How do you include the right level of detail in your data model?

Crafting an adept data model requires a nuanced understanding of your audience. In the realm of data modeling, experts emphasize the paramount importance of tailoring the level of detail to resonate with diverse stakeholders. Delve into the intricacies of audience analysis, discerning the unique needs and technical acumen of decision-makers, analysts, and end-users. By aligning the granularity of your data model with the cognitive frameworks of your audience, you unlock the power to convey complex information intuitively. This strategic approach not only fosters comprehension but also enhances collaboration, positioning you as a visionary in the ever-evolving landscape of data modeling expertise.

Capture everything** but display what is needed. All talk about AI, ML, Data analytics, Data modelling is worthless without high quality curated datasets to back them up. For any org, having an automated pipeline of capturing granular task level data that can be rolled up to higher activities and business outcome is a goldmine. This data warehouse can then be mined for patterns, run thru models for predictions, or shown to stakeholders as per need and intended usage. ** within reason, considering laws, privacy, security, retention and downstream use.

In spite of the fact that I believe every researcher ought to have a complete comprehension of the topic at hand, there is still a possibility that they will struggle to collect accurate data or even fail to do so entirely. The approach that was taken and the steps that were taken to obtain the data will be of the utmost significance.

To include right level of detail in your data model, define the business requirements and objectives > This involves identifying essential entities, relationships, and attributes that directly contribute to achieving the desired outcomes. Additionally, you can use the CALCULATE function as a level of detail expression in Power BI to apply filters and manipulate data > To edit the data model in Power BI, users can open the data model from various locations such as the workspace content list or the semantic model details page > From there, users can create measures, calculated columns, calculated tables, and manage relationships to ensure the data model reflects the necessary level of detail for analysis and reporting.

It's also imperative to consider the data literacy of the audience. For stakeholders with limited exposure to data analysis, integrating real world analogies and relatable examples can make complex data more accessible. This approach creates a connection between the data and the everyday experiences of the audience. Such relatable narratives ensure that the data resonates with the audience, regardless of their technical background.

Precision in data modeling commences with a meticulous definition of purpose. Establishing clarity on the objectives of your data model is foundational to determining the appropriate level of detail. Industry experts stress the significance of delineating the specific insights and decisions your data model is intended to support. This strategic alignment ensures that the granularity of your model resonates with the intricacies of the tasks at hand. By elucidating the purpose with utmost clarity, you pave the way for a data model that not only meets but surpasses the expectations of precision, positioning you as a discerning authority in the data modeling domain.

Begin with the end in mind : when applied to the data model would mean that we should clearly articulate the purpose of the data model and what we would like to achieve with it . We must also identify the key requirement and constraints which includes the data sources , data type , performance or regulatory requirements . The other key important point from my perspective is to ensure we build a strong base data model which meets the current requirement however it is extendible for future changes and expansion.

Further to defining the purpose, it's beneficial to adopt a goal oriented approach that goes beyond mere data presentation. For instance, if the objective is to drive strategic decision making, the model should highlight predictive analytics and trend projections. This approach shifts the focus from descriptive statistics to actionable insights, encouraging a forward thinking perspective in data analysis. This purpose driven structuring is importent for models intended to inform future strategies and business directions.

It's about clarifying the overarching goal, be it unveiling intricate patterns or offering a succinct summary. Purpose not only serves as a compass for what data to include but also shapes the very structure and presentation of your model. If the aim is deep exploration, diving into granular details might be the route, while a purpose centered on clarity and succinctness may call for a more aggregated approach.

Determining data granularity is a crucial step when building your data model for reporting purposes. A data model comprises of facts and dimensions. Its important to maintain the same level of granularity in both your facts and dimensions so that you can create a perfect one (dimension) to many (fact) relationship between your tables. It depends on the business context - at what level the business want to visualize their KPI's and drill down into details. Example - business might want to visualize "COGS" by "Date,SKU,Customer" so your level of detail in fact would be Date-SKU-Customer. However, its important to align with the business and choose the ideal granularity that would prevent any future changes/modifications to the data model.

In shaping a data model, the art lies not only in choosing the right format but in aligning it with the intricacies of your data landscape. Industry leaders emphasize the judicious selection of formats, advocating for an approach that mirrors the nuances of the information at hand. Opting for a format that encapsulates the essence of your data ensures a harmonious integration of detail. This nuanced decision-making process, guided by technical acumen and domain expertise, exemplifies your commitment to precision in data modeling. It positions you as a luminary, adept at orchestrating data formats that resonate with the complexities of contemporary data ecosystems.

Here are some things that can be done - Grouping tables by purpose - annotating aspects on the diagram or color code to make the necessary aspects clearer - For data that will potentially flow downstream create a separate naming convention or color code in the data model. - add statistical information to give everyone a sense of what the data looks like in reality.

