The Primary Means to Narrate Stories in Tableau

We can represent the visuals using the following three formats on Tableau:

1. Sheets: Spaces where we can build individual visuals are called sheets. A worksheet has a single view in its sidebar, as well as shelves, cards, legends, and the Data and Analytics panes. A workbook is a sheet file structure along the lines of Microsoft Excel. It includes sheets that can function as a worksheet, a dashboard, or even a story

Source: https://help.tableau.com/current/pro/desktop/en-us/inspectdata_describe.htm

2. Dashboards: Most commonly used reporting format, a dashboard is a layout where sheets are arranged in a meaningful manner. It is a collection of views that helps you to compare a variety of data at the same time. If you have a set of views that should be reviewed every day, then you can create a dashboard that displays all of the views at once rather than navigating to separate worksheets. Think of the efficiency gains that this can bring about.

Source: https://www.tableau.com/about/blog/2020/5/6-dashboards-tableau-partners-help-you-mitigate-covid-19

3. Story:Sheets or dashboards arranged in a sequence to convey information. A story is a collection of visuals that work together to convey information. Stories can be created to tell a data narrative, provide context, show how decisions affect outcomes, or simply make a compelling case.

Source: https://help.tableau.com/current/pro/desktop/en-us/stories

Default Charts in Tableau

Through a set of default charts created with Tableau, data sets can be displayed in a comprehensible way. Let's have a look at a few types of charts:

1. Area Chart:An area chart is a line chart with a colour shaded area between the line and the axis. These charts constitute the most common approach to illustrate stacked lines and are often used to represent accumulated totals over time.

Source: https://help.tableau.com/current/pro/desktop/en-us/qs_area_charts.htm

2. Bar Chart:Place a dimension on the Rows shelf and a measure on the Columns shelf to make a bar chart or vice versa. We may compare numerical data such as integers and percentages using bar charts. Each variable's value is represented by the length of each bar. Bar charts, for example, might demonstrate how much money a small business spends on various expenses.

Source: https://www.tableau.com/data-insights/reference-library/visual-analytics/charts/bar-charts

3. Box-and-whisker Plots:When demonstrating the distribution of data points across a specified metric, box-and-whisker plots, also known as box plots, are an excellent chart to employ. The ranges within the variables measured are represented in these graphs. These graphics are useful for comparing the distributions of multiple variables.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_boxplot.htm

4. Bubble cloud: In bubble clouds, data is displayed in a cluster of circles. Individual bubbles are defined by dimensions, while individual circles are defined by measures. A bubble chart's design allows it to display multiple variables. Individual bubbles represent dimension field values, while measure field values define the size and colour of the bubble. As a result, we can examine a plot with at least three variables, one dimension and two measure fields.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_bubbles.htm

5. Bullet Graph:Bullet graphs are a type of bar graph that was created to replace dashboard gauges and metres. When comparing the performance of a major metric to one or more other measures, a bullet graph is beneficial. A bullet graph can help you visualise your objective, the current data set, and past data sets; all in one visualisation if you have a target goal that you need to meet on a regular basis

Source: https://www.tableau.com/data-insights/reference-library/visual-analytics/charts/bullet-graph

6. Cartogram:Choropleth Maps, also known as Filled Maps, are a powerful tool for studying geographic data, especially for maps with a lot of detail (e.g., US by counties or ZIP codes). They make it simple to detect geographical hotspots and then drill down into these areas using several visualisation options.

Source: https://www.pluralsight.com/guides/build-filled-maps-in-tableau

7. Click View:The circle view is a useful representation for comparative analysis. It's the same as using the circle marker on a scatter plot. Every mark is in the shape of a circle and can be used for subsequent actions.

Source:https://interworks.com/blog/ccapitula/2014/10/17/tableau-essentials-chart-types-circle-view/

8. Gantt Chart:Gantt charts are used in project management to depict the length of time between events or activities. As a project management tool, it highlights the interdependencies between activities and illuminates the workflow timeline.

