Beinex Wins 2021 AWS Rising Star Consulting Partner of the Year – MENA Award

As a frequent user of Tableau Desktop and Server, check out my top picks from the list. • Improved dynamic parameter • Multiple marks layer support for maps • Block comments in calculations • Web authoring enhancements • Offline map support for Tableau Server • Tableau Server Management (TSM) improvements • Multiple key activation on Tableau Server prior to TSM initialization • Analytics Extension for Tableau Online • Merge duplicate external assets • Tableau Prep Builder in the browser Tableau Desktop Improved Dynamic Parameter The dynamic parameter was one of the features which I used extensively starting from 2020.1. The feature allows us to populate parameter values based on a specified field whenever the workbook is opened. We will be able to select only the field with matching data types. The feature update announced in Tableau 2020.4 gives me more privilege to customize the Dynamic Parameter. I can now set a parameter’s range to a field’s min and max value upon opening the workbook, or when the field’s data source is refreshed. So, whenever I open the workbook or the extract is refreshed, Tableau displays the unique values within the range specified, giving more control over the parameter values displayed. Multiple Marks Layer Support for Maps This is an exceptional feature to bring multiple spatial layers and context together to better understand and analyze geospatial data and map views. I will be able to include multiple marks layers from a data source to map visualizations and enhance the geospatial analysis. I can present more context in a single map view and perform further analysis with this feature. Block Comments in Calculations Block comments is a simple yet one of the most useful features for me, which Tableau has announced in 2020.4. I often used to add comments in complex calculations for easy understanding in future references. Earlier, only single-line comments were possible, limiting the description I could add. But from now on, I can add comments of any length to calculation windows with block comments by simply starting the comment with /* and ending with */. What makes it unique is that the new multi-line block comment feature is consistent with other popular programming languages. This feature is an example of Tableau’s on-going effort to provide its customers with an intuitive user experience. Tableau Server Web Authoring Enhancements Starting from Tableau 2020.4, it is possible to author dashboards from the browser just like how we design it in Tableau Desktop. With this web authoring enhancements, I can include Highlight actions, Format Mark Labels, apply filters to worksheets, create fixed sets, and even create extract in my workbooks from the browser itself. I no longer have to make changes from Tableau Desktop and publish it to the server. I can directly do it from the browser itself. Offline Map Support for Tableau Server Rendering dashboards with maps is now faster compared to previous versions. Now, I can create maps using the offline map style in web authoring, ensuring the performance of map views in Tableau Server. Offline map support is a great deal for organizations with strict internet access restrictions, assuring that map view access to all its users. Tableau Server Management (TSM) Improvements Tableau Server Administration activities like installation, upgrade and backup are now easy like never before. I can retry installation or upgrade from the last checkpoint in case of an unexpected issue or error during installation or upgrade, saving my efforts to obliterate tableau server. Backups can be performed twice as fast as previous versions, and I can monitor the progress with the new progress bar giving visibility into what step the backup is on and how much time is remaining. Backups can also be scheduled using TSM command starting from 2020.4 and that is awesome. I no longer need to prepare batch script and depend on the windows task scheduler to schedule the backups on regular intervals; instead, I can schedule it with just a single command. Multiple Key Activation on Tableau Server Prior to TSM Initialization During Tableau Server installation, it is now possible to activate multiple license keys prior to TSM initialization. I will be able to save a lot of time by eliminating the need to restart after the installation is completed to activate multiple licenses and experience a smoother installation. Analytics Extension for Tableau Online The power of the Analytics extension is now unlocked in Tableau Online too. The feature was already available in Tableau Server, and it helped us dynamically perform advanced analysis with models and functions in R, Python and other platforms. Analytics extension in Tableau Online significantly enhances the scope of using Advanced Analytics by the common users. Merge Duplicate External Assets Earlier, the Database or Table with similar names used to appear as multiple assets within Tableau Catalog. But, the new feature helps me to merge those multiple assets into a single one. I can manage assets easily and keep an organized view of External assets by merging the common ones. Tableau Prep Tableau Prep Builder in the Browser Prepare the data for visualizations from anywhere using a browser! Starting from 2020.4, Tableau is bringing the data prep process into one integrated platform on the web. Now I can easily prepare and manage prep flows from anywhere using a browser. Conclusion Tableau is progressively evolving as a single platform for data preparations, visualization, and collaboration with every update and version release. Author : Firdous Maqbool Images Courtesy : Tableau

AI in Healthcare: Building a Health-Smart Future

AI can leverage high-volume patient datasets to detect patterns, enabling medical experts to personalize treatments, optimize costs, manage medication, improve general health management, enhance healthcare research, help clinicians make informed decisions, etc. Besides automating hospital administrative tasks, AI implements technologies that improve patient care. However, medical institutions must also address challenges like data privacy and the need for human support while implementing AI in healthcare. Accurate diagnosis, early disease detection, tailored treatments, and prediction of outcomes powered by AI help build a health-smart future by delivering swift and efficient care to people around the globe. In short, AI is truly transforming patient care by enhancing outcomes and survival rates while mitigating expenses for service providers.

