Budgeting Made Easy: Beinex Budget Allocation Accelerator (Infographic)

With AWS Glue DataBrew, we can transform and prepare datasets from Amazon Aurora and other Amazon Relational Database Service (Amazon RDS) databases and upload them into Amazon S3 to visualise the transformed data on a dashboard using Tableau.

Here is how to do it:
With AWS Glue DataBrew, we can:
1. Transform and prepare datasets from:
a. Amazon Simple Storage Service (Amazon S3)
b. Amazon Aurora
c. Amazon Relational Database Service (Amazon RDS) databases
2. Upload them into Amazon S3
3. Visualize the transformed data on a dashboard using Tableau

Method:
1. You can create a JDBC connection for Amazon Redshift and a DataBrew project on the DataBrew console.
2. DataBrew queries data from Amazon Redshift by creating a recipe and performing transformations.
3. The DataBrew job writes the final output to an S3 bucket in Tableau Hyper format.
4. You can now upload the file into Tableau for further visualisation and analysis.

Result: Creation of predictive dashboards on top of the S3 bucket

About AWS Glue DataBrew
AWS Glue DataBrew is a tool for data analysts and scientists that simplifies cleaning and standardising data to prepare it for machine learning and analytics.

Why is marketing optimization important?

Marketing Optimization is a continuous process that intends to increase your ROI and refine strategies by analyzing accessible data from marketing channels and granular data of ads and campaigns and visualizing them in one place. In short, it covers collecting data, analyzing it, and initiating action. The process of optimization includes: • Gathering data
• Analyzing data for intelligent insights
• Making strategic decisions about the campaigns and ads
• Repeating the process on a routine basis
For marketing campaigns to be successful, businesses need to target suitable customers and deliver personalized experiences. Optimizing the campaigns can make customers more likely to respond positively. The following points stress why the optimization of marketing campaigns is important. • Ensures the money is spent effectively to maximize ROI
• Delivers personalized campaigns to boost customer engagement.
• Makes the most of data-driven insights to prioritize initiatives, develop better strategies, and improve decision-making.
• Optimizes resource allocation based on the campaign results
• Evaluates and modifies strategies to enhance campaign performance

Optimizing Marketing Campaigns with Alteryx

Customers prefer personalized experiences and are more connected and empowered than before, making it challenging for Chief Marketing Officers to balance customer engagement across multiple channels. Despite the rising significance of data-driven marketing, many marketers find it hard to employ analytics effectively. It has become imperative for marketing teams to integrate diverse data sources, enhance customer insights, and create intuitive, cost-effective workflows beyond traditional tools. Alteryx, the drag-and-drop and end-to-end analytics platform, harnesses data-driven insights and innovative techniques to reach the right audience and boost marketing efforts. Alteryx provides tools that help businesses leverage advanced analytics to comprehend customer behavior, measure ROI, optimize campaigns and costs, analyze campaigns' efficiency, segment customers, and optimize marketing spend. Alteryx empowers marketers to recognize high-value prospects and tailor strategies accordingly to increase conversion rates and drive revenue growth. Let's take a look at the two major aspects of Alteryx that optimize marketing campaigns for maximum efficiency and impact: • Real-Time Analytics Alteryx facilitates quick and more informed decisions by offering up-to-date data, allowing businesses to modify strategies in real time based on the current performance metrics and make necessary adjustments to the campaigns. The real-time data enables the monitoring of KPIs, which helps promptly detect any issues or possibilities that come up during a campaign. The rapid responsiveness of Alteryx retains the agility and efficacy of your marketing efforts, allowing businesses to respond quickly to the dynamic nature of the market. For continuous tracking and optimizing campaigns, businesses must leverage Alteryx's real-time analytics to enhance process efficiency, gain faster results, and stay competitive. • Data-driven Decisions Alteryx maximizes the full potential of your data to make better and more informed decisions. Businesses can get a comprehensive audience perspective by unifying customer data from diverse sources, which improves customer segmenting and targeting. The advanced analytical faculty of Alteryx equips businesses with tools paramount to optimizing campaigns that help in predictive modeling and acquiring customer insights. Besides, Alteryx helps you foresee trends, allocate resources efficiently, and develop target group-aligned marketing strategies by analyzing historical data. In short, Alteryx's data-driven approach ensures your marketing campaigns are efficient and cost-effective. Alteryx also facilitates personalized marketing to attract digitally empowered customers. By automating the integration of campaign data with third-party data, Alteryx helps marketers create campaigns that resonate with each customer's preferences. By assessing the performance of campaigns and delivering detailed insights into the success of campaigns across multiple channels, Alteryx helps identify what's working—and what's not— fostering continuous improvement.

