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

5 Steps in Alteryx Predictive Analytics Process 

The five major stages of the predictive analytics process cycle include selecting a target variable, examining the data, collecting the data, creating the model, and scoring the model.

A detailed description of the steps involved in the predictive analytics process in Alteryx:

• Step 1: Select a Target Variable
• Step 2: Analyse Your Data
• Step 3: Run Calculations/ Collect New Data
• Step 4: Model Building
• Step 5: Score the Model

Step 1: Select a Target Variable

Select the target variable which is the column that should be predicted. It could be a binary or non-binary categorisation or a numerical value and it can be continuous or time-based. Each of these target variables helps in finding business solutions. But just because time is a variable in the problem does not mean that a time-based model will be the best way to solve it. Simultaneously if a field has a numeric value, it does not mean that a binary model cannot be utilised in finding insights.

Step 2: Analyse Your Data

The largest contributor to excellent predictive models is the sample size. Anything less than 5000 records is counted as under-sampled and using it is not considered the best practice.
Alteryx and Tableau Prep are both excellent tools for understanding data by creating histograms, scatterplots, and correlation matrices. Before step 3, in the data transformation procedure, it is better to know what types of variables are in the data. There are various sorts of predictor variables and several types of target variables, and each must be structured differently.

1. Categorical Data: String fields with no order are categorical data. It contains data in the form of text.
2. Ordinal Data: String fields with an order are ordinal data. It can be substituted into numeric order in a predictive workflow.
3. Numeric Data: It represents information with a measurable value.
4. Cyclical Information: Data which gets repeated as such in a cyclic process is cyclic information.

Step 3: Run Calculations/ Collect New Data

Obtaining the greatest data or inferring fields from present data, such as adding seasonality, can be a powerful predictor variable. Always be inventive in the choice of variables. It is crucial to note that if there is to infer a piece of data, it is sometimes unwise to include both that data and the original data column in the same model because the predictive model would automatically give higher weight to this column. It is also critical to recognise that while it is beneficial to include factors with correlation, variables that drown out all other variables must occasionally be removed.

Step 4: Model Building

a. Make Use of the Decision Tree
Using a Decision Tree, it is possible to rapidly discover which of the factors are the most crucial for predicting the target variable. This model will not be utilised in the final forecast since it will over-fit, but it will show whether some of the variables are overly connected to the target variable.

b. Experiment with Different Models
Data Science is complicated, and it is difficult to know which model will yield the best results, therefore a variety of models, such as Random Forest, Boosted Models, and Neural Networks can be employed for better results.

Step 5: Score the Model

Alteryx offers a scoring tool that may be used to score models. During this step, data should be withheld for the model to test and score. Even though different models can provide different scores, through testing and reconfiguring, accurate predictions can be made.

What makes Alteryx an exceptional tool for predictive analytics?

These remarkable capabilities make Alteryx an excellent tool to carry out predictive analytics tasks easily:

• • No or Low coding required
• • Predictive analytics by drag and drop
• • Predictive tool kit for specifically performing predictive analytics
• • Integration to R and Python
• • Variety of built-in and custom ML models are available
• • Model customizations are possible
• • Automation and/or scheduling of predictive analytics workflows

Predictive Analytics Tools

Predictive analytics solutions use the power of data to help businesses in identifying trends in customer behaviour, making predictions, and developing optimised marketing plans.

The tools that aid in predictive analytics are enlisted below:

1. Data Investigation Tools
2. Predictive Tools
3. Tools for the Modern Statistical Learning Method
4. Tools for Predictive Model Comparison and Hypothesis Testing
5. Tool for Predicting Values for All General Predictive Modeling Tools

1. Data Investigation Tools

Data investigation tools contain tools that help to get a better understanding of data. To better understand the data used in a predictive analytics project including both visualization tools and tools that provide tables of descriptive statistics.

The list of data investigation tools is given below:

1. Field Summary Tool
2. Heat Plot Tool
3. Histogram Tool
4. Plot of Means Tool
5. Scatterplot Tool
6. Violin Plot Tool

2. Predictive Tools

This category contains general predictive modelling tools for classification and regression models, and also tools for predictive modelling related to model comparison and hypothesis testing.

