Smartload 101

How to Access Smartload

Accessing Smartload is simple and straightforward. Once enabled by your EBM representative, it becomes available to you if you're subscribed to it. Additionally, you have the option to hide it from certain users if needed. You'll find Smartload conveniently located towards the bottom left of the Catalyst navigation pages, clearly labeled for easy access.

Smartload Navigation

In this section, we'll provide an overview of every aspect of Smartload in Catalyst. From sections and subsections to buttons and options, we'll guide you through each element so you can navigate with confidence. Think of it as your roadmap to understanding the layout and functionality of Smartload.

Fact Tables

• Description: This tab displays a list of fact tables available within the system. Each row represents a separate fact table, showcasing various details and actions related to them.

• Subsections:

  1. Fact Table List: Displays all existing fact tables with relevant information such as creation date, row count, linked attribute tables, status, and refresh timestamps.

  2. Fact Table Options:

     • Add a Consolidated Table: Enables users to add a new consolidated table.

     • Upload a New Table: Allows users to upload a new fact table.

     • Edit Cube: Provides a detailed editing interface for the selected fact table called Input Data Management, allowing modifications to column headers, data types, inclusion of Catalyst hierarchy, and addition of standard measures.
       

     • Manage Relationships: Facilitates linking of fact table columns to attribute table columns.
       

  3. Cube Status: Understanding Row Statuses and Buttons
     
     
     
     

     Each row on the fact table page provides key information about the table's status and available actions. Here's what the different elements mean:

     1. Linked Attribute Tables:
     The chain icon indicates linked attribute tables. Hovering over this icon shows the linked Location and Attributes tables.

     2. Row Count and Creation Date:
     Displays the number of rows in the table and when it was created. For example: "Created: 09/11/2024 11:55 AM | Rows: 0"

     3. Hierarchy Refresh Status:
     Shows if the table is linked to a Catalyst Hierarchy. "Not Provided" indicates no linked hierarchy.

     4. Refresh Status and Timestamp:
     Indicates when the last successful refresh occurred, if applicable. For example: "Successful refresh 09/18/2024 01:39 AM"

     5. Cube Status:
     - Blue cube icon: Indicates the cube has been built.
     - White cube icon: Indicates the cube hasn't been built yet.

     Note on White Cube Status:
     A white cube status doesn't necessarily mean the table's data isn't being used elsewhere. Even if a table has zero rows, it may be:
     - Awaiting further development
     - Used in consolidated higher-level cubes (e.g., L1 or L2 data feeding into an L3 cube)
     
     Rule of thumb:
     If a table with a white cube icon has a non-zero row count, it's likely being used somewhere in Smartload and should not be removed without investigation.

     6. Additional Options:
     The three-dot ellipsis menu provides access to more actions for each table.

  4. Ellipsis Menu:

     • Provides various options such as downloading cube, processing cube, adding data to table, replacing data in table, refreshing table, deleting table, syncing new records, and more.

       

        

Attribute Tables

• Description: This tab showcases a list of attribute tables present in the system along with relevant details and actions.

• Subsections:

  1. Attribute Table List: Presents all attribute tables with information like creation date, standard name, row count, linked fact table(s), and additional actions. Use Data Input Management to access the editing interface.

  2. Attribute Table Options:

     • Manage Relationships: Allows users to establish relationships between attribute table columns and fact table columns.

       

  3. Ellipsis Menu:

     • Offers options like input data management, adding/replacing table data, assigning a standard name, downloading a sample, changing table to a fact table, duplicating table, deleting table, and refreshing table.

       

Unassigned Tables

• Description: Similar to the previous tabs, this section displays tables that are currently unassigned, providing users with the option to assign them as needed.

• Subsections:

  1. Unassigned Tables List: Shows tables awaiting assignment.

  2. Assignment Options:

     • Allows users to assign tables to appropriate categories or modules within the system.

Module Setup

• Description: This tab serves as a creation page for various modules or data types, offering insights into status and logs.

• Subsections:

  1. Module Creation: Provides a user interface for creating different modules or data types such as financial or transaction data.

  2. Status and Logs: Displays the status of module creation processes along with detailed logs for troubleshooting purposes.