The Alteryx Approach to Marketing

Let's explore the major components of Alteryx that help optimize marketing campaigns. Leveraging these tools helps deliver in-depth insights about customer data and optimizes your marketing campaigns for measurable results. • Data Integration Tools Alteryx supports various data sources like cloud storage, databases, and spreadsheets to facilitate effortless data integration and consolidation of customer data from disparate sources. Integrating the various data sources helps businesses leverage customer data, get a holistic picture of the audience, run audience segmentation, develop predictive models, and make data-driven decisions for optimizing campaigns. • Visual Workflow Designer It helps you develop and customize data analytics workflow, allowing marketers to create tailored analytics processes. Alteryx's drag-and-drop interface streamlines complex tasks, shifting your focus to data analysis rather than firefighting with technical challenges. Visual Workflow Designer supports diverse functions like data cleaning, transformation, and analysis, enabling the streamlining of marketing operations.

Alteryx’s Predictive Capabilities for Marketing Campaign Optimization

The advanced predictive capabilities of Alteryx enable users to predict market trends, assess probable outcomes, and make better and more informed decisions. Alteryx's predictive analytics helps optimize marketing campaigns by: • Predicting customer behavior
• Personalizing campaigns to cater to specific customer segments
• Optimizing resource allocation
• Monitoring campaign performance and evaluating its impact in terms of leads and sales.
Here are some powerful predictive capabilities of Alteryx that allow marketers to boost ROI, optimize marketing strategies, and make data-driven decisions: • Market Basket Analysis: It allows marketers to identify frequently purchased products and tailor campaigns based on the purchase patterns and behaviors of customers, unlocking hidden patterns and prospects. It assesses the possibility of customers buying particular products together by analyzing additional customer needs. Alteryx Designer uses MB Rules and MB Inspect tools to run market basket analysis. • Clustering: Alteryx facilitates precision customer segmentation by grouping customers based on their behavior, demographics, and choices. The predictive grouping of customers into different clusters makes it easier to customize campaigns for a cluster of similar customers, boosting engagement and chances of conversion. • Forecasting: Alteryx's forecasting capabilities analyze historical data, trends, and patterns, enabling the marketing team to predict demand, sales, and revenue with remarkable accuracy. Forecasting is the key to running campaigns efficiently by anticipating market fluctuations and making decisions accordingly.

Alteryx's Robust Data Connectivity

Alteryx enables smarter analysis and decision-making with seamless data connectivity, empowering marketing teams to connect directly to marketing platforms such as Marketo, Salesforce, and Google Analytics. This powerful data connectivity: • Facilitates faster insights from different sources
• Eliminates the intricacies of extracting data
• Enhances collaboration between marketing, sales, and analytics teams
• Makes marketing data easily accessible
• Increases focus on high-impact analysis
Marketers can instantly gather campaign data from Marketo, track customer journeys through Salesforce, or analyze website performance metrics from Google Analytics—all within Alteryx. It streamlines workflows and expedites the process from collecting raw data to acquiring actionable insights, making analysis faster and more effective. Businesses can utilize Alteryx to automate diverse data sources, streamline data access, and tailor marketing strategies to specific customer groups. The capability of Alteryx to deliver quick results is attributed to marketing campaign optimization and staying competitive. The Alteryx approach saves time and boosts the overall effectiveness of marketing campaigns. In short, employing Alteryx equips organizations to analyze data faster, detect trends, and deploy useful marketing strategies.

What is Predictive Analytics?

Predictive analytics is a branch of advanced analytics that uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. The primary goal is to go beyond knowing what has happened to provide the best assessment of what will happen in the future.