Predictive tools are enlisted below:

1. Count Regression Tool
2. Gamma Regression Tool
3. Linear Regression Tool
4. Logistic Regression Tool
5. Naïve Bayes Classifier Tool
6. Neutral Network Tool
7. Stepwise Tool
8. Support Vector Machine Tool

3. Tools for the Modern Statistical Learning Method

1. Boosted Model Tool
2. Decision Tree Tool
3. Forest Model Tool
4. Spline Tool

4. Tools for Predictive Model Comparison and Hypothesis Testing

1. Cross-Validation Tool
2. Lift Chart Tool
3. Model Coefficients Tool
4. Model Comparison Tool
5. Nested Test Tool
6. Test of Means Tool
7. Variance Inflation Factors Tool

5. Tool for Predicting Values for All General Predictive Modeling Tools

1. Score Tool

6. Time Series Tools

1. ARIMA tool
2. ETS tool
3. TS Compare Tool
4. TS Covariate Forecast Tool
5. TS Filler Tool
6. TS Forecast Tool
7. TS Forecast Factory Tool
8. TS Model Factory Tool
9. TS Plot Tool

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

Benefits of AWS Cost Optimization

Optimizing AWS costs is a vital part of effectively utilizing AWS services. Businesses can leverage the wide array of AWS services and pricing models to learn how to optimize costs and save considerable amounts of money. AWS Cost Optimization offers several key benefits to organizations looking to manage and reduce cloud expenses while maximizing efficiency. Let's take a look at the benefits of cost optimization with AWS: Enhanced flexibility: AWS offers flexible and cost-effective purchase plans like Reserved Instances or Amazon EC2 Spot Instances, enabling businesses to select the suitable pricing models that best fit their requirements. Boosted efficiency in resource utilization: Allocate resources effectively by rightsizing your instances, identifying the optimal size for your compute workloads, selecting the right data storage and transfer options, and eliminating unused resources. This prevents overprovisioning and ensures optimal usage of available services. Increased cost savings: AWS identifies unused resources and helps businesses scale down their infrastructure, reducing cloud costs and saving big. AWS cost optimization also ensures that you pay only for what you need, enhancing the efficiency of your cloud operations. Improved budgeting and forecasting: AWS Cost Optimization gives businesses greater visibility into spending patterns, enabling them to forecast costs and create accurate and realistic budgets based on seasonal trends, historical usage patterns, and business growth projections. Increased scalability: By employing cost optimization strategies, businesses can seamlessly scale resources on demand and pay for the requirement only when needed. Optimizing costs for seasonal fluctuations, changing workloads, and business growth ensures scalability in your AWS environment.

A Quick Look into AWS Cloud Cost Optimization Tools

AWS Cost Explorer: A tool that offers insights into the cost and usage of AWS services by giving a detailed picture of AWS spending. Its benefits include: • Identifying areas where costs are optimized • Forecasting future costs • Planning and budgeting AWS costs AWS Budgets: A tool that allows users to create custom cost and usage budgets for AWS services. It enables users to take corrective action by sending alerts when cost and usage thresholds exceed. Its benefits include: • Handling AWS spend by setting and monitoring budgets • Avoiding unforeseen expenses and cost overruns • Providing suggestions for cost optimization AWS Trusted Advisor: A tool offering tailored recommendations for optimizing AWS costs, performance, and security. It is free of charge for all AWS users. Its benefits include: • Delivering actionable insights from usage data • Offering personalized recommendations from usage data • Identifying areas where costs can be lowered AWS Pricing Calculator: A tool that helps users get estimates before choosing a service and enables the assessment of calculations based on the estimates. Its benefits include: • Identifying possible areas for cost-saving • Planning budget effectively • Providing insights into the costs you expect for a service

