THE KEY FEATURES OF TABLEAU 2019.1

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How AI Decision-Making is Improving Enterprise Outcomes

The Key Features of Tableau 2019.1

I’ve always been a huge Tableau fan and one of the biggest reasons why is that I can always count on Tableau for bringing out new exciting features in every release. With new quarter, Tableau has yet again released a new version after a thorough research based on people’s experience, thoughts and comments. As a result, Tableau 2019.1 has plenty of new and insightful features amongst which is a feature called Ask Data which will make our life so much easier! There are so many other new features which everyone will appreciate, such as Prep Conductor, Automatic Dashboard Phone Layout, URL Action improvements, Nested Sorting improvements, connector for Microsoft Azure SQL data warehouse and many more! Here are my top picks from the list. Ask Data Are you not a data analyst but still love to play with data? You have a question in mind but don’t know how to get to the answer from all the data? Just Ask Data! Ask or type the question in your natural language and Tableau will not only give you the answer but also visualize it in a stunning way! We thought this one deserves its own blog with detailed information and so we decided to share one with you, keep your eyes open as it is coming soon to our blog! Prep Conductor Keep your data fresh with Prep Conductor. Tableau Prep is helping organizations with preparing and sorting their data easily. This new addition allows you to automate this process on Tableau Server or Online. You can schedule and manage your data flows created on Prep. It can be seamlessly integrated to your existing schedules of Server or Online. You can manage the permissions to your flow regarding who should run, edit, download and so much more. Nested Sorting Thinking that sorting a hierarchical field would make your work easier? It is the most talked about and wanted feature and Tableau 2019.1 has got you covered! With this feature, you can visually define how you want to sort within multiple dimensions helping to capture the trends in categorical data bringing out wonderful insights. Mobile View Everybody is using smart phones. For everything. Everywhere. Period. Tableau knows it and continues to create more phone friendly features. Now you can create customized mobile layouts for your dashboards even if it’s not listed in default options. This comes in handy when your phone is not listed in the model. Tableau 2019.1 brings to its users customized phone layout for specific uses including interactive offline previews and automatically adding phone layouts to new dash board. Now, others can view your visually stunning creative dashboards via their phone in a sophisticated way. URL Improvements Decide how you want the URL action to work with the new URL improvements in Tableau 2019.1. Target specific URL actions enable the user to control the view of any external information linking to your dataset. Develop intuitive, integrated and innovative dashboards by connecting to all the necessary information outside your data source. Microsoft Azure SQL Data Warehouse Connector Be it on cloud or on premises, Tableau keeps on adding more data connections to make it easy for users to leverage the native way of connecting. This came up with the connector to Microsoft Azure cloud service in the current release. This helps in avoiding ODBC in making connections to the data stored in Microsoft Azure Data warehouse. I’m very excited about all these new features, what about you? Are you interested in learning more about Tableau’s new features or the features that are yet to come in the next versions? Are you a Tableau user and would like to become a Tableau Champion? Or are you new to Tableau and it just seems like something you could use to work with your data? 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.

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

AWS and GRC Framework: A Comprehensive Approach to Secure and Compliant Data Management

AWS and GRC Tools

Amazon Web Services (AWS) provides a comprehensive set of GRC tools and practices that can be integrated into the Governance, Risk Management, and Compliance (GRC) framework that empower businesses to effectively manage their data while adhering to industry standards and regulations. Below are some examples of AWS and GRC (Governance, Risk, and Compliance) tools and practices:

Governance: Establishing a Solid Framework

In the AWS ecosystem, governance refers to setting up policies, guidelines, and processes to ensure effective data and resource management. This framework supports business objectives while promoting ethical practices, transparency, and adherence to regulatory requirements. AWS provides several GRC tools and practices that centralize governance and enhance control over accounts, resources, and configurations. • AWS Organizations: This tool helps organizations manage multiple AWS accounts in one place, allowing for the easy implementation and enforcement of policies across the enterprise. Through Service Control Policies (SCPs), organizations can control permissions and ensure only authorized actions occur. • AWS Config: AWS Config is essential for governance as it tracks changes in AWS resource configurations. By providing detailed insights into the state and relationships of these resources over time, AWS Config ensures compliance with internal and external standards. • AWS Service Catalog: The AWS Service Catalog allows organizations to maintain oversight of their IT environment by managing catalogs of approved services. This guarantees that only pre-approved and compliant software, databases, and configurations are deployed. • AWS Control Tower: AWS Control Tower simplifies setting up and managing a secure, multi-account AWS environment. It automates governance best practices and continuously monitors compliance with pre-configured guardrails.

