A DEEP DIVE INTO TABLEAU 2019.3

Blog

How AI Decision-Making is Improving Enterprise Outcomes

TABLEAU CATALOG What could be better than a feature that could provide better visibility and better data management at the same time, and also ensure that the right data is always used for analysis. This is possible with the new feature -Tableau Catalog.

This feature provides a complete picture of the data and how each data is connected. Another use of Tableau Catalog is linear and impact analysis. This not only shows which assets will change but also who will be affected by it, which makes work easier for many and avoids wastage of time. EXPLAIN DATA Tableau 2019.3 is up with a new Al-driven feature called the “Explain Data”, which helps people go from the “what” of the data to the “how” of it. With explain data, we can get an explanation for each unexpected value in the data by just a single click. On selecting the desired data point, the ‘explain data’(lightbulb) icon appears. For each value there might be a number of explanations. Each of these explanations are checked and only the most likely ones are provided as visualizations.

Now these visualizations can be used for further explorations. TABLEAU SERVER MANAGEMENT ADD-ON Organizations that run critical deployment of Tableau Server at a large scale, have mentioned concerns over manageability and scalability. They have been in search for tools that could organize the management process in an efficient way, which could save a lot of time. Tableau solved this problem by introducing the Tableau Server Management Add-on – a new feature designed to help organizations manage the deployment of Tableau Server. With this, they can quickly react to the changing needs of the business as well as save time by organizing the management process in the most efficient way. Tableau Server Management Add-on, which makes running the critical deployment of tableau at a large-scale server much simpler.

The server management add-on feature can help in optimising the performance of deployment by customizing which nodes process background jobs such as extract refreshes and subscriptions and isolating these workloads, to specific nodes. This makes it easier to scale deployments to the needs of their organization. This feature has a few tools, including two for better reliability and scalability and one for content migration, all of which helps the organizations to govern their data effectively. If you are interested in learning more about the latest Tableau release and use cases, please contact us at training@beinex.com/ info@beinex.com and we would be happy to schedule a Tableau demo or training for you and your company.

Note: The Server Management Add-on is not available for Tableau Online, as they manage everything from scaling, performance, and security on behalf of their Tableau Online customers. The Tableau Server Management Add-on can be separately purchased from the Tableau Server deployment.

With every release, Tableau proves that it takes user suggestions into account when developing new features. Tableau has always tried to keep customers convenience and flexibility as their matters of concern. Tableau’s latest version 2019.3 has been included with exciting new features like Tableau Catalog, Explain Data and Tableau Server Management Add-on. Let us have a look at what these new features are:

Spatial Parameters: Enhancing Geospatial Analysis and Adding Interactivity to Your Maps

What Are Spatial Parameters?

Spatial parameters in Tableau allow you to dynamically interact with geospatial data within your visualizations. These parameters enable you to select spatial objects like points, polygons, multi-polygons, lines, or collections for calculations. Unlike traditional parameters, which work with values like text or numbers, spatial parameters offer the flexibility to work with geospatial data like coordinates and spatial shapes. You can create spatial parameters in two ways: 1. From a data source: If your data contains spatial fields (like latitude and longitude), you can load these as spatial parameters. 2. Using Well-Known Text (WKT) : This method allows you to manually input spatial data in text format to create custom spatial parameters. Spatial parameters can be used in the same way as other parameters in Tableau, including parameter controls, actions, and dynamic values. Note: Spatial parameters can only be created from spatial data fields (such as latitude and longitude). Creating spatial parameters from text string fields, like a "Country" field, is not feasible as it might be assigned as a geographic role but remains a text field.

