Going Further, Together with AI-driven Sentiment Analysis (Infographics)

Selected Category = VALUES(Orders[Category])

Selected Colour bar = 
var selected_category = VALUES ('Selected Category'[Category]) 
var category_to_highlight = SELECTEDVALUE (Orders [Category])
var filtered = ISFILTERED ('Selected Category'[Category])
var result = 
    SWITCH(TRUE(),NOT(filtered),"#0055cc", category_to_highlight in selected_category && filtered,"#0055cc","#9cd0ed")
return result

var selected_category = VALUES ('Selected Category'[Category]) // taking values from category table

var category_to_highlight = SELECTEDVALUE (Orders [Category]) // Using selected value function 

var filtered = ISFILTERED ('Selected Category'[Category]) // checks if the column is filtered and will return true or false

var result = 
SWITCH(TRUE(),NOT(filtered),"#0055cc", category_to_highlight in selected_category && filtered,"#0055cc","#9cd0ed") 

return result

Setting Up Metrics Is a Breeze

Configuring metrics in Tableau Pulse takes just minutes. Whether it’s setting up your own custom metrics or utilizing pre-defined ones, the process is straightforward and user-friendly. And once set up, the visuals, descriptions, and insights are generated automatically, enabling team members to stay updated on performance with ease.
Pro Tip: You can also customize existing metrics to meet specific goals. For example, a custom metric was developed to track interactions generated by teams in the field, with a target of reaching a high engagement level within a set timeframe. This tailored approach has streamlined the process of monitoring performance and recognizing accomplishments.

Integrating Data into the Flow of Work

Decipher Trends with Intelligent Metrics

Monitoring metrics is not solely focused on tracking numbers; it also involves identifying opportunities and addressing challenges. With AI-generated summaries of key metrics like Annual Contract Value (ACV) and Pipeline Generation, Tableau Pulse helps you understand the context behind the data. The live updates ensure that any changes are reflected immediately, providing real-time insights that are always up-to-date.

Get a Clear View of Business Performance

Tableau Pulse’s intuitive visualizations and natural language summaries make it easy to compare current and past performance, helping you identify areas that may need closer attention. For instance, if Pipeline Generation is up 10.9% year over year but showing signs of slowing, Pulse allows you to dig deeper, understand the cause, and take timely action to keep growth on track.
Tableau Pulse is available on mobile, and these insights are accessible wherever you are, making it easy to stay connected to your data on the go.

Analyze Business Segments with Precision

Ask Smart, Data-Driven Questions

Powered by AI, Tableau Pulse does more than just report numbers. It actively helps you explore trends by suggesting relevant questions and providing insights in clear, understandable language. Whether it’s identifying which sales regions are thriving or where potential issues may arise, Pulse empowers you to make data-driven decisions with confidence.

Foster a Data-Driven Culture

Tableau Pulse is designed to be accessible to everyone, regardless of their level of data expertise. By integrating insights into tools like Slack, Pulse ensures that critical information is always at hand, promoting a culture of informed decision-making across the organization. This democratization of data means that every team member can contribute meaningfully to achieving our shared goals.

Tableau Pulse has truly transformed how we approach strategic decisions, placing personalized, contextually relevant insights directly in our workflow. It empowers everyone to be data-driven, aligning efforts towards achieving your company objectives and navigating the dynamic challenges of business with agility and insight.

Want to Transform Your Business?

Top Differences between Dynamic Set and Fixed Set

1. Set members change when the underlying data changes.
2. It has a single dimension.

1. Set members do not change.
2. It can be single-dimensional or multidimensional.

Steps to Create a Dynamic Set

The process to create a dynamic set is as follows: In the Data pane, right-click on the sub-category dimension and choose Create > Set. (Figure 1)

Figure 1: Creating a Set

• In the Create Set dialog box, set up your set. You can configure it using the following tabs:
1. General: Use the General tab to choose one or multiple values to be considered when computing the set. Alternatively, you can choose the Use All option to consistently consider all members, even when new members are added or removed.

If you know the top-selling products beforehand, you can manually select the products as shown in Figure 2 below.

Figure 2: Creating a Set using General Tab

2. Condition: Utilize the Condition tab to establish criteria that decide which members should be incorporated into the set.

You can specify this condition and create the set if you need products with sales greater than $50,000. (Figure 3)

Figure 3: Creating a Set using Condition Tab

3. Top: Employ the Top tab to set restrictions on which members should be included in the set.

For instance, you can establish a limit based on total sales, where only the top 5 products with the highest sales are included. (Figure 4)

Figure 4: Creating a Set using Top Tab

• Once you have completed the configuration, click the "OK" button.
• The newly created set will appear at the bottom of the Data pane within the Sets section. You can identify it by the set icon, which denotes a set field.

Steps to Create a Fixed Set

The process to create a fixed set is as follows:

• In the developed visualisation, select one or more marks from the view (Figure 5).