Risk Management: Safeguarding Against Threats

Identifying and managing risks is crucial in today's digital landscape. AWS offers a range of services to help organizations monitor their infrastructure, detect potential vulnerabilities, and respond to security threats swiftly. • AWS CloudTrail: CloudTrail logs and monitors API activity within AWS, allowing organizations to trace user actions and conduct investigations after security incidents. It’s also useful for forensic analysis to determine the cause of issues. • AWS Security Hub: As a centralized security dashboard, AWS Security Hub aggregates findings from multiple AWS services, helping businesses streamline incident response and maintain compliance. • Amazon GuardDuty: GuardDuty is a security service that detects suspicious activities and unauthorized behavior in AWS accounts. By leveraging machine learning and threat intelligence, it helps organizations preemptively address potential risks. • AWS Config Rules: These customizable rules automatically check resource configurations for compliance. If a resource deviates from the expected settings, alerts are triggered, enabling proactive issue resolution.

Compliance: Ensuring Adherence to Regulations

Compliance is vital for any organization handling sensitive data. AWS simplifies the process with a variety of tools to help organizations meet regulatory requirements and reduce audit burdens. • AWS Artifact: AWS Artifact offers on-demand access to important compliance documents, such as SOC reports and certifications like ISO and PCI DSS. This is especially helpful during audits, providing immediate access to evidence of AWS's adherence to standards. • AWS Shield & AWS Web Application Firewall (WAF): These services protect against DDoS attacks and unauthorized access attempts, ensuring web application availability and data integrity. Shield and WAF work together to filter out malicious traffic and ensure the security of incoming requests. • AWS Key Management Service (KMS) & AWS Certificate Manager (ACM): Data encryption is critical for compliance, and AWS KMS helps manage encryption keys securely. ACM automates the deployment of SSL/TLS certificates, protecting data both in transit and at rest. • AWS Audit Manager: AWS Audit Manager automates evidence collection for audits, continuously assessing your environment against industry standards. This reduces manual effort and ensures that organizations remain consistently compliant. AWS’s robust GRC framework empowers organizations to effectively manage data, mitigate risks, and ensure regulatory compliance. Leveraging AWS’s advanced tools not only supports meeting industry standards but also fosters innovation, resilience, and future-readiness within businesses.

Beinex: AWS Partner for the Middle East

Beinex is an AWS consulting partner, and we empower customers with AWS managed services to host their BI solutions and much more on the cloud. Our cloud migration experts bring in best-in-class stability and reliability by understanding your business strategy and working closely with you to deploy AWS infrastructure as a service. Connect with Us for a Free Demo Interested in our services? Try AWS for free: https://beinex.com/beinex-amazon-web-services/

BEINEX CONSULTING WINS ALTERYX 2020 PARTNER OF YEAR AWARDS, MIDDLE EAST

Beinex Consulting has been awarded as the Alteryx 2020 Partner of the Year, Middle East at the Alteryx Partner Summit and Awards virtual event along with 15 other winners from North America, LATAM, and EMEA.

During the Alteryx Summit, ‘Your Road to Revenue’, Alteryx celebrated the achievements and commitment of their partners to the Alteryx business and its customers. Beinex Consulting was awarded on the level of engagement in the Alteryx partner program and its efforts around driving innovation, growing revenue, and empowering Alteryx customers to solve our world’s most pressing business and societal issues in the Middle East Region. Selected among top Middle East Alteryx partners, Beinex demonstrated excellence in delivering end-to-end analytics transformation services that revolutionised multiple industries in the Middle East. Beinex Consulting Founder and Managing Director, Indumon Das indicates further growth for the digital transformation organisation soon: “Beinex continues to make strategic investments to enhance our association with Alteryx and clients in major Middle East markets. This award is a recognition to our continuous growth strategy and focus to be the best Middle East partner” “Through their ongoing pledge to the Alteryx Partner Program, our partners have demonstrated their commitment to helping Alteryx customers break down barriers and deliver game-changing insights.” – Josh Lewis, VP, Global Channels, Alteryx About Beinex Consulting Beinex is a digital transformation organization with a broad range of analytics modernization and training services. As a pioneer in analytics and cloud transformation, Beinex’s mission is to transform the way individuals and the organizations work with the data through innovation and experience. Beinex offers a broad range of robust and scalable business intelligence and analytics services to drive effective decision-making and create business value.