Getting Started with Smartload

Getting started with SmartLoad involves a structured approach to efficiently manage data ingestion, configuration of fact and attribute tables, and the subsequent building of a cube for comprehensive data analysis.

Step 1: Initial Data Setup

1. Data Integration:

   • If your data is already integrated from your source ERP(s), Smartload will automatically populate your fact tables. This ensures that your data is up-to-date without manual intervention.

   • If automated integration isn't set up, you can still manually upload data to Smartload.

2. Manual Upload:

   • Navigate to the Smartload tab in your application and select "Upload New Table."

   • If you're dealing with data from multiple ERPs and need to consolidate them, use the "Add Consolidated Table" option. This allows you to create a unified view of your data much like a SQL view.

   • Choose the file containing your data. It's important to note that the name of the fact table will be based on the tab chosen from the file upload, not the file name itself.

Step 2: Fact Table Configuration

1. Input Data Management:

   • If you're creating a Fact table, you'll need to set up input data management selections. This includes defining how Smartload should handle incoming data. This is found by clicking Edit Cube.
     

     

   • Once configured, click "Next" to proceed to the core configuration panel.

2. Core Configuration:

   • In the core configuration panel, you can set up scenarios, choose which columns to include in your analysis, configure currency and calendar date settings, and more.

   • This step allows you to tailor the fact table to meet your specific analytical needs.

     

3. Calculated Columns:

   • After configuring the core settings, you can add calculated columns using DAX measures. These columns can perform calculations on your data, providing additional insights.

     
     

     • Simple Definition: Think of calculated columns as new pieces of information you create from your existing data. You use calculations or rules to make these new columns. For instance, you might calculate total sales by multiplying the quantity sold by the price. These columns help you uncover more insights and make your data more useful for analysis without changing the original data.

     • Full Definition: Calculated columns are new columns in a dataset that are created using calculations or expressions based on existing columns within the dataset. These calculations can involve mathematical operations, text manipulations, conditional logic, or any other transformation that is needed to derive new insights from the data. For example, in a sales dataset, you might create a calculated column to calculate the total revenue by multiplying the quantity sold by the unit price. Similarly, you could create a calculated column to categorize customers based on their purchase behavior or to calculate profit margins. Calculated columns are useful for performing on-the-fly calculations without altering the original dataset. They allow analysts to derive additional insights and perform complex analyses without the need for external tools or programming languages.

4. Calculated Measures:

   • Similarly, you can add calculated measures to further analyze your data. These measures can be customized based on your business requirements.

     

     • Simple Definition: Calculated measures are like custom-made calculations that work on summarized data. They help you analyze your data in a more specialized way by calculating things like averages, percentages, or growth rates. These measures give you deeper insights into your data's trends and performance without altering the original data.

     • Full Definition: Calculated measures are customized calculations applied to aggregated data in a dataset or data model. They help analyze data in a more sophisticated way by performing calculations on the fly based on specific business requirements. Unlike calculated columns, which create new fields at the row level, calculated measures operate at the aggregated level, providing insights into overall trends or performance. For example, in a sales dataset, a calculated measure might calculate the average order value or the year-over-year growth rate in sales. These measures can involve complex calculations, such as ratios, percentages, or comparisons, and are often used in data analysis and reporting to provide deeper insights into business metrics.

5. Process Cube:

   • Once all configurations are set, click "Process Cube" to build the cube. This step prepares your data for analysis and reporting.

Step 3: Attribute Table Setup

1. Repeat Process:

   • Follow a similar process as for Fact tables to add attribute tables. Upload the data, configure settings, and process the cube.

   • Attribute tables provide additional context and details about your data, enhancing the depth of your analysis.

     

2. Manage Relationships:

   • Use the "Manage Relationships" feature to link your Fact and Attribute tables. This ensures that your data is properly connected, allowing for comprehensive analysis.

Step 4: Data Analysis

1. Download Smartload Cube:

   • Once all tables are set up, you can download a full Smartload cube. This cube contains all your data and can be manipulated and analyzed using standard pivot tables or other reporting tools.

Step 5: Data Maintenance

1. Add Table Data:

   • To upload more data for the same data type, choose "Add Table Data" under the hamburger ellipsis menu in the Fact table row. This option is useful for adding new data without uploading the entire dataset again.

