What are the most effective ways to identify business processes for dimensional modeling?

Understand the Business Goals and Objectives Engage Stakeholders Conduct Workshops and Interviews Review Existing Documentation Identify Key Business Processes Define Business Dimensions Determine Grain and Granularity Identify Fact Tables and Measures Consider Time Prototype and Validate Adopt Best Practices Iterate Based on Feedback

If we start from the customer and the key insights that are needed, then we work backwards from the data sources needed and the business logic transformation needed.

By following some steps, you can effectively identify key business processes for a dimensional model that drives informed decision-making and propels your business forward. 1. Why?: Align processes with core business goals. 2. Map it out: Chart key processes, find data goldmines. 3. Listen up: Engage stakeholders, understand their needs. 4. Follow the trail: Trace data back, unlock hidden insights. 5. Prioritize & focus: Start strong, expand later.

Los objetivos empresariales, a mi parecer, han de ser un acopio de "insights" que, poco a poco, vayan mejorando los resultados de la compa?ía, eso se consigue con una buena planificación de esos objetivos, con la colaboración de todos los miembros de esa empresa, todos han de opinar, ha de ser una simbiosis de todos los componentes, desde abajo arriba, es muy recomendable dar voz a tus empleados, por las importantes aportaciones que pueden aportar desde su visión...

To identify the business processes for dimensional modelling, you can follow these steps: 1.Choose the business process that you want to analyze. 2.Determine the level of detail for the business problem/solution. 3.Identify the dimensions that describe the business process. 4.Identify the facts that describe the business process. Examples of business processes are customers, profit, sales, organizations, and products. Dimensional models are typically used in two environments: the data warehouse and OLTP systems. The data warehouse is subject-oriented and contains specific, selected subject areas in the organization, such as customer and product.

In terms of the fact table grain, what has worked well in the past is the day level grain. Even if the metrics asked is monthly or quarterly level, getting it down to the day level gives the ultimate flexibility for current and future needs. Now if there is a need to go to the hour level , one must really question this and challenge this requirement harder. Unless there is real- time analytics feeding back into the product, there is no need to get down to the hour level for typical use cases.

Identifying the grain of the facts is a critical step in dimensional modeling as it provides clarity about the specific business events or transactions that are the focus of analysis. It also helps in avoiding aggregation pitfalls. If the grain is too coarse, valuable information may be lost during aggregation. If it's too fine, the data warehouse may become impractical to query. Grain should be detailed enough to capture the interrelationships between dimensions and provide meaningful insights.

Identifying the grain of the facts refers to specifying the level of detail or granularity at which you want to store and analyze your data in the data warehouse. This step is crucial because it determines the scope, size, and structure of your data warehouse. The grain of the facts is essentially the level of detail at which individual records or transactions are stored. Here are some considerations to help identify the grain of the facts: 1)Business Requirements 2)Key Metrics and KPIs 3)Temporal Aspects 4)Data Source Characteristics 5)User Requirements 6)Transaction vs. Summary Data 7)Normalization vs. Denormalization 8)Data Volume and Performance 9)Regulatory and Compliance Requirements

Identifying the grain of facts is a critical step in dimensional modeling, as it establishes the most detailed level at which data is captured and analyzed. By defining the grain, we can accurately map the business processes and ensure that the model aligns with the specific needs and nuances of the business. This granular approach allows for precise and meaningful data analysis, aiding in detailed insights and informed decision-making. It ensures that the model is not only comprehensive but also relevant, making it a powerful tool for understanding and optimizing various aspects of the business process.

To create a robust dimensional data model, it is essential to understand the business requirements thoroughly. To identify business processes, select the specific process and prioritize requirements based on significance, data quality, and feasibility. Gather essential metadata, including business requirements, source systems, data quality issues, common terms, and other relevant business-related metadata. In a data warehouse environment, partition data based on subject areas, such as customers or products, to align with specific business processes. In operational environments, partition data by application or function, such as OLTP systems. Engaging stakeholders, subject matter experts, and end-users is crucial for gathering insights.

