The Conceptual Modelling Journey Map for Data Citizens

Executive Summary

‘The Conceptual Modelling Journey Map for Data Citizens’ presents an overview of key data management, governance, and modelling topics. Howard Diesel covers the significance of concepts in concept modelling, the implementation of models in biology and business processes, and the role of representation, concept space, and concepts in data models. The webinar explores the complexity of UML modelling, the importance of business process models and their role in workflow analysis, and UML’s concept space and hierarchy. Howard also includes the trilogies dimension of business concept, communication and linguistics’s role in data modelling, challenges and approaches in data modelling and decision-making, training and assessment of data citizens, and data-driven decision-making and data quality modelling.

Webinar Details

Title: The Conceptual Modelling Journey Map for Data Citizen
Date: 20 August 2024
Presenter: Howard Diesel
Meetup Group: Data Citizens
Write-up Author: Howard Diesel

Data Management and Challenges in Data Governance

Howard Diesel opens the webinar by reflecting on last week’s data management journey mapping session. He notes that The focus was on data governance’s benefits from data modelling. The real-world challenges of engaging data citizens in data modelling were also discussed, including the importance of data literacy, one-on-one engagement with business personnel to build conceptual data models, and strategies for adoption and simplification. Key takeaways, Howard emphasises, the importance of conceptual models and practical application for data-driven decision-making, highlighting the need for professionals to discuss various conceptual models and implementation frameworks, and for executives to introduce these into the organisation.

Figure 2 Data Citizen Journey Map

Understanding the Characteristics and Importance of Concepts in Concept Modelling

Howard shares that the webinar focuses on key characteristics of a conceptual model. Developing a conceptual model involves observing and representing the origin, focusing on critical aspects of the business, and ensuring clear communication of business rules and requirements for implementation. Understanding the purpose of the model, selecting the appropriate model type, and determining the level of detail are crucial initial steps. Characteristics of conceptual models include their relationship to the origin, their domain and perspective, and considerations of concern and usage.

Figure 4 Conceptual Model Characteristics

Figure 5 Characteristics of Models & Conceptual Models

Understanding and Implementing Models in Biology and Business Processes

The use of models to understand and communicate natural phenomena is touched on, particularly in the context of biology. Howard highlights the importance of developing a communication mechanism to agree on how things work. He also delves into conceptual modelling, which involves specifying what is to be implemented and its purpose and function, such as explaining, exploring, or predicting. It emphasises understanding the origin, analysing, assessing, planning, and designing to bring the model into realisation and implementation within a specific domain and context. The context of the modeller, the potential application domain, the language of the model, and social and temporal contexts are also considered. Finally, Howard notes that while conceptual models are typically used in practice, they are not mandatory, and he emphasises the importance of focusing on relevant areas for a given purpose.

Understanding the Role of Representation, Concept Space, and Concepts in Data Models

Howard discusses representation and communication, emphasising the role of symbols and terminology as carriers of understanding. He introduces the idea of a concept space, which encompasses the terminology and definitions used, and highlights the importance of understanding concept relationships within data models. The passage underlines the necessity of effectively communicating these concepts to stakeholders, particularly data citizens when building models or applications for the business or community.

Concept and Complexity of UML Modelling

The distinction between conceptual and logical models in UML modelling is discussed. A debate centres on whether a specific UML model qualifies as conceptual, with an attendee noting that conceptual models should represent the business domain without technical details. Additionally, the conversation touches on the importance of well-defined concept space and semantics in identifying a conceptual model.

Figure 6 Conceptual Model: Y/N?

Figure 7 Four Layer Hierarchy of Modelling Languages

Understanding Business Process Models and Their Importance in Workflow Analysis

Howard describes a high-level business process model, discussing the flow of the process, activity-based costing, aggregated data, simulation, and digital twins in business processes. He also touches on different modelling languages, data and object representations, and the limitations of Entity Relationship (ER) modelling in handling certain aspects such as exclusivity and temporal aspects. Howard emphasises the importance of choosing the right ontology and language constructs based on the type of relationships and business requirements to avoid conflicts in representation.

Figure 8 Business Process Type & Instance Model

Figure 9 Modelling Hierarchies

Understanding the Concept of Space and Hierarchy in UML

The discussion revolves around Unified Modelling Language (UML) and conceptual models. Howard elaborates on the different levels of modelling, such as conceptual, logical, and physical, emphasising the importance of understanding the concept space in modelling. He presents various diagrams, including an electronic circuit diagram and a family communication model, prompting participants to determine if they qualify as conceptual models based on the defined concept space and a priori semantics. Howard highlights the significance of grasping the concept space for effective modelling.