2. Replace Data:

   • If you need to replace existing data while keeping configurations intact, upload a file with the same columns containing new data. Click "Process" to update the cube after such changes.

Nice work. You've successfully set up and managed your data using Smartload. Take advantage of its features to gain valuable insights and drive informed decision-making in your organization.

How to Create a New Fact Table

Creating a new fact table using a file from Excel or CSV containing a large amount of data can seem daunting, but with the right approach, it can be a smooth process. Here's some advice to help you through it.

1. Prepare Your Data: Before uploading your file, ensure that your data is clean and organized. Remove any unnecessary columns or rows, special characters, fix formatting issues, and ensure consistency in data types.

2. Understand Your Data: Take some time to understand the structure and content of your data. Identify which columns contain the metrics or facts that you want to analyze and which columns contain the dimensions or attributes.

3. Choose the Right File Format: Smartload supports both Excel and CSV file formats. Choose the format that best suits your data and ensure that it is compatible with Smartload's requirements.

4. Upload Your File: Once your data is prepared, navigate to the Smartload section and select "Upload New Table." Choose your file from Excel or CSV and follow the prompts to upload it.

5. Select the Tab: Remember that the name of your fact table will be based on the tab chosen from the file upload, not the file name itself. Select which tab you want to pull in data from.

6. Choose Type: Once uploaded, Smartload will ask you if this is a fact table or attribute table. Select Fact table.

7. Configure Your Fact Table: After uploading your file, you'll be prompted to configure your fact table. This includes selecting the appropriate columns for analysis, setting up input data management selections, and configuring any necessary scenarios or hierarchies.

8. Process Your Cube: Once your fact table is configured, click "Process Cube" to build the cube. This step prepares your data for analysis and reporting.

9. Review and Validate: Before proceeding, review your fact table to ensure that it has been created correctly and that the data appears as expected. Validate the results to ensure accuracy.

10. Iterate as Needed: If you encounter any issues or need to make adjustments, don't hesitate to iterate on the process. Smartload allows you to make changes and updates as needed to ensure that your fact table meets your requirements.

How to Create a New Attribute Table

Creating a new attribute table from an Excel or CSV file is straightforward. Here's how:

1. Get Your Data Ready: Before you start, make sure your data is clean and tidy. Remove any extra rows or columns you don't need and make sure everything's in the right format.

2. Pick Your File: Choose whether you're uploading from Excel or CSV. Either works fine, just go with what you're comfortable with.

3. Upload the File: Head over to the Smartload section and pick "Upload New Table." Find your file and click upload.

4. Select the Tab: Remember, the table is named off of the tab in the file, not the filename. Select which tab you want to pull in data from.

5. Choose Type: Once uploaded, Smartload will ask you if this is a fact table or attribute table. Select Attribute table.

6. Choose Columns: Now, pick the columns you want in your attribute table. These are the details you'll use to describe your main data later on. For example, if you're uploading a location table, you might have columns for city, state, and country.

7. Process Your Table: Once you're happy with your selections, hit "Process Cube." This gets everything ready for analysis.

8. Check Your Work: Take a quick look to make sure everything's how you want it. You might want to link this attribute table to your sales or customer data to make it more useful. For example, you could link the location attribute table to your sales data to see where your customers are buying from.

9. Repeat as Needed: Since you'll likely have lots of attribute tables, like one for each type of detail you want to track, you can repeat this process as many times as you need. Just keep it simple and organized, and you'll be all set!

Defining and Managing Relationships in Smartload

Managing relationships between fact tables and attribute tables is essential for ensuring effective data analysis within Smartload. Here's some advice on how to do it:

1. Understanding Relationships: Before managing relationships, it's essential to understand the concept. In Smartload, relationships define how attribute tables are linked to fact tables based on common fields or keys. For example, a customer ID in a sales fact table might be linked to a customer ID in a customer attribute table.

2. Navigating to Relationship Management: To manage relationships, users can typically find this feature within the Smartload interface. Look for options like "Manage Relationships" or "Link Tables" in the navigation menu or within the settings of individual tables.

3. Establishing Relationships: When linking tables, identify common fields or keys that can serve as the basis for the relationship. For instance, in a retail scenario, a product ID in a sales fact table might be linked to a product ID in a product attribute table.

