An Ultimate Guide to All the Tableau Certifications

One of the highlights of Alteryx is Data Connections. It can be relied upon to generate and handle your data connections from a central location. The user can access it using their username and password.

The main advantages of Alteryx that set it apart :

• • data connectivity is high
• • direct access to the data sources/ bases

With the help of the Manage Data Connections window, it is possible to:

• • view connections you have already created
• • view connections shared with you
• • add new connections.

3 Steps to Create and Manage Your Data Connections with Alteryx

1. Adding
2. Testing
3. Sharing

Let’s get to know the steps in detail:

1. To Add a Data Connection

If the user wants to add a data connection, select Add New Data Connection on the Data Connections page. Then from the Connection drop-down select the connection type.

2. Testing Data Connections

The second step is to test the data connections. A controller and two or more worker machines make up a multi-node setup of the Server. With this arrangement, the test functionality examines the connection on the controller machine rather than the individual worker computers. One should verify the identical database drivers and driver versions are installed on every system to make sure the connection will function on any of them. Connection Test Failures

The connection tests can fail due to multiple reasons. The user can save data connections even though they have failed the connection test.

These are the most typical causes of connection tests failing:

• • The server and database can be inaccessible to you in some circumstances. For instance, only the connection end user can access the server or database in certain cases.
• • Your ability to connect to the server or database may also be blocked by network security.
• • Sometimes the server is unable to connect to the host of the database server. At that time ping the database server host while logged into the server where Server is installed to check for network connectivity. The credentials for the database are incorrect or do not have the necessary access permissions. Get in touch with the database manager.
• • The database is unavailable. To ensure that the database is operational and performing as expected, get in touch with the database administrator.
• • The Server configuration you are using has several nodes.

3. To Share a Data Connection

After creating a Data Connection, go back to the Data Connections page to share it with users or custom groups for use in Designer.

Note: Keep in mind that you must connect with a Curator or the gallery administrator to make sure that they have access to the necessary data connections if you want to allow the workflow to be used by particular users or groups.

Follow the steps mentioned below to share a Data Connection:

• • Choose the shareable data connection by clicking the pencil icon on the Data Connections screen.
• • Select Users or Custom Groups on the Modify Data Connections screen.
• • Enter a user's or a group's name here.
• • Choose the user or the group.

The appropriate tab lists the individual, group, or studio that you are connected to. For more information on how users can access and manage shared connections in Designer, go to Input Data Tool and Manage Data Connections in the help section of Designer.

Note: Verify that the user's machine is installed with the same or a more recent version of the Microsoft SQL Server Native Client before initiating a Microsoft SQL Server connection. Go over to Troubleshooting.

a. Cancelling Access to a Data Connection
Choose the "x" icon next to a user's name to remove their access to a connection.

b. Modify a Data Connection

• 1. Choose the pencil icon on the Data Connections page.
• 2. Alter the Name or Connection String fields on the Modify Data Connections screen.
• 3. You can share the connection with people and groups using the Users or Custom Groups pages.
• 4. Choose Save.

4. Delete a Data Connection
1. Choose the trash can icon next to the connection name to remove the connection.

Tools in Alteryx Designer

Let’s have a look at the input tools available in Alteryx Designer:

1. Input Data Tool

By connecting the Input Data tool to a file or database, you can utilise it to add data to your workflow. This tool has a wide range of configuration possibilities.

2. Directory Tool

The Directory tool can be used to return a list of all the files present in the given directory. The tool returns file names along with additional details about each file, including file size, creation, and modification dates, and more.

3. Dynamic Input Tool

Using the Dynamic Input tool, it is possible to change file names, amend database queries, or alter input pathways that were created using data from your workflow are all possible If you're reading from a database, Alteryx will do it at runtime and will dynamically select which entries are read.

4. Connect In-DB Tool

When compared to conventional analysis techniques, the Connect In-Database tool can significantly enhance performance by allowing blending and analysis on large data sets without removing the data from the database.

