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.

Snowflake Data Sharing

Data sharing in Snowflake equips you to share specific objects with another Snowflake account or a designated reader account. The beauty of this process lies in the fact that the data isn't duplicated or moved between accounts.
Now, why is this a game-changer for organisations? When constructing data pipelines and developing data products, it's a common practice to shuttle data between databases and diverse systems to blend different datasets.
Consider this scenario: You have transactional data within your online transactional processing (OLTP) database, and you wish to integrate it with external data for a machine learning model. Traditionally, organizations would export data into a data lake, import external data, and then employ tools like Apache Spark for analysis.
But what if, instead, you could simply deposit your data into Snowflake, and the external data source could seamlessly share its data with your organisation, eliminating the need to load it separately? This eradicates the challenge of keeping data copies synchronised, resulting in savings on storage, computing costs, and maintenance efforts.
Imagine your company possesses valuable information that can guide other companies in making informed decisions. For instance, let's say your company can provide precise estimates for product delivery times based on proprietary data, and you want to offer this information for sale to your customers.
Enter Snowflake data sharing—it empowers you to precisely do that.

Case Studies: Snowflake Data Sharing

Citing two instances where leading organisations use Snowflake to improve actionable data sharing, collaboration and reporting capabilities.

1. A Pioneering Technology Leader A well-known Swedish-Swiss multinational corporation successfully implemented a streamlined data strategy using the Snowflake Data Cloud, adopting an "extract once, use everywhere" approach that simplified data consolidation and enablement. By transitioning from nightly extracts, which caused significant system overhead, to a single, near real-time Change Data Capture (CDC) process, the company achieved efficient replication of information to Snowflake with minimal impact. The utilisation of Snowflake Secure Data Sharing facilitated secure and governed data collaboration across the four business areas.
2. A Leading fast-food Restaurant Chain Snowflake's data-sharing capabilities have revolutionised decision-making for a fast-food restaurant chain. They can effortlessly share crucial sales, inventory, and operational data with external entities, expanding from three to over 30 parties.
With a high-performance database platform hosting over 2 million transaction records, the restaurant chain has established a robust data management and analysis infrastructure through Snowflake, empowering its operational and marketing endeavours.
Moreover, by consolidating all data onto Snowflake, the organisation has achieved a remarkable 70% reduction in operational IT costs, demonstrating the platform's efficiency and cost-effectiveness.
Centralising and sharing data with Snowflake significantly eased the development of data products for various purposes, including marketing campaign analytics, quotation success metrics, production line tools, and supply chain dashboards. These data products are utilised by thousands of users globally, including internal stakeholders and external vendors, enhancing collaboration and efficiency across the organisation.

What are the best practices for Snowflake data sharing?

Optimize your Snowflake data sharing experience with these essential practices. Ensure data security by utilizing secure views to filter and mask sensitive information. Enhance clarity and understanding by employing descriptive names and comments for your shares. Monitor and fine-tune your sharing activities using Snowflake Information Schema or Account Usage views. Foster communication and collaboration with your consumers to create a seamless workflow.
Take command of your data sharing environment by setting quotas and limits with the ALTER SHARE command. Keep your consumers informed about any changes or updates to your shares, and actively seek feedback to refine your data-sharing strategy. Explore additional data sources through Snowflake Data Exchange or Data Marketplace to enrich your analytics.
These best practices safeguard sensitive data, ensure compliance with data privacy regulations, clarify the purpose of each share, and provide insights into usage and performance, ultimately enhancing your data analysis capabilities. Below are some best practices for data sharing with Snowflake:

1. Understand Snowflake Data Sharing
Familiarize yourself with Snowflake's data sharing features, such as Secure Data Sharing (SDS) and Sharehouse, to leverage the platform effectively.

2. Role-Based Access Control (RBAC)
Implement strong RBAC policies to control who can share data and who can access shared data. Define roles and permissions to ensure data security and compliance.