We are thrilled to recognize Beinex Consulting for being named Alteryx Middle East Partner of the Year!https://t.co/xwmp7HbsMp #TogetherWeSolve pic.twitter.com/4zic9mdlgD
— Alteryx (@alteryx) October 1, 2020

Employee Churn Prediction Model: Experience Certainty in HR Matters

Many businesses make a concerted effort to create work cultures that increase job happiness, encourage people to find meaning and satisfaction in their work, reward and recognize employees for their actions, and foster both personal and professional growth.

While many tactics are adopted for the company’s benefit and to considerably increase retention, leaders cannot rely only on them. They must face the uncomfortable reality that they will eventually lose significant talent if they do not keep an open mind and a realistic outlook on the future. Influential leaders act daily to safeguard themselves, their teams, and their companies from the risk of attrition because wishful retention thinking is not a viable business strategy.

Is it possible to foresee attrition so that it only impacts the business a little less, given that it is impossible to stop employees from leaving? Well, with the help of technology, it is possible. The churn model, among others, can help in this situation. Are you wondering how? Through an employee churn prediction model, we can make it happen. After understanding which employees are on the verge of leaving using the churn model, it is possible to reach out to them and understand their grievances.

Employee Churn Prediction Model

It's a predictive model that calculates the likelihood (or vulnerability) of each employee leaving. It tells us how likely we will lose employees or a specific employee in the future at any given time. It classifies employees into two groups (classes): those who quit and those who don't. It will typically tell us the probability of the employee belonging to which of the groups in addition to placing them in one of the two groups. Thus, a churn model can be used to estimate the chances of resignation.

Explaining the Model

Modern churn models frequently draw their foundation from machine learning, more specifically from binary classification methods. There are several of these algorithms; therefore, it's important to test which one works best in each circumstance. Here we have made use of four machine learning models:

Random Forest

Supervised machine learning algorithms like random forest are frequently employed in classification and regression issues. On various samples, it constructs decision trees and uses their average for classification and majority vote for regression.

The Random Forest Algorithm's ability to handle data sets with both continuous variables, as in regression, and categorical variables, as in classification, is one of its most crucial qualities. In terms of classification issues, it delivers superior outcomes.

KNN

One of the simplest machine learning algorithms, based on the supervised learning method, is K-Nearest Neighbour. The K-NN algorithm assumes that the new case and the existing cases are comparable, and it places the latest instance in the category that is most like the existing categories.

A new data point is classified using the K-NN algorithm based on the similarities after storing all the existing data. This means new data can be quickly and accurately sorted into a suitable category using the K-NN method. Although the K-NN approach is most frequently employed for classification problems, it can also be used for regression.

Decision Tree

The supervised learning algorithms family includes the decision tree algorithm. The decision tree technique, in contrast to other supervised learning methods, can handle classification and regression issues.

By learning straightforward decision rules derived from previous data, a Decision Tree is used to build a training model that may be used to predict the class or value of the target variable (training data).

Support Vector Machine

One of the most well-liked supervised learning algorithms, Support Vector Machine, or SVM, is used to solve Classification and Regression problems. However, it is employed mainly in Machine Learning Classification issues.

The SVM algorithm's objective is to establish the best line or decision boundary that can divide n-dimensional space into classes, allowing us to quickly classify new data points in the future. A hyperplane is a name given to this optimal decision boundary.

SVM selects the extreme vectors and points that aid in creating the hyperplane. Support vectors representing these extreme instances form the basis for the SVM method.

