical and “consistent.”

Data Modeling Challenges

There are a variety of challenges organizations face when Data Modeling. These challenges may provide a flawed data analysis, followed by false insights. Sometimes making a small change in the structure may end with modifying the entire application. It should be understood the use of Data Modeling can become very complicated, very quickly.

Two common challenges for Data Modeling are:

  • Finding Accurate (High Quality) Data Sources: Identifying both accurate and inaccurate data contributors (and creating two lists) can greatly benefit any organization doing research. The Data Modeling process can become essentially useless for good decision-making if sources are providing inaccurate data. Organizations should ensure the data is accurate before attempting to develop meaningful conclusions.
  • Data Silos: Data is often stored in a variety of locations, which includes hidden or overlooked internal resources. This, in turn, leads to analyzing incomplete data, producing a poor analysis and inaccurate insights. Businesses should make every effort to centralize their data and eliminate data silos prior to the Data Modeling process.

The Three Primary Data Models   

It is normal for businesses to develop a data model in three basic phases. (If possible, start off with a white board and get input from different sources.) Each phase of modeling helps to bring the data model closer to a representation of reality.

The three primary Data Modeling steps lay a foundation for the data model as a whole, and support the development of additional layers:

  • The conceptual model roughs out the entities that will be represented and defines the relationships that exist between them. This phase focuses on the extent, or range, of the database being modeled, and establishes the general rules for the database.
  • The logical model takes these entities, and develops the details of their qualities and characteristics, and how the relationships work. This phase defines the data structure, but is not focused on the technical aspects of the database and its construction.
  • The physical model moves the design from theory to reality. In this phase, how the Database Management technology will be used is the focus. The design of the tables actually making up the database are considered, as are the keys that will symbolize the relationships between the tables.

Additional Layers of Data Modeling Techniques

After the base model has been set up, there are different Data Modeling techniques available for creating and adding layers to the data model. Although the basic concepts of Data Modeling are used with all techniques, each of the additional technique is designed for a specific purpose. Listed below are some additional techniques:

  • Graph Data Modeling: This modeling technique also acts as a foundation for the object model and the entity-relationship model. The graph data model (referred to as being “whiteboard-friendly”) is often designed on a whiteboard. During this process it is decided which entities should be nodes, how relationships are linked, and which entities should be discarded. The result is a visual layout of the organization’s data entities, relationships and properties.

The process is repetitive and often relies on a trial-and-error process before eventually getting it right, which is followed by the continuing evolution of the data model.

  • Relational Data Modeling: Used to describe the different relationships between data entities. It minimizes complexity and provides a clear overview of the model. This technique organizes data into related tables and supports analytics.

Relational Data Modeling uses SQL (structured query language) to access and record tables.

  • Network Data Modeling: Offers a flexible map of objects and relationships between entities. Objects are placed inside of nodes with the relationships between each node being shown as an edge.

This Data Modeling technique makes conveying complex relationships as records easier, and can be linked with multiple “parent” records.

  • Entity-relationship Data Modeling: Shows entities and the relationships between them using a graphical format made up of relationships, entities, and attributes. It is used to define and describe data that is important for certain business processes. The model does not define these business processes, but presents business data in graphical form, and is usually drawn as boxes (entities) that are connected by lines (relationships).

The entity-relationship model is a high-level model used for defining data elements and their relationships to support a sophisticated information system.

  • Object-oriented Data Modeling: A model of objects based on real-life scenarios, which are represented as objects. In this model, real world problems are shown as objects with different features. Data and relationships are stored within a single structure. These single structures are referred to as objects. All of the objects show multiple relationships between them.

The object-oriented Data Modeling technique is excellent for dealing with complex and constantly changing data.

  • Dimensional Data Modeling: Used primarily for data warehousing purposes. This model helps organize large sets of data into manageable chunks. Dimensional Data Modeling uses fact tables. These contain rows and columns that group related information dealing with a specific subject. A strength of this model is its ability to easily create detailed reports on specific data subsets (for example, customer-specific information).

A weakness with the dimensional data model is that small changes can cause a ripple effect through the entire design.

  • Hierarchical Data Modeling: Follows a tree-like structure, with its nodes sorted into a particular order. It was a popular concept for a variety of fields, such as computer science, design, architecture, systematic biology, mathematics, and social sciences. This model lost popularity, because of its clumsiness in retrieving and accessing data, but may still be useful for certain projects, such as storing geographic information or filing systems.

Hierarchical Data Modeling continues to be used by the telecommunication, banking, and healthcare industries.

Supplemental Videos

Two webinars are available that provide useful supplemental information and some extremely useful visuals. The first is presented by Peter Aiken, and titled DataEd Webinar: Data Modeling Fundamentals. The second is titled DAS Webinar: Data Modeling Techniques and presented by Donna Burbank, the Managing Director of Global Data Strategy, Ltd.

What’s Next?

Some significant Data Modeling trends for 2022 include the modeling of data lakes, new toolkits, and an increase of non-relational modeling techniques. These trends will continue to grow as the importance of Data Modeling grows into the future.

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