Source:https://help.tableau.com/current/pro/desktop/en-us/buildexamples_gantt.htm

9. Heat Map:In a heat map, data is displayed along with colours. Using one or more Dimensions members and the Measure value, a heat map can be created. Heat Map helps to compare data by colour. For example, how many products have failed to meet the company's expectations, and how many products have exceeded expectations, and so on.

Source:https://help.tableau.com/current/pro/desktop/en-us/buildexamples_highlight.htm

10. Histogram:A histogram is a graph that depicts a distribution's form. It divides values for a continuous metric into bins and segregates a set of data points into user-specified ranges. The histogram, which resembles a bar graph in appearance, condenses a data series into an easily interpreted visual by grouping many data points into logical ranges or bins.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_histogram.htm

11. Scatter Plot (2D or 3D):Scatter plots are a type of graph that is used to show the correlations between numerical data. They are used to depict the link between three variables by plotting data points on three axes. Each column on the X, Y, and Z axes is represented by a marker, whose position is determined by the values in the columns.

Source: https://www.dataplusscience.com/TabCharts/scatterplotsize.html

12. Streamgraph:Streamgraph shows how a number value (Y-axis) changes in response to another numeric value (X-axis). It is a sort of stacked area chart. The relative proportions of the entire can be studied using a stream chart.

Source: https://greatified.com/2018/09/17/how-to-build-a-stream-graph-in-tableau-software/

13. Text Tables:Text tables (also called cross-tabs or pivot tables) are created by placing one dimension on the Rows shelf and another dimension on the Columns shelf. Then, on the Marks card, slide one or more measures to Text to complete the view.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_text.htm#:~:text

14. Treemap:Treemaps are used to show data in the form of nested rectangles. Dimensions define the structure of the treemap, while measures define the size or colour of the individual rectangles. It is a simple data visualisation that can provide information in a visually appealing format.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_treemap.htm

15. Word Cloud:The word cloud is an excellent visual for representing the frequency of words in a given volume of Text. In a word cloud, the most important or unique words from the data are arranged in groups. The main goal of making a word cloud is to provide the viewer with a quick understanding of the important and unique words in the data.

Source: https://www.edupristine.com/blog/creating-word-cloud-tableau

Custom Visuals in Tableau

Tableau also provides a range of custom visuals. Creating them is just a question of one's expertise in Tableau.

1. Dot Distribution Map: Maps that help to spot visual clusters are known as point or dot distribution maps. Dot distribution maps are excellent for displaying how data points are dispersed.

Source: https://help.tableau.com/current/pro/desktop/en-us/maps_howto_pointdistribution.htm

2. Network:Nodes and edges make up a network graph. By connecting nodes with similar features, network visualisations show relationships between items. A network graph is a type of data visualisation that allows consumers to quickly grasp data relationships. Nodes are single data points with edges connecting them to other nodes. The relationship between two or more nodes is represented by edges. This enables the user to easily visualise clusters and establish linkages.

Source: https://ladataviz.com/2019/12/15/build-a-network-graph-in-tableau-in-three-steps/

3. Polar Area:The Polar Area Chart, also known as the Coxcomb chart, resembles a pie chart except that all of the slices have the same angle and the length of the slice that extends spirally from the centre represents quantity.

Source: https://tableau.toanhoang.com/creating-a-polar-chart-in-tableau/

4. Radial Tree:A radial bar chart is a type of pie chart. Like a pie chart, it depicts the relationship of parts to the whole, but it can also include subcategories for each part of the total. Each category in the data series plotted in a radial bar chart is assigned a different colour, whereas all subcategories are assigned the same colour.

Source:https://boraberan.wordpress.com/2014/12/31/radial-treemaps-bar-charts-in-tableau/

5. Timeline:The timeline chart, as the name implies, depicts the significant events that occur in the month, year, or even day. The timeline can also be used as a calendar to display forthcoming events.

Source: https://playfairdata.com/how-to-make-a-timeline-in-tableau/

What is Agentic AI?