A Step-By-Step View of the Process

Step 1: Loading data to databricks

In the initial stage, CSV data collected are loaded to the churn model. Any type of data sets can be employed here depending on the situation.

Step 2: Transformation: converting to the requisite format

While uploading, objective data sets are transformed into integers.

Step 3: Feature selection

There are four feature selection algorithms from which we take the best one to filter out undesired features. The selection of filtering features differs for each type of data based on the algorithm.

Step 4: Splitting the data

After the feature selection, the next step is to split the data for training and testing—then divide the data into a 7:3 ratio. In the training set, we train our model with data to understand the attrition patterns and later test it with data in the testing set.

Step 5: Standardisation

In this step, data is converted into a standard format that allows for large-scale analytics.

Step 6: Model selection

During model selection, datasets are provided to the machine algorithms like Random Forest, KNN, Decision tree and Support vector machine. Each algorithm produces its own sets of accuracy values; from that, the most accurate predictions are selected. Using the same procedure, we can categorize the employees into groups, for example, those who are planning to resign and those who are not.

Step 7: Result generation

The result is built on how each machine learning model performs with the dataset. The accuracy value depends on the performance of each model—the higher the accuracy, the higher the probability of accurately predicting the outcomes for each employee.

Building Smarter Dashboards with Tableau's AI Features

The Rise of AI-Driven Analytics

Artificial intelligence (AI) has transformed the analytics landscape, enabling organizations to automate complex tasks, identify patterns, and predict future trends. Tableau's AI-powered capabilities are designed to augment human intelligence, streamlining the analytics process and uncovering new opportunities.

Tableau's AI Features: A Brief Dive

1. Tableau Pulse: Powered by AI, Tableau Pulse offers contextual, personalized, and intelligent insights at your fingertips. It redefines data experience by making data accessible to everyone despite their expertise in data visualization tools. The Insights Platform within Tableau Pulse automates the identification of contributors, trends, drivers, and outliers for relevant metrics and streamlines and democratizes data analysis. Tableau Pulse's Metrics Layer lets KPIs and key metrics and KPIs be defined and utilized consistently across the enterprise, creating a unified source of truth for decision-making. Tableau Pulse also offers a Next-Gen Experience by offering intuitive, user-friendly metrics directly into the tools users already work with, such as email or the Tableau web app, enabling users to engage with data in real-time and effortlessly scale insights.