3. Secure Data Sharing
Use Secure Data Sharing (SDS) to securely share data with external parties without copying or moving the data. Implement encryption and access controls to protect sensitive information.

4. Sharehouse Best Practices
If using Sharehouse, follow best practices for creating and managing share objects. This includes defining share schemas, tables, and using the appropriate share options for your use case.

5. Data Masking and Redaction
Apply data masking or redaction policies to shared data to protect sensitive information. Ensure that shared data complies with privacy regulations and internal data governance policies.

6. Query Performance Optimization
Optimize query performance for shared data by using clustering keys, partitioning, and indexing. This helps enhance the efficiency of queries on large datasets.

7. Versioning and Change Tracking
Implement versioning and change tracking mechanisms to keep track of updates and changes in shared data. This ensures data lineage and helps with auditing and troubleshooting.

8. Documentation and Metadata
Maintain comprehensive documentation and metadata for shared datasets. Include information about the source, purpose, and any transformations applied. This helps users understand the shared data context.

9. Governance and Monitoring
Establish governance practices for data sharing, including regular reviews of shared data objects and access logs. Monitor data-sharing activities to identify any anomalies or potential security issues.

10. Educate Users
Provide training and documentation for users involved in data-sharing activities. Ensure they understand the best practices, security protocols, and the impact of data sharing on performance.

11. Regular Audits and Reviews
Conduct regular audits and reviews of shared data objects, permissions, and access controls. This helps maintain data integrity, security, and compliance with organizational policies.

12. Cost Monitoring

Keep an eye on the costs associated with data sharing, especially if you're sharing data externally. Understand the pricing model and optimize data sharing configurations to manage costs effectively.

By adhering to these best practices, you can:
1. Shield Sensitive Data: Employ secure views to fortify sensitive information.
2. Navigate Data Privacy Regulations: Ensure compliance with data privacy regulations by controlling access and usage.
3. Illuminate the Purpose of Each Share: Maintain transparency regarding the intended purpose and content of each shared dataset.
4. Efficiently Monitor Usage and Performance: Keep a finger on the pulse of usage patterns and optimize performance for streamlined data sharing.
5. Elevate Your Data Analysis Journey: Enrich your analytics by exploring diverse data sources and unlocking fresh perspectives.

What’s Next

1. Enhanced Data Collaboration Tools:

Snowflake is expected to introduce advanced features for more streamlined and efficient data collaboration, addressing the evolving needs of industries.

2. Innovations for Privacy-Preserving Analytics:

Snowflake aims to develop and implement solutions that uphold data privacy while enabling powerful analytics, aligning with the growing concerns in this area.

3. Adaptation to Changing Industry Dynamics:

Snowflake is likely to evolve its platform to meet the challenges posed by changes in industry dynamics, ensuring it remains a robust solution for data management and collaboration.

4. Integration of Cutting-Edge Technologies:

Snowflake may integrate emerging technologies, enhancing its capabilities to support new use cases and data-intensive analytic methods.

5. Continued Emphasis on Secure Data Collaboration:

Future updates from Snowflake may include even more robust security measures and innovations in data clean rooms, reflecting a commitment to secure data collaboration in the face of a dynamic data landscape.

Best Way to Share Data for Your Business

For secure collaboration, old ways of copying data are no longer the best. If you're working with trusted partners and it's privacy-compliant, Snowflake Secure Data Sharing is a quick and secure option. But, if you're dealing with sensitive or regulated data, especially when the risk is high, consider using a data clean room for an extra layer of security and compliance.

Beinex + Snowflake Offerings

Beinex’s partnership with Snowflake enables us to offer you advanced features like automated tuning and elastic compute, along with analytics modernisation services, to help your organisation realise exponential Return on Investment.

