Tableau-CRM-and-Einstein-Discovery-Consultant Questions and Answers

When deploying a dashboard such as SalesPerformance_1 from a sandbox to production in Salesforce Tableau CRM using a package, the correct metadata reference in the XML file is typically formatted with a suffix that identifies the type of component. For dashboards in Tableau CRM, the suffix "waveDashboard" is used, hence the correct reference would be "SalesPerformance_1 waveDashboard."

Two benefits of designing using the "Progressive Disclosure" principle are:

• A: Improved ease of use for end users. By only displaying essential information initially and allowing users to access more detailed data as needed, dashboards become less cluttered and more user-friendly.
• D: Better dashboard performance. This design principle can also lead to better performance as less data is processed and rendered initially, speeding up load times and responsiveness.

To convert the "CloseDate_Month" numerical field into a month name in the dataset, the consultant should use:

• C: computeExpression. This Dataflow node allows the transformation of existing data fields using expressions, such as converting a month number into a month name. By applying a suitable expression, the 'CloseDate_Month' can be transformed into a string representing the month’s name, enhancing readability and usability in filters.

The "Progressive Disclosure" design principle in dashboard design means: A. Only provide the user with the level of detail they need to see, with the option to drill down deeper into more details. This approach helps to keep the user interface simple and clean, avoiding information overload while still offering more detailed data as needed. This principle ensures that dashboards are user-friendly and efficient, focusing on user engagement and information relevance. Reference: Salesforce Documentation on Progressive Disclosure

Creating an Einstein Discovery story begins with selecting a dataset in Tableau CRM. Users can navigate to the desired dataset and initiate the story creation process by selecting the "Create Story" option. This feature allows users to explore data automatically, uncovering patterns and predictive insights that help in making data-driven decisions. The process does not involve copying texts or uploading presentations but rather utilizes the existing datasets to generate meaningful and actionable insights. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_create_story.htm &type=5

In Salesforce Analytics Query Language (SAQL), the order of functions plays a crucial role in how data is processed. The 'filter' and 'order' functions can be used interchangeably depending on the specific needs of the query. However, 'offset' should always follow either 'filter' or 'order', and 'limit' must be specified after 'offset'. This order ensures that the query first narrows down the data set, then offsets into this subset, and finally limits the number of records returned.

Predictive insights are primarily beneficial for:

• C: Drilling down into the underlying reasons behind a prediction. This capability enables users to understand not just what might happen in the future but why those outcomes are likely, based on the data being analyzed. This understanding is crucial for strategic decision-making and for optimizing processes based on predictive analytics.

In the configuration wizard of Tableau CRM apps, required fields or questions without default values are typically indicated by the system to guide the setup process. By proceeding with the configuration ("Looks good, next"), the wizard will highlight any mandatory questions that have not been answered, usually by marking them in red. This visual cue allows users to easily identify and address the required inputs that are missing defaults, ensuring that essential configurations are not overlooked during setup. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_apps_configure.htm &type=5

The maximum number of concurrent API Analytics calls per organization in Salesforce Tableau CRM is 200. This limit helps to ensure that the system can handle multiple API requests efficiently without degrading the performance for users across the organization.

Creating a regression timeseries in Salesforce Tableau CRM involves the use of the SAQL (Salesforce Analytics Query Language). Specifically, the timeseries function is used within SAQL to model and forecast data points across time. Here are the required steps:

• Partition: This argument in the timeseries function specifies how the data should be grouped. In most cases, this will be based on a time dimension such as date or month.
• Seasonality: Setting this parameter to 12 is typical for capturing yearly patterns and trends in the data.

To write predictions back to the Predicted Amount field in the Opportunity object across all records, the best approach is to enable a Bulk Scoring Job under the Prediction Definition. This feature allows for the bulk processing of records to update the predictive field based on the latest model predictions. Here are the steps:

• Navigate to the Prediction Definitions in Tableau CRM.
• Select the prediction model applied to the Opportunity object.
• Configure the settings to specify the Predicted Amount field as the target for the output.
• Enable and configure the Bulk Scoring Job to run at desired intervals (e.g., daily, weekly), depending on the business requirement.

This process ensures that all Opportunity records are scored consistently with the most current predictions, keeping the Predicted Amount field updated.