Real-World Application: Employing Tableau Pulse in the supply chain helps track and monitor shipping times, inventory levels, and supplier performance in real time, facilitating informed and proactive decision-making. 2. Tableau Agent: A conversational AI assistant, Tableau Agent facilitates the data journey for new analysts using generative AI to expedite the analysis process from data collection to exploration. Tableau Agent is built on the Einstein Trust Layer, ensuring enterprises' data remains safe and accessible. It streamlines the stages of analysis by effortlessly creating calculations using natural language, making the process easier and faster for technical and non-technical users. It also creates AI-generated descriptions for data sources to convey their objective and essence consistently and informatively. Real-World Application: In dealing with customer engagement, Tableau Agent helps personalize marketing strategies and campaigns to augment customer interaction based on customer preferences and behavior. 3. Einstein Copilot for Tableau: Quickly identify trends and explore data at every analysis phase with the reliable AI assistant, Eintein Copilot for Tableau. It doesn't matter if you are a novice or a seasoned analyst, as Einstein Copilot for Tableau enhances your Tableau experience by making analytics accessible to facilitate actionable insights and informed decisions. With this feature, you can easily produce AI-generated descriptions of data assets and explore data utilizing natural language directly from Tableau Cloud Web Authoring. Real-World Application: Utilizing Einstein Copilot streamlines data analysis for business users by offering instant and expert-level insights to the questions asked without needing any technical expertise in data analysis. 4. Tableau Explorer: This feature employs machine learning algorithms to automatically identify key insights, patterns, trends, and correlations within your data. Discovering hidden data insights with AI offers intelligent recommendations for charts, dashboards, visualizations, and dashboards. Further, Tableau Explorer generates data-driven narratives, emphasizing key findings clearly and concisely. It enables users to grasp complex data insights and make informed decisions quickly. Real-World Application: Using Tableau Explores optimizes operational efficiency by analyzing production data, facilitating the identification of areas of process improvement, and reducing costs. 5. Predictive Analytics: Tableau's predictive models enable you to forecast future outcomes, identify potential risks, and optimize business strategies. Regression analysis identifies relationships between variables while clustering groups similar data points for segmentation. Decision trees visualize complex decision-making processes, making understanding and optimizing outcomes easier. Real-World Application: Predictive Analytics develops predictive models to forecast revenue and sales, allocate resources efficiently, and analyze data to check for potential setbacks. 6. Natural Language Processing (NLP): Tableau's NLP capabilities allow users to ask questions and receive answers in natural language, simplifying the analytics process. Tableau's Ask Data feature enables users to ask queries in everyday language and get answers instantly. The Explain Data feature automatically describes data patterns and trends and patterns, improving understanding and insight. Besides, text analysis facilitates sentiment analysis, entity recognition, and topic modeling, unlocking valuable insights from unstructured data. Real-World Application: NLP facilitates the sentiment analysis of customer feedback, that is, analyzing the data from reviews, surveys, and social media to understand customers' sentiments and base decisions accordingly. 7. Automated Data Preparation: Tableau's AI-driven data preparation streamlines data cleaning, joining, and aggregation, freeing up valuable time for analysis. The AI-powered algorithms help identify and rectify errors, propose optimal data formats, and detect inconsistencies and missing values. It ensures data accuracy, completeness, and trustability, allowing users to concentrate on higher-level analysis and decision-making. Real-World Application: Automating data preparation for financial reports simplifies the process by reducing manual effort and enhancing the accuracy of results. Organizations across various industries have successfully leveraged Tableau's AI features to: 1. Enhance customer experience as predictive analytics helps identify customer churn patterns. 2. Optimize operations as automated data preparation streamlines supply-chain management. 3. Drive revenue growth as AI-driven insights uncover new business opportunities.

Dashboards that Talk: Revolutionizing Business Insights

Interactive dashboards that engage with you have revolutionized how businesses interact with data, ushering in an era of conversational analytics. Tableau, a pioneer in data visualization, has been at the forefront of this revolution, empowering organizations to unlock hidden insights and drive informed decision-making. With Tableau's natural language processing (NLP) capabilities, users can ask questions and receive answers in a conversational format, eliminating the need for tedious data analysis. This seamless interaction lets stakeholders focus on strategic decision-making rather than getting bogged down in data intricacies. Furthermore, Tableau's automated insights and predictive analytics capabilities allow businesses to forecast trends, identify potential risks, and optimize operations, giving them a competitive edge in today's fast-paced market. The implications of interactive dashboards extend far beyond data analysis, transforming the very fabric of business operations. Executive dashboards can now provide real-time strategic guidance, while sales dashboards can predict performance and identify areas for improvement. Operational dashboards can monitor processes in real time, enabling swift corrective action. Moreover, customer service dashboards can deliver personalized support, fostering enhanced customer experiences. As AI and NLP continue to advance, the possibilities for talking dashboards will only expand, enabling businesses to tap into unprecedented levels of insight and innovation. By embracing these cutting-edge technologies, organizations can unlock new avenues for growth, efficiency, and competitiveness, ultimately redefining the future of business intelligence. With Tableau at the helm, the era of conversational analytics has truly begun, and the potential for transformation is vast.

Building Smarter Dashboards

To harness the full potential of Tableau's AI features, consider the following best practices: 1. Define Clear Objectives: Align your dashboard with key business goals and objectives. 2. Select Relevant Data: Focus on high-quality, relevant data to ensure accurate insights. 3. Leverage AI-Driven Insights: Use Tableau Explorer and predictive analytics to uncover hidden trends. 4. Design Intuitive Visualizations: Create clear, concise dashboards that facilitate decision-making. In today's data-driven landscape, building smarter dashboards is critical for business success. Tableau's AI features empower organizations to unlock hidden insights, drive informed decision-making, and stay ahead of the competition. By embracing these cutting-edge capabilities and following best practices, IT professionals, business leaders, and data analysts can create dashboards that propel business growth and innovation.