How can You Use Alteryx and Tableau for Advanced Analytics

1. Data Preparation with Alteryx

Alteryx provides powerful data preparation capabilities, including data cleaning, data integration, and data transformation. You can make use of it for:

1. Importing data from various sources such as databases, spreadsheets, or APIs.
2. Creating data preparation workflows, connecting different tools to cleanse, filter, aggregate, and manipulate your data. Use Alteryx's visual workflow interface.
3. Deriving additional insights from your data to leverage Alteryx's advanced analytics tools like predictive modelling, time series analysis, or clustering.

2. Advanced Analytics with Alteryx

Alteryx offers a list of advanced analytics tools, such as predictive analytics, spatial analytics, and text analytics, that can be utilised for:

1. Building machine learning models and performing regression analysis or classification tasks.
2. Analysing geographic patterns, performing spatial clustering, or conducting network analysis.
3. Performing sentiment analysis or topic modelling and extracting insights from unstructured text data by using Alteryx's text mining tools

3. Data Visualization and Reporting with Tableau

Once your data is prepared and enriched in Alteryx, you can connect Tableau to the output data and create interactive visualisations, and perform the following:

1. Use Tableau's drag-and-drop interface to create charts, graphs, dashboards, and reports to visualise your data.
2. Leverage Tableau's advanced visualisation features like calculated fields, table calculations, or trend lines to enhance your analysis.
3. Combine multiple data sources, including the output from Alteryx, to create comprehensive dashboards that provide a holistic view of your data and insights.

4. Integrating Alteryx and Tableau

When it comes to pushing data from Alteryx to Tableau, there are indeed a couple of approaches you can consider ensuring a smooth integration between the Alteryx and Tableau platforms. Alteryx allows you to export the prepared and enriched data as a Tableau Data Extract (.tde) or Tableau Hyper Extract (. hyper) file. You can make use of it for the following functions:

Publishing Data Source Directly to Tableau Server:

Connect Tableau directly to the files to visualise and explore the data.

Alteryx allows you to publish data sources directly to Tableau Server, making it accessible to Tableau users.

You can connect Alteryx and Tableau, enabling Tableau users to access and analyse the data from Alteryx in real time.

You can set up a workflow where Alteryx automatically exports updated data to Tableau on a scheduled basis, ensuring your visualisations are always up to date.

Writing Data in Tableau’s hyper Format:

To integrate Alteryx with Tableau, you can:

Write the data in Tableau's proprietary hyper format and

Directly import it into Tableau for visualisation and analysis.

Beinex partnership with Tableau & Alteryx

As the premium partner of Alteryx and Tableau, Beinex offers a unique advantage in leveraging the combined power of these two tools for your business. Our experts can help you unlock the full potential of your data through sophisticated data preparation, advanced analytics, and compelling visualisations that provide deeper insights into your business operations.

With our expertise, you can effectively make data-driven decisions and communicate complex analytics. Whether you need help with implementation, training, or ongoing support, Beinex is your go-to partner for all your data analysis needs. Get in touch with us today and see how we can help you transform your business with the combined power of Alteryx and Tableau.

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

The Principles of Data Ethics

And there are five of these principles:

1. Ownership: The individual, himself/ herself/ themself, possesses the ownership of the data related to the person. A firm cannot take that data without the consent of the person lest it be deemed stealing.
2. Transparency: The individual, aka data subject, has the right to know how a particular enterprise intends to collect, store and utilise the data concerned with the person.
3. Privacy: Any bit of Personally Identifiable Information (PII) should not be made publicly available unless otherwise consented to. This includes the name, address, phone number etc.
4. Intention: If the firm is collecting data on the individuals to fulfill unstated malicious intentions, it goes against the spirit of ethics.
5. Outcome: If the collected data, despite the right intentions, come to have an unwanted outcome vis-a-vis the owner of the data, thanks to an algorithmic bias or any other reason, then the data ethics stand violated.

Characteristics of Data Ethics

Largely there are four characteristics that portray data ethics.

