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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.

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About Beinex Cloud Services

By partnering with Beinex, a leading cloud engineering and enablement partner in the Middle East, you can leverage our expertise to optimize your cloud infrastructure, enhance performance, and ensure security and compliance. Our technical prowess allows us to design and implement tailored cloud solutions that align with your business needs, enabling you to maximize the benefits of cloud technology and drive innovation.

Partnering with industry-leading cloud technology providers like Google Cloud Platform, AWS, and Microsoft Azure to build scalable, secure, cost-effective solutions and deliver transformative cloud technology solutions. Beinex has also forged strong partnerships with Snowflake, Tableau, Alteryx, Alation, and Databricks. These collaborations enable us to offer comprehensive solutions that leverage the best-in-class capabilities of these platforms. We can provide our clients with cutting-edge technologies and expert guidance by working closely with our partners.

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We are delighted to achieve this award with our relentless dedication and unparalleled expertise on the Alteryx platform. With ample product knowledge, technical expertise, and sufficient focus on customer satisfaction, we were able to demonstrate the value that we offer as a partner. Our determination and dedication aided in supplementing our customers with the power to deliver faster and better business outcomes by intelligent decisioning using Alteryx. Last year, we were fortunate enough to successfully transform the majority of our clients’ businesses with Analytic Process Automation by quickly automating analytics and the entire data-driven business processes, resulting in quick wins and faster returns on ROI. We were also awarded with Alteryx 2020 Partner of the Year award, Middle East. With the preferred partner status, we will be able to make even greater collaboration with the Alteryx team, helping us extract its possibilities to the next level. Alteryx always stands for developing data-driven technical solutions to business problems by empowering its clients to be self-sufficient in handling data analytics and continues to provide unmatched services, like;

• • Collecting data from multiple sources for quick analysis and faster insight generation.
  • Exploration of data from on-prem databases, the cloud, and big or small data sets, and more.
  • Analysis with maps, addressing solutions to deeply understand your customers and locations.
  • Augmenting your team’s analytic output to gain insights by using data without any coding or analytics expertise.
  • Embracing automation to effectively communicate with your stakeholders and enable intelligent decision-making to drive better, faster business outcomes.

We are incredibly honored to receive this award, and we look forward to a continued partnership as a preferred partner in 2021! Thank you, 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.

Data preparation is purging imperfectly formed data, reorganising unclean data, and merging various data sets for analysis. Rows and columns in the data structure must be changed, and data types and values must be cleaned up. The amount of time it takes to find insights directly depends on how quickly and well you prepare your data. The entire process can be sped up by being aware of the extent of the data you're studying and observing the modifications you make to the data. With the help of Tableau Prep Builder, data can be combined, shaped, and cleaned more quickly and easily for analysis within Tableau. You can swiftly obtain high-quality data using the linear and visual approach when preparing your data. When ushering in data into a data prep tool, it is always better to know what you're working with and whether you're looking at the complete data set or just a subset. Before you begin cleaning, you might also need to undertake some data research. Five crucial steps are to be taken care of for prepping the data. Let's have a look:

1. Adjust the data sample size

2.Evaluate the data

You'll probably want to start by counting the number of distinct values in each field. A simple check at the column header at the top reveals how many states are represented in the data set. You'll also want to understand how various values connect to identify data outliers or problems. You can utilise highlighting in Tableau Prep to find correlations between different fields. The data grid view is condensed to only display the records with the selected value in the chosen field when you click on a value in the profile pane. Tableau Prep highlights the corresponding values in blue, and the values span areas.

3.Filter the data

Limit the fields you import into Tableau Prep to those you'll need for your analysis to maximise the overall effectiveness of your data preparation process. By filtering your data, you can verify that you're performing the proper analysis while saving time. For instance, if you need to look at sales data from the previous two years, you may use the range or relative date filters to limit the date field to that period. You might want to eliminate any incorrect or irrelevant data. A value in the data pane can be excluded with a single click. You can do this at any time during your flow.

4.Assess and tidy up the data

Tableau's data types will have an impact on your analysis. Therefore, it's critical to correctly identify each field before beginning. Even though Tableau allows you to update aliases, alter data types, split lots, and create calculations, it is far simpler to carry out these tasks beforehand, particularly when preparing the data set for someone else. Tableau Prep includes built-in capabilities to aggregate and replace recurring characters or pronunciation, saving you from having to edit each one individually so that you don't have to; these solutions use algorithms to make cleaning easier. Or, if you foresee a missing value, you may manually add it so that it will be included when the flow processes the complete data set. You can apply a computation if you know that a field must be cleaned or filtered, but it takes more than the user interface offers.

5.Understand the data results

Deciding about the final data set's appearance while you begin to prepare your data can be difficult. For Tableau to effectively analyse your data, you might need to merge numerous data sources or pivot your data from columns to rows. One technique to get beyond this obstacle is visualising the data pane in Tableau Desktop as to how it should appear. Do you have columns with the same value in several places? Should each product be in a single field with the sales transactions stated below, or should each product have its column with the sales transactions listed underneath? The latter is more likely, and a pivot is necessary for this situation. You will be joining the data if you need to combine two tables. By using a join, you can increase the number of fields in your data source that you can investigate. Although a join can be added at any point during the data preparation process, the sooner you use it, the sooner you will comprehend the data set and identify areas that require immediate attention. Like appending two data sets together, a union enables you to do so. For instance, you might have an Excel file where each sheet displays transactions from different years. You may maintain the same structure with extra rows by using a union rather than joining the tables. After your data has been organised, processed, and filtered, it's time to interpret what it is trying to tell you. Tableau Prep connects with your entire business intelligence platform like many other data preparation products. To allow others to begin their analysis, publish the extract to Tableau Server or Tableau Cloud. Bring it into Tableau Desktop to start posing and investigating more in-depth queries. The hardest part of the data analysis process is now complete. It's time to share the breakthroughs that resulted from your hard work.

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.