Figure 5: Selecting the marks.

• Right-click on the selected mark and select “Create Set” (Figure 6).

Figure 6: Creating the set.

• Type a name for the developed set (Figure 7).

Figure 7: Typing the Set Name.

• When finished, select “OK”. This newly created set can be accessed from the data pane. When this set is placed in the filter, the view will be filtered to show only the relevant set values.

Top Benefits of Sets in Tableau

• Top N or Bottom N Analysis: Sets can filter the data to display only the top or bottom N values based on a specific condition. For example, you could create a set to show the top 10 products by profit or even combine sets and display the top N and bottom N products by profit in a single chart. (Figure 8)

Figure 8: Top 3 and Bottom 3 Products by Profit

• Segmentation Analysis: Sets can also segment data into groups based on a specific condition. This can be useful for analysing performance differences between different groups. For example, you could create a set to segment customers based on their geographic location.

• Excluding Data: Sets can be used to exclude specific data points from a visualisation. For example, you could create a set to exclude customers who have not purchased in the last six months.

What is Set Actions in Tableau

Set actions allow users to modify the values within a set, on selection of marks within a view. This enables your audience to engage directly with a visualisation or dashboard and control various aspects of their analysis.

To utilise set actions:

1. Create sets associated with your data source.
2. Build set actions using the created sets.
3. Optionally, create calculated fields that incorporate the sets.
4. Construct visualisations referencing the sets.
5. Test and adjust the set actions for desired behaviour.

To create a set action that helps in drilling down category:

1. Create a set that selects a particular category (Figure 9 shows creating a set using the general tab selecting only furniture)

Figure 9: Creating Category Set using General Tab

2. If you are in a worksheet, go to Worksheet > Actions.
If you are in a dashboard, go to Dashboard > Actions.

3. In the Actions dialog box, click “Add Action” and choose "Change Set Values."

4. In the Add/Edit Set Action dialog box:

• Provide a descriptive name for the action.
• Choose a source sheet or data source. By default, the current sheet is selected. If you opt for a data source or dashboard, you can select specific sheets within it.
• Choose the desired method for users to execute the action:

1. Hover: The action will trigger when a user hovers the mouse cursor over a mark in the view.
2. Select: The action will activate when a user clicks a mark in the view.
3. Menu: The action will initiate when a user right-clicks (or control-click on Mac) a selected mark in the view and then selects an option from the context menu.

• To specify the target set:

1. First, choose the data source from the available options.
2. Then, select the desired set from the Target Setlist.

Figure 10: Setting up set action

• Specify what happens when the action is run in the view:

1. Assign values to set - Replaces all values in the set with selected values.
2. Add values to set - Adds individually selected values to the set.
3. Remove values from the set - Removes individually selected values from the set

• When the selection is cleared in the view:

1. "Keep set values" will retain the current values in the set without any changes.
2. "Add all values to set" will include all possible values in the set.
3. "Remove all values from set" will remove all previously selected values from the set.

5. After configuring the desired behaviour, click "OK" to save the changes and return to the view.
6. To ensure the set action functions as intended, interact with the visualisation, and test its behaviour.

Benefits of Set Actions

1. Filtering: Set actions can filter data based on user selections. For example, you could create a set step that filters the data to show only the top 10 customers in a particular region.
2. Highlighting: Set actions can also highlight data based on user selections. For example, you could create a set action highlighting all the customers who have purchased in a particular month.
3. Drill-downs: Set actions can create drill-downs that allow users to explore the data in greater detail. For example, you could create a set action enabling users to drill down from a high-level view of category by sales to a more detailed view of sales by sub-category. (Figure 11)

1. Snowflake Iceberg Tables Now Generally Available

Snowflake Iceberg Tables have moved to general availability, offering full storage interoperability with the Apache Iceberg open table format. This feature facilitates easier governance and collaboration on Iceberg data stored externally, enhancing the flexibility of data lakehouses, data lakes, and data meshes. Over 300 customers have already adopted Iceberg in its public preview, highlighting its potential to broaden Snowflake's data footprint.

2. Advancements in Snowflake Cortex AI

Snowflake introduced several enhancements to Cortex AI, including:

• • Cortex Analyst: Built with Meta's Llama 3 and Mistral Large models, this tool allows businesses to build applications securely on top of Snowflake's analytical data.
• • Cortex Search: Leveraging Neeva's retrieval and ranking technology, it facilitates the development of apps against documents and text-based datasets.
• • Cortex Guard: Aiming to ensure model safety, it filters and flags harmful content, including violence and hate speech.
• • Document AI: This feature, powered by Snowflake Arctic-TILT multimodal LLM, will soon allow users to extract data from documents such as invoices and contracts.

3. Introduction of Polaris Catalog

The Polaris Catalog is a vendor-neutral, open catalog implementation for Apache Iceberg, providing cross-engine interoperability and greater flexibility. It will become open-sourced within 90 days, supporting a variety of engines, including Apache Flink, Apache Spark, and Trino.

