Data Modeling: The “How” of Data Management and Effective Analysis

Data modeling is the foundation of 21st century business and data-informed projects. As industry leaders embrace trends such as IoT and digital twins, this process is the key to managing information as a resource. However, this process is complex, which is why this blog will briefly explain the process and how it relates to decision making.

What is data modeling?

In the physical world, a model replicates a situation on a small scale to guide individuals through it, enlightening the circumstances to capitalize upon in the large scale. It lays out the available resources in a digestible form to manage and use later.

Data modeling in the digital and information world functions similarly. A data model is a digital data organization system: it represents the vast volume of data in a condensed space. It is implemented in a business’s technology backend and used to guide decision-making on the commerce front-end.

Data modeling should not be confused with database building or data analytics. Data modeling tells what the business requires while a database and analysis give ideas of how to achieve goals. A database, built on the foundation of the data model, is a structured, visual graphic interface form of the defined data. Analysis is any number of tools used to see the patterns in the displayed data or deliver reports on performance.

What does the data modeling process look like?

There are three stages of data modeling. Depending on the purpose of the data modeling, a business team may only go to the logical model. However, most projects will go all the way to the physical data level.

1. Conceptual data model — This is the first stage of modeling where requirements are outlined. In this stage, entities and subtypes of these entities within the data are defined. Their attributes are outlined, and the relationships between entities are identified. The rules that maintain the integrity of the model are determined as well. At this stage, there is no consideration of how the data is visualized or used.
2. Logical data modeling — This is the second stage of modeling where the conceptual model is organized in a logical manner. These are descriptions of how the concepts outlined in the first stage are structured. For example, the logical model may say it is tabular data or describe it as object-oriented classes.
3. Physical data modeling — The final layer, this stage specifies the actual fields created to be displayed in a database. It also details where the data is stored, in what form and to what extent or manner it can be queried.

A company may have multiple data models, each specific to a different branch or project within the enterprise. The models may change over time as new activities generate new data that recontextualizes archived data. Rather than being hard and fast rules that must be redone every few months or years, they are living digital documents that are updated to reflect changes.

There is no one right way to model data or notate it. Furthermore, there are many tools one can use in data modeling. A retailer modeling data may not need the same data model as a finance executive. Many companies choose models that allow agility. Some are pre-built, like IBM’s industry data models. The biggest factor to account for is standardization across models for cross-company use.

What is data modeling for?

Data modeling is used by information systems. An information system is a process or program that uses information to create tasks that advance the project or company. An example of an information system is a Customer Relationship Management System (CRM). These systems advance marketing and sales efforts in tandem by tracking customer activities and product trends. A single enterprise may have many different information systems in use throughout it.

Data modeling is especially helpful for organizations that rely on data producing systems. Since it surveys how the data is managed in an instance, data modeling will help streamline IoT data management and diminish dark data incidences. It allows for a high-level exploration of collected information and established concepts that results in more pattern analysis and relationship diagraming. Data modeling does the nitty gritty for big picture results.

Data modeling in action: Digital Twins

Digital twins, a vital tool for industry, have blossomed in use in transportation, healthcare and military. They use physical data modeling in complex interfaces beyond spreadsheets. Their entities are beyond sensor points and include weather patterns, whole machines and other large items. The living document nature of data modeling and its flexible organization give digital twins its power.

From a Sealevel blog on the subject: “A digital twin, according to the paper, is an exact digital model of the factory reproduced on a 1:1 scale, using real-time data to ensure that the digital twin (the model) is identical to the physical twin (the manufacturing facility) at all times.”

This physical layer of modeling gives industry a powerful diagnostic tool. When potential changes are made to a project, those can be modeled on the digital twin and queried for reports. New relationships can be defined on the twin model and tested for success in analysis before being implemented on the original model.

Data modeling is no longer just in the toolbox of developers and engineers. It belongs to business analysts, financial markets, cold chain management and transportation or any other industry that relies heavily on data.