The importance of data modeling to business analysis

Nowadays, organizations collect large amounts of data. They use business analysis as a systematic approach to analyze the data, identify patterns, and draw meaningful insights to facilitate strategic decision-making. Central to this process is data modeling, a technique for graphically representing complex data sets for analytical purposes.

In the following sections, we define the fundamental aspects of business analysis and data modeling. We also explore the role of data modeling in business analysis, present its advantages and disadvantages, and conclude by outlining the usefulness of Cardanit in this context.

What is business analysis?

Business analysis enables organizational change and is essential in various fields, including software development, product management, and project management. It involves understanding the structure, policies, and operations of an organization, identifying business needs, and recommending suitable solutions (for example, technology or process improvements) to achieve the organization's goals. Many of the solutions often involve the development of new or the improvement of existing IT systems.

Business analysts perform all business analysis activities in an organization. They work across all organizational levels and use various techniques to analyze and assess the business environment, processes, and systems. Business analysts have a wide range of tasks, including but not limited to:

• identifying, clarifying, and documenting business objectives, challenges, and opportunities;
• gathering information through interviews, surveys, workshops, and other techniques;
• using tools and techniques (such as data modeling and process modeling) to analyze the collected information to understand patterns, trends, and potential issues;
• creating visual models, diagrams, and reports to convey complex technical information to non-technical stakeholders;
• defining the objectives and requirements for projects or programs;
• evaluating actions to improve a business system;
• developing strategies to manage change management processes.

Overall, business analysis as a practice helps organizations make informed decisions, improve processes, increase efficiency, and achieve business goals.

What is data?

Before exploring what data modeling is and how it connects to business analysis, let's first understand what data is.

The existing literature and dictionaries provide many different definitions of data, all of which have one thing in common. And that's the understanding that data is 'recorded facts, events, transactions and similar.' Data refers to raw facts, figures, and symbols that can represent data points. It's unprocessed and lacks context or meaning - for instance, 27.9, $51 and 2023-9-17 are forms of raw input.

Furthermore, the definition of data is usually used for defining information. An often-heard description of information is that it is ‘data placed in context’. Thus, if data has some meaning attributed to it, it becomes information. In other words, information represents processed, organized, and meaningful data that its recipient can use. It provides context, relevance, and purpose to the raw facts.

Everything said until now is correct, and data and information are indeed related concepts, each with a distinct meaning. However, when it comes to business analysis and data created and held in IT or information systems, the interpretation of what data is can take a slightly different direction.

According to the standard ISO/IEC 2382-1 1993, data is 'a re-interpretable representation of information in a formalized manner suitable for communication, interpretation or processing.' Thus, information can become data, which is then interpreted and has meaning applied to it, making it information again.

Business analysts usually work with both data and information. They use data modeling techniques to structure and understand the data and convert it into valuable information for the organization.

What is data modeling?

Generally, data modeling is the process of graphically representing data structures and their relationships within a system or database in a precise form called the data model. The process may involve the creation of three data models defined at different abstraction levels, namely:

• Conceptual data model - this model defines high-level entities or concepts and their relationships in a business, leaving further details about the entities (for example, their attributes or types) for the next steps of modeling. The model is a rather technology-independent specification of the data in the database. The main focus is on what data is being used and how it's related, without including technical details.
• Logical data model - this model defines the structure of the data elements and their relationships, regardless of how the data will be stored physically. It's a refined version of the conceptual model and provides a blueprint for the physical database design.
• Physical data model - this model represents how the data will be stored in the database. The model incorporates any changes necessary to achieve adequate performance and includes details like data types and indexes. It's the basis for actual database implementation.

There are various data modeling tools and techniques available to business professionals. For example, Entity-Relationship Diagrams (ERDs), Data Flow Diagrams (DFD), Unified Modeling Language (UML) diagrams, and Data Definition Language (DDL) statements.

Finally, data modeling is important for designing and developing databases, software applications, and information systems within an organization.

What is a good data model?

A good data model serves as a roadmap for organizing and structuring data to meet business requirements. It ensures data accuracy, consistency, and efficiency. It represents all the information requirements of an organization in a clear, concise, and explicit way to avoid misunderstandings and errors during implementation. Any overlapping and conflicting requirements are excluded from it.

Furthermore, a good data model should tick the following boxes:

• accurately represents the real-world entities, their attributes, and relationships;
• covers all the necessary aspects of the business requirements, including all relevant entities, attributes, relationships, constraints, and business rules;
• anticipates future requirements to some extent, ensuring scalability and adaptability;
• should maintain consistency across all its components;
• is properly normalized to eliminate redundancy and improve data integrity;
• uses standardized notations, making it clear and easy to understand by all stakeholders;
• can handle increased data volumes and user loads without significant loss in performance;
• can accommodate changes in business requirements without requiring a complete redesign;
• is optimized for efficient data retrieval and manipulation, considering all the types of queries to be performed on the data;
• aligns with the overall business objectives, supporting the organization's business processes, reporting needs, and analytical requirements.