1. Understand Your Audience

The best dashboards are built with a specific audience in mind. Consider who will be using your dashboard and what their goals are. Are they busy executives who need to quickly grasp key performance indicators (KPIs), or are they analysts who need to drill down into details? Once you know your audience, you can tailor your dashboard design to their needs. Source: https://www.tableau.com/learn/whitepapers/10-best-practices-building-effective-dashboards?utm_medium=blog

2. Optimize Layout

In today's mobile-first world, it's important to design dashboards that look good and function well on a variety of devices. When designing your dashboard, consider how your audience will be consuming the information. If they're primarily using mobile phones, you'll need to prioritize the most important KPIs and optimize the layout for a small screen.

3. Minimize Page Load Times

No one wants to wait for a dashboard to load. Slow load times can be caused by a variety of factors, including the size of your data set and the complexity of your visualizations. To improve load times, you can optimize your data queries, use extracts, and leverage Tableau's built-in performance optimization features.

4. Insights-Based Design

When designing your dashboard layout, consider how your audience will naturally "read" the information. Group related pieces of information together and use visual cues like shading, lines, and white space to guide the eye. The most important information should be placed in the upper left corner of the dashboard, as this is where most viewers will start looking.

5. Minimize Views and Colors

It's tempting to include every relevant piece of data in your dashboard, but this can lead to information overload. Stick to two or three key views, and use color strategically to highlight important trends or patterns. Avoid using too many colors, as this can make your dashboard visually overwhelming.

6. Incorporate Interactive Features

Dashboards are most powerful when they allow users to interact with the data. Use filters, highlights, and actions to let users explore the data in different ways. This will help them to gain a deeper understanding of the information and answer their own questions.

7. Arrange by Hierarchy

When formatting your dashboard, it's best to start with the largest elements and work your way down to the smallest. This will help you to avoid accidentally overwriting your changes. The hierarchy of dashboard formatting typically follows this order: theme, workbook, and worksheet.

8. Utilize Tooltips and Whitespace for Effective Design

Tooltips are a great way to provide additional information without cluttering up your dashboard. Use tooltips to display detailed data points or to link to external resources. White space is also essential for good dashboard design. It helps to improve readability and make your dashboard less visually overwhelming.

9. Prioritize Clarity

Every element on your dashboard should serve a purpose. If a title, legend, or axis label isn't necessary, get rid of it. Consider using a floating layout to create more white space and improve readability.

10. Ensure Dashboard Effectiveness

Once you've built your dashboard, it's important to test it with your target audience. Ask them how they're using the dashboard and if it helps them answer their questions. Use their feedback to iterate on your design and create dashboards that are truly effective.
By following these best practices, you can create dashboards that are clear, concise, and actionable. Effective dashboards will help your audience understand your data and make better decisions.

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 who are passionate about Tableau and delivering a world-class experience. Connect with us for a Tableau free trial: www.beinex.com/free-tableau-software
Image source: https://www.tableau.com/learn/whitepapers/10-best-practices-building-effective-dashboards?utm_medium=blog

What is AWS KMS?

AWS KMS is a managed service that enables you to create, control, and use encryption keys to protect data across your applications. By leveraging AWS KMS, businesses can confidently manage data encryption with a variety of flexible tools and practices. Here’s a breakdown of the key components and functionalities of AWS KMS.

What are KMS Keys?

KMS keys are central to AWS KMS’s operations. They are cryptographic keys used for data encryption and decryption. Each KMS key has its own unique characteristics:
• Symmetric KMS Keys: These are 256-bit Advanced Encryption Standard (AES) keys, used for both encrypting and decrypting data and cannot be exported from AWS KMS.
• Data Keys: Data keys can encrypt larger volumes of data. AWS KMS can generate and encrypt these keys, though it doesn’t store or manage them directly. Source: https://docs.aws.amazon.com/kms/latest/developerguide/overview.html

Why Use AWS KMS?

When you encrypt data, you need to protect your encryption key. If you encrypt your key, you need to protect its encryption key. Eventually, you must protect the highest-level encryption key (known as a root key) in the hierarchy that protects your data. That's where AWS KMS comes in. AWS KMS protects your root keys. KMS keys are created, managed, used, and deleted entirely within AWS KMS. They never leave the service unencrypted. Additionally, you can create and manage key policies in AWS KMS, ensuring that only trusted users have access to KMS keys.