Identifying data sources and events for dimensional modeling involves: 1. Stakeholder Engagement: ? Collaborate with stakeholders to understand key business processes and events. 2. Documentation Review: ? Examine existing documentation, process flows, and reports to identify relevant data. 3. System Exploration: ? Investigate systems, databases, and logs to pinpoint where data is generated. 4. Data Profiling: ? Profile data to understand its structure and content for dimensional modeling. 5. IT Collaboration: ? Work closely with IT teams managing databases 6. Bi tooks 7. External Data consideration 8. Data Governance and Metadata: Leverage

Identifying data sources and events for dimensional modeling involves: 1. Stakeholder Engagement: ? Collaborate with stakeholders to understand key business processes and events. 2. Documentation Review: ? Examine existing documentation, process flows, and reports to identify relevant data. 3. System Exploration: ? Investigate systems, databases, and logs to pinpoint where data is generated. 4. Data Profiling: ? Profile data to understand its structure and content for dimensional modeling. 5. IT Collaboration: ? Work closely with IT teams managing databases 6. Bi tooks 7. External Data consideration 8. Data Governance and Metadata: Leverage

Find the Data Sources and Events: Operational Systems: Identify the operational systems where events are recorded. This could include transactional databases, logs, or other sources. Data Exploration: Explore the structure and content of these data sources to understand the types of events captured and the format in which they are stored.

Validating the data quality and availability is a critical step in the process of building a data warehouse. important steps: 1) Reliability of Analysis 2) Decision Making Confidence 3) Alignment with Business Objectives 4) Efficient Resource Utilization 5)Timely and Consistent Data 6)Enhanced Data Governance 7) Mitigation of Risks

Validating data quality and availability is pivotal in dimensional modeling to ensure the business process is accurately represented. High-quality, accessible data is the backbone of effective modeling, providing the necessary detail and accuracy for insightful analysis. This validation step helps in identifying any gaps, inaccuracies, or inconsistencies in the data, ensuring that the model is built on a reliable foundation. Such rigor in data validation not only enhances the model's effectiveness but also boosts confidence in the insights derived, leading to more informed, data-driven decisions that closely align with the business's operational realities and strategic objectives.

Validate the Data Quality and Availability: Data Quality Assessment: Assess the quality of the data in identified sources. Identify any inconsistencies, missing values, or outliers that may impact analysis. Availability Check: Ensure that the required data is available for extraction. Assess whether the data is regularly updated and if historical data is accessible.

This is best achieved via a Data Governance Center of Excellence with key representation across business units with an Executive 'sponsor'.

Documenting business processes and data flows is a key step. This documentation provides a clear blueprint of how data moves and transforms within the organization, highlighting key processes, decision points, and data dependencies. It serves as a guide for aligning the dimensional model with actual business operations, ensuring that the model captures the essential elements and dynamics of the business. Effective documentation aids in communication among stakeholders, clarifies requirements, and helps in identifying improvement opportunities, making it an indispensable tool for developing a coherent, comprehensive, and functional dimensional model. Azure Purview is a great tool to do this.

Document the Business Processes and Data Flows: Process Mapping: Create process maps that illustrate how data moves through the organization. This includes the generation, capture, and transformation of data from source to destination. Data Flow Diagrams: Develop data flow diagrams that visually represent the movement and transformation of data within and between systems.

One thing to consider is how scalable the data model is. Is it easy to add new columns. , new tables as business needs grow. Are we having a unified data model that will power different data products without heavy-lifting ? Those are some questions to ask.

As a reference, apart from the classics by the Kimball Group and Christopher Adamson on dimensional modeling, I highly recommend the book Agile Data Warehouse Design by Lawrence Corr and Jim Stagnitto. In this book, the authors propose an adaptable and agile method for dimensional design called BEAM (Business Event Analysis & Modeling), alongside many techniques and "tools", like modelstorming, 7W's for dimensional models, etc. These "tools" facilitate design discussions, detailed design work, communication, implementation, and provide concise yet thorough documentation.

Your goal is to make a team or department more efficient. By spending quite a lot of money. Chances are high that the transition between a less effective team and a more effective one will be handled by someone else. That someone is looking for blood sacrifice. So your real job is to find someone who will spill the beans and tell you what they are really doing. Then you can model it. Automate it. Efficiency it. And someone can pay the blood sacrifice. It's easier the first time. When the victims still trust. But after a few goes, or if the staff are more experienced, you will need to find new ways to trick them into exposing their secrets. Their knowledge. Their Power.

When identifying business processes for dimensional modeling, it's crucial to consider additional factors beyond the basics. This includes understanding the evolving business environment, anticipating future data needs, and being aware of technological advancements. Integrating feedback from diverse stakeholders is key to capture varied perspectives and needs. Considering data privacy and security from the outset is vital in today's data-driven world. Additionally, scalability and flexibility of the model should be considered to accommodate growth and changes in business processes. Lastly, ensuring that the model aligns with the overall business strategy and goals is essential for long-term effectiveness and relevance.

Ralf Kimball created an bus matrix where business processes and shared entities or more formally conformed dimensions are presented together.