In Einstein Discovery stories, insights show how different variables, or combinations of variables, explain the variation of the outcome variable. The outcome variable is the target variable whose behavior and influencing factors are being analyzed and predicted by the model. Insights in the story will help understand how each explanatory variable and their interactions impact the outcome, which is central to predictive analytics and machine learning in Einstein Discovery. Reference: Salesforce Help - Einstein Discovery Insights

When you configure the story, you tell Einstein Discovery to maximize/minimize the variable. The variable is 'outcome variable' in your story

When clients express disappointment that Einstein Discovery only detected known patterns, an appropriate response is to highlight the efficiency benefits, specifically how Einstein significantly reduces the time to insight compared to traditional methods. This emphasizes the value of using Einstein Discovery to quickly validate known patterns and potentially explore nuances within those patterns more swiftly than through manual analysis.

For Discovery in Salesforce Tableau CRM, the limits on the number of columns (variables) are set at a minimum of 2 and a maximum of 50. These boundaries ensure that the Discovery models are both manageable and robust enough to generate meaningful insights without overwhelming the computational resources.

Einstein Discovery presents insights based on their importance in explaining the variation in the outcome variable, in descending order. This approach prioritizes insights that have the most significant impact on the outcome, helping users to focus on the most influential factors first. By sorting insights in this manner, Einstein Discovery ensures that users can quickly identify and address the key drivers of their business metrics, facilitating more effective decision-making based on the most critical data points. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_story_overview.htm &type=5

Salesforce Tableau CRM (formerly Einstein Analytics) provides specific prebuilt permission sets that allow various levels of access and capabilities within the platform. The two prebuilt permission sets that are relevant here are:

• Einstein Analytics Platform Admin: This permission set grants administrative privileges within Einstein Analytics, allowing users to manage users, permissions, and overall platform settings.
• Einstein Analytics Platform User: This permission set enables users to access and use Einstein Analytics for data exploration and dashboard creation, but without the administrative privileges.

These permission sets are designed to simplify user management by providing role-based access controls that can be easily assigned to different users depending on their needs within the platform. Reference: Salesforce Documentation - Einstein Analytics Permission Sets

To access Einstein Analytics (now known as Tableau CRM), having just an Einstein Analytics Platform license is not sufficient. Users also require specific permissions to use analytics functionalities. The critical permission is "Use Analytics," which should be granted via a permission set. Without this permission, users can't access or use Tableau CRM, even if they have the necessary license.

In Tableau CRM (formerly Einstein Analytics), you can restrict access to data at the row level using security predicates. Security predicates are filter expressions added to datasets that dynamically restrict data access based on user attributes or record specifics. This method allows administrators to enforce data security policies directly within the dataset, ensuring that users only see data relevant and permitted to them, without altering the underlying data structure like removing rows manually or adding flags. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_security_predicates.htm &type=5

Predicate is a name for a filter condition that defines row-level access to records in a dataset Example: "rowLevelSecurityFilter":"'AccountOwner' == \"$User.Name\""

To automate the loading of data from a Microsoft Azure SQL Data Warehouse into an Einstein Analytics dataset, transform it, and merge it with company data daily, the following Einstein Analytics user interface features can be used:

• A: Dataflow: This tool allows for the creation and management of complex data transformation and integration processes necessary for combining data sources.
• D: Recipes: Recipes provide a user-friendly interface for performing data transformation tasks without writing code, which can be used for merging and transforming data.
• E: Analytics REST API: The API can automate data ingestion and management tasks, providing flexibility and control over data integration from external systems.

To load data from an external, on-premise Oracle database into Einstein Analytics, the recommended options are:

• Use the Analytics External Data API: This API facilitates the integration of external data sources with Einstein Analytics, allowing for automated data uploads.
• Load the data as a CSV file: This method involves extracting data from the Oracle database into a CSV format and then uploading it to Einstein Analytics.

Using Chart Markers in a bar chart is an effective way to dynamically emphasize certain values, making it easier for users to focus on key information. Chart markers can be configured to highlight data points based on specific conditions or thresholds, such as highlighting values above or below a certain number, or showcasing changes over time. This visual differentiation aids in

To improve dashboard performance in the face of growing datasets, the Einstein Analytics consultant can take the following actions:

• B: Distribute steps among separate pages: This reduces the load on any single page, enhancing performance by only loading data necessary for the view being accessed.
• D: Replace separate step filters with a global filter: This reduces the number of queries and processing needed as the global filter applies universally, streamlining data handling.
• E: Move calculations to a dataflow: Offloading calculations to a dataflow minimizes the processing required at runtime, which can significantly improve dashboard performance.

