As organizations come to see their data as strategic assets, firms are finding that they need to make analyzing, understanding, and managing these assets a priority. What data modeling is, essentially, is a way to visualize metadata, which lies at the heart of enterprise data management and governance, and better understand complex datasets. A data model can help break down the complexity of diverse data sources and help users find and make connections in their data—a must when using big data for analytics.
With today’s modern BI and analytics tools, data analysts lacking data engineering skills and business users, who might not have known what data modeling is until now, can easily design, define, and develop their data models. Smart, modern data analytics platforms like Qlik Sense® can even automate the data modeling process, making it easier than ever before for non-technical users to process and work with data.
For those unfamiliar with the term, what data modeling is, essentially, is a formal representation of the content and structure of data and determines how data is stored and accessed. It shows the logical inter-relationships and data flow between different data elements, helping users map their data landscapes. A data model is used to organize a company’s data for internally developed applications and systems, for reporting and analytics, for example, or for data residing in a data warehouse or BI environment. It helps ensure that information flowing in and out of an application or system will be compatible and interoperable.
A data model is critical because it provides the structure for supporting analytical needs. It helps:
In considering what a data model is, it helps to understand the traditional process of data modeling. Manually building a data model typically involves three stages progressing from the conceptual to the physical.
In the first stage, business stakeholders and data architects outline the requirements for an information system, defining the entities within the data and identifying their attributes and relationships.
In the second stage, business analysts and data architects organize the concepts outlined in the first stage in a logical manner. They create a technical map showing the structure of the data elements, or how the attributes, entities, and relationships among entities will be implemented.
This logical model can then serve as the basis for a physical model, which details where the data will be stored, in what format, and how it can be queried.
Looking at the techniques used for data modeling is another way we can better understand what a data model is. There are two basic techniques used for building data models. The traditional modeling technique used for operational systems is entity-relationship modeling. In this model, an “entity” represents an object in the real world such as an employee or project while “attributes” represent the entity’s properties and “relationships” represent the associations among entities.
Another technique used for data modeling is the dimensional model, which uses facts and dimensions instead of entities, attributes, and relationships and is a more efficient way of representing data. “Facts” are generally numerical information such as granular transaction details or metrics used to measure business processes while “dimensions” describe the context of a transaction or business process event.
Because the dimensional model allows for more rapid querying and analyses of data, it is frequently used for performance management, analytic processing, reporting including executive and operational KPI reporting, and advanced analytics including data mining and predictive analytics. It is also the de facto standard for managing data in a data warehouse because of its predictability, absence of bias, and extensibility. The two most used dimensional models are the star schema, which offers better query performance, and the snowflake schema, which provides better referential integrity.
The benefits of data modeling done correctly are many. Through visualizing metadata and schema, it helps all stakeholders involved in the process better understand the connections between different data elements while fostering data literacy across users. It helps augment and accelerate data governance efforts, improve the agility of data architectures, and ensure data integrity. And with tools like Qlik Sense®, data modeling doesn’t have to be difficult.
Qlik Sense is an innovative and robust BI and analytics platform that allows users of all skill levels to explore data freely using interactive selections and global searches. Powered by a one-of-a-kind Associative Engine, Qlik automatically indexes all relationships in your data, so there’s no need to fully clean or model data in advance. With smart, self-service data preparation, non-technical users can visually combine, transform, and load data from multiple sources using drag-and-drop functionality. Intelligent data profiling shows users the relationships amongst tables and how they would associate, letting each user create their own data model or use the suggested associations to build one faster. No complex data modeling is required. As users remove or add tables, Qlik Sense automatically adjusts the data model.
Designed specifically for interactive, free-form data exploration and analysis, Qlik Sense instantly updates analytics and highlights relationships in the data, exposing both associated and unrelated values with each click. Critical thinking isn’t interrupted by data preparation tasks, and users are empowered to explore data in any direction—not limited by pre-aggregated data or predefined queries.
It refers to the process of mapping out the relationships between items in a dataset and designing data structures for use in BI tools, analytics applications, and other use cases. It helps users better understand the data landscape and enables organizations to better analyze and extract value from their data stores.
It provides an overview, or visual representation, of data that will be used for an application, such as analytics or reporting, for example, or to build a database or information system. It determines how data will be exposed to the user, helps improve data quality, and ensures consistency in naming conventions, default values, semantics, and access policies. It creates optimal conditions for data analysis.
They are important because they provide the structure necessary for supporting reporting and analytical tasks. They help to ensure the functionality of an application or system by validating business user requirements, prevent data redundancy, generate confidence that the data used is trusted and reliable, and set the stage for data analysis.