Unlike traditional AI models that rely on static predictions, Agentic AI operates dynamically, making independent decisions, adapting to new situations, and optimizing processes autonomously.

Key Capabilities of Agentic AI

Agentic AI is more versatile than GenAI, and it can: • Sense & Interpret – Understand real-time data, identify anomalies, and detect trends.
• Act & Decide – Take actions based on goals, risk factors, and evolving scenarios.
• Learn & Adapt – Improve continuously through feedback loops and reinforcement learning.

Agentic AI vs. Generative AI: The Key Differences

While traditional AI analyzes data and identifies patterns, GenAI creates content and generates images and code through prompts or instructions. Agentic AI is autonomous and doesn’t rely entirely on prompts. It makes independent decisions, acts, and adapts to changing situations with minimal human input. The main differences between Agentic AI and GenAI are as follows:

GenAI	Agentic AI
Creates new content like text, images, videos, audio, code, etc.	Makes autonomous decisions and performs tasks
Reactive response to user input, i.e., the output generated will be based on the prompt received.	Proactive approach: It acts independently with minimal or no human oversight.
Generates creative and original content	Does not create content but makes decisions.
Limited to generating responses based on trained data	Analyzes situations, reasons, and takes actions autonomously
Engages with users by generating text, images, or media based on input	Interacts with its environment, making real-time decisions
Use cases: SEO content, marketing copy, chatbot responses, code generation	Use cases: Self-driving cars, autonomous virtual assistants, workflow automation, financial risk analysis
Examples: ChatGPT, DALL·E, MidJourney, Copilot	Examples: Tesla Autopilot, virtual assistants (e.g., Google Assistant with enhanced autonomy), AI-driven workflow management systems

Top Benefits of Agentic AI: Why Businesses Need It

In a world of complex risks, volatile markets, and increasing regulatory pressure, static AI models fall short. Organizations need AI that proactively thinks ahead, mitigates risks, and unlocks new efficiencies across industries.

The key benefits of Agentic AI are listed below:

• Execute autonomous, goal-directed actions and decision-making.
• Engage in proactive problem-solving instead of reactive responses.
• Carry out complex reasoning and long-term planning.
• Learn and adapt autonomously with minimal human oversight.

How Agentic AI Gives Businesses a Competitive Edge

The rise of AI-first companies means businesses that embrace Agentic AI will gain a competitive edge, leveraging: • Self-learning AI agents that evolve with business needs.
• Automated decision systems that operate with minimal human intervention.
• AI-native enterprise ecosystems that seamlessly integrate with digital workflows.

Comprehensive List of Agentic AI Tools & Use Cases

Agentic AI refers to AI systems that act autonomously, making decisions and performing multi-step tasks with minimal human intervention. Below are some real-world tools and use cases of Agentic AI across various industries:

Industry	Agentic AI Use Cases	Agentic AI Tools / Examples	Business Impact
Virtual Assistants	Autonomous AI task execution	AutoGPT, BabyAGI	Automates workflows, reduces manual effort
Self-Driving Vehicles	Autonomous navigation & decision-making	Tesla Autopilot, Waymo	Enhances safety, reduces human intervention
Robotics	AI-powered automation in warehouses	Boston Dynamics’ Spot, Amazon Robots	Reduces human risk, improves efficiency
Finance & Trading	AI-driven autonomous trading	Numerai AI Hedge Fund, Kavout Kai AI	Optimizes investments, minimizes risk
Healthcare	AI-powered diagnosis & treatment planning	IBM Watson Health, Qventus AI	Improves accuracy, streamlines patient care
Cybersecurity	Threat detection & autonomous response	Darktrace, CrowdStrike Falcon AI	Prevents cyberattacks, enhances security
Smart Cities	AI-driven traffic & energy optimization	Google DeepMind AI, AI Traffic Systems	Reduces congestion, lowers energy costs
Enterprise AI	AI-driven workflow automation	UiPath AI RPA, Adept ACT-1	Increases productivity, minimizes errors
Retail & E-commerce	Autonomous inventory & order management	Amazon Warehouse AI, AI Chatbots	Optimizes supply chains, improves CX
Personal AI Assistants	AI-driven memory & task management	Rewind AI, Inflection AI Pi	Enhances productivity, personalized insights