Be careful of unintended inferences people may derive from your graphs etc. Eg when scales or colors are different on subsequent graphs, viewers may make assumption on the legend and infer incorrectly. In the end this is art, story telling and science at the same time.

Interactivity can be key in crafting visually appealing, accessible, and balanced visualizations. For R users, the ‘plotly’ package offers tools to build interactivity into your plots and graphs. You can use these tools to zoom into/out of detailed results, pinpoint selected indicators, and quickly conduct on-demand exploratory data analysis. Interactivity also motivates audience buy-in. Additionally, adding other flairs—such as animation features to show rate of change over time—can influence the impact of your “data story.” These features and tools are more than painting a pretty picture; rather, they are key strategies that can be used to paint an accurate and meaningful “portrait” of your findings.

Choosing the right data format and visualization is like setting the stage for a play - it determines how your audience will engage with and understand the story you're telling. Imagine a company, where objectives cascade from top management down to operational teams, each requiring different data granularities. 1. At the top, an executive might need a broad overview, like whether products are shipped on time. 2. For a supply chain director, it's about zooming in on critical delays across various accounts. 3. Meanwhile, the supply chain specialist needs a microscope view of every shipment, with a sharp focus on potential delays for proactive management. This design not only ensures consistency, but drives organizational alignment.

There isnt a fixed solution for all purposes. The principle to follow however is to set a balance that ensures clarity and usability without overwhelming the model with extraneous details. Some helpful guidelines.... Set strong foundation rules - Essential entities, relationships, constraints and mgmt to ensure accurate representation and integrity in the model. Design models to adapt to changing business requirements - The model's complexity should align with the users requirements and understanding. Strike a balance between normalization and denormalization for flexibility and develop the model iteratively, refining details as the project/user-journey progresses.

To make your data model clearer, simplify it by removing unnecessary details without losing its meaning. This helps highlight the important information, making it easier to understand and remember. You can use filters, groups, or categories to reduce the data points, and colors, sizes, shapes, or symbols to emphasize key aspects. Titles, captions, labels, legends, or annotations provide context, and consistent naming conventions, formats, and scales maintain clarity. Choose appropriate charts or graphs based on your data type and size, and use white space, alignment, or layout to organize and separate data elements.

For a successful data model, it is critical to bring all required data sets from all different data sources. To achieve it, one must have a complete requirement captured from the business. It is also critical to keep the data model simple and optimized. Control duplication of attributes and keep the number of attributes as low as possible without losing focus on the short-term to long-term business requirements.

Nothing will lose an audience faster than adding too much to the presentation of a model and its results. And once the audience becomes lost in the message that the model is intending to communicate, it will be difficult to secure support for future such exercises. One way to avoid this is to “practice” the presentation with “safe” audiences as much as possible before showcasing it to the target audience.

To incorporate the appropriate level of detail in your data model, strive for simplicity and clarity. Eliminate unnecessary or redundant information, focusing on the most crucial data elements that directly contribute to achieving your purpose. Employ clear and consistent naming conventions for entities, attributes, and relationships. Use groupings, filters, or categories to reduce the number of data points or dimensions, making the data more manageable and understandable. Consider visualizations like charts, graphs, and maps to effectively convey trends, patterns, and relationships within the data. By simplifying and clarifying your data model, you enhance its accessibility, usability, and overall value for decision-making and analysis.

To figure out how detailed your data model should be, it's important to check and test it before showing it to others. This ensures that your data model is correct, complete, and makes sense for your purpose and audience. To do this, you should look for errors or problems in your data sources, methods, and calculations. Check if your data model has any logical, conceptual, or visual issues. See if it's easy to understand and use, and evaluate how relevant and important it is. It's also helpful to get feedback from a colleague or someone from your audience. Look at your data model from their perspective to make sure it answers their questions or conveys your message well.

Validation and testing are crucial for ensuring the accuracy, completeness, and consistency of data models in the realm of analytics. Meticulous examination of data sources, methods, and calculations is necessary before unveiling a model to the world. To ensure that the model resonates with the audience, it must be clear, readable, and usable. A thorough evaluation for logical or visual flaws guarantees that the model is fortified against errors and tailored to meet the unique needs of its purpose and audience. Seeking external feedback acts as a fail-safe, providing diverse perspectives. Ultimately, validation and testing transform a data model from a mere concept into a reliable and impactful tool.

Validating and testing your data model is crucial to ensure that it is accurate, complete, and consistent, and meets the needs of your purpose and audience. You can use the following steps to validate and test your data model: 1) Create the development, validation, and testing data sets. 2) Use the training data set to develop your model. 3) Compute statistical values identifying the model development performance. 4) Calculate the model results to the data points in the validation data set. 5) Compute statistical values comparing the model results to the validation data. 6) Calculate the model results to the data points in the testing data set. 7) Compute statistical values comparing the model results to the test data.

