The digital continues to advance; it is important to use data tools and analytics within the business, as it affects decision-making, which depends directly on how data is structured and organized. Data Modeling makes it possible to transform large volumes of information into a valuable and understandable resource.
It not only helps to improve data management, but also optimizes processes by detecting patterns and predicting trends, helping companies to anticipate and act positively.
In this article, we explore what Data Modeling is, its benefits and best practices to structure data and improve decision making within any company.
Data modeling is a process that consists of creating a visual and organized representation of the data held by a company.
It is generally done by means of diagrams and the objective is to ensure that all information is accessible, accurate and efficient for analysis.
Thanks to Data Modeling, companies can visualize data at different levels of abstraction, in order to understand the company's situation in depth and define an appropriate path or strategy for the organization.
It is the general overview model that presents the structure and content, but not the data plan in depth.
It is usually the starting point for data modeling, since it allows detecting the flow of information throughout the company and generating the data architecture.
This second level is more related to the general definition of Data Modeling, since it describes the content of the database and the data flow.
Details are added to the general structure of the conceptual model, but there are no specifications for the database as such, as it can be applied to different products or technologies.
This describes the details of how the logical array will be realized. It must have the necessary details to allow technologists to create the database structure in software and hardware to support the applications that will be used.
This level of abstraction of Data Modeling is specific to a given database software system.
Thanks to these three levels, data modeling provides a systematic approach to organizing data, from business understanding to technical implementation.
Companies usually collect a large amount of information from different sources for decision making, but just having data is not enough.
It is vital to analyze them to obtain actionable information that dictates the guidelines to act on certain scenarios and boost a company's profitability.
Accurate data analysis requires not only collecting but also efficiently processing the information. Data Modeling is very functional, as it allows them to thoroughly understand the data they have and make appropriate decisions for storage and management.
Thanks to this, benefits are obtained such as:
Data modeling can help for several purposes in terms of decision making within companies:
One of the great advantages of Data Modeling is that it improves the quality of the information obtained and, by structuring the data in a logical way, it avoids errors that can affect the operation of companies.
This helps analysts to work more effectively, with updated, consistent and clean data, improving the accuracy of reports and analysis.
In addition, by having a good organization, users can access this data efficiently and without wasting time.
Nowadays, organizations obtain data from different sources: social networks, ERP systems, external databases, etc.
Data Modeling facilitates the transmission of such data and allows to have them in a single coherent model, which enables them to be used more efficiently regardless of their origin.
This facilitates analysis, making more informed decisions, according to the global vision of the company and its environment.
Helps improve analytical processes by providing a structure that facilitates access to information for real-time reporting.
When data is properly organized, queries are easier, which helps to make decisions more quickly and effectively.
Data Modeling, in addition to organizing information, reduces duplication of data and actions.
By identifying and eradicating redundancies, organizations make fewer errors, as there is no duplication of records and incorrect decisions are not made because of erroneous or outdated information.
Some recommendations and techniques should be taken into account in data modeling to get the maximum benefits. Let's take a look at the main ones:
You need to ensure that all data is up to date so that it is properly managed and all teams, or new team members, can understand the data model and use it to their advantage, according to their specific needs.
This way, it is easier to identify any areas that require adjustments or improvements as the business evolves.
Data Modeling has to be designed to be scalable and flexible because as companies advance and grow, their data and analysis needs also change.
A data modeling that works well in the initial phase of the company may not be sufficient for when there are expansions, so it has to be able to adapt to new data sources and information volumes.
This process is not unique, but requires constant monitoring and adjustments to ensure its efficient operation.
This involves verifying that the areas can perform queries in an agile manner, that the indexes are well configured and that data quality is not diminished over time.
Likewise, adjustments must be made when required, such as restructuring relationships between tables or optimizing the database.
Through this, you ensure that Data Modeling remains effective as your organization's needs change.
Visual modeling platforms, such as flowcharts, are key to plan and design Data Modeling, especially when there is complex information, as it helps to make it more digestible and understandable for everyone.
In this way, teams also improve their communication and enhance the different processes, streamlining them and making fewer errors, by better understanding all the data.
Having a visual model makes it possible to identify improvements, detect failures and have a clear vision of how data interacts within a company.
There are different ways to visualize data modeling. Below, we are going to see the most common ones so that you can apply it:
It is based on the notion of real-world entities and the relationships between them. It creates a set of entities, relationships, general attributes and constraints and tries to analyze how they relate to each other.
This type of diagram is used to design or debug databases in the field of software engineering.
It is responsible for organizing data in the form of a tree with a root, to which other data are connected.
The hierarchy starts from the root and spreads out like a tree; in this way, it is easier to explain several relationships in real time with a single one-to-many relationship between two different types of data.
This Data Modeling allows for many-to-many relationships between connected nodes.
The data is organized in a graph-like structure, i.e., there is no fixed or hierarchical structure, and secondary nodes, known as members, may have several primary nodes, known as owners.
This data model is organized in tables, which have columns and rows, each one cataloging an attribute present in the entity.
In this way, it is easier to identify the relationships between data points.
Data modeling is a very effective tool for companies that want to stay competitive and make strategic decisions according to current demands.
In this context, in London Consulting Group we can help you achieve this goal, as we have the solution in Analytics and Data, with which we facilitate companies to understand their operations through the information generated. In this way, decision-making is more agile and effective internally and externally.
Through the implementation of appropriate tools, the generation of unreliable or outdated data is avoided, building instead dashboards and customized reports that help measure the success of the company and enhance decision-making.
Contact us and find out how we can help you optimize your decision-making through Data Modeling and other tools!