Top Benefits of Using Spatial Parameters

Spatial parameters have offered immense possibilities for Tableau experts to analyze and visualize geospatial data. Key benefits are listed below: 1. Cross-Data Source Spatial Exploration With spatial parameters, you can explore spatial relationships between data sources that don't support joins. Unlike traditional data sources, which can be limited by join constraints, spatial parameters allow you to compare spatial regions from different datasets. This means you can dynamically analyze relationships across multiple data sources without the need for complex joins. 2. Skip Long Joins Previously, working with large spatial datasets required time-consuming spatial joins. Now, spatial parameters enable you to bypass these long joins and conduct spatial analysis much faster. You can use parameters to compare regions across multiple data sources instantly, even with vast datasets. 3. Interactive and Dynamic Analysis Spatial parameters allow users to visually interact with geospatial data in real time. Like other parameter types, you can use parameter controls to input points, lines, or polygons and adjust your analysis on the fly. This flexibility allows you to dynamically change spatial boundaries and relationships during your analysis. 4. Distance-Based Queries With spatial parameters, you can perform distance-based queries without complex calculations. For example, you can control the size of a buffer and see what lies within that buffer directly from the worksheet. This makes it easy to conduct proximity analysis without the need for complicated formulas. 5. Create Custom Regions Spatial parameters allow you to create custom regions by selecting points, lines, and polygons and combining them into a single parameter. For instance, you can create a sales region by combining multiple states into one spatial parameter. This enables you to tailor regions for specific analysis scenarios.

Spatial Calculations in Tableau 2024.3: What’s New

Tableau 2024.3 also introduces three new spatial calculations to help you evaluate spatial relationships: • SYMDIFFERENCE: Identifies areas that are unique between two regions. • INTERSECTION: Shows the overlap or intersection between two regions. • DIFFERENCE: Displays areas present in one region but not in the other. These new spatial calculations provide powerful tools to analyze and compare geospatial data.

Union Aggregation for Spatial Data

In previous versions of Tableau, the only aggregation available for spatial data was Collect, which groups spatial elements together. Union Aggregation has been introduced in Tableau 2024.3, allowing you to dissolve the boundaries between regions and providing even greater flexibility in working with spatial data.

Validating Spatial Calculations

Tableau 2024.2 introduced the Validate Calculation feature, which can help you detect and correct errors in your spatial data. This tool is handy when working with complex spatial datasets. Tableau's new Spatial Parameters and related features, such as spatial calculations and union aggregation, provide powerful capabilities for geospatial analysis. Whether comparing spatial regions, performing proximity-based queries, or creating custom areas, these features enable dynamic and flexible analysis beyond traditional methods.

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 for a free demo: https://www.beinex.com/free-tableau-software/

Understanding What is Data Catalog: A Guide to Data Cataloging Best Practices

What is a Data Catalog?

A centralized repository, Data Catalog, stores metadata about an organization's data assets. It provides a single source of truth for an organization's data, making it easier to discover, access, and manage. A data catalog is a directory that helps users navigate and understand the organization's data landscape. Here are some of the key features of a data catalog. • Managing metadata, including data descriptions and relations, about the data assets of an enterprise
• Enabling easy discovery and locating of data assets through a user-friendly interface
• Categorizing data assets based on criteria like confidentiality, sensitivity, etc.
• Supporting data lineage by providing information about data assets' origin, movement, and transformation.
• Enhancing data governance by providing data stewardship, data quality management, and compliance management features.
• Facilitating integration with various data sources, including relational databases, cloud storage, and big data platforms.

Data Cataloging Best Practices for Effective Management

1. Start with a clear goal

Before implementing the data catalog, define the reasons you need. If you have clear goals, you can decide which data sources to prioritize, which features to enable, and how success is measured. The general goals are: • Improving data coverage
• Enhancing data governance
• Enabling self-service analytics support
• Ensuring compliance with official compliance
• Promoting cooperation between teams

2. Focus only on the data that rely on catalogs

Avoid the temptation to catalog all the data you have. Instead, focus on high-quality data assets, reports, dashboards, and pipelines commonly or critically used in business processes. This keeps the catalogs manageable and relevant.