Benefits of Predictive Analytics

1. Enhanced Decision Making: Make informed decisions based on data-driven insights rather than gut feelings.
2. Cost Savings: Optimize resources and reduce waste by predicting demand and managing inventory effectively.
3. Risk Management: Identify potential risks and take preventive measures to mitigate them.
4. Improved Customer Satisfaction: Anticipate customer needs and preferences, leading to better products and services.

Predictive Analytics Techniques

Predictive analytics techniques offer a wide range of applications powered by various types of models that generate valuable insights. To determine the best predictive analytics techniques for your organization, start with a clearly defined objective. Once you know the specific question you want to answer, you can select the most suitable model.

List of Predictive Analytics Models

1. Regression Models: Used to predict continuous outcomes.
2. Classification Models: These models categorize data into predefined classes.
3. Clustering Models: Group similar data points together based on defined criteria.
4. Time Series Models: Analyze data points collected or recorded at specific time intervals to forecast future values.

1. Regression Models in Predictive Analytics

Regression models estimate the relationship between variables, tracking how independent variables impact dependent variables to predict future outcomes. These models range from simple (one independent and one dependent variable) to multiple linear regression (multiple independent variables). Various regression techniques can be applied based on the specific use case.
By defining variable relationships, organizations can conduct scenario or 'what-if' analysis, testing how changes in independent variables affect outcomes.
Application of Regression Models For example, a company might use a regression model to analyze how product qualities influence purchase likelihood, such as identifying a correlation between blue shirts and higher sales. These insights help refine marketing strategies and product development, optimizing future performance.

2. Classification Models in Predictive Analytics

Classification models categorize data based on historical knowledge. Using a labeled training dataset, the classification algorithm learns correlations between data and labels and then categorizes new data. Popular techniques include decision trees, random forests, and text analytics.
These models are highly adaptable and can be retrained with new data, making them useful across various industries.
Application of Classification Models For example, banks use classification models to detect fraudulent transactions. By analyzing millions of past transactions, the algorithm identifies patterns indicative of fraud and alerts customers to suspicious activity.

3. Clustering Models in Predictive Analytics

Clustering models group data based on similar attributes. Using a data matrix that associates items with relevant features, the algorithm clusters items with shared features, uncovering hidden patterns. Organizations use clustering models to group customers for personalized targeting strategies.
Application of Clustering Models A restaurant might cluster customers by location and mail flyers only to those within a certain driving distance of a new location.

4. Time-series Models in Predictive Analytics

Time series models analyze data points in relation to time, making time one of the most common variables in predictive analytics. These models use historical data to predict future metrics. For example, analyzing data from the past year can help forecast the upcoming weeks.
Time series analyses are versatile, used for applications like seasonality analysis (predicting how assets are affected by certain times of the year) and trend analysis (determining asset movements over time).
Application of Time-series Models Forecasting sales for the next quarter, predicting store visitor numbers, or even determining peak flu seasons.

Predictive Analytics with Tableau

Tableau empowers users to not only visualize their data but also to gain actionable insights through advanced predictive capabilities. Whether you're looking to forecast sales, predict customer behavior, or optimize business operations, Tableau is the right choice.

3 Ways to do Predictive Analytics in Tableau

1. Forecasting in Tableau Desktop

Tableau Desktop offers robust forecasting features that allow users to make data-driven predictions effortlessly. Using exponential smoothing models, Tableau enables you to forecast future data points based on historical trends. Here’s what you can do: Let’s explore the ways to forecast data in Tableau Desktop: • Creating a Forecast: Users can add a forecast to a view by simply dragging a time dimension to the Columns shelf and a measure to the Rows shelf. By right-clicking on the view and selecting "Show Forecast," Tableau generates a forecast based on the selected model. • Customizing Forecasts: Forecast settings can be customized to adjust the prediction length, forecast model, and season length. Users can access these settings through the "Forecast Options" dialog box. • Evaluating Forecasts: Tableau provides a forecast description that includes details about the model, prediction intervals, and underlying statistics. This helps users understand the reliability and accuracy of their forecasts. • Visualizing Forecasts: Forecasts are visualized as shaded areas or lines on the chart, making it easy to compare predicted values with actual data.