4. Private Preview of Snowflake Horizon Updates

Snowflake launched a private preview of an internal model marketplace within Snowflake Horizon. This marketplace enables users to publish and curate models, applications, and data products for internal use ensuring controlled access and preventing unintended external sharing. Other upcoming features include AI model sharing and AI-powered object descriptions.

5. AI & ML Improvements

• • Snowflake AI & ML Studio: A no-code interactive interface for AI development, now in private preview.
• • Cortex Fine-Tuning: In public preview, allowing customization of pre-trained models for specialized tasks.
• • ML Lineage: In private preview, offering traceability across ML life cycles.
• • Feature Store: Now in public preview, for creating, managing, and serving ML features.

6. Snowflake Native Apps with Snowpark Container Services — Preview

Snowflake introduced the integration of the Native App Framework with Snowpark Container Services on AWS. This integration provides developers with configurable GPU and CPU instances for various applications, from computer vision to geospatial data analysis. Over 160 Snowflake Native Apps are now available in the marketplace.

7. Developer Tool Enhancements

Several updates aimed at developers include:

• • Snowflake Notebooks: Snowflake Notebooks is now in preview, offering an interactive, cell-based programming environment for Python and SQL within Snowsight. It enables exploratory data analysis, machine learning model development, and other data science tasks all in one place.
• • Snowpark pandas API: Allows the use of pandas syntax for AI and pipeline development within Snowflake. The Snowpark pandas API is now in preview, allowing you to run pandas code directly on Snowflake data. This API offers a pandas-native experience with Snowflake's scalability and security, handling larger datasets without rewriting pandas pipelines.
• • Database Change Management: A public preview feature for DevOps, including Git integration.
• • Python API and CLI: Soon to be generally available, facilitating CI/CD practices.

8. Expanded Cloud Footprint and Governance

Snowflake announced a new data boundary for the EU, ensuring regional data residency and compliance. Additionally, a Department of Defense (DoD) environment meeting IL4 security controls will be available, highlighting Snowflake's commitment to robust data governance and security.

9. Snowflake Trail

The new Trail set of observability capabilities was unveiled, providing developers with tools to monitor, troubleshoot, and optimize workflows. Trail integrates with platforms like Grafana, Metaplane, and Slack, adhering to OpenTelemetry standards.

10. Snowflake Cortex Fine-Tuning — Preview

Cortex Fine-Tuning, now in preview, lets users adapt pre-trained models for specialized tasks. This managed service fine-tunes popular large language models using your data within Snowflake, enhancing model performance for specific use cases.

The preview of Snowpark Native Apps with Snowpark Container Services enables running containerized services within Snowflake Native Apps. This feature supports provider IP protection, security, data sharing, monetization, and integration with compute resources.

11. Snowpark Python Local Testing Framework — General Availability

The Snowpark Python local testing framework is now generally available. This emulator allows you to test Python code locally with Snowpark Python DataFrames, facilitating development and CI pipeline integration without needing a Snowflake account connection.

12. Universal Search and Snowsight Updates

Universal Search, now generally available, allows users to search for content across Snowflake storage, external Iceberg storage, and third-party providers. Snowsight also received a dark mode feature, enhancing user experience in low-light conditions.

13. New Geospatial Functions in Preview

Four new functions for GEOGRAPHY objects are now available in preview:

• • H3_TRY_COVERAGE: A special version of H3_COVERAGE that returns NULL if an error occurs when attempting to return an array of IDs (INTEGER values) identifying the minimal set of H3 cells that completely cover a shape.
• • H3_TRY_COVERAGE_STRINGS: Similar to H3_TRY_COVERAGE but returns hexadecimal IDs (VARCHAR values).
• • H3_TRY_POLYGON_TO_CELLS: Returns an array of INTEGER values of the IDs of H3 cells with centroids contained by a Polygon, returning NULL if an error occurs.
• • H3_TRY_POLYGON_TO_CELLS_STRINGS: Similar to H3_TRY_POLYGON_TO_CELLS but returns VARCHAR values.
With innovations in AI, data governance, and developer tools, Snowflake continues to drive forward the capabilities of its platform, ensuring customers can leverage data more effectively and securely. The future looks promising as Snowflake expands its offerings and strengthens its ecosystem, providing powerful solutions.

Agentic AI is redirecting AI to autonomous systems that can plan, decide, and execute tasks independently. These AI agents can manage workflows, interact with software systems, and trigger actions without continuous human input. While this autonomy promises efficiency and scalability, it also introduces new governance challenges.

Research shows that 86% of enterprises expect higher risk levels with agentic AI, yet only 2% of organizations currently meet responsible AI standards. This gap highlights a critical reality: organizations must adopt a structured agentic AI governance framework to balance autonomy with accountability.