6 Vital Data Modeling Steps and Key Benefits to Know
Solutions Review’s Expert Insights Series is a collection of contributed articles written by industry experts in enterprise software categories. In this feature, Pimcore CEO Dietmar Rietsch offers an overview of the vital data modeling steps and key benefits of each.
As a foundational blueprint for transforming data into information, data modeling remains critical for creating an enterprise information system. It structures and organizes enterprise databases, converting data into high-utility information by describing entities, fields, and attributes while ensuring completeness and creating validation rules. By articulating the exact information demands of various business processes, it establishes a framework for capturing and generating usable information for data-led procedures such as process governance, business intelligence, and application architecture.
To that extent, it greatly influences the cost and time of designing a new application by providing uniform schemas and systematic methods for standardized, predictable approaches to defining data. Consequently, it streamlines complex workflows, improves output quality, enables application scalability, and secures data usage while reducing the time to value by detecting discrepancies faster and aiding long-term data maintenance. Additionally, once a data model is implemented efficiently, interconnections and data processing automation become more accessible, accurate, and effective.
Given below are the necessary data modeling steps:
Data Modeling Steps
Understanding Business Challenges
A highly efficient data model is one that addresses your business challenges and is able to translate raw data into a meaningful form for furthering product growth sustenance. Businesses must provide guidelines and criteria to cater to the needs of business stakeholders. For example, an eCommerce company will need to figure out the best way to create their data models in order to position themselves differently, target certain types of customers ensure campaign success rates, product visibility, , cart abandonment rates, and likewise.
This will guide them in strategizing better customer experience and offer-based personalized services. For mapping out specific business challenges and data needs, it is important to lay out the groundwork for prioritizing, gathering, storing, and altering data to meet business needs by making relevant data available for use. As data models are built around business objectives that guide how they are designed, it is imperative to specify a set of qualifiers using feedback from the stakeholders and to assimilate them into the framework.
Collect & Organize the Data
When collecting data, it is crucial to figure out the purpose of its application and the data type required to accomplish it. For the data to be compatible with its intended application, it must be standardized and organized. Various conventions govern data representation, model compilation, and business requirement communication to put data together in a data modeling system. The goal is to establish an application functionality overview by engaging with different business stakeholders, such as end-users, decision-makers, clients, and technical experts.
As data modeling aims to eliminate inaccurate, incomplete, or duplicate information in data, it identifies relevant data and collects and organizes it to decide where and how it should locate. Data requirements at this stage can be broad without focusing on specific variables. A point to note here is the data intended for import must be free of errors and duplicates, as unorganized and inconsistent data negatively impacts the quality of information.
Build a Conceptual Model
Referred to as domain models and typically created as part of gathering initial project requirements, conceptual models offer a big-picture view of what the system will contain, how it will organize, and what business rules it will involve. Being the core of a data model a conceptual model provides the means to convey to a business which entities are deemed essential to its success, and how they relate to one another within that business. Created and designed for industry audiences, conceptual models offer a pan-organization view of the business system and focuses on data representation in a real-world context.
For instance, conceptual data modeling in the industrial automation industry can be applied to controlling complex data sets within a product life cycle.
The conceptual model is not a part of hardware infrastructures such as data storage or software specifications. It is important to note that conceptual models do not need to define the specific attributes that relate to an entity. In general, a conceptual model will explicitly reveal what types of entities are relevant for a business to capture, what their characteristics are, what their constraints are, and how they relate to each other. Essentially, the creation of a conceptual model involves figuring out how to organize different types of data in order to meet the requirements of the company.
By letting users control and check for possible errors, pitfalls, and uncertainties in the designed model, data validation qualifies entities’ coherence, clarity, correctness, and relationships within the data model. Data validation eliminates errors such as misspellings and missing fields while also weeding out duplicates, as it filters the quality of the data set for importing. It ensures data consistency, as any inconsistency in the data can negatively impact the quality of information that is disseminated to your customers.
The data governance rules establish administrative norms in the data model to provide data access to authorized users. For the purposes of analyzing and validating data models and databases in a normalization technique, numerical identifiers, called keys, are assigned to groups of data to represent relationships between them without having to repeat the data. For example, in the retail industry, assigning each customer a unique key can link the address and order history without repeating the information in the table of customer names.
Build Data Taxonomy
Taxonomies serve as a structured framework for analyzing data and assisting with managing data in various situations. The main difference between them and metadata is that taxonomies help organize assets and content into hierarchical relationships. Using taxonomy to classify data makes it easier to search for it in a database or content management system. Taxonomy ensures that data is continuously categorized across multiple sources and channels so that businesses make informed decisions.
A structured hierarchical list makes it simpler for users to find information in a much more timely and organized manner within a collection. By providing a framework for the data, a taxonomy facilitates the analysis of the data and organizes products into categories to increase data processing efficiency. Furthermore, it ensures that data classification across various sources and channels remains consistent.
Define Attributes & Entities
It is necessary to identify the things, events, or concepts represented in the data set to be modeled before data modeling can begin. Each entity should be logically distinct and cohesive. Defining product attributes can accelerate product data enrichment, vital for increasing sales and leads. By defining entities precisely, businesses can see how they relate to one another. Typically, this is accomplished by translating each entity into a separate database table, with each row representing a single instance of a specific employee or project and each column representing an attribute.
Businesses must decide which variables to know and how they will format them based on identified and applied rules. Data models are created by examining existing information, identifying entities within the system, and determining where those entities fit together. Instead of highlighting lines of authority, it shows how data is arranged.
Making the most of Data Modeling
An effective data model enables developers, data architects, business analysts, and other stakeholders to understand the relationships between data in a database or data warehouse. Besides reducing software and database errors, it increases the enterprise’s documentation and system design consistency. It also improves application and database performance and facilitates data mapping throughout the organization. It is a backbone that allows communication between developers and business intelligence teams while simplifying, expediting, and streamlining the process of designing databases conceptually and logically. Effective data modeling reduces resource waste, predicts issues before they arise, enhances cross-functional communication, and ensures data quality, security, and accessibility while enforcing compliance.
In the last few years, some had predicted that data modeling would retire with the advent of disruptive technologies. However, it stood the test of time and remains more crucial today than ever, with the datasphere ever expanding in volume and veracity. Without a model to make sense of it, deciphering insights from such a massive amount of data would be like finding a needle in a haystack!