Optimizing Cloud Costs with AWS Cost Explorer

A robust tool that enables users to visualize, analyze, and monitor AWS costs, AWS Cost Explorer offers a granular view of your AWS spending and usage. It allows you to track your spending over time, detect cost trends, and investigate specific resources to understand where the money is spent. Cost Explorer provides a detailed report on cost, usage, and Reserved Instances and a main graph that helps you drill down into costs and usage. Users can access data for up to the last thirteen months, get suggestions for RI purchases, and predict expenses for the next twelve months. Besides, Cost Explorer offers insights into user spending by identifying areas for examination. Users can also set up preconfigured views to track overall cost trends while customizing views to align with user requirements. AWS Cost Explorer has some incredible features that assist you in comprehending and managing your spending. Data Exploration: Access AWS Cost Explorer directly from the Billing and Cost Management console. With the ability to filter and group data by dimensions such as service, usage type, region, and tags, you can dive deep into specific areas and identify key cost drivers. Report Generation: Build custom reports to monitor your costs and usage trends over time. These reports can be saved and scheduled for regular updates, offering continuous insights into your spending patterns. Costs Forecasting: Leverage Cost Explorer's forecasting feature to predict future expenses based on historical data. The predictive faculty supports better budget planning and more efficient resource allocation. Businesses that do not optimize costs can face higher costs, inefficient use wastage of resources, and severe impact on profit. Therefore, AWS cost optimization is essential for utilizing cloud resources efficiently and cost-effectively, potentially saving money for businesses in the long run. The benefits of cost optimization include lowering costs, offering predictability, preventing unforeseen expenses and cost overruns, and enhancing resource utilization, which drives business growth and sustainability.

I’ve always been a huge Tableau fan as they never fail to impress me by bringing out new exciting features in every release. Tableau has yet again released a new version after a thorough research based on people’s experience, thoughts, and comments. As a result, Tableau 2020.3 has an exciting feature with respect to Predictive modelling functions, especially useful to advanced analytics geeks. There are so many other new features which everyone will appreciate – such as IN operator for calculations, Search improvements in data pane, Data quality warnings on Tableau Server and many more! Here are my top picks from the list. The IN Operator The much-awaited feature for the SQL lovers. The IN operator is a shorthand replacement for multiple OR functions. By using IN operator, we will be able to create concise calculation fields instead of never-ending lines of ‘OR’. Let’s look into a simple use case by assuming that I am interested to know the sales value of ‘Apple Products’ compared to all other products, then I can use IN operator to with IF statement to group the products to “Apple Product” and “Other Products” IF [Product Name] IN (‘Apple iPhone 5′,’Apple iPhone 5C’,’Apple iPhone 5S’) THEN “Apple Products” ELSE “Other Products” END Predictive Modelling Function Now the predictive genes would be happy to see more features on the prediction module of Tableau. With the added advantages of the functions MODEL_QUANTILE and MODEL_PERCENTILE, now the prediction function goes beyond the existing trendline analysis. Both the functions will help to build a model that understands how your data is distributed around a best-fit line. MODEL_QUANTILE is a table calculation that returns a target value at a specified percentile, based on other predictors MODEL_PERCENTILE is a table calculation that returns the probability of the expected value being less than or equal to the observed mark, based on other predictors Search improvements in Data pane While developing business specific dashboards, we need to create several calculations and its needs to be adjusted or new calculations needs to be created from existing calculations as per the business unit’s requirements. With the newly added Search improvement feature, our lives are made easy by searching or filtering specific filed based on field name, type, or comments. Search improvements in Data pane Write to external databases in Tableau Prep Write to external databases in Tableau Prep The wow moment for prep is here with the introduction of the ability to output to a database. In all previous versions of Prep, we have been able to write to an extract file or csv, and now from the latest 2020.3 introduces the ability to output to a database. Currently supported databases are SQL Server, MySQL, PostgreSQL, Amazon Redshift, Snowflake, Oracle, and Teradata. As shown in the below example, result from the flow can be saved to a data base table from the output step by choosing “Database table” option, specify your server connection (with login credentials) , choose the data base and then the table. And now, Write to database will power the analytics journey by helping to solve the issues related to, data sources, data security and data governance Open or Upload Workbooks On The Web within Tableau Server Sharing your work and exploring insights from others just got easier. You can now open a Tableau workbook or upload it straight to the web without having to use Tableau Desktop. Simply select the workbook (.twb or .twbx) you want to upload and publish directly to your site on Tableau Server or Online. Only users with the appropriate publishing permissions will have the ability to upload content. Open or Upload Workbooks On The Web within Tableau Server Three refresh options are available, they are Earlier prep was used mainly for Set Data Quality Warnings on Tableau Server Data quality warnings make it possible to let users know when a data asset is flagged, whether because the data is under maintenance, it’s stale, or some other reason. With high visibility data quality warnings, not only let users know about the issue but make sure they do not miss it. Are you interested in learning more about Tableau’s new features or the features? Are you a Tableau user and would like to become a Tableau Champion? If the answer to the above questions is a big “YES”, 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.