AI-Powered Diagnosis & Treatment Enhanced accuracy in diagnoses Faster diagnosis and treatment Tailored treatment plans	REIMAGINING HEALTHCARE MANAGEMENT WITH AI	Personalized Medication Prediction of potential side effects Optimized treatment efficacy Continuous monitoring of medication responses
Virtual Nursing Assistants 24/7 support Medication and appointment reminders Enhanced patient engagement		Real-time Analysis of Patient Data Continuous monitoring Early detection of potential health issues Improved patient outcomes
Predictive Insights Risk prediction Implementing preventive care Personalized prevention strategies		AI Chatbots Patient support Symptom assessment Appointment management

From AI chatbots to personalizing treatments to robotic surgeries, the prospect for AI to revolutionize patient care is vast and exciting.

AI-Powered Diagnosis & Treatment

Detects patterns in patient histories and laboratory results, resulting in more accurate diagnoses, enabling early disease detection, and developing tailored treatment plans.

Personalized Medication

Tailors treatment plans to an individual based on specific attributes like lifestyle elements, medical histories, and genetic profiles, helping identify possible responses to specific medications.

Virtual Nursing Assistants

Offers personalized guidance and support, monitoring remotely in real-time and improving patient engagement to ensure quality care.

Real-time Analysis of Patient Data

Analyzes high volumes of medical data in real-time, resulting in prompt insights for faster decision-making, earlier detection for timely intervention and enhanced results, and discovery of possible safety issues.

Predictive Insights

Identify high-risk patients earlier and deliver personalized recommendations by detecting patterns and anticipating future patient outcomes.

AI Chatbots

Provides services like initial symptom evaluation, medication management, and health support by analyzing the patient's responses regarding their inquiry about symptoms, resulting in effective communication and enhanced healthcare delivery.

How does AI in Healthcare Transform Patient Care?

Given the rising adoption of AI, the healthcare sector is experiencing a profound transformation. With the advancement of AI technologies in healthcare, hospitals can now redefine the boundaries of patient care, allowing healthcare providers to deliver personalized and effective treatment options. Let's look at how AI is revolutionizing the patient care experience.

Supporting Healthcare Professionals through Virtual Assistants

To align with the demand for healthcare services, healthcare experts avail the support of virtual assistants to provide tailored support based on patient queries regarding symptoms, medical advice, medication reminders, monitoring vital signs, and scheduling appointments. Besides, virtual assistants generate routine reports based on patients' health, helping improve patient outcomes and offer a better experience.

Accelerating Drug Recovery & Research

AI expedites drug discovery by analyzing genomic data, clinical trial results, and more to predict drug safety and how they interact with the body. It facilitates the faster detection of effective treatments, leading to quicker medical breakthroughs. Moreover, AI can help tailor treatment plans to patients based on their medical histories, genetic profiles, and lifestyle factors, enabling more effective targeted therapies.

Facilitating Early Disease Detection & Diagnosis

AI analyzes vast amounts of medical data, including images, lab results, and patient histories, to detect anomalies and patterns that may signify the presence of a disease. This can lead to improved accuracy, earlier intervention, and personalized medicine. It also predicts the likelihood of disease progression or response to treatment, enabling healthcare providers to take proactive measures and improve patient outcomes.

Offering Mental Health Support

AI supports mental health by offering personalized and accessible patient care through digital applications. Research has proven the efficacy of AI-powered cognitive-behavioral therapy (CBT) in diagnosing and managing conditions like depression. Such tools offer 24/7 support to track patient progress and improve treatment adherence to ensure effective and empathetic treatment.

Improving Patient Education

AI has become an effective tool for patient education, with AI-powered chatbots being used for diet recommendations, smoking cessation, and cognitive-behavioral therapy. Educating patients about their diagnoses, treatments, and preventive care improves adherence and health outcomes. AI can personalize and simplify medical information, empowering patients to take control of their health. The future of AI in healthcare holds enormous prospects for augmenting diagnostics, treatment, and patient management. The capabilities of advanced AI tools extend to predicting disease progression and improving surgical assistance through remote collaboration. Though challenges exist in integrating AI into clinical practice, the power of AI to enhance outcomes, efficiency, and patient experiences is undeniable. Beinex collaborated with Sultan Qaboos University Hospital (SQUH), University Medical City, and Novartis to combat Atherosclerotic Cardiovascular Disease (ASCVD) using AI and analytics, reimagining cardiovascular health. Beinex adopted a data-driven methodology to manage ASCVD risks, facilitating proactive disease identification and intervention. By employing NLP, BI, Data Engineering, and Data Science and leveraging innovative data analytics and real-time insights, we helped revolutionize cardiovascular disease risk management, improve prevention strategies, and offer personalized patient care in the GCC region. Explore the success story and get in-depth insights into how we redefined healthcare and established new standards with life-saving intelligence. Download the case study titled 'Revolutionizing Healthcare in 2025: How AI is Transforming Hospitals of the Future' to discover how AI disrupts healthcare and builds a health-smart future. Click here: https://beinex.com/artificial-intelligence-in-healthcare-case-study-download