4. Example Use-Cases:

   • Sales Analysis: Linking a sales fact table with attribute tables containing information about products, customers, and regions allows for detailed sales analysis by product category, customer segment, and geographic location.

   • Inventory Management: Establishing relationships between a sales fact table and attribute tables containing inventory levels, product specifications, and supplier information facilitates inventory tracking and replenishment analysis.

   • Customer Segmentation: Connecting a sales fact table with attribute tables containing demographic data, purchase history, and customer feedback enables segmentation analysis to identify high-value customers and target marketing efforts effectively.

5. Testing and Validation: After establishing relationships, it's crucial to test and validate the connections to ensure that data is properly linked and that analysis results are accurate. Use sample queries or reports to verify the integrity of the relationships.

6. Iterative Approach: Relationships may evolve over time as data requirements change or new insights are discovered. Be prepared to revisit and refine relationships periodically to adapt to evolving business needs.

Updating and Adding Data to a Smartload Cube

In Smartload, keeping your data up-to-date is easy with the "Table - Add Data" option. This feature allows you to append new data to a pre-existing fact table without the need to recreate the entire table. Here's why you'd want to know about it:

Use-Cases:

1. Month-End Close: At the end of each month, you may need to update your sales or financial data with the latest transactions. Instead of starting from scratch, you can simply add the new data to your existing fact table, ensuring that your analyses are always based on the most current information.

2. Iterative Business Practices: Business data is dynamic, with new information constantly being generated. Whether it's daily sales figures, weekly inventory updates, or quarterly financial reports, the ability to append data allows you to seamlessly incorporate new data into your analyses without disrupting your existing configurations.

How It Works:

• Simply navigate to the ellipsis menu under the respective fact table in Smartload and select "Table - Add Data."

• Upload your new data file, ensuring that the column structure matches the existing fact table. The file name and tab name don't matter; the system looks for the data itself.

• The new data will be appended to the existing fact table, maintaining all previously configured settings, relationships to attribute tables, calculated columns, and measures. This means you can add data to the cube without reinventing the wheel each time you introduce new data.

By utilizing the "Table - Add Data" option, you can streamline your data management processes, ensuring that your analyses are always based on the latest information without the need for repetitive setup tasks.

Replacing Data in a Smartload Cube

In Smartload, the "Table - Replace Data" option offers a powerful way to update or overwrite existing data in a fact table or Smartload cube. Here's how to use it effectively:

Use-Cases:

1. Data Refresh: When you need to refresh your data with the latest information, replacing existing data is a straightforward approach. For instance, if you receive an updated version of your sales data file, you can replace the existing data in the fact table with the new file to ensure that your analyses are based on the most recent data.

2. Starting Over: Sometimes, you may need to start over with your data but retain the same configurations, relationships, calculated measures, and other settings. Instead of recreating the entire fact table from scratch, you can use the "Table - Replace Data" option to overwrite the existing data while preserving all other configurations.

How It Works:

• To replace data in a fact table, navigate to the ellipsis menu under the respective table in Smartload and select "Table - Replace Data."

• Upload the new data file, ensuring that it has the same column structure as the existing fact table. Unlike with appending data, where the file name and tab name don't matter, when replacing data, the column structure must match exactly.

• Once the new data is uploaded, it will replace the existing data in the fact table or Smartload cube while maintaining all previously configured settings, relationships to attribute tables, calculated columns, and measures.

Additional Use-Cases:

1. Data Cleanup: If your existing data contains errors or inconsistencies that need to be corrected, replacing the data allows you to start fresh with clean and accurate information.

2. Scenario Testing: Before making significant changes to your data analysis approach, such as modifying calculated measures or introducing new scenarios, you may want to test these changes with a fresh set of data. Replacing the data in your fact table enables you to do so without affecting your existing configurations.

By leveraging the "Table - Replace Data" option in Smartload, you can easily update or overwrite existing data in your fact tables or Smartload cubes while maintaining consistency and integrity in your data analysis processes.

By efficiently setting up data ingestion or upload mechanisms, configuring fact and attribute tables accurately, and building out a well-structured cube, organizations can transform raw data into valuable insights. SmartLoad's robust features empower users to navigate complex datasets with ease, facilitating informed decision-making and driving business growth.