Figure 10 Conceptual Model: Y/N?

Figure 11 Conceptual Model: Y/N? continued

Understanding the Triptych Dimension of Concept Modelling in Business

The triptych dimension of conceptual modelling is discussed, and Howard uses the example of a diptych to explain the concept. He emphasises understanding the physical, mental, and social worlds and creating artefacts to implement a system. The linguistics and encyclopaedic dimensions are explained, highlighting language definition and ontology. The importance of terminology transport and concept modelling is underscored, focusing on building a conceptual model to derive different structure models such as banking data structure models or business process models.

Figure 12 The Triptych Dimensions of Conceptual Modelling – a Paradigm for Conceptual modelling

Figure 13 The Closed Triptych (DIPTYCH)

Figure 14 Triptych – The model perspective right wing open

Figure 15 Triptych – Open

Figure 16 Notion, Term, Concept

The Importance of Communication and Linguistics in Data Modelling

Howard emphasises the importance of linguistic and conceptual clarity in communication and modelling. He highlights the need for shared language and grammar among communication partners to avoid misunderstandings in presenting conceptual and subject-area models. Emphasising terms’ role as message carriers, Howard stresses the importance of aligning the meanings of terms for effective communication. He also underscores the significance of establishing clear business glossaries and definitions to enhance understanding. Additionally, Howard questions the disparity in the prominence of process diagrams versus data models in companies, suggesting a potential issue in effectively communicating data modelling language to professionals.

Figure 17 Linguistic Dimension: the Term Space

Challenges and Approaches in Data Modelling and Decision-Making

The challenges of working with data citizens involve Data literacy and User adoption. It’s essential to communicate the importance and practical application of data modelling literacy and how it informs data-driven decision-making. This includes conceptual modelling, data modelling literacy, and different types of database tables, such as system tables, file tables, external tables, and graph databases. Understanding and applying these concepts can help address concerns about complexity and oversimplification.

Figure 18 Data Model Literacy: How-To Read

Building and Understanding Data Models: A Discussion on Training and Assessing Data Citizens

Howard outlines the training process on the linguistic dimension in modelling and business grammar. He emphasises the importance of understanding and conveying modelling and business terms effectively and developing business rules, definitions, and naming standards. Howard also highlights the significance of guiding data citizens through assessing and arguing about data models, ensuring quality and completeness, and discussing the level of abstraction. The focus is empowering individuals to critically engage with data models and assess their quality and relevance within the business context.

Figure 19 Data Model Literacy: How-to Argue

The Art and Training of Data Modelling and Its Implementation

The process of writing a data model involves several key steps. First, it’s important to understand the concept and detail required before starting the model. Adequate training in reading and arguing is essential for creating a good data model. Consensus on what constitutes a good data model is crucial, achieved through defining concepts and diagramming principles. Prioritising elements and getting adoption right are also key considerations. Additionally, creating shared understanding through consistent business definitions and ensuring effective communication are essential. Lastly, the design and training phases are critical in shaping the data model and ensuring all stakeholders have the necessary skills and knowledge.

Figure 20 Data Model Literacy

Process of Concept Modelling and Simplification in Complex Environments

An effective method for simplification is conceptual modelling, which is essential for capturing and understanding complex environments. Multi-level modelling allows for abstraction at different levels – presentation, conceptual, logical, physical, and instances. Focal point modelling aids in choosing the appropriate language based on purpose and intention, with UML focusing on classes, objects, behaviour, and entity relationships, highlighting entities and relationships. Other important techniques include Elm for ensemble logical modelling, data vault modelling, hubs anchor modelling, and anchors. These methods help to separate areas of the business that can change and focus only on what’s essential.

Figure 21 User Adoption (Change Management – ADKAR)

Data-Driven Decision Making and Data Quality Modelling

Howard concludes with a focus on the importance of data modelling in improving decision quality. An attendee emphasises the need to understand critical elements in the decision-making process and ensure that data models are of the right quality to support key decisions. They underscore the significance of addressing issues related to uniqueness, completeness, and accuracy in data modelling to guarantee adequate support for decisions and actions. The session ended with a thank-you note and an invitation to attend the next week’s session.

Figure 22 Simplification

Figure 23 Data-Driven Decision-Making