3. Automate metadata collections

Documenting manual data is time-consuming and error-prone. Record schedules, table relationships, data lines, and usage patterns directly from data sources using a data catalog tool with automated metadata harvesting. This will keep your catalog up to date with minimal manual effort.

4. Promote collaboration

Large data catalogs combine machine-generated metadata with human knowledge. To improve their value, data managers, analysts, and business users must:

• Add explanations and relevant business areas. • Assess and label data assets (reliable, certified, etc.) while providing insights on how data records are used in your project. • Share queries and analysis to enhance accessibility and understanding.
This collaborative approach transforms catalogs into dynamic, valuable resources rather than static inventories.

5. Define the database

Each data record must have a clear owner responsible for ensuring the data's quality, documentation, and suitability. Data owners (often data managers or specialists) are key actors who can trust catalogs and keep them from date to date.

6. Define and implement governance guidelines

Data catalogs are about more than just discovering data. It is also a powerful tool that supports data governance. Strong governance practices help build trust in your data catalog and ensure it supports regulatory needs. The key governance measures include: • Follow anyone with data, access, or modifications.
• Apply data classification (sensitive, published, internally).
• Enforce access control.
• Document compliance requirements (such as GDPR and HIPAA).

7. Enable easy and intuitive search for better data discovery

Data catalogs should work like a fast, intuitive, keyword-friendly search engine, enabling users to search for technical and business terms. Search results should show useful contexts (explanation, usage statistics, popularity). Filters and tags help narrow down your results easily. A user-friendly search experience drives acceptance and makes data coverage faster.

8. Monitor catalog consumption and commitment

Track how users interact with the data catalog to see what works and where there are gaps. Certain useful indicators include: • Most terms were searched.
• Most of the data records considered
• Contribution rate (how often users add descriptions, reviews, or comments)
• User recruitment rate across all teams
This data helps continually improve the catalog and translate it into user requirements.

9. Review and organize regularly

Like other systems, data catalogs can become overcrowded over time. A clean and well-maintained catalog makes navigating easier and encourages more trust. Some best practices include: • Setting up a regular catalog audit
• Archiving outdated or unused data records • Delete duplicate entries • Updating the old document
• Identifying data assets that new owners need

Unlocking the Power of Data Cataloging with Alation

An effective data catalog is not just a tool—it’s a foundation for a data-driven culture. By following these data cataloging best practices, organizations can transform their catalogs into trusted, collaborative resources that drive informed decision-making. Alation, a leader in data intelligence, empowers businesses with an AI-driven data catalog that streamlines metadata management, enhances data governance, and fosters collaboration. Alation’s advanced capabilities include: • Automated metadata harvesting
• AI-powered data discovery and recommendations
• Robust governance and compliance tools
• Self-service analytics enablement
Alation’s data catalog is designed to help organizations like yours build trust in data, enhance compliance, and improve decision-making efficiency.

Get Started with Alation and Beinex

In collaboration with Alation, Beinex helps businesses implement a modern data cataloging strategy, ensuring seamless integration and regulatory compliance. Whether you’re just starting or refining your existing catalog, our expertise can accelerate your data governance journey. Connect with us for a free demo: www.beinex.com/beinex-alation

ASK DATA: TABLEAU’S NEW NATURAL LANGUAGE

Ask Data: Tableau’s New Natural Language Capability Engine

Hello Tableau Fans! As promised here is the blog about the most awaited feature of Tableau 2019.1, ASK DATA! I have been waiting for this since the day it was announced in beta. Now it’s available with 2019.1 version on server and online. For those who don’t know what AskData is, its natural language capability engine integrated within Tableau server and Online. It takes self-service analytics to next level by allowing end users to get answers to their questions by simply typing. All we need to do is curate the dataset to make it easy for end user, publish it and start questioning. Although it gives you powerful ability to bring out the insights, data also needs to be curated in a way that end user can easily understand and make use of it. Key points to keep in mind when you publish the data source for AskData.

Try to limit data fields as less as possible where it supports up to 1000 data fields.

