Learn how Tableau has aided the business in finding lucrative new sources of income and ensuring that airport operations run like clockwork even in challenging circumstances.

The pre-Tableau era in Aviation

The aviation industry used to spend a lot of time doing the same operations on a daily, weekly, or monthly basis before Tableau was introduced and used because most of the data processes lacked the ability to be automated. They had to manually enter this information into Excel and re-create the same report each week to give senior management the most recent traffic data from the airport, for example. Additionally, it may be difficult to find key insights buried in spreadsheets of flight and traffic data, which means that sometimes the companies might have missed opportunities to improve their operations altogether.

Tableau as a problem solver

Now that most of these previous manual operations are fully automated, hundreds of man-hours can be saved each month. Data is prepared in Alteryx, staged in SQL server databases, and then connected straight to Tableau, where it can be instantly refreshed. A new dashboard can be created at once, and it will never require manual upkeep or updating again. Because data is available whenever they need it, employees become more independent and can effectively address their own queries and analytics requirements. Different teams, including those in finance, capacity planning, operations, engineering services, investment planning, and more, use Tableau in this fashion.

Because of Tableau's focus on visual analytics, particularly mapping, it is much simpler to spot important patterns and trends in the data, such as newly popular airline destinations. Complex metrics are much easier to comprehend when you have that visual feel of the marketplace. Tableau is made to maximise geographic data, so you can understand both the "where" and the "why." Without the need for specialised software, everyone can conduct geospatial analysis thanks to out-of-the-box geocoding and beautiful interactive maps of Tableau.

Tableau drives new revenue streams

The Business Development team's main goal is to increase airport activity and attract additional airlines. To do this, companies must create a strong business case showing that there are unmet passenger demands. For instance, Tableau discovered sizable passenger traffic volumes that were travelling indirectly through other hubs to Dubai. They were able to identify and develop new market prospects for passengers who weren't previously served thanks to this information.

It used to take a lot of figures to be done crunching to uncover these insights, but today the teams can investigate the data using an automated Tableau dashboard that is updated monthly with ticketing data from the International Air Transport Association (IATA) database. To examine the most recent trends, Tableau can quickly explore the data on a global heatmap. This method saves companies days of manual analytics effort by providing them with the evidence that they need to develop airline offers quickly and persuasively.

Tableau and day-to-day operations

The airports that are open 24 hours a day, 7 days a week, must function flawlessly 24 hours a day. The weather is one of the biggest risks to this since it can abruptly cause serious interruptions. Fortunately, Tableau may be used to lessen the worst consequences. When the meteorological division issues a weather warning, Tableau examines the planned itinerary for the impacted timeframe and looks for airlines that operate several flights to/ from the same location. Then, to lessen the effects of this interruption while maintaining connectivity and customer service standards, Tableau collaborates with these airlines to proactively reschedule or cancel flights and combine the demand with fewer flights.

Beinex being Tableau Premium Partner provide sustainable analytics solutions and help organisations to build superior data visual analytics capabilities internally through our bespoke training programs. We have 100 years of combined experience in Tableau and are led by professionals who have successfully delivered Tableau projects in the region for large private and public sector organisations. Our team of Tableau-certified consultants are real-life Tableau business users who are passionate about Tableau and delivering a world-class experience. We have successfully implemented Tableau in various industrial sectors in the Middle East and in other countries too.

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What is Time Series Analysis?

Time series analysis involves examining data that changes over time or where time is a variable in the outcomes. Time isn't just a data point; it's the primary axis on which the data is based.

The main difference with time series analysis is that data is collected at regular time intervals. This helps identify patterns in the data, forming trends, cycles, or seasonal variations. With a consistent time frame of historical data, time series forecasting becomes a valuable tool for predicting future data. Time's crucial role as a variable in data across industries makes time series analysis widely applicable. Explore a few examples outlined below.

6 Real-world Examples of Time Series Analysis in Various Industries

Example 1: Health Authority Enhances Patient Care

Problem: Inefficient resource use and rising costs of care and operations over time.

Solution: Utilizing data analytics, a prominent health authority conducted a comprehensive analysis of patient data. Examining historical data on patient stays, treatments, and conditions, they identified optimal times for administering medication, resulting in reduced average length of stay and cost savings for both patients and the system.
Insight: Time series analysis in healthcare extends beyond patient care to chronic disease research and epidemic-scale studies. Tracking chronic diseases over time and analyzing patient data using time series methods contribute to advancements in the field.