1. Vouching for and ensuring data security and protecting customer info: When you handle customer data, as an enterprise, you are bound to protect it, prevent breaches, and ensure data never gets compromised. This is easier said than done. IBM India, in a report, outlines that “data breach average cost increased 2.6% from USD 4.24 million in 2021 to USD 4.35 million in 2022.”
2. Offering clear benefits: It is a kind of social contract clause. You give your consumers greater speed, convenience, value and savings, and they (users, patients, clients, employees, customers and partners) will not be hesitant to part with their data as long as they are guaranteed and followed on the guarantee of data in safe hands not prone to misuse.
3. Provision for consumer agency: Look at this scenario from a McKinsey report: “If a customer receives an offer and says, ‘I think I got this because of how you’re using my data, and that makes me uncomfortable. I don’t think I ever agreed to this,’ another company might say, ‘On page 41, down in the footnote in the four-point font, you did actually agree to this.’ Here, the customer has no agency. Worse than that, he feels he has been duped by the company. Game over! Remember, your reputation as an enterprise and the trust that you painstakingly cultivated over the years with customers can vanish in as much time as it takes for the customer to hit the post button on social media.
4. Doing what you promise: The company should do what it has promised it will do or risk credibility and reputation.

In short, companies that adhere to the principles of fairness, privacy, transparency, and accountability in data matters can earn and retain the trust of their customers or clients. Trust is one power of attorney. It empowers a firm to not only ensure better customer service and experience by exercising the power of data it has been granted but also preserve and enhance its reputation.

Regulations and Data Ethics

Regulatory requirements and ethical obligations are mutually related and complementing. The European Union’s General Data Protection Regulation (GDPR) went into effect (only) in May 2018. But the Internet and data collection using the Internet predate it. Does it mean that companies could have done whatever they wanted to do with data prior to GDPR? Negative.

Ethics is your enterprise’s shadow. It is born with it as its twin. Regulation or law is the caretaker that comes afterwards.

“The bar here is not regulation. The bar here is setting an expectation with consumers and then meeting that expectation—and doing it in a way that’s additive to your brand,” an expert noted.

No wonder you are obliged to build company-specific data usage rules rather than await the regulators and legislators to chip in with guidelines and laws which could be too late or sometimes too little. Ascertain what are the no-go areas; areas where you cannot take the data to.

Once it is done, it is important that you communicate the data values internally and externally so that everyone is on the same page. You also need to set up an agency (e.g. Data Ethics Board) and institutionalise and propagate the values that you designed. C-suite should also be made a part of this ethics board or should be kept posted on the developments in the board.

Most of the companies operating today are data-driven as they have started identifying the value of data. The value of data and data marketplaces are evolving with the growth of big data and data requirements of companies.

Data Marketplace

In simple terms, these are online marketplaces where we can buy and sell data of any sort. Data marketplaces offer several kinds of data from a wide range of different data sources. These data include Business Intelligence, demographics, research, and marketing data. Data types are structured and offered to clients by data providers. Providing buyers with more choice of high-quality data generates more engagement and encourages fair pricing between the sellers. Every company has the potential to earn revenue from the information it generates. In a recent study of more than 400 organizations, only 1 in 12 were monetizing their data to its fullest extent. Modern data monetization strategies can help you open brand new revenue streams. There are 3 key steps to monetize your data and drive new revenue streams.

Understand your Data Offerings - What types of data or data services are you producing that would be valuable to other organizations?

Decide How to Price Data Products - What should be the pricing strategy followed, and how easy would it be to put into action?

Know About Data Sharing Methods - Which distribution channel should be selected for data sharing that adheres to GDPR and data governance policies?

Once you have decided on the data offerings and priced the data products, the key to successfully monetize your data is by sharing the data with the clients/ prospects. The customers and business partners would be eager to get data from you, especially if you can make the process easy and secure. Traditional data sharing technologies often use FTP, Cloud Storage, or API. These methods generally do have few disadvantages.

1. Storage costs for both vendors and buyers
2. ETL costs and effort
3. Security vulnerabilities
4. Service and support costs
5. Latency and potential errors leading to poor customer experience

How do we overcome these challenges? There are platforms that make revenue generation easier with secure data sharing methodologies. Snowflake is one of the most secure and innovative platforms.