In Salesforce Analytics, the basic actions within a query that are fundamental to building meaningful visualizations and data summaries include:

• A. Aggregate by a measure: This involves summarizing data using statistical measures like sum, average, maximum, etc., which help in understanding the distribution and trends of data.
• C. Filter to narrow down your results: Filtering allows users to limit the data shown in the dashboard to only those records that meet certain criteria, thus focusing on more relevant subsets of data.
• D. Grouping by a dimension: Grouping involves categorizing data based on specific fields (dimensions), which helps in analyzing the data across different segments or categories.

An Analytics app in Salesforce is a collection of datasets, dashboards, lenses, and a dataflow. These components are bundled together within an app to provide a comprehensive analytics solution tailored to specific business needs or project requirements. The app facilitates organized access to related analytics assets, making it easier for users to navigate and utilize data for decision-making and insights. Reference: Salesforce Help - Understanding Analytics Apps

The likely missed step in the deployment process of a Tableau CRM analytic app that includes a recipe and several datasets and dashboards, where the datasets aren't appearing post-deployment, is that the recipe needs to be run. Here's what should be done:

• Navigate to the Tableau CRM Analytics Studio.
• Locate the deployed recipe within the app.
• Manually initiate the recipe to run, or verify if it's scheduled to run automatically. If not, set up a schedule.
• Ensure that once the recipe runs, it processes and creates the datasets as designed.

This step is crucial because recipes are responsible for transforming data and generating the datasets needed for the dashboards.

Assigning any Analytics permission set to users in a Salesforce org does not automatically assign the Analytics Platform permission set license. The license must be explicitly assigned to users before they can utilize the features enabled by the permission sets. This ensures that only authorized users have access to powerful analytics capabilities.

To meet the requirement where each sales rep can view attainment data for reps in their division but only see their own commission data, applying a security predicate on the existing single dataset is the most efficient and secure method. This approach allows granular control over data visibility based on user-specific criteria, directly aligning with the security and privacy needs specified.

The "Unexplained Bar" in the "Why it Happened" chart of Einstein Discovery represents the difference between the predicted outcome and the observed outcome. This metric is essential as it highlights the variance that the model's explanatory factors do not account for, providing insight into the limitations of the current model. This understanding aids in interpreting the accuracy and reliability of the model predictions.

Based on the "Why it Happened" graphic: A. Germany performs lower than average but better when the product is Call Center. This interpretation is supported by the presence of the Call Center product in Germany as a factor that increases the probability of Opportunity Won, contrary to its general performance. D. Germany sells more Call Center product and that helps them increase their win rate. The graph indicates that Call Center products have a positive impact in Germany on the win rate, suggesting that the higher sales volume of this product helps improve overall performance in the region.These insights are derived from understanding the visualization in the context of what contributes positively and negatively to the outcomes, which is a key feature of Einstein Analytics. Reference: Einstein Discovery Insights

The best practice to ensure Einstein Analytics dashboards are easily used across both desktop and mobile devices is to create multiple layouts and ensure the layout selectors match the device. Additionally, resizing and hiding widgets as necessary helps ensure that the content is appropriately displayed and interacted with on the device screen size.

In Salesforce Tableau CRM, the timeout for ETL (Extract, Transform, Load) jobs that have been scheduled but not executed is 15 minutes. This timeout setting helps manage system resources efficiently by ensuring that jobs do not indefinitely occupy system capacity if they fail to initiate.

In Salesforce Tableau CRM, selecting the correct root object when using the dataset builder is crucial because the root object determines what related objects can be added to the dataset. The root object sets the base or primary focus of your dataset, and its relationships define the scope of additional data that can be included through joins and links to other Salesforce objects. Selecting an inappropriate root object might restrict the range of data that can be effectively utilized or require complicated workarounds to incorporate necessary related data. Reference: Salesforce Help - Selecting the Right Root Object

If an administrator is unable to upload a custom geoJSON file via the user interface, the recommended approach is:

• C: Upload the geoJSON via the API because it is not a function in the UI. This method allows for the integration of custom geoJSON maps into Salesforce dashboards, even when UI support is not available.

To open the Actions menu for a particular case shown in a dashboard in Salesforce Tableau CRM:

• B: Hover over the case number column of the values table widget and click the arrow that appears. This action will display the menu related to that specific case, offering various actions that can be performed directly from the dashboard.