Key Features of AWS KMS:

• Key Creation and Management: You can easily create and manage various types of cryptographic keys, including symmetric keys and asymmetric key pairs. • Key Rotation: KMS supports automatic key rotation, which helps to enhance security by minimizing the lifetime of keys. • Key Usage Restrictions: You can define specific usage restrictions for your keys, such as limiting them to certain AWS services or requiring additional authentication factors. • Centralized Key Management: KMS allows you to manage all of your cryptographic keys from a single location, simplifying key management tasks. • Integration with AWS Services: KMS integrates seamlessly with many AWS services, such as Amazon S3, Amazon RDS, and Amazon EBS, making it easy to encrypt data at rest. • Compliance and Auditing: KMS helps you meet compliance requirements by providing features such as key audit logs and access controls. • HSM-Based Key Protection: KMS offers HSM-based key protection, which provides an additional layer of security for sensitive data.

Key Benefits of Using AWS KMS:

• Enhanced Security: KMS helps to protect your data by providing strong encryption and key management capabilities. • Simplified Key Management: KMS streamlines the process of creating, managing, and controlling access to cryptographic keys. • Improved Compliance: KMS can help you meet compliance requirements by providing features such as key audit logs and access controls. • Cost-Effective: KMS is a cost-effective solution for managing cryptographic keys. • Scalability: KMS can scale to meet the needs of your growing business.

Types of KMS Keys

AWS KMS provides three types of KMS keys to suit different security needs: • Customer Managed Keys: You create and control these keys, customizing policies and permissions. This control extends to managing key rotations, setting up aliases, and even scheduling deletions. • AWS Managed Keys: These are managed by AWS on behalf of a specific AWS service, though they cannot be directly managed or used outside that service. • AWS Owned Keys: AWS owns and manages these keys across multiple accounts, making them inaccessible for direct management or auditing by customers.

How Does AWS KMS Encrypt Data?

AWS KMS employs several methods for data encryption:

Data Keys and Envelope Encryption

In envelope encryption, data is first encrypted with a data key, which is then encrypted by a KMS key known as the root key. This two-layer encryption ensures an additional level of security.

Encryption Context

KMS operations accept an optional encryption context, a set of key-value pairs providing additional information about the data being encrypted. This helps add context to the encrypted data, making it easier to identify and validate in future processes.

Managing AWS KMS Keys and Access

Managing access to KMS keys is critical for security, especially when multiple teams or departments are involved. Here are some key components for effective key management: • Key Policies and Grants: Permissions to access KMS keys are managed through key policies. Alternatively, temporary access can be granted using grants, which can be applied, utilized, and revoked as needed without affecting long-term permissions. • Grant Tokens: To mitigate delays in grant permissions due to eventual consistency, grant tokens can be used for faster access rights activation.

Custom Key Stores and VPC Integration

For highly sensitive data, AWS KMS allows integration with AWS CloudHSM (Hardware Security Module) clusters through custom key stores. A custom key store provides a dedicated, secure storage location for cryptographic keys. Additionally, KMS allows you to connect directly through a private VPC endpoint, ensuring that all communication between your Virtual Private Cloud (VPC) and AWS KMS remains within the AWS network, bypassing the public internet for added security.

Rotating, Importing, and Deleting KMS Keys

Managing the lifecycle of encryption keys is a vital aspect of data security. AWS KMS provides several features to handle key management effectively: • Key Rotation: AWS KMS can automatically rotate customer-managed KMS keys annually, maintaining historical key material for decryption purposes. • Importing Keys: Businesses can import their own key material to AWS KMS, with options to set expiration dates or delete key material on demand. • Deleting Keys: When a KMS key is deleted, all associated metadata and key material are removed, making it impossible to decrypt any data previously encrypted with that key. AWS KMS also offers the ability to disable keys temporarily if access restrictions are needed.

