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.

AWS + Tableau: Together, a Match Made in Data Heaven

By embracing the synergy between Tableau and AWS, you're not just investing in tools; you're investing in a future fueled by data-driven insights. This powerful combination paves the way for a more agile, data-centric organization ready to thrive in the ever-evolving digital landscape. The digital landscape is evolving rapidly, and businesses are increasingly turning to the cloud for their analytics needs. This shift is driven by the cloud's ability to:

1. Faster Time to Insights: The seamless integration between Tableau and AWS allows you to quickly get up and running with your analytics, enabling you to make data-driven decisions sooner.
2. Effortless Data Management: Leverage the power of AWS data warehousing and management services to ensure your data is clean, organized, and readily accessible for analysis in Tableau.
3. Advanced Analytics Capabilities: Tap into the power of AWS machine learning and artificial intelligence services to uncover hidden patterns and gain deeper insights from your data within the Tableau environment.
4. Handle complex data integration: Seamlessly connect and analyze data from various sources, regardless of size or location.
5. Empower self-service analytics: Enable users to explore and gain insights from data independently, fostering data-driven decision-making across the organization.
6. Support digital transformation: Meet the growing demands of digital transformation with scalability, flexibility, and cost-efficiency.

Image source: https://aws.amazon.com/solutions/partners/tableau-server/

Tableau: The Master of Data Visualization

Imagine transforming raw data into captivating, interactive visualizations that tell a clear story. Tableau is a game-changer in the world of data visualization. It empowers users of all technical backgrounds to:

1. Connect to Diverse Data Sources: Tableau seamlessly connects to a wide range of data sources, both on-premise and in the cloud. This includes databases, spreadsheets, cloud applications, and even big data platforms.
2. Effortlessly Drag-and-Drop Analysis: The user-friendly interface allows users to drag and drop data fields, explore trends, and create stunning visualizations without writing a single line of code.
3. Craft Interactive Dashboards & Reports: Go beyond static reports. Tableau empowers you to create dynamic dashboards that users can interact with, filter data, and gain deeper insights on the fly.
4. Foster Data-Driven Culture: Tableau democratizes data by making it accessible and understandable to everyone in the organization, fostering a data-driven culture where decisions are based on evidence, not intuition.

AWS: The Cloud Powerhouse for Scalability and Security

While Tableau excels at data visualization, the underlying infrastructure needs to be robust and scalable. This is where AWS, the world's leading cloud computing platform, comes into play. Here's how AWS empowers your Tableau deployment:

1. Unmatched Scalability: AWS offers virtually limitless scalability to accommodate your growing data volumes and user base. As your data needs evolve, your cloud infrastructure can easily scale up or down to meet those demands.
2. Enhanced Security: Security is paramount when dealing with sensitive data. AWS offers robust security features and compliance certifications, ensuring your data remains protected throughout its lifecycle.
3. Cost-Effectiveness: The pay-as-you-go model of AWS allows you to optimize your costs. You only pay for the resources you use, eliminating the need for upfront investments in expensive hardware infrastructure.
4. Wide Range of Services: AWS offers a comprehensive suite of services beyond just compute power. These services include data warehousing, machine learning, and data management tools, giving you a complete cloud ecosystem to manage your entire data pipeline.

Tableau and AWS work together to accelerate your digital journey with Modern Cloud Analytics (MCA). Let’s see how:

Modern Cloud Analytics: A Collaborative Powerhouse

Modern Cloud Analytics is a collaborative initiative leveraging the expertise and resources of Tableau, AWS, and their extensive partner networks. Its objective is to maximize the value you extract from your data and analytics investments throughout your entire digital transformation journey, encompassing:

1. Data Strategy and Migration: Develop a comprehensive plan to securely and efficiently migrate your data and analytics operations to the cloud.
2. Optimization: Fine-tune your cloud analytics environment for peak performance and cost-effectiveness.
3. Deployment and Scaling: Securely deploy and seamlessly scale your Tableau environment on AWS to adapt to your evolving needs.

Benefits of Modern Cloud Analytics:

1. Faster Time to Value: Get up and running with cloud analytics quickly, enabling data-driven decisions sooner.
2. Reduced Costs: Leverage the cloud's inherent cost-efficiency and scalability to optimize your analytics spending.
3. Minimized Risks: Mitigate potential risks associated with cloud adoption by utilizing validated migration processes and expert guidance.