2. Bringing R/Python Calculations into Tableau

Integrating R and Python into Tableau Desktop enhances its analytical capabilities, allowing users to perform complex statistical analysis and machine learning tasks. Users can create calculated fields using MODEL calculations, or by using SCRIPT functions that include R or Python scripts to perform custom calculations. These scripts can be used for various purposes, such as regression analysis, clustering, and predictive modeling. Tableau connects to R using Rserve and to Python using TabPy.

3. How to Do Predictive Analytics with Tableau Prep

Tableau Prep enhances your data preparation process by integrating with Einstein Discovery, Salesforce's AI-powered analytics tool. This integration allows you to infuse your data workflows with advanced predictive capabilities. • Einstein Discovery in Tableau Einstein Discovery, part of Salesforce's suite of AI (Artificial Intelligence) tools, is integrated into Tableau to provide advanced predictive analytics capabilities. In Tableau Prep, Einstein Discovery can be used to build and integrate predictive models directly within the data preparation workflow. This feature is available in Tableau Desktop as well. • Generate predicted values by integrating R/Python in Tableau Prep Tableau Prep allows for the integration of R and Python to perform advanced data transformations and generate predicted values.

Here's how you can do it: • Script Steps:

1. Tableau Prep includes a "Script" step that lets users run R or Python scripts as part of their data flow.
2. This step can be used to perform complex transformations, calculations, and predictions.

• Connecting to R/Python:

1. Similar to Tableau Desktop, Tableau Prep connects to R using Rserve and to Python using TabPy.
2. Users need to set up these servers and connect them to Tableau Prep to execute scripts.

• Implementing Predictive Models:

1. Users can import trained models from R or Python into Tableau Prep.
2. The "Script" step allows these models to be applied to the data, generating predicted values as part of the data preparation process.

• Dynamic Data Preparation:

1. Using R and Python, users can create dynamic and flexible data preparation workflows that include predictive analytics.
2. This enhances the overall data preparation process by integrating advanced analytical techniques.

Real-life Scenarios/ Use cases of Predictive Analytics

Predictive analytics can be applied in numerous business scenarios to enhance decision-making, efficiency, and customer satisfaction. Here are some real-life examples:

1. Customer Churn Prediction:
• Scenario: A telecom company wants to reduce the number of customers leaving for competitors. • Application: By analyzing customer usage patterns, support interactions, and billing history, the company can predict which customers are at risk of churning and take proactive measures, such as targeted promotions or personalized outreach.
2. Fraud Detection:
• Scenario: A financial institution wants to identify fraudulent transactions. • Application: By examining transaction histories, user behavior, and other data points, predictive models can flag suspicious activities in real-time, allowing for immediate investigation and action.
3. Sales Forecasting:
• Scenario: A manufacturing company needs to predict future sales to plan production and manage resources. • Application: Leveraging past sales data, market trends, and economic indicators, the company can generate accurate sales forecasts to inform production schedules and supply chain management.
4. Marketing Campaign Optimization:
• Scenario: A marketing team wants to improve the effectiveness of their campaigns. • Application: Predictive analytics can help segment customers based on their likelihood to respond to different types of campaigns, enabling more targeted and effective marketing efforts.
5. Risk Management:
• Scenario: An insurance company needs to assess risk for new policy applicants. • Application: By analyzing historical claims data and applicant information, the company can predict the likelihood of future claims and set premiums accordingly.

Tableau offers a powerful platform for integrating predictive analytics into your data strategy. With its robust forecasting capabilities, seamless integration with R and Python, and advanced features in both Tableau Desktop and Tableau Prep, you can transform raw data into actionable insights. Whether you are aiming to predict future trends, optimize operations, or make data-driven decisions, Tableau equips you with the tools needed to gain the full potential of your data. To know more, connect with us: https://www.beinex.com/tableau-beinex