Snowflake & Data Monetization

Snowflake is an analytic data warehouse provided as Software-as-a-Service (SaaS). It provides a data warehouse that is faster, easier to use, and far more flexible than traditional data warehouse offerings. Snowflake allows companies to easily publish a variety of data sets that become immediately available for use or purchase for clients. Snowflake Data Exchange, a modern data sharing method, reduces the time to market and significantly influences customer success. Data Exchange is your own data hub for securely collaborating around data between a selected group of members that you invite. It enables providers to publish data that can then be discovered by consumers. The benefits of Snowflake Data Exchange over Traditional Data sharing Methods are:

1. Secure Data Sharing
   • Exchange data within your organization between different business units. Collaborate with external parties such as vendors, suppliers, partners, and customers.
2. Reduce Time to Market
   • Break down data silos and reduce time to market.
   • Interchange data with third-party vendors to help augment internal datasets.
   • Break down data silos by scaling multiple data sets from different sources within your organization.
   • Find and consume data on other Data Exchanges to get business insights.
3. Speed of Processing
   • Snowflake’s multi-cluster shared data architecture is designed to process enormous quantities of data with maximum speed and efficiency.
   • All data processing horsepower within Snowflake is performed by one or more clusters of computing resources.
   • Data is cached locally within computing resources, along with the caching of query results, to improve the performance of future queries.
4. Cost Benefits
   • The costs for sharing data with Snowflake are minimal and straightforward.
   • Simply pay for the data you store, i.e., you only pay for what you use.
   • Reduce extract, transform, load (ETL), and data pipeline maintenance costs.
5. Control and Govern Access
   • Managing membership
   • Granting and revoking access to data through standard and personalized listings
   • Auditing data usage
   • Applying security controls to your data

Snowflake promises to accelerate the data exchanges by reducing the barriers to access data and time to market. It increases the intrinsic value of data by allowing to share and access of data between providers and consumers.

Real-life Implementation

A famous telecom organization in Europe was sitting on large silos of data that they could not monetize properly because of the complex architecture of the data warehouse operations and data security challenges involved in the data sharing process. The company has Customer Daily Records (CDR) of its subscribers that contains location data of the users. This data can be used to identify the places people visit and help with building consumer profiles. The gathered data allows advertisers to target messages to specific users while tracking whether they visited a retail store after seeing a mobile ad. This helps them plan personalized marketing strategies and business goals based on demography profiles for targeted users. However, due to the data privacy policies of the European Union like GDPR, organizations were struggling to share data with their potential clients. The GDPR policy makes it mandatory for organizations to ensure that the customer's personal information is not shared with third parties without the customer's consent and involves hefty fines and penalties for the data breach. Even the data sharing process was a source of concern as the data was often shared in text/excel files because of the different database architecture of the clients. With growing data privacy concerns and challenges in creating datasets adhering to the GDPR policies, organizations are strictly asked not to share customer data with third parties. The current system architecture forced the organizations to employ a large number of resources to extract the data from the database system and ensure that customer data is not compromised at any point. The companies were evaluating the possibilities of a potential system that would help them monetize the data they currently hold. The introduction of Snowflake into the organizational architecture solved the data monetization problem and improved the overall data culture in the organization. The unique architecture of Snowflake separates the data storage and computation layer to enhance organizational productivity. The pay as you use policy of the Snowflake and the zero maintenance of infrastructure helped the organization phase out the complex on-premise solutions required to handle the huge data volume. Easy connectivity with the existing solutions used for data analytics practice and on the fly scalability of the computation layer helped the organization increase productivity. It also paves the way for seamless integration to the organization's architecture. The Data Marketplace of Snowflake ensured secure data sharing with third parties adhering to the GDPR policies. The in-built data security policies and features minimize the role of organizations to provide data privacy as well. This enables the organizations to make only those data points visible to end-users that they seemed apt for sharing. It also ensures that the data always resides in the organizational Snowflake database rather than on third-party databases. Moreover, the organizations could reach out to thousands of potential clients through Snowflake Data Marketplace without relying on any intermediatory sources. All this ultimately brings out the scope of using the existing data to drive revenue to the organization and highlights the importance of having a complete environment like Snowflake to capture, preserve, access, and transform data. Authors: Rahul Vijayan, Firdous Maqbool