Unified Integration for Unparalleled Insights

Tableau and AWS offer a comprehensive solution for cloud-powered organizations. Both Tableau Server and Tableau Cloud operate flawlessly on AWS infrastructure, providing you with:

1. Effortless Data Access: Streamlined workflows and effortless access to data stored within various AWS sources directly within the AWS ecosystem.
2. Market-Leading Connectivity: Tableau serves as the ideal platform for analyzing data residing in diverse AWS data sources like:
3. Amazon Redshift: A blazing-fast data warehouse designed to handle large datasets with efficiency.
4. Amazon RDS: A managed relational database service offering high availability and scalability.
5. Amazon EMR: A managed Hadoop framework for processing and analyzing massive datasets.

Enhanced Security and Broader Connectivity

Tableau's commitment to continuous improvement extends to its AWS integrations. Here's a glimpse into the exciting advancements:

1. Enhanced Security: The updated Amazon Athena connector now supports secure authentication using third-party identity providers like Azure AD and Okta, adding an extra layer of security with multi-factor authentication options.
2. Expanded Connectivity: The Tableau Exchange offers a plethora of new connectors, further extending your connection options within the AWS ecosystem.
3. Amazon OpenSearch Connector: Effortlessly visualize and analyze data residing in Amazon OpenSearch.
4. Amazon DocumentDB Connector: Gain valuable insights from your DocumentDB data through seamless interaction.
5. Amazon Neptune Connector: Explore connections within your data by directly connecting to your Neptune graph database.

By embracing the synergy between Tableau and AWS, you're not just investing in tools, you're investing in a future fueled by data-driven insights. This powerful combination paves the way for a more agile, data-centric organization ready to thrive in the ever-evolving digital landscape.

How Beinex Can Assist You

Beinex is a premier Tableau partner and AWS consulting partner providing sustainable analytics solutions to organizations. Our Tableau and AWS-certified consultants help organizations build superior data visual analytics capabilities through bespoke training programs. We empower customers to host their BI solutions on the cloud with AWS infrastructure as a service. Get in touch with us for free trials and experience our expertise in providing analytics and cloud solutions.

Tableau's 2024.1 Release: What’s in it for you

Do your employees struggle to understand complex data visualizations? Does your organization lack a consistent approach to defining metrics? Are you looking for ways to make data more accessible for everyone? If you answered yes to any of these questions, then the new features in Tableau 2024.1 are for you! This release addresses these common challenges head-on, offering solutions like Viz Navigation for Text Tables, the Metrics Layer in Tableau Pulse, and more. Keep reading to learn more about these innovations and how they can revolutionize your data analysis workflows:

1. Tableau Pulse: Personalized Insights Delivered

Tableau Pulse is a real lifesaver, transforming how your organization interacts with data. Here's how it empowers your workforce:

Personalized Metrics: Get AI-driven insights and relevant metrics delivered directly to your inbox and Slack, eliminating the need for complex visualizations.

Simplified Data Exploration:Gain a deeper understanding of business performance with natural language summaries that explain the "why" behind the data.

Mobile Access:Stay on top of key metrics on the go with the Tableau Pulse mobile app for iOS and Android.

To learn more about Tableau Pulse, click here: https://beinex.com/topics/tableau-ai-and-tableau-pulse-tableaus-dynamic-duo/

2. The Metrics Layer: Building a Unified Language for Data

Achieving consistency and clarity in data analysis is key to making informed decisions. The Metrics Layer within Tableau Pulse tackles this challenge by providing a central hub for defining and managing metrics.

Here's how it works:

Standardize Metric Definitions:Create clear and consistent definitions for your metrics, including real-world business context.

Single Source of Truth:Ensure everyone across the organization uses the same definition for a particular metric, eliminating confusion and maintaining consistency.

Effortless Updates:Any changes made to a metric definition are automatically applied to all related metrics, saving time and ensuring accuracy.

Deeper Insights: The Metrics Layer understands the underlying business context associated with your data. This allows Tableau Pulse to provide more relevant and insightful answers to your "what" and "why" questions about your metrics.

By creating a unified language for data, the Metrics Layer empowers collaboration and drives data-driven decision making throughout your organization.

3. Tableau Pulse on Mobile: Stay on Top of Your Data Anywhere

Monitor key metrics and gain actionable insights, even when you're on the go. Tableau Pulse seamlessly integrates with Tableau Mobile, allowing you to access your personalized AI-powered insights directly from your smartphone or tablet.

Get a quick snapshot of your metrics:

See real-time trends and track how your performance is shaping up.

Understand the "why" behind the numbers with natural language summaries provided by Tableau Pulse.

Dive deeper for a comprehensive analysis:

Simply click on a metric within your email or Slack digest notification.

Tableau Mobile will automatically launch, taking you directly to the relevant insights exploration page within the app.

