This topic will introduce the job roles, industry applications, terminology and definitions relevant to modelling data processes in the systems analysis and design field. We will also discuss the skills one should possess to design and communicate process models effectively.
In this topic, you will learn about:
- the role of a process modeller
- applications of process models
- industry terms and definitions
- required skills for process modelling.
Let us begin.
The role of a process modeller
Meet Henry!
Henry works in the systems analysis and design field as a 'process modeller', applying a broad range of knowledge and skills, often within complex projects.
This module covers the skills and knowledge that enable someone like Henry to gather process data and business information to model data processes within an organisation, which includes high-level tasks such as developing the model's scope, gathering process data, developing the model and validating the data model with stakeholders.
In the following video, Henry outlines what is involved in the role of a 'Process modeller'.
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Process modelling: An overview
Process models, in general, are used to describe the sequential flow of activities or work tasks and for documenting operational procedures. Process models are further categorised as follows depending on the purpose and fields of their use.1
- Business process models - describes the sequential workflow of organisational or a specific business function’s tasks or activities.
- System process models - describes the sequential flow of control in computer system programs or units.
- Program process flows - describes the sequential execution of software program statements.
The following video provides an overview of the process modelling technique.
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Examples of industry applications
Process models are widely used in various industries to streamline operations, improve efficiency, and ensure consistency.
When used in any industry, process models would provide several benefits, including standardisation of procedures, improved visibility into workflows, better compliance with regulations, and enhanced security measures. They also facilitate training and documentation, making it easier for personnel to follow established procedures and efficiently respond to challenges or changes in their respective fields.
The following examples outline the use of process models in cybersecurity, data management, and systems administration.
Expand each industry sector name to see relevant examples of process model applications.
- Incident Response - In cybersecurity, process models help to define and document incident response procedures. These models outline the steps to be taken when a security incident occurs, helping organisations respond quickly and effectively to mitigate threats.
- Vulnerability Management - Process models are employed to establish a systematic approach to identifying, assessing, and remediating vulnerabilities in a network or system. They help prioritise and track vulnerability mitigation efforts.
- Security Policy Compliance - To ensure compliance with security policies and regulations, process models define the steps for security assessments, audits, and compliance checks. They provide a structured framework for maintaining security standards.
- Data Governance - Process models are used in data governance frameworks to establish policies and procedures. For example, data ownership, data quality management, and data access controls. These models ensure that data is properly managed and protected.
- Data Integration - In data integration projects, process models define the flow of data from source systems to target systems. They specify data transformation, cleansing, and validation steps, ensuring the consistency and accuracy of the data.
- Data Backup and Recovery - Process models guide data backup and recovery procedures, including regular backups, data retention policies, and disaster recovery plans. They help organisations safeguard critical data assets.
- Patch Management - Process models outline the steps for patching and updating software and systems. They ensure that patches are applied systematically to address vulnerabilities and maintain system security.
- Server Provisioning - In systems administration, process models define the provisioning process for new servers or virtual machines. They specify hardware or virtual resource allocation, OS installation, and configuration steps.
- Change Management - Process models are used to manage changes to IT systems and infrastructure. They define change request submission, review, testing, and approval processes to minimise disruptions and risks.
Understanding business processes
A process can be defined as:
A coordinated set of activities designed to produce a specific outcome.2
There is a process for almost everything we do.
Consider common activities such as making a cup of coffee, installing a new digital device (e.g. printer, external monitor, smart devices) or constructing a piece of furniture from a flat-pack. We follow a process when performing each activity. Furthermore, an activity can be broken down into smaller tasks that are completed in a specific order or sequence; some may perform these tasks using fewer/more steps than others.
What is a business process?
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Gartner defines business processes as:
An event-driven, end-to-end processing path that starts with a customer request and ends with a result for the customer. Business processes often cross departmental and even organisational boundaries.4
The typical components of a business process include:
- the data required to complete the task and achieve the desired outcome
- the work tasks that manage or manipulate the data in some way
- the decisions that may impact the data in the process or the method of conducting the process
- the movements of the data (input, output and flows) between the tasks in the process
- the individuals and groups who perform the tasks in the process.
The following video outlines the elements that make up a business process.
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Business process modelling (BPM)
Business process modelling is crucial for understanding how organisations function and adapt to changing market and user demands. It visually represents an organisation's activities, illustrating how they contribute to achieving desired outcomes and goals.
Gartner defines business process modelling as follows.
Business process modelling (BPM) links business strategy to IT systems development to ensure business value. It combines process/workflow, functional, organisational and data/resource views with underlying metrics such as costs, cycle times and responsibilities to provide a foundation for analysing value chains, activity-based costs, bottlenecks, critical paths and inefficiencies.6
The following video explains what is involved in process modelling and why organisations use it.
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Policies, processes and procedures
When modelling data processes, it is important to understand the distinctions between business policies, processes, and procedures of data management and data governance.
Definitions
- Policy - A high-level statement or guideline that outlines the organisation's goals, objectives and the principles that should govern data-related activities. It sets the overarching framework for managing data within the organisation.
- Processes - A series of structured activities and tasks designed to achieve a specific business goal or outcome. Furthermore, a business process defines how data is collected, processed, stored, and used to support the organisation's operations.
- Procedures: - A step-by-step set of instructions or guidelines that specify how a particular task or operation should be performed. Procedures are highly detailed and provide specific guidance for carrying out activities.
Application of policies, processes and procedures in process modelling
Policies provide the strategic direction, business processes define how data is handled in a broader context, and procedures offer detailed, step-by-step instructions for specific data-related tasks. All three elements are interrelated and play a vital role in modelling data processes effectively, ensuring data is managed efficiently and securely and complies with organisational policies and external regulations.