If a data model has all these characteristics, it becomes an invaluable asset for the organization and enables effective data management, analysis, and decision-making.

The role of data modeling in business analysis

Data modeling and business analysis are two closely related concepts in the field of business management and information technology. They're both essential for understanding, organizing, and optimizing business processes and data resources.

However, it is data modeling that plays a key role in business analysis in many ways - for instance:

• Data modeling helps business analysts understand and document the data requirements of an organization, as well as gain insights into what kind of data is necessary to support various business processes.
• Data modeling provides a clear and standardized way of illustrating data structures, allowing business analysts to effectively communicate data requirements to different stakeholders.
• Business analysts can use data models to identify gaps in existing data structures and inconsistencies in business rules, hindering the successful implementation of business processes and systems.
• Data models provide a framework for organizing the data that business analysts need to analyze to make informed decisions and provide recommendations to stakeholders.
• Data models help software developers understand the data requirements and design systems that align with the business needs.
• By using data modeling, business analysts can design integrated data structures that facilitate the data exchange between different systems and applications.
• Data models provide business analysts with a structured way to assess how changes in data requirements affect business processes and ensure their continuous alignment with business objectives.

Overall, data modeling is essential for business analysis as it helps understand, document, and communicate complex business processes and data requirements. Without it, business analysts cannot develop solutions that align with business goals and efficiently manage and use data resources.

Why use data models in business analysis

As previously mentioned, data models have an important role in business analysis. They provide a clear graphic representation of complex processes and enhance stakeholder communication. However, although data models facilitate understanding, their creation and maintenance require considerable time and effort.

The following sections present the key advantages and disadvantages of using data models in business analysis.

Data modeling advantages

Data modeling brings many benefits to business analysis. For instance, data models:

• help business analysts and stakeholders gain a clear understanding of the structure, relationships, and flow of data within an organization;
• act as a common language between technical and non-technical stakeholders;
• assist in defining validation rules and data constraints, ensuring data quality;
• help maintain a standardized view of data across the entire organization by requiring consistency and accuracy in data definitions;
• serve as a basis for designing databases, data storage systems, and other information systems that align with business requirements;
• aid in data-driven decision-making;
• provide the structure necessary for effective data governance;
• act as a blueprint for organizing and structuring data, facilitating data exchange between different systems.

Data modeling challenges

Although data modeling does offer numerous advantages to business analysts, it also presents certain challenges - for instance:

• The process of creating detailed data models is often very time-consuming, especially for large and complex systems.
• It's difficult to balance completeness and simplicity in data models as they can quickly become overly complex and difficult to understand, maintain, and modify.
• Existing stakeholders might resist changes proposed by new data models, especially if they disrupt established processes.
• Developing and implementing accurate and effective data models requires expertise in data modeling techniques and tools.
• Data models focus on structural aspects and might not capture all contextual or semantic nuances related to data.

How does Cardanit help in this context?

As an online BPMN and DMN modeler, Cardanit can significantly facilitate business analysts in the process of data modeling.

Business analysts can use Cardanit to create intuitive diagrams illustrating how data flows within business processes and how decisions are made, consequently helping them understand complex processes and data relationships. They can easily link business rules and decision logic directly to corresponding process steps. Cardanit helps them identify dependencies and optimize data-driven decisions within the processes.

Cloud-hosted, Cardanit also facilitates collaborative work by allowing multiple users to work on the same diagrams in real time. Comments and annotations can be embedded directly within the models. Thus, entire teams can analyze, discuss, and refine data models together from anywhere, anytime, and ensure everyone is on the same page. The version control features further help ensure the effective management of data models. Users can track changes, revert to previous model versions, and maintain a history of edits.

Finally, Cardanit provides business analysts with different educational resources supporting their continuous learning and skill development. For instance, video tutorials, process templates, and documentation. They can use the materials to understand specific concepts, create correct data models, and learn best practices for modeling data.

In conclusion

Effective decision-making is based on a thorough analysis of reliable data. Organizations can make well-informed decisions based on data-driven insights by using data modeling in business analysis. As always, there are many advantages and disadvantages to using data modeling. However, an online BPMN and DMN tool like Cardanit can help even inexperienced business analysts model data easily. They can create, refine, communicate, and manage data models, ensuring the effective analysis and optimization of business processes and decisions.