This one-click access empowers everyone in your organization to make data-driven decisions, regardless of location. Tableau Pulse is available on both iOS and Android versions of Tableau Mobile.

4. Tableau Available Through AWS Marketplace: Streamlined Procurement and Deployment

For organizations leveraging the power of AWS cloud infrastructure, Tableau Cloud is now available on the AWS Marketplace. This integration offers several benefits:

Simplified Procurement:IT leaders can leverage existing AWS purchasing processes for Tableau Cloud, streamlining procurement workflows and reducing administrative burden.

Consolidated Billing:Unify your cloud bills, bringing together your AWS and Tableau Cloud expenses for a clearer picture of IT spending.

Faster Deployment:Benefit from the ease and familiarity of the AWS Marketplace for a quicker and more streamlined Tableau Cloud deployment process.

5. Viz Navigation for Text Tables: Enhanced Accessibility

Tableau 2024.1 introduces a groundbreaking feature for exploring data visualizations: Viz Navigation for Text Tables. This innovative capability empowers users to navigate and interact with text tables using their keyboard or assistive technologies, removing the need for a mouse.

Benefits for all users:

Improved Accessibility:Users with disabilities or those who prefer keyboard controls can now easily explore text tables within dashboards and stories.

Effortless Navigation: Navigate through rows and columns of data using keyboard shortcuts, making data exploration quicker and more efficient for everyone.

Screen Reader Compatibility:Screen reader users will hear the information for each cell as they navigate the text table, providing a seamless data exploration experience.

Deeper Insights with Text Tables: Viz Navigation for Text Tables empowers dashboard authors to create more comprehensive visualizations. Text tables can be used alongside charts and graphs to provide supplementary details, allowing users to delve deeper into the data without needing to switch to the "View Data" option.

This new feature signifies Tableau's commitment to making data accessible to everyone, regardless of their ability or preferred method of interaction.

6. Tableau Prep: Identify Duplicate Rows

Tableau Prep enables you to take control of your data quality. By removing or correcting duplicates, you can build trust in your data and generate reliable insights that drive better business decisions.

Effortlessly Spot Duplicates:

Leverage Tableau Prep's intuitive interface to quickly pinpoint duplicate rows across your data.

Gain a clear visual representation of duplicates, allowing you to easily understand the cause of the duplication.

Cleanse Your Data with Confidence:

Based on your specific data preparation needs, you can either:

1. Remove identified duplicates to ensure a clean and accurate dataset.
2. Fix the underlying issues causing the duplication, preserving valuable data points.

A New Era of Data-Driven Decision Making

With Tableau 2024.1 and Tableau Pulse, you have everything you need to democratize data across your organization. Empower your employees to leverage insights and make data-driven decisions that drive real business impact.

How Beinex Can Assist You

Beinex, a premier Tableau partner, provides sustainable analytics solutions to organizations and help 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 who are passionate about Tableau and delivering a world-class experience. Connect with us for a Tableau free trial.

Organisations take advantage of advanced analytics using the techniques given below:

Data Mining

Data mining is extracting useful information from large, raw chunks of data to find trends, plan new business strategies, increase revenue, decrease costs, reduce risks and enhance customer relationships. It establishes relationships and finds patterns and correlations to detect dangers and frauds and to make a profit out of businesses.

Sentiment Analysis (Opinion Mining)

Sentimental Analysis technique is used by businesses to detect emotion or feelings in textual data. It categorises the tone of writing as positive, negative or neutral. Organisations are benefitted in many ways by aiding in crisis prevention and understanding and analysing customers' opinions about their particular products or services. The companies monitor online conversations to learn about the customers' tastes, needs, and expectations.

Sentiment analysis' fully automated tools assist businesses in extracting information from unstructured and unorganised material found on the internet, such as blog posts, email, webchats, social media channels, and comments.

Cluster Analysis

It's a popular data-mining technique that matches unstructured data fragments based on commonalities discovered between them. Cluster analysis is instrumental for companies to identify different consumer groups and sales transactions or detect fraud. It is used in Machine Learning, image analysis, pattern recognition, information retrieval, data compression, bioinformatics and computer graphics.

Cluster analysis is a powerful data-mining tool for any company that wants to recognise discrete groupings of consumers, sales transactions, or other types of behaviours and things. Insurance firms use cluster analysis to identify fraudulent claims, and banks use it for credit scoring.

Retention Analysis

Studying user analytics to determine how and why consumers churn is known as retention analysis (or survival analysis). Retention analysis is crucial for learning how to keep a lucrative client base by increasing retention and new user acquisition.