Here's how policies, processes and procedures differ in their purpose and the role they play in process modelling.
| Role in process modelling | Purpose | Examples | |
|---|---|---|---|
| Business Policies | Business policies influence the design of data models and processes by establishing overarching goals and constraints. They help determine the objectives that business processes should achieve. |
Provide the strategic direction for data management and governance. They communicate the organisation's values, priorities, expectations and alignment with legislative and compliance requirements regarding data. |
"Data privacy is a top priority for our organisation." "All data must be accurate and up-to-date." |
| Business Processes | Business processes are essential in data modelling because they dictate the flow of data within an organisation. Data models are designed to align with and support these processes, ensuring that data is utilised effectively. | Provide a detailed roadmap for executing specific tasks and activities related to data management. |
"Customer onboarding process." "Data backup and recovery process." |
| Business Procedures | Procedures play a crucial role in data modelling by providing detailed instructions on specific data-related tasks. They inform the design and implementation of data processes. | Procedures are practical documents that ensure consistency and standardisation in executing tasks. They are vital for ensuring data quality and compliance. |
"Data entry procedure for customer information." "Procedure for data access request handling." |
Data dictionary
A data dictionary is a centralised repository that stores definitions, descriptions, and metadata about data elements, attributes, and relationships.
A data dictionary is a valuable tool for data modellers because it provides a centralised and organised repository of metadata and information about the data elements within a database or data system. Data dictionaries provide comprehensive information about data elements, relationships, constraints, and documentation.
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Skills overview
The following skills are critical for effectively designing and communicating data-related models and requirements. Furthermore, these skills play a pivotal role in data process modelling, ensuring that data models accurately represent business requirements, facilitate effective communication, and support data management in a rapidly evolving digital landscape.
Here is how each skill applies to data process modelling.
Analytical skills
Critical analysis of complex documentation
In data process modelling, understanding existing data documentation, standards, and requirements is crucial. Data modellers often need to read and interpret complex technical documents, data dictionaries, and system specifications to gather information about data structures, relationships, and business rules.
Data processes often require input and specifications from multiple sources, and the ability to determine specific criteria and requirements from these diverse documents is crucial for creating accurate process models.
This skill helps data modellers consolidate information from various sources to determine specific data requirements accurately. It involves extracting essential details from documentation to inform the design of data models that meet business needs and align with data governance policies.
Systematic and analytical decision-making
Process modelling involves making decisions about how data flows, transformations and interactions should be represented in the models. These decisions often pertain to complex and non-routine situations.
The skill of applying systematic and analytical decision-making processes ensures that modelling choices are well-founded and based on a thorough understanding of the data and its requirements.
Analytical decision-making processes are employed when dealing with non-routine situations or making design choices that impact data processes. Systematic thinking helps identify and address potential issues in data models.
Documentation
Data modellers are required to produce clear and concise documentation that conveys the structure and behaviour of data processes. This documentation helps stakeholders understand how data flows, transformations, and storage are orchestrated within an organisation.
Writing skills come into play when creating diagrammatic models as well as other associated documentation. These models visually represent complex relationships between data entities, processes, and systems. Associated documentation is often used to provide detailed information on the diagrammatic models.
Visual modelling and diagramming skills
The following video discusses the importance of visual modelling and diagramming skills.
Note: Although this video discusses the skills specific to a Business Analysis role, note that these skills apply to any individual responsible for developing process models.
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Interacting with others
Establishing trust and open communication channels with stakeholders is crucial for successful data modelling projects. It ensures everyone is aligned and invested in the modelling process, leading to more accurate and useful data models.
Communication skills
Process modellers often engage with stakeholders, such as business analysts, subject matter experts or business end-users, to gather requirements for process models. Effective oral communication skills are crucial for conducting interviews, workshops, and meetings to elicit these requirements.
Active listening and questioning techniques help ensure the data modeller fully understands the client's needs. Reading verbal and non-verbal signals during discussions helps clarify information and confirm mutual understanding.
Collaboration skills
Collaboration is key in process modelling. Process modellers work with diverse colleagues and clients, including subject matter experts, IT teams, and business users.
Building rapport and fostering strong relationships with these stakeholders is important for effective communication, eliciting requirements, and ensuring the modelling process aligns with the needs and expectations of different parties involved.
Note: Although this video discusses the skills specific to a Business Analysis role, note that these skills apply to any individual responsible for developing process models.
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Planning, organising and technology skills
Managing complex tasks
Process modelling is a multi-step and complex task requiring careful planning and sequencing. This skill involves taking responsibility for organising and managing the modelling process, including setting priorities and timelines. Effective task planning and workload management skills ensure that modelling activities progress smoothly.
It also entails negotiation and collaboration with team members and stakeholders to ensure that the modelling process aligns with the capabilities, efficiency, and effectiveness of the organisation's resources.
Understanding how technology can support BPM
The following video explains how Information Technology (IT), specialised hardware/software use and the vast amounts of data generated by business transactions make up and support Business Process Modelling (BPM).
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Maintaining currency in new digital technologies
Investigating, learning, and using new digital technologies and applications is vital in data modelling. Data professionals must explore and leverage new tools and technologies to manage, manipulate, and communicate data effectively in a secure and stable digital environment.
How did you go?
Congratulations on completing the topic Industry insights. You should now understand what is generally involved in process modelling.
In this topic, you learnt about:
- the role of a process modeller
- applications of process models in different industries
- business process modelling
- basic industry terms and definitions relevant to process modelling
- the required skills for process modelling.
