Along with a deep understanding of the data and metadata, data stewards must thoroughly understand the business processes needed for optimum performance. An excellent way to gain this understanding is to model, carefully and completely, business processes and the relevant data. Business processes represent the flow of data through a series of tasks that are designed to result in specific business outcomes. They are an important component of a data steward’s responsibility, and data stewards should understand the fundamentals of modeling processes as well as the data underpinning them.
Understanding Data Stewardship in Process Management
Data stewardship forms the foundation of effective data governance frameworks within organizations. Data stewards act as custodians of organizational data, ensuring the data’s quality, accessibility, and security throughout its lifecycle. This role becomes particularly crucial when modeling business processes because data stewards must understand both the technical and business aspects of the data and the data flow.
Core Responsibilities of Data Stewards in Process Management
Data stewards contribute to process modeling and optimization in several key ways, including:
- Managing data quality through defined standards and monitoring mechanisms, ensuring that business processes operate with accurate and consistent information.
- Collaborating with data owners and business functions to establish clear data definitions and usage guidelines that align with process requirements.
- Implementing data quality rules and verification steps within process workflows to maintain high data quality.
- Coordinating between technical and business data stewards to ensure seamless data flow across process boundaries.
Integration with Data Governance
Effective data stewardship practices support broader data governance programs by:
- Establishing clear data ownership and accountability within process workflows.
- Maintaining data lineage documentation to track how data transforms throughout business processes.
- Addressing data quality issues before they impact process execution.
- Supporting master data management initiatives that standardize critical business data elements.
Impact on Process Optimization
When data stewards actively participate in process modeling, organizations experience:
- Improved data accuracy and consistency across interconnected processes.
- Reduced data silos through better communication between business units.
- Enhanced data security and compliance with regulatory requirements.
- More efficient data sharing and collaboration between process stakeholders.
Understanding these aspects of data stewardship provides context for how process modeling supports organizational goals while maintaining data integrity and quality.
What Is a Business Process?
A process is a coordinated set of activities designed to produce a specific outcome. There are processes for saving a file, constructing a building, and cooking a meal. In fact, there is a process for almost everything humans do. A business process is a type of process designed to achieve a particular business objective.
Components of a Business Process
Essential data needed to accomplish the specific business objective. Forms the foundation of the process.
Individual activities that manipulate, review, or act upon the data to achieve process objectives.
Key decisions that affect the data or influence how the process is conducted.
The movement and transformation of data between different tasks within the process.
Individuals and groups responsible for performing various tasks within the process.
Manual or Automated
Documented or Knowledge-Based
Simple or Complex
Formal or Flexible
Business processes consist of many components, including:
- The data needed to accomplish the desired business objective
- Individual work tasks that manipulate, review, or act upon the data in some way
- Decisions that affect the data in the process or the manner in which the process is conducted
- The movement of data between tasks in the process
- Individuals and groups that perform tasks
Processes can be manual or automated, fully documented or simply knowledge in the minds of one or more people. They can be simple or complex. They can be formal, requiring exact adherence to all details; or flexible, provided the desired outcome is achieved.
Logical Process Modeling
Logical Process Modeling is the representation of a business process, detailing all the activities in the process from gathering the initial data to reaching the desired outcome. These are the kinds of activities described in a logical process model:
- Gathering the data to be acted upon
- Controlling access to the data during the process execution
- Determining which work task in the process should be accomplished next
- Delivering the appropriate subset of the data to the corresponding work task
- Assuring that all necessary data exists and all required actions have been performed for each task
- Providing a mechanism to indicate acceptance of the results of the process, such as electronic “signatures”
All business processes are made up of these actions. The most complex of processes can be broken down into these concepts. The complexity comes in the manner in which the process activities are connected together. Some activities may occur in sequential order, while some may be performed in parallel. There may be circular paths in the process (a re-work loop, for example). It is likely there will be some combination of these.
Logical Process Models: Business-Centric Activity Mapping
The movement of data and the decisions made that determine the paths the data follow during the process comprise the process model. The logical process model contains only business activities, it uses business terminology (not software acronyms, technical jargon, etc.…), and it completely describes the activities of the business area being modeled, and is independent of any individual or position working in the organization. Like its sibling, Logical Data Modeling, Logical Process Modeling does not include redundant activities, technology dependent activities, physical or systems limitations or requirements. The process model is a representation of the business view of a set of activities under analysis.
Heretofore, many applications and systems were built without a logical process model or a rigorous examination of the processes needed to accomplish the business goals. This resulted in applications that did not meet the needs of the users and / or were difficult to maintain and enhance. Stewards can participate in the development of logical process models to improve the understanding of the organization’s activities and relate those activities to the relevant data.
Problems with an un-modeled system include the following:
- Not knowing who is in possession of the data at any point in time
- Lack of control over access to the data at any point in the process
- Inability to determine quickly where in the process the data resides and how long it has been there
- Difficulties in making adjustments to a specific execution of a business process
- Inconsistent process execution
Logical Process Modeling Types and Stages
Logical process modeling methods provide a description of the logical flow of data through a business process. They do not provide details about how decisions are made or how tasks are chosen during the process execution. They may be either manual or electronic, or a combination of methods. Some of the logical modeling formats are:
- Written process descriptions
- Flow charts
- Data flow diagrams
- Function hierarchies
- Real-time models or state machines
- Functional dependency diagrams
A function is a high-level activity of an organization; a process is an activity of a business area; a sequential process is the lowest-level activity. Therefore:
Functions consist of Processes. Usually, functions are identified at the planning stage of development, and can be decomposed into other functions or into processes. Some examples of Functions would include: human resource management, marketing, and claims processing.