It is essential to conduct thorough validation and testing. This process includes verifying data accuracy, confirming alignment with the intended objectives, and ensuring calculations yield expected results. For instance, in a healthcare analytics model, validation might involve checking that patient records are accurately represented and that algorithms for predicting disease trends produce reliable outcomes. Testing helps uncover and address potential issues, guaranteeing the reliability of the data model. By rigorously validating and testing, you ensure that when shared, the model provides trustworthy insights, as exemplified in healthcare scenarios where precise data is paramount for decision-making.

Normalize your data model to eliminate redundancy and improve consistency at the logical level. However, consider denormalization when performance is a critical factor at the physical level. You can also Collaborate with stakeholders, including business users, analysts, and developers, to gather requirements, understand their needs and expectations regarding the data model's level of detail.

Maintaining the appropriate level of detail in your data model requires continuous updating & refinement. Ensure that your data model reflects these changes by adding, removing, or modifying entities, attributes & relationships. Regularly review & assess the granularity of the data, ensuring that it aligns with your purpose & audience needs. Use data profiling techniques to identify anomalies, inconsistencies, or redundancies that may impact the accuracy and interpretability of the data. Embrace feedback from users to understand their pain points & areas for improvement. By actively updating and refining your data model, you can maintain its relevance, accuracy, and overall effectiveness in supporting informed decision-making and analysis.

Recognize that data models are dynamic and subject to change. Regularly update your model to incorporate new data, account for evolving business requirements, and adjust to changes in the external environment. Periodically refine your model based on feedback, emerging trends, and advancements in data analysis techniques to maintain its relevance and effectiveness.

Updating and refining a data model is an ongoing process which must include stakeholder input. It is imperative that business processes are always changing, system requirements are also evolving hence continuous updating and refinement of data models is indispensable. This will ensure that the data model adapts to these unavoidable changes. It is foolhardy to keep an obsolete data model as it negatively impacts integrations, results in data inaccuracies and in other risky industries it may result in lawsuits due to security and compliance issues. When updating and refining a data model it is important that all changes are documented and communicated clearly to the stakeholders.

In the end, keeping your data model in top form isn't just about maintenance, but is commitment to relevance, effectiveness and perpetual improvement. - Keep incorporating new or updated data. - Adjust your data model to reflect new queries, goals, or challenges since agility to pivot ensures your insights remain aligned with organizational needs. - Address issues surfaced through feedback and keep enhancing the model to deliver more value. I believe, with every iteration the data model evolves and matures.

Repasa datos y la historia que quieres contar. Mantén actualizados el origen de datos.En la preparación está el 90% del éxito de la presentación.

Consider putting data models into auto pilot. It may mean auto delivery of insights on a predetermined cadence for some. For others it may mean managing it by monitoring anomalies or informing downstream data consumers about the delay or data drift. Regardless plan it to develop once and consume it multiple times.

One of the most common errors in data and analytics is the failure to declare the data grain in fact tables when beginning the design process. The declaration of data grains establishes what the table represents. Every fact or candidate dimension should be consistent with the grain and maintain the right level of granularity when moving data around tables

A good data model need to be... PRECISE - * Precision: Ensuring accurate and specific representation of data. * Reliability: Consistent and dependable performance in handling information. * Efficiency: Optimal use of resources for data processing and storage. * Clarity: Clear and understandable structure for easy comprehension. Also includes proper documentation, modular design, and version control. * Integrity: This involves enforcing data integrity through constraints, validation rules, and referential integrity. * Simplicity: Striving for simplicity in design to enhance usability and maintenance. * Extensibility: Capability to easily adapt and incorporate new data elements or features.

Machine Data Analytics significantly influences every aspect of a real-time digital system and its associated Data(Objects)Model. It plays a crucial role in digital transformation by utilizing technology to enhance explainability, sustainability, reliability and efficiency, and facilitate new business adaptability. This involves analyzing data from sensors, IoT devices, and digital logs to interact with the model and capture essential details in realtime , while ensuring the data remains manageable for analysis. Machine data; eg log files, signals, sensor readings, and event streams, offers a detailed and complete view of an organisation's digital landscape health. A goldmine for real-time, contextual insights for informed decision-making.

Beyond the aforementioned steps, consider factors such as data security, ethical considerations, and legal compliance. Ensure that your data model adheres to privacy regulations and ethical standards. Additionally, provide documentation that explains the model's assumptions, methodologies, and any limitations, fostering transparency and trust among stakeholders.