When the sliver of thought -- that we have seen all that Tableau can do -- crosses our mind, just pay witness to a meteor shower of new features lighting the horizon up! Tableau is officially upgraded to 2021.3. Tableau 2021.2 was a remarkable update in itself: it included ‘ask and explain data’ for viewers, allowing connected desktops, Collections, and many more valuable options and features. So, what does the latest upgrade feature? In one go, the following list encapsulates the whole gamut of supplements:

1. Improvements to Data Prep Experience
   1. Linked tasks
   2. Generate rows
2. Improvements to Tableau Catalog
   1. Data quality warnings in subscription emails
   2. Inherited descriptions in web authoring
3. Slack Integration
4. Additional Features
   1. Customise the set of workbooks on the homepage
   2. Rename published data sources directly in Tableau Online or Server

Improvements to Data Prep Experience Tableau Prep, an indispensable backstage prior to staging your real and original data show, has found a high-power addition. With Linked Tasks in Tableau Prep Conductor, one can now bring enhanced harmony to his data preparation. Creators with Data Management or DM can now schedule flows to run one after the other on Tableau Online or Server. This feature has a wide range of applications; for instance, if the sales data for a designated time is yet to show up on the system and fails at source for some reason and there are multiple flows connected to that data source, stale data issues will unnecessarily pop up in all the workflows. With linked tasks, this issue can easily be prevented from manifesting. Linked Tasks on, it’s now simple to automate the orchestration of multiple flow jobs, ensuring they happen in sequence after each task completes successfully. Essentially, if your data didn’t update and there are two workflows linked to the same datasets at different time intervals, they both run, consuming time and resources; the scenario can be avoided, and the other linked task can be stopped from running; thanks to the new feature. A multitude of benefits come power-packed as the feature unfurls:

• Authors of a flow can get alerted automatically provided any of the jobs fail and can set up an appropriate warning on the data for consumers well in advance.
• Any flow can be scheduled by customers, or they can extract refresh to run when new data arrives, saving them time and resources.

Please see Image 1 to have a better understanding.

(Image 1: Linked Tasks on Tableau Prep)

Besides, Tableau Prep Conductor can generate a set of rows that are otherwise missing based on dates, date times, or integers. This is of huge importance as it allows users to fill gaps in data quite easily to ultimately ensure that processes downstream have all the requisite datasets to work on and create highly accurate and precise visualisations. Please see Image 2 for a quick understanding of the feature:

(Image 2: Generate rows on Tableau Prep)

Improvements to Tableau Catalog Next in the line comes improvements to Tableau Catalog. Two features need special mentioning:

1. Data quality warnings in subscription emails, and
2. Inherited descriptions in web authoring

Data quality warnings are of paramount importance. Accurate warnings, when they are around, lends confidence to the customers that the right, trusted data is being utilised to arrive at sound business decisions. Formerly, this was restricted to Tableau Online or Server, but now it is delivered straight into your inbox bundled with the subscriptions to the dashboards you care for. The power of data visualization with visibility into the quality of the data feed add top-notch credibility and renders confidence to the customer who spends a good portion of the day paying attention to emails. In the 2021.3 version, Data Quality Warnings are available in Web Authoring. This will soon be available in Tableau Desktop as well. Slack Integration Yes, Tableau notifications can now be directly accessed on Slack! Admins and Users will control what notification types can be dispatched on Slack. Notifications include:

• Shared content
• Data-driven alerts
• @mention

This is just the commencement of the Tableau + Slack integration. Other Stellar Features Up until the upgrade, the same three sample workbooks have occupied the Desktop Homepage of Tableau. They may give a good starting point for new users to get acquainted with Tableau. But the datasets were always generic and did not get updated. Now with the customizable set of workbooks on the home page, this has undergone a radical change. (See image no.3)

(Image no.3)

Add to this the ability to rename published data sources directly in Tableau Online or Server on the data source page; the upgrade is a real treat to data rockstars. (See image no. 4) Practitioners point out that The REST API can also be used when changing a large number of workbooks to minimise efforts.

(Image no.4)

No need to generate a newly published data source to change the name. No need to manually change all workbooks on the Desktop to use that newly published data source, which was highly frustrating! So, welcome to Tableau 2021.3. Let us uncomplicate and perpetually so! Co-Author : Rakesh Neelakandan

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.