Before users can access Analytics in Salesforce, they need two things: an Analytics permission set license (PSL) and a permission set containing at least one Analytics user permission. The PSL enables access to the Analytics functionality at a license level, while the permission set defines what specific actions the user is authorized to perform within Analytics.

Posting to Chatter from a Salesforce Wave (Tableau CRM) dashboard is done by selecting "Post to Chatter" from the Actions drop-down menu at the top of the dashboard. This feature allows users to directly communicate findings, share insights, or discuss data visible on the dashboard with other team members through Salesforce Chatter, enhancing collaboration and information sharing. Reference: Salesforce Documentation - Post to Chatter from Dashboards

In Tableau CRM's Insights list, insights are ranked according to their statistical significance. This means that the insights considered most impactful or relevant based on the underlying statistical analysis are presented first. This ranking helps users quickly identify the most critical insights derived from their data.

The steps in the data exploration process in Salesforce Tableau CRM are as follows:

• View: Initially viewing the data to understand what is available and identify areas of interest.
• Explore: Digging deeper into the data to discover patterns, anomalies, and insights.
• Refine: Adjusting the exploration by filtering, sorting, or enhancing the data visualization for clearer insights.
• Save: Saving the state of your exploration to revisit or continue working on later.
• Share: Sharing your findings with others in the organization, either through direct dashboard access, downloadable reports, or URLs.

Each prebuilt Analytics app has its own PSL. When you set up permissions for those apps, select the right PSL. For Service Analytics, select "Service Analytics Apps." For Event Monitoring, select "Event Monitoring Analytics Apps," and so on.

When discrepancies arise between the aggregate sum in a dataset and manual calculations in Salesforce, common issues include: C. Differences involving formula fields. Formula fields in Salesforce are calculated dynamically and may not always be calculated during data sync if they depend on other fields that aren't triggered by record updates. D. Differences involving trigger-updated fields. Fields that are updated by triggers might not have their latest values captured if the triggers were executed after the data was replicated. These scenarios can cause inconsistencies in data reflected in Einstein Analytics compared to what is stored directly in Salesforce. Reference: Salesforce Documentation on Data Sync and Replication

In Salesforce Tableau CRM, with data sync enabled, the maximum number of dataflow definitions an organization can have is 25. This limit is designed to balance the need for comprehensive data operations with system performance and manageability.

Data that has already been loaded into Einstein Analytics as datasets can be utilized directly in Einstein Discovery without requiring a separate load process. This integration within the Salesforce platform allows seamless transition of data from analytics to discovery, facilitating a streamlined workflow for data analysis and enhanced decision-making. Therefore, Danielle can use existing datasets in Einstein Analytics for her analysis in Einstein Discovery without needing additional permissions or import steps. Reference: Salesforce Help - Using Data in Einstein Discovery

In Salesforce Tableau CRM (Einstein Discovery), one of the straightforward ways to get data into Einstein Discovery for analysis is by using an Einstein Analytics dataset. These datasets can be directly used to create stories in Einstein Discovery, allowing for deep analysis and predictive modeling based on the data already collected and structured within the Salesforce environment. This method leverages the integrated nature of Salesforce products to provide a cohesive and efficient analytics experience. Reference: Salesforce Help - Data Preparation for Einstein Discovery

https://trailhead.salesforce.com/content/learn/modules/wave_exploration_smart_data_discovery_basics/wave_get_smart_data_discovery_and_data

In the context of Einstein Discovery in Tableau CRM, "Second-order analysis" refers to the type of insight that examines how one variable explains variation of the outcome variable. This analysis delves deeper into understanding the relationships between variables, particularly how they interact to influence the outcome. Second-order analysis is more advanced than first-order, which typically looks at direct relationships without considering interactions between multiple variables. This level of insight helps in identifying underlying patterns that might not be apparent from simple direct analysis. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_understanding_analysis.htm &type=5

The Service Analytics Overview dashboard in Tableau CRM is designed to provide comprehensive insights into various aspects of service operations. It includes:

• Key metrics such as open cases, average time to close, first contact resolution, and customer satisfaction, offering a quick overview of service efficiency.
• Drill-down capabilities into more detailed dashboards like agent performance, channel review, and telephony metrics, enabling deeper analysis.
• Availability on both desktop and mobile platforms, ensuring accessibility across different devices.
• It serves as an excellent starting point for service analytics, providing a holistic view of the service department’s performance.

This dashboard is a central tool for managing and analyzing service operations effectively, helping organizations to optimize their customer service strategies. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_service_analytics.htm &type=5