Customer retention is a crucial practice in every business; companies can quickly decrease churn rates and increase customer satisfaction by tracking and taking advantage of customer behaviour.

Complex Event Analysis

Complex data analytics is the application of complex algorithmic approaches to effectively process huge unstructured data volumes. Computers perform data analysis; this was done mainly by individual machines acting on well-defined data structures in the past. This method uses technology to forecast high-level occurrences that are likely to occur due to a series of low-level factors.

Predictive Analysis

Predictive analysis is a technique used to analyse data and forecast the possibility of an event occurring in the future, allowing businesses to plan. It uses historical data combined with statistical modelling, data mining techniques and Machine Learning to predict risks and opportunities. Predictive analysis uses a scientific approach to forecast the future with a high degree of accuracy.

Machine Learning

Machine Learning is a crucial part of the AI subset of advanced analytics. This advanced analytic tool uses computational approaches to find patterns in data. It then uses them to build statistical models that can produce solid results without human participation. It falls into the following categories:

Supervised learning: The more common type of Machine Learning is supervised learning, which uses labelled data sets to allow you to search for specific patterns in the data. It requires vast datasets for the process; the more the amount of data, the more chances of getting accurate results.

Unsupervised learning: It employs various methods to find patterns and correlations in a subset of data. On the other hand, these algorithms are unable to recognise specific data sets, but they sort the information based on similarities and anomalies. However, it is applied in cybersecurity to find patterns from data.

Semi-supervised learning: It combines the benefits of supervised and unsupervised learning approaches. This technique uses unlabelled and labelled data to help the systems understand the challenge. The labelled data set is then utilised to aid in the model's training, with the results being used to mark the remaining unlabelled data. When all of the data has been labelled, the model is trained on it.

Reinforced learning: A relatively new advancement in Machine Learning, a reinforcement learning algorithm learns and develops to achieve a specific goal through trial and error. It tries out numerous choices before using rewards or penalties to help it make the best decision to achieve the goal.

Data Visualisation

Data representation in a visual or graphical style is known as data visualisation. It allows decision-makers to see analytics visually, making it easier to grasp complex topics or spot new patterns. Data visualisation aids in telling tales by transforming data into a more understandable format and showing trends and observations. A good visualisation tells a story by reducing noise from data and emphasising the essential facts. The common types of data visualisation include charts, tables, graphs, maps, infographics and dashboards.

Cohort analysis

Cohort analysis is employed to analyse the data and group it based on shared user behaviours during a specific period. It is a beneficial technique for boosting customer retention and happiness. By analysing behavioural patterns, it is possible to gain valuable information about what type of campaign is most likely to be successful, which customer group is most likely to buy your goods, and their expectations from a product. Cohort analysis can bring several advantages to a company:

Increased Customer Lifetime Value (CLV): Cohort analysis' capacity to assist a firm in improving client retention improves the CLV, which is the total money a business generates from a customer throughout their relationship.

Stronger relationships with loyal customers: Cohort analysis helps you discover your most loyal customers, allowing you to target them more precisely and encourage them to stay with you for as long as possible.

Better testing of new designs: In most cases, tests cannot predict how well a new design of a product will perform in the market. With the aid of cohort analysis, generate a cohort based on interactions with the latest design and compare it to the conversion rate of those that haven't.

Regression Analysis:

It is a powerful statistical method used to estimate the link between dependent (outcome) and independent (features) variables. The goal of regression analysis is to figure out how one or more factors may influence the dependent variable to spot trends and patterns. It is crucial for projecting future trends and generating forecasts.

To perform a regression analysis, you must first establish a dependent variable that you believe is influenced by one or more independent factors. After that, you'll need to create a comprehensive dataset to work with. Using surveys to get data from your target consumers is a great way to get started. All of the independent variables you are interested in should be addressed in your survey.

Different sectors like banking, insurance, retail, pharmacy, e-commerce and others used regression techniques to yield valuable, actionable business insights.

Advanced Analytics gives companies a greater understanding of data patterns and behaviour, allowing them to forecast future actions. It provides a substantial strategic advantage by revealing new business prospects and potential innovations, a deep awareness of customer and employee behaviours, fresh ways of looking at existing problems, and operational improvement opportunities, increasing revenue or lowering costs.Advanced Analytics analyses information from various data sources using predictive modelling, Machine Learning, and business process automation.