Processes consist of Sequential Processes. Processes are activities that have a beginning and an end; they transform data and are more detailed than functions. They can be decomposed into other processes or into Sequential Processes. Some examples of Processes would be: make payment, produce statement of account, and verification of employment.
Sequential Processes are specific tasks performed by the business area, and, like a process, transform data. They cannot be further decomposed. Examples of Sequential Processes are: record customer information, validate Social Security Number, and calculate amount due.
Each business activity in a logical process model is included in a decomposition diagram, given a meaningful name and described in detail with text. As in Logical Data Modeling, naming conventions are quite important in process modeling. Names for processes begin with a verb and should be as unique as possible while retaining meaning to the business users. Nouns used in the activity name should be defined and used consistently. In a decomposition diagram, each level completely describes the level above it and should be understandable to all appropriate business users.
Data-Driven Approach to Process Definition
This approach analyzes the life cycle of each major data entity type. The approach defines a process for each phase or change the data undergoes, the method by which the data is created, the reasons for the change, and the event that causes the data to achieve its terminal state. This method assures that all data actions are accounted for, and that there are meaningful associations between the data and its processes. However, in a data-driven method, the logical data model must be completed before the process modeling and analysis can begin.
Major points of interest in constructing a Logical Process Model are:
- The purpose of the process: Writing the purpose and referring to it frequently enables the analyst to recognize a step in the process that does not make sense in the context of the process.
- Who will participate in the process: The participants may be people, groups of people, or electronic applications.
- The order in which the steps of the process are done: Order in a process model is essential, and is one of the main ways a process model differs from a data model.
- The data to be included in the process: There is an initial set of expected data, and the data stewards should know what data might be modified or added during the process. Part of this step is deciding which subset of the data is appropriate at each task in the process.
- Decisions made during the execution of the process: These include decisions about which path the process should take, and whether all the required data is present at any given point in the process.
- The rules used to define the various parts of the process: Also, note any naming conventions that are important for the business.
- The disposition of the data at the end of the process: That is, will the data be retained or deleted? If the data will be stored, where and in what form will the data be kept? Do future process-related reports need to access the data?
The more complete the model, the easier it will be to implement an accurate representation of the business area responsibility for the data stewards, and the more successful the organization will be in achieving its goals.
Process definition also helps an analyst or steward know when a process should be broken into smaller, sequential processes (tasks). If the definition of a process is ambiguous or lengthy, it is usually a candidate for decomposing into sequential processes. All functions are decomposed to processes, and all processes are ultimately decomposed into sequential processes/tasks.
A good check of the accuracy of any model is to simulate it by walking through the process manually. This allows the analyst and steward to locate any points in the processes that are not valid before system construction.
Once the process has been successfully simulated, review the results with the appropriate data stewardship team and leaders who understand the expected results from each function and process. This verification step allows the business representatives to understand the process and to confirm the validity of the process and data.
Evolving Landscape of Data Stewardship and Process Modeling
The data stewardship model continues to evolve as organizations face new challenges in data management. Modern data stewardship roles must adapt to emerging technologies while maintaining the core principles of process modeling and data governance.
Artificial Intelligence Integration
Technical data stewards increasingly work with AI-powered data management tools. These systems transform how organizations handle data collection and processing, requiring updates to traditional process modeling approaches. Data professionals must develop frameworks that account for automated decision-making while ensuring data quality management remains robust.
Real-Time Data Processing
Modern data stewardship focuses on real-time monitoring through data dashboards and automated alerts. Operational data stewards must implement business rules that support immediate data validation, making process modeling more dynamic and responsive to changing conditions.
Cross-Organizational Collaboration
The value of data increases through sharing and collaboration. Data stewardship programs now emphasize frameworks that enable organizations to treat data as an enterprise asset while maintaining security. This requires sophisticated process models that account for data usage across organizational boundaries.
Regulatory Compliance Evolution
As regulations grow more complex, data stewards represent their organizations in maintaining compliance. Implementing data stewardship practices requires careful attention to emerging legal requirements, particularly in process models that handle sensitive information.
Enhanced Data Governance
Modern data stewardship success depends on embedding data stewardship practices throughout an organization. Data custodians work closely with data users across different domains, requiring process models that support collaborative data management decisions while maintaining data related security.
Like Logical Data Modeling, Logical Process Modeling is one of the primary techniques for analyzing and managing the information needed to achieve business goals. It is important that data stewards understand the concepts of Process Modeling, and the methods used in process discovery and definition, and participate by relating and explaining the data and processes used by a business area. Properly performed, logical process modeling can greatly assist an organization in its efforts, producing functional business processes and appropriate data for use across the enterprise.
Conclusion: The Strategic Value of Process Modeling in Data Stewardship
Process modeling stands as a cornerstone of effective stewardship and data governance strategies. Data stewardship consists of multiple interconnected responsibilities across each data domain, from defining data standards to resolving data issues. When data stewards leverage data through well-designed process models, organizations transform their data assets into strategic resources. The data steward’s role has become increasingly important as organizations recognize that high-quality data drives business success.
Through structured process modeling, data analysts and professionals in various data governance roles can better understand and manage their organization’s data assets. As the data management body of knowledge continues to expand, data stewardship helps bridge technical and business perspectives, leading to improved data quality across the enterprise. This comprehensive approach to governance and data enables organizations to meet current challenges while preparing for future innovations in data management.