Create hierarchies to give drilldown and drill up flexibility to users.

Rename the fields to make it more relevant and meaningful

Be cautious when using geographical data. Make sure to map the geo codes properly.

Create calculated fields if necessary.

I’m using sample super store to show AskData in action. Below shows how the aggregation works and how easily data fields can be changed on the fly. It also leverages the ShowMe functionality that’s already available to change the type of chart. It lists down all the available charts based on your data fields. You can also observer the suggestions by NLP while you type questions. You can click on the one which is more relevant to your question. It’s obvious that users tend to enter similar words while asking these questions. For example, they might type products instead product names. We can create synonyms for the data fields, so that it understands the question better. It takes no time to do this. It understands all your questions and takes the advantages of existing analytical capabilities like aggregation, sorting, filtering, etc. It does auto-sorting when you select a bar chart. AskData ships with a very powerful algorithm which understands the data, automatically indexes and gives you the right viz for your question. Here are some best practices to make it more useful.

Curate your data source keeping the end user in mind

Create all the calculations and synonyms before exposing to end user

Apply your existing governance and access to the published data source.

Keep your question short

In conclusion, AskData will certainly change the way we interact with data by shortening the time taken for data driven decisions. I hope you have enjoy reading my blog, please keep up with us as we will be adding many more! Please contact us at info@beinex.com to schedule a Tableau demo or training for you or your company.

Snowflake and GenAI: Revolutionizing Data Analytics

Snowflake Cortex: AI-Powered Data at Your Fingertips

Snowflake Cortex provides access to industry-leading AI models, including large language models (LLMs) and vector search functionality. These serverless tools simplify everyday analytics and AI development, all within a single line of SQL or Python. Snowflake-Governed Data Source: https://www.snowflake.com/en/blog/use-ai-snowflake-cortex/

Key benefits include:

• Instant Access to AI Models: Snowflake users can leverage specialized machine learning and LLM models without managing expensive infrastructure. • Enhanced Data Insights: With Cortex, users can analyze vast datasets using Snowflake’s powerful AI capabilities, unlocking strategic insights to improve decision-making. • Simplified AI App Development: By removing technical barriers, Cortex democratizes AI access, enabling users of all skill levels to build AI applications.

LLM-Based Models for Unstructured Data (in private preview):

• Answer Extraction: Extract key information from unstructured datasets. • Sentiment Detection: Identify the sentiment in textual data. • Text Summarization: Create concise summaries of lengthy documents for quicker insights. • Translation: Perform large-scale text translation efficiently.

ML-Based Models (available soon):

• Forecasting: Automatically forecast time series based on historical data, adjusting for seasonality and scaling. • Anomaly Detection: Detect outliers in time series data, useful for monitoring data pipelines. • Contribution Explorer: Identify key factors contributing to changes in metrics between two time intervals. • Classification: Categorize data into predefined classes to offer recommendations based on trends.

General Purpose Models for Broader Use Cases:

• Complete: Generate text completions using state-of-the-art open-source LLMs like Llama 2. • Text2SQL: Convert natural language queries into SQL, powered by Snowflake’s LLM, similar to the Snowflake Copilot feature. These serverless functions offer out-of-the-box capabilities that can be integrated into analytics workflows and app development in Snowflake. For example, with just a few lines of code, developers can embed these functions into chatbots using Streamlit. This allows Python-savvy users to build secure and powerful LLM applications quickly, often within hours.