List of Key Features in Tableau 2023.2

1. Enhanced Multi-row calculations in Tableau Prep
2. Line Patterns
3. Personal Access Token Admin Control
4. Editable Viz Alt Text
5. Salesforce Data Cloud Connector
6. Effortless Tracking of Known Issues

1. Enhanced Multi-row calculations in Tableau Prep

Gone are the days of complex formulas and tedious coding. With just a few clicks, Tableau Prep empowers you to perform multiple-row calculations, making your data preparation process a breeze. Calculate the difference from, percent difference from, or even execute moving calculations effortlessly without breaking a sweat.

Tableau Prep's enhanced calculation capabilities are like a magic wand in your hands, eliminating the need for intricate calculations or diving into complicated code. You're now equipped with the tools to effortlessly transform your raw data into actionable insights, paving the way for smarter decision-making and unlocking the true potential of your analytics journey.

Get ready to unleash the full potential of your data with LOOKUP calculations. Seamlessly compose complex expressions that enable you to access and analyse data across multiple rows, empowering you to unveil hidden insights and patterns.

2. Introducing Line Patterns

The next game-changing feature in Tableau 2023.2 is Line Patterns. This exciting addition provides a whole new level of flexibility and customisation when it comes to creating visually stunning data visualisations.

With Line Patterns, you now have the power to choose from a variety of line styles, including solid, dashed, or dotted lines. These patterns allow you to effortlessly highlight specific areas of interest within your visualisations, making it easier than ever to convey complex information with clarity and impact.

Imagine comparing actual data against modelled values with striking visual cues or effortlessly differentiating lines in dynamic or crowded visualisations. Line Patterns give you the ability to create eye-catching visuals that captivate your audience and make your data come to life. No longer will you be limited to static, monotonous lines. Line Patterns empower you to add a touch of creativity and personalisation to your visualisations.

3. Personal Access Token Admin Control

Gone are the days of compromising on security compliance. With this latest release, Site Admins are granted an array of tools to manage PAT creation privileges, tailor their validity periods to organisational needs, and wield their data protection prowess like never before. You hold the keys From a single dashboard to determine who can create PATs and fine-tune their lifespans, ranging from a day to a full year.

But the excitement doesn't stop there! We've taken proactive security measures to the next level. For Tableau Cloud sites created after version 2023.2, PATs come disabled by default, providing an additional layer of protection. Moreover, we've listened to your feedback, and the default expiration period has been thoughtfully shortened to 180 days, aligning with best practices and ensuring optimal data security.

4. Editable Viz Alt Text

With the latest feature, content authors have the power to edit the automatically generated alt text, opening the doors to endless possibilities. Whether through the Data Guide panel or the Accessibility option in the Worksheet drop-down menu, you can now add up to 2,500 characters of detailed alt text to craft captivating descriptions that bring your visualisations to life. Step into a realm of creativity and inclusivity, ensuring that everyone can fully comprehend and appreciate the story behind your data.

5. Salesforce Data Cloud Connector

With Tableau's enhanced Salesforce Data Cloud connector, say goodbye to complex installations as this driverless connector effortlessly connects your data sources. Use Data Spaces, where you can create customised visualisations tailored to specific contexts, organising metadata, data, and processes to empower stakeholders. Enjoy a user-friendly interface that replaces object API names with object labels, making table selection a breeze. With support for 11 additional functions, including datetime capabilities, supercharge your content creation and data analysis processes.

6. Effortless Tracking of Known Issues

We've streamlined the process of tracking known issues, making it effortless to stay in the loop. Discover the centralised Known Issues page, where you can effortlessly monitor the status of experiences, from working as expected to be in review, in progress, scheduled, or deployed. With intuitive drop-down filters, you can easily navigate through Tableau components and subscribe to specific issues for automatic email updates.

Additionally, our revamped Tableau Help now includes comprehensive "Release Notes" documentation, offering insights into new features and updates across Tableau Cloud, Tableau Server, Tableau Desktop, and Tableau Prep. Stay informed, empowered, and ahead of the curve with our enhanced status tracking and knowledge resources.

Upgrade to Tableau 2023.2

The release of Tableau 2023.2 brings a host of exciting features and enhancements that empower users to unlock the full potential of their data. With each release, Tableau continues to evolve and revolutionise the data analytics landscape, empowering users to discover insights, tell compelling stories, and easily make data-driven decisions. Upgrade to Tableau 2023.2 and embark on a data exploration and innovation journey like never before.

If you are interested in learning more about earlier versions of Tableau, click the links below:

Tableau New Features Unveiled: Tableau 2023.1 Release

Tableau Version 2021.4: Bringing Automation to Data Management

How Beinex Can Assist You

Beinex, a premier Tableau partner, provide sustainable analytics solutions to organisations and help 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 who are passionate about Tableau and delivering a world-class experience. Connect with us for a Tableau free trial.