List of Key Applications of Agentic AI

The major applications of Agentic AI are diverse and can be employed across various domains such as: • Banking & Financial Services – AI-driven fraud prevention, adaptive risk management, and autonomous trading.
• Healthcare & Life Sciences – Personalized AI-powered treatments, real-time diagnostics, and proactive patient monitoring.
• Risk & Compliance – AI-driven audits, self-regulating compliance frameworks, and dynamic risk assessment.
• Supply Chain & Logistics – Autonomous demand forecasting, intelligent inventory management, and AI-driven logistics.
• Cloud & Digital Transformation – Self-optimizing cloud infrastructures, real-time automation, and AI-powered cybersecurity.

How Beinex is Leading the Agentic AI Revolution

At Beinex, we are pioneering Agentic AI solutions that enable businesses to shift from reactive decision-making to intelligent automation. Our expertise spans: • AI-powered GRC (Governance, Risk & Compliance) – Ensuring real-time regulatory adherence.
• AI-driven financial transformation – Fraud detection, customer intelligence, and risk modeling.
• Cloud-first AI ecosystems – Enabling adaptive, self-learning enterprise solutions

The Future of Agentic AI: Are You Ready?

As AI governance, ethical AI, and real-time automation evolve, businesses embracing Agentic AI will define the next digital transformation era. The question is no longer if AI can optimize business operations; it’s how fast your business can adapt to AI that thinks, learns, and acts. Interested? Let’s connect for a free assessment: https://beinex.com/contact-us/

What’s New in Version 2

Version 2 of the Cost Optimizer app brings a host of innovative features aimed at empowering organizations to better understand and manage their Snowflake-related costs. The highlight of this release is the introduction of Cortex Usage Insights, which provides unprecedented visibility into resource utilization and optimization opportunities.

Changelog:

• Cortex Usage Insights: This new analytics feature allows users to track and optimize Cortex resource utilization. By providing detailed insights into how Cortex services are being used, organizations can identify inefficiencies and make data-driven decisions to reduce costs without compromising performance. • Enhanced Cost Transparency: Version 2 also includes improved reporting capabilities for Cortex-related expenditures. Users can now access granular cost breakdowns, enabling them to understand exactly where their Snowflake budget is being allocated and how to optimize it further. These enhancements make the Cost Optimizer app an indispensable tool for organizations leveraging Snowflake’s advanced capabilities, particularly those utilizing Cortex services.

What is Cortex Services from Snowflake?

Snowflake Cortex is a powerful suite of services designed to simplify and accelerate AI and machine learning (ML) workflows directly within the Snowflake Data Cloud. Cortex enables organizations to harness the power of AI without the need for extensive coding or specialized expertise. Key features of Snowflake Cortex include: • AI and ML Integration: Cortex allows users to build, train, and deploy machine learning models using SQL, making AI accessible to a broader range of users. • No-Code Development: With Cortex, even non-technical users can leverage AI capabilities to derive insights and make data-driven decisions. • Advanced Analytics: Cortex provides pre-built models and functions for tasks like anomaly detection, forecasting, and sentiment analysis, enabling organizations to unlock the full potential of their data. • Cortex LLM: Snowflake Cortex LLM Functions offer businesses seamless access to industry-leading large language models (LLMs) with enhanced retrieval capabilities and improved AI safety. This update introduces support for new high-performing LLMs.