Document AI

Document AI (currently in private preview) leverages large language models (LLMs) for seamless data extraction. By utilizing a pre-trained model and a user-friendly interface, customers can process various document types—such as PDFs, Word files, text files, and even screenshots—to quickly obtain answers to their queries. This capability can be scaled to build pipelines that automate data extraction, significantly reducing manual effort and saving time. Snowflake-and-GenAI-Revolutionizing-Data-Analytics Source: https://www.snowflake.com/en/blog/use-ai-snowflake-cortex/

Snowflake Copilot: Your AI-Powered SQL Assistant

Snowflake recently introduced Snowflake Copilot, an AI-driven solution that makes SQL query generation faster and more efficient. With Snowflake Copilot, users can ask data-related questions in plain English, and the AI will generate SQL queries to deliver the desired insights. Snowflake-and-GenAI-Revolutionizing-Data-Analytics-3

Key Features of Snowflake Copilot:

• Text-to-SQL: Users can interact with their data using natural language, eliminating the need for complex SQL coding. • Enhanced Accuracy: The AI continuously refines its understanding of user queries, providing more accurate SQL code suggestions. • Data Exploration: Ask open-ended questions about your data and receive detailed insights without writing complex queries.

The Future of Generative AI with Snowflake

Snowflake is pushing the boundaries of Generative AI with its continuous development of AI tools like Snowflake Cortex AI and Snowflake Copilot. These innovations pave the way for a future where natural language becomes the primary interface for data analysis, enabling businesses to extract more value from their data while maintaining robust governance.
By integrating AI capabilities directly into the data platform, Snowflake empowers users to streamline workflows, reduce processing times, and unlock new levels of productivity—all without needing deep AI expertise.

A Transformative Partnership for the Future

Generative AI, coupled with Snowflake’s powerful data platform, is a game-changer for businesses looking to innovate and scale. Whether it’s enhancing productivity, improving decision-making, or driving customer engagement, Snowflake’s AI solutions are built to transform how enterprises interact with their data.

As AI continues to evolve, so will Snowflake's offerings, bringing more capabilities, deeper insights, and greater efficiencies to businesses worldwide. Ready to experience the future of data analysis with Snowflake and AI? The journey has just begun!

Snowflake + Beinex Partnership

Beinex is a Snowflake Services Partner Premier Tier, and the partnership reaffirms Beinex’s commitment to delivering exceptional data solutions and positions the company at the forefront of industry advancements. Harnessing the true potential of the data, partnership drives innovation and success in the digital era.
Belonging to Snowflake Services Partner Premier Tier, Beinex leverages Snowflake’s advanced capabilities and seamlessly integrates them into its comprehensive data solutions. This enables Beinex to accelerate the pace of Digital Transformation for its clients, providing them with the tools necessary to extract maximum value from their data and thrive in an increasingly data-centric world.
Connect with us for a free demo: https://beinex.com/contact-us/

Data Calculations in Tableau: Sum and Running Sum

[sc name="quote" quote="“The functions, even as they may look simple, are pretty useful for any Tableau Consultant especially if they are working on finance-related projects,”" author="Joseph John, Senior Consultant, Analytics, Beinex"][/sc]

What is Tableau Sum and Running Sum?

Sum

SUM is one of the commonly performed functions in Tableau. The Tableau Sum function seeks out the Sum of records under consideration. It is the total of the values present in a field. The screenshot provided below exhibits the total sum of sales for each of the three categories as given in Sheet 1.

In the sample dataset shown below, the sum of sales is shown corresponding to each of the corresponding values in the dimension "Category". For example, "Furniture" has a total sale of 754,748, which could be comprising of furniture related products such as Tables, Chairs etc.

Running Sum

A RUNNING SUM is a cumulative total in a row or column from the first value to the final value in the respective row or column. For instance, in the example given below, the cumulative values of Furniture and Office Supplies stand at 1,486,641 and that value when added to Technology’s value of 839,893 gives 2,326,534.

You can summarise or modify the granularity of your data using aggregate functions. An aggregate part combines the values of multiple lines to provide a single value. Examples of aggregate functions apart from sum are measurements based on Count, Count Distinct, Fixed Calculations, and other standard integration functions.

Every time you include a measure in your view, an aggregate is automatically applied to that measure. Depending on the context of the view, different aggregation techniques are used. Analysts can well utilise these features to simplify the whole complex process of data analysis, and organisations can harness them for insightful decisions.