Monitoring Key Usage with CloudWatch

To ensure robust monitoring, AWS KMS integrates with CloudWatch to track key usage and send notifications when certain actions are taken. For instance, you can create a CloudWatch alarm that triggers an alert if someone tries to use a key that’s pending deletion. AWS KMS is an indispensable tool for organizations needing to secure sensitive data efficiently. By understanding KMS keys, managing access, and leveraging encryption best practices, companies can create a reliable data protection framework that scales with their needs.

AWS + Beinex Partnership: How Can We Help You

Beinex is proud to be an AWS consulting partner dedicated to empowering our customers with AWS-managed services to host their BI solutions and beyond in the cloud. Our cloud migration experts deliver top-tier stability and reliability by aligning with your business strategy and collaborating closely to implement AWS infrastructure as a service. Experience AWS for free: https://beinex.com/beinex-amazon-web-services/

The cloud-based approach delivers:

1. Better performance
2. More flexibility
3. Enhanced cost savings
4. Improved security
5. Facilitates excellent teamwork opportunities

Specifically, Tableau on AWS lets you process data more quickly and scale up or down resources as needed. Plus, you can access a range of AWS services to optimise your Tableau setup. AWS also provides tools to help you save money and a secure environment to protect against cyber threats and data breaches. Ultimately, Tableau on AWS enables teams to collaborate more efficiently, taking their data analysis and business intelligence to the next level.

There are several other benefits to using Tableau on AWS beyond scalability, cost, and security. Here are some additional insights:

1. Faster Deployment: With Tableau on AWS, you can deploy new instances of Tableau in minutes rather than days or weeks as you would with on-premises infrastructure. This is because AWS has pre-configured templates for Tableau that make it easy to spin up new instances quickly. 2. Better Performance: Tableau on AWS is designed to exploit AWS's high-performance infrastructure. Tableau runs faster and more efficiently on AWS than on traditional on-premises infrastructure. 3. Integration with Other AWS Services: Tableau on AWS integrates seamlessly with other AWS services, such as Amazon S3 for data storage, Amazon Redshift for data warehousing, and Amazon EMR for big data processing. This makes building a complete analytics solution easier by using Tableau and other AWS services. 4. Improved Disaster Recovery: With Tableau on AWS, disaster recovery is built. AWS provides automated backup and recovery services to quickly recover your Tableau environment and data if there is a disaster or outage. 5. Global Reach: AWS has data centres worldwide, meaning you can deploy Tableau in the region closest to your users for better performance. This is especially important for organisations with a global presence.

Overall, Tableau on AWS offers several advantages over on-premises infrastructure. By leveraging AWS's scalability, cost-effectiveness, and security, organisations can run Tableau more efficiently and with better performance. Additionally, AWS's integration with other services and global reach make it an attractive option for organisations looking to build a comprehensive analytics solution.

Tableau Server on AWS deployment options

The following list outlines the available options for deploying Tableau Server on AWS:
1. Self-Deployment on EC2 Instance: This option involves users provisioning and configuring an EC2 instance and deploying Tableau Server. This provides the most significant control over the deployment process and can be customised to specific needs. However, it also requires more expertise and effort from the user.
2. Quick Start Deployment: The Tableau Server on AWS Quick Start provides an automated deployment process using AWS CloudFormation templates. This simplifies the deployment process and ensures that best practices are followed. However, it may be less customisable than self-deployment.
3. AWS Marketplace Deployment: Tableau Server is also available on the AWS Marketplace with pre-built AWS CloudFormation templates. This provides a quick and easy way to deploy Tableau Server, with different pricing and instance options public. However, users may have less control over the deployment process than over self-deployment.

Users should evaluate their specific needs and expertise when selecting a deployment option. Self-deployment provides the most significant control and customisation, while Quick Start and AWS Marketplace deployment offer simplified and quick deployment options.