By integrating Cortex Usage Insights into the Cost Optimizer app, Beinex is helping organizations maximize the value of their Snowflake Cortex investments while keeping costs under control. The Cost Optimizer app is available on the Snowflake Marketplace, making it easy for organizations to access and deploy this powerful tool. Whether you’re looking to optimize costs, gain deeper insights into resource utilization, or enhance your Cortex-related analytics, the Cost Optimizer app is your go-to solution. Get the Cost Optimizer App on Snowflake Marketplace Version 2 of Beinex’s Cost Optimizer app represents a significant step forward in cost management and optimization for Snowflake users. With its new Cortex Usage Insights and enhanced cost transparency features, the app empowers organizations to make smarter, data-driven decisions while keeping costs in check. Explore the Cost Optimizer app on the Snowflake Marketplace today and take the first step toward unlocking the full potential of your Snowflake investment.

The Struggles of Traditional Business Intelligence

Traditional BI platforms, while valuable, suffer from several limitations:

Limited Accessibility: These tools often require extensive technical expertise to navigate and analyze data. This restricts valuable insights to a select few, hindering widespread organizational adoption.

Slow Insights: Extracting insights from traditional BI can be a time-consuming process. Data preparation, analysis, and model building can take weeks, delaying crucial decision-making.

Limited Data Exploration:: Traditional BI tools often restrict users from analyzing a predetermined set of variables. This can lead to overlooking important data points and potentially skewed insights.

These limitations force businesses to make a tough choice: sacrifice the quality of insights by limiting analysis or invest significant time and resources into data preparation and model building.

The Rise of Augmented Analytics

Augmented analytics emerges as a powerful solution, addressing the shortcomings of traditional BI. It leverages the power of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to revolutionize data analysis:

Democratization of Data: Augmented analytics empowers users of all technical skill levels to gain valuable insights. Natural language interfaces allow users to ask questions and receive insights in plain English, eliminating the need for complex queries or coding expertise.

Real-Time Insights: AI and ML algorithms can analyze massive datasets in real-time, delivering insights within minutes. This allows businesses to capitalize on fleeting opportunities and react swiftly to market changes.

Deeper Analysis: Augmented analytics goes beyond basic data exploration. It can automatically identify anomalies, uncover hidden patterns, and drill down to the most granular details within your data, providing a more comprehensive understanding of business performance.

How Does Augmented Analytics Work?

Augmented analytics enhances the four core stages of data analysis:

1. Data Preparation: Traditionally, data preparation involves manual tasks like data cleaning and integration. Augmented analytics automates these processes, allowing data scientists to focus on more strategic tasks.
2. Insight Discovery: Traditional BI requires pre-defined models to uncover insights. Augmented analytics utilizes ML algorithms to analyze all available data, regardless of size or complexity, to deliver targeted and nuanced insights in response to user queries.
3. Insights Sharing: Sharing insights often involves generating reports and charts, a time-consuming task. Augmented analytics utilizes Natural Language Generation (NLG) to present insights in real time through online dashboards. These dashboards explain the "why" behind the data, giving decision-makers the context they need.

The Benefits of Augmented Analytics

By implementing augmented analytics, businesses can unlock a multitude of benefits:

Real-time decision-making: No more waiting weeks for crucial insights. Augmented analytics delivers results in minutes, allowing businesses to seize time-sensitive opportunities.

Deeper data exploration:: Discover the hidden depths of your data. Augmented analytics empowers detailed analysis, revealing the "why" behind trends and enabling more accurate predictions.

Scalability: Seamlessly handle huge amounts of datasets without compromising the performance.

Breaking down data silos: Unify your data and insights. Augmented analytics analyzes data from all integrated sources, providing a holistic view of business performance and fostering cross-departmental collaboration.

Better business decisions: Make smarter choices with comprehensive data insights. Augmented analytics empowers informed decision-making, leading to improved business outcomes.

Augmented Analytics Use Cases

Let's explore how augmented analytics is transforming various industries:

1. Pharmaceutical Companies: Analyze vast datasets to optimize go-to-market strategies and uncover hidden patterns in market share data.
2. Financial Lenders: Assess credit risk with greater accuracy by analyzing all relevant data points in real-time, leading to faster loan approvals and better risk management.
3. Consumer Goods Companies: Gain real-time insights into product sales, customer churn, and satisfaction, allowing for proactive customer engagement strategies.