How Alteryx Designer Can Help You

A handy drag-and-drop, code-friendly tool, users of data analytics can easily extract, transform, and load data from virtually any source using Alteryx Designer. Using repeatable workflows, it facilitates predictive, statistical, and geospatial analysis, enabling the generation and sharing of insights within hours.

Key Features of Alteryx Designer

The platform is incredibly reliable and suitable for employing in almost any sector or industry. Major features of Alteryx Designer that you should be in the know of:

1. Go Code- free or Code- friendly
You can use a code-free or code-based interface regardless of your level of coding expertise. Use C++, Python, or R languages to write code in this interface.

2. Lesser Time, Greater Efficiency
Alteryx Designer rapidly extracts and integrates data from many sources producing faster insights that help to make smarter decisions.

3. Automated Workflows
It is possible to automate repetitive workflows or update them as needed to save time by enabling analytics scaling.

4. Easy Integration
Alteryx Designer provides a wide range of software for companies to select according to their requirements. It directly integrates with Alteryx Analytics Gallery, Alteryx Analytics Server, Alteryx Connect, and Alteryx Promote. Integration with R, Python, Tableau, Power BI, SAP, Sharepoint, Salesforce, Github, and Microsoft Azure tools is also made possible.

5. Spatial Analytics
Data analysis based on demographic, firmographic, and geospatial intelligence helps to produce insightful business decisions.

6. Predictive Analytics
Predictive analytics is provided across the entire analytics workflow, and by employing Alteryx Designer, accessing, preparing, and modelling data can be done in a single platform. Sharing the results can be done using the same.

7. Macro
A macro is a group of tools that help to save repeated analytic processes. It saves time by automating repetitive tasks.

8. Assisted Modelling
As a new feature in Alteryx Designer, it enables users to create ML pipelines and make predictions based on historical data.

9. Reporting
With the help of the user-friendly reporting tools in Alteryx, users can produce high-quality data-driven reports. The user can create top-notch reports with text, data, charts, maps, and images using various designs. A variety of output formats, including HTML, PDF, RTF, DOCX, XLSX, and PCXML, are supported by the reporting engine.

10. Alteryx Community
One of the main advantages is Alteryx community support. Let's say you cannot design a workflow or be unsure about how to perform some tasks. The Alteryx community will then be of great assistance to you in providing instant and informative replies.

11. Intelligence Suite
It is quite easy to extract concepts and insights from structured and unstructured data using Alteryx’s Intelligence Suite. Using the sentiment analysis tool, it is easier to identify the emotion hidden in the data and share the insights. The computer vision tool quickly processes huge data sets automatically, and it is possible to play with Data Science with automated Machine Learning tool.

Click here to Download Alteryx Designer Opting for a Free Trial

1. Snowflake Iceberg Tables Now Generally Available

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

2. Advancements in Snowflake Cortex AI

Snowflake introduced several enhancements to Cortex AI, including:

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

3. Introduction of Polaris Catalog

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

4. Private Preview of Snowflake Horizon Updates

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

5. AI & ML Improvements

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

6. Snowflake Native Apps with Snowpark Container Services — Preview

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

7. Developer Tool Enhancements

Several updates aimed at developers include:

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

8. Expanded Cloud Footprint and Governance

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

9. Snowflake Trail

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

10. Snowflake Cortex Fine-Tuning — Preview

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

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

11. Snowpark Python Local Testing Framework — General Availability

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

12. Universal Search and Snowsight Updates

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

13. New Geospatial Functions in Preview

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

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

What is Spatial Analysis?

Spatial analysis is the art and science of extracting insights from data that has a geographic component. Think of it as giving your data a physical address! Traditionally, this involved complex Geographic Information Systems (GIS) software. But today, spatial analysis is more accessible than ever, thanks to data science and machine learning.

Pinpointing locations on a map is just the first step. Spatial analytics goes far beyond that, offering a powerful lens to understand how relative location impacts your business. It allows you to see the bigger picture: how customers, stores, services, and other factors interact with each other geographically.

This magic happens by blending spatial data (think zip codes, store addresses) with your existing data sets (sales figures, customer demographics). By analyzing these combined datasets, you gain a wealth of insights that can transform your decision-making process.