AI-augmented analytics marks a paradigm shift in how businesses leverage data for informed decision-making. It's not just a novel technology but a strategic necessity in today's data-driven landscape. By embracing AI's power, organizations can reveal a treasure trove of deeper insights, streamline data handling, and foster a more intuitive user experience with data analysis. As this field continues to evolve, we can anticipate a wave of groundbreaking applications that empower businesses to make smarter choices and achieve success. The future of business intelligence BI is intelligent, and the time to act is now.

What is Tableau Server Health Check?

Tableau Server Health Check comprehensively evaluates the server's performance, configuration, and overall health. It aims to ensure that the server operates optimally, providing a seamless and efficient user experience. Regular health checks can prevent performance degradation, identify potential issues before they become critical, and ensure that the server environment is configured according to best practices.

Critical Components of a Tableau Server Health Check • Server Performance • Server Configuration • Resource Utilization • Content and Workbook

When Should You Perform a Tableau Server Health Check?

1. Regular Maintenance
• Quarterly or Bi-Annually: Conducting a health check every three to six months helps identify potential issues early and maintain optimal performance over time.
2. Performance Issues
• Slow Load Times: If users experience slow load times for dashboards and reports, it clearly indicates that a health check is needed to diagnose and resolve performance bottlenecks. • Frequent Server Crashes: Regular crashes or downtime are signs of underlying issues that need immediate attention.
3. Major Updates or Upgrades
• Before and After Upgrades: Perform a health check before upgrading to a new version of Tableau Server to ensure the current environment is stable. Conduct another check after the upgrade to verify everything is working correctly and optimize new features.
4. Changes in Usage Patterns
• Increase in User Base: A health check ensures the server can handle the new load effectively if the number of users or the volume of data being processed significantly increases significantly.
5. User Feedback
• Negative User Feedback: If users report consistent issues with dashboard performance, accessibility, or other aspects of Tableau Server, it’s crucial to perform a health check to address these concerns.
6. System Configuration Changes
• Infrastructure Changes: Changes to the underlying infrastructure, such as hardware upgrades or network configuration changes, should be followed by a health check to ensure everything is configured optimally.

What are the Benefits of Tableau Server Health?

• Improved Performance • Better User Experience • Improving allocation of server resources and license management. • Increasing server stability, reliability, and uptime. • Scalability and Future Planning

How Does Beinex Conduct Tableau Health Check?

Our certified Tableau experts will evaluate your server environment and deliver a comprehensive report to help you identify and mitigate risks. Our process is structured into three main phases: Evaluation, Diagnosis, and Recommendation. Each phase focuses on different aspects of the server's operation to ensure that it meets your organization's needs and performs optimally. • Evaluation - We assess the size and configuration of your server, the performance of all data extracts and dashboards, server security, and more • Diagnose - We parse through server logs and review usage metrics to diagnose the root cause of any issues • Recommend - We present findings, best practices, and recommendations that you can act on immediately

Why Choose Beinex?

• Certified Team of Server Experts At Beinex, we pride ourselves on having a team of certified Tableau experts who bring extensive knowledge and experience to every project. Our specialists have the latest certifications and are continually updated with the newest Tableau features and best practices. • 300+ Tableau Deployments & Upgrades With over 300 successful Tableau deployments and upgrades under our belt, Beinex has a proven track record of delivering exceptional results. Our extensive experience spans various industries and business sizes, allowing us to understand and address the unique challenges different organizations face. • Tableau Premier Partner As a Tableau Premier Partner, Beinex stands among a select group of top-tier Tableau partners worldwide. This prestigious partnership recognizes our commitment to excellence and ability to deliver high-quality Tableau solutions.

How Beinex Can Assist You

Beinex, a premier Tableau partner, provides sustainable analytics solutions to organizations and helps to build superior data visual analytics capabilities internally through our bespoke training programs. Our team of Tableau-certified consultants are real-life Tableau business users passionate about Tableau and delivering a world-class experience. Connect with us: https://www.beinex.com/tableau-beinex/