What Spatial Analytics Can Do for You

Here's how spatial analytics can help you understand and optimize key areas:

• • Customer Behavior and Inventory: Analyze nearby consumer buying habits for specific products and services. This allows you to customize inventory and service experiences at each location based on local demand.
• • Strategic Location Planning: Optimize your location strategyby determining how the proximity of competitors or existing locations impacts new site expansion. You can also understand how far customers are typically willing to travel for your product or service.
• • Improved Customer Experiences: Ensure service availabilityand minimize service gaps by strategically locating key hubs within an appropriate distance from each other. This translates to a smoother and more efficient experience for your customers.
• • Targeted Marketing: Drive efficiencies in your marketing programs by customizing your offerings to match demographic purchasing preferences in specific locations. This targeted approach allows you to reach the right audience with the right message.

How Alteryx Enables Data Blending for Spatial Analytics

Forget complex GIS! Alteryx's no-code tools make spatial analysis a breeze, unlocking location intelligence for all data users. Optimize resources, plan assets, manage logistics, and more - all in a user-friendly platform.

Alteryx offers an intuitive workflow that streamlines the entire process:

• • Import and Unify: Easily bring in various datasets, regardless of format.
• • Effortless Geocoding: Transform addresses and other location data into usable geographic coordinates with a few clicks.
• • Spatial Blending: Combine your location data seamlessly with traditional datasets for a holistic view.
• • Advanced Analytics Made Simple: Perform complex spatial analyses without needing specialized coding skills.
• • Data Enrichment: Boost your insights by adding demographic, firmographic, or industry-specific data to your spatial datasets.
• • Visualize and Explore: Discover hidden patterns and relationships through interactive maps and visualizations.

A 6-Step Recipe for Blending Spatial Data in Alteryx

Alteryx makes blending spatial data with your existing information a breeze. Here's a step-by-step guide to get you started:


• • Gather Data: Gather all the data sets you need for your analysis, from customer information to market demographics.
• • Translate Your Addresses: Use Alteryx's geocoding tools to transform addresses and other location data into usable geographic coordinates.
• • Define Your Trade Zone: Create a virtual boundary to analyze specific locations based on radius
• • Blend Datasets Together: Seamlessly combine your spatial data with traditional datasets to create a comprehensive picture of customer-location relationships.
• • Use Advanced Spatial Analytics for Additional Insights: Perform complex spatial analyses within Alteryx's user-friendly interface, unlocking hidden insights without needing specialized coding skills.
• • Visualize and Share Your Findings: Prepare your data for reports and interactive visualizations that effectively communicate your insights. Alternatively, export the data for further analysis or integration with downstream processes.


Find a detailed 6-step guide for blending spatial data using Alteryx:


1. Gather Data

Alteryx's Input tool lets you grab data from anywhere – spreadsheets, databases, even social media! Just connect to your desired sources, and Alteryx will get your data ready for spatial exploration.

2. Turn Addresses into Locations:

The Street Geocode tool in Alteryx quickly transforms your standard addresses (like customer locations or branch sites) into geographic coordinates (latitude and longitude). This "spatializes" your data, adding a new data point for each location. In this example, we'll use it to geocode both customer and site data.

3. Define Your Trade Zone:

The Trade Area tool lets you see what's happening within a specific area around each location. For example, you can create a 10-minute drive time polygon. This "draws" a zone around each location, encompassing all areas reachable within a 10-minute drive using the road network.

4. Blend Datasets Together:

The Spatial Match tool lets you see how different sets of locations relate to each other. For instance, you can use it to find out how many customers live within (or outside) the 10-minute drive time zone you created for each location. It essentially compares your customer data points (spatial points) with the trade area polygons (spatial objects) to identify matches based on spatial relationships (like "contains" or "intersects").

5. Use Advanced Spatial Analytics for Additional Insights

Alteryx offers a range of additional tools for advanced spatial analysis, making it accessible to users beyond data specialists. Additional tools include:

6. Visualise and Share Your Findings:

Alteryx doesn't just help you crunch data - it enables you to share your insights clearly.

Visualize Your Success: Overlay data on detailed maps or satellite imagery using advanced mapping tools.

Spread the Knowledge: Export your analysis in various formats like Excel, ESRI, or even Tableau and Qlik for seamless integration with other data workflows and presentations.

Beinex + Alteryx Offerings

As a Premier Alteryx partner, we have extensive experience and a proven track record of success. Our team is highly skilled in Alteryx solutions and can help you unlock the full potential of this powerful platform.
Contact us today to learn more about how Alteryx and our partnership can take your business to the next level.
Image Source: https://community.alteryx.com/pvsmt99345/attachments/pvsmt99345/general-discussions/2303/2/Spatial_Cookbook_Victa.pdf