Example 2: Retail Giant Identifies Sales Opportunities

Problem: Slow data analysis affecting decision-making days before events.

Solution: A leading retail giant's front-office team integrated all data sources to gain a comprehensive view. Implementing time series analysis dashboards, they swiftly identified sales opportunities by forecasting against seasonal trends. Proactive measures were taken to increase ticket sales for upcoming events based on real-time insights.
Insight: Time series analysis aids retail giants in making data-driven decisions, predicting consumer trends, and strategizing marketing efforts, ensuring maximum impact.

Example 3: Manufacturing Company Improves Forecasting Accuracy

Problem: Inaccurate and time-consuming operations, manufacturing, and sales forecasting.
Solution: A manufacturing company revamped its forecasting process using time series analysis and modeling. By leveraging better data and faster analysis, they reduced analysis time from one day to one hour. Accurate forecasts in supply chain and manufacturing processes led to significant cost reductions in inventory, supply chain, labor, and capital equipment.
Insight: Time series analysis proves invaluable in optimizing forecasting models, enhancing accuracy, and streamlining operations in manufacturing.

Example 4: Marketing Analytics Scaling for Media Clients
Problem: Evolving marketing technologies making it challenging to quickly analyze information for media clients.
Solution: An analytics firm centralizes over 100 data sources using Tableau, allowing quick data retrieval and the creation of custom dashboards. Time series analysis aids in media forecasting, enabling the firm to develop insightful "what if" analyses. This empowers clients to make informed decisions about marketing investments.
Insight: Time series analysis combined with data centralization is crucial for scaling marketing analytics, providing clients with actionable insights.

Example 5: Streamlining IT Costs Through Self-Service Analytics
Problem: Business decisions based on static reports, consuming time and resources.
Solution: A retail group enables department staff with self-service analytics in Tableau, reducing IT costs by 20%. Time series analysis is applied for accurate forecasting of retail and IT trends, optimizing product orders and resource allocation.
Insight: Time series analysis aids in accurate forecasting for retail and IT trends, optimizing resource allocation, and reducing IT costs.

Example 6: Innovative Use of Data Analytics in Auditing Processes
Problem: Traditional audits were time-consuming and lacked value addition.
Solution: An organization in the energy sector used Tableau to analyze a year's worth of data, uncovering trends in financial processes. This data-driven approach revolutionized their auditing processes, providing deeper insights into the financial health of the organization.
Insight: Time series analysis proves instrumental in data-driven audits, providing a comprehensive view of historical data and uncovering trends for better decision-making.
These real-world examples showcase the versatility and impact of time series analysis across different industries, emphasizing its crucial role in data-driven decision-making and business optimization.

Under the thematic area of “Decoding Barriers to Pave the Way for UAE’s Digital Future”, Digital Transformation Summit brings together UAE’s 200+ CTOs, CIOs, CISOs, heads of digital transformation, IT infrastructure, cyber security, information and communication technologies and other experts in the domain.

Beinex is a multinational firm exploring the endless possibilities of data for Cloud, Analytics, Artificial Intelligence, Machine Learning, and Automation.

Partnerships are what make Beinex stronger. The company has solid partnerships with some of the leading technology firms, research labs, and universities around the globe. Beinex has robust, substantive business alliances at multiple levels with Microsoft, Salesforce, AWS, Tableau, Alteryx, Snowflake and other leading techno-ecosystems.

By virtue of these partnerships and its constant urge to drive past excellence benchmarks, Beinex became a deserving candidate to win this prestigious award.

And the Award Goes to Beinex

In effect, Beinex architects, guides, leads, and implements solutions in Analytics, AI, and ML for the spheres of Digital Transformation, GRC, and Risk & Audit Transformation. Present in three continents, Beinex enables its clients to analyze data, mitigate risks, identify opportunities and automate processes.

The award recognizes the relentless pursuit of excellence by Team Beinex in the domains it is into.

“It is a moment of pride and happiness for Beinex. The award secured in the Best in Data & Analytics category at the Digital Transformation Summit, UAE, 2022, is a testimony to the fact that Beinex has continuously and without fail pursued business excellence powered by innovation and experience. We acknowledge and understand that with great recognition comes responsibilities of a greater degree, a calling of a higher order. And we will continue to honor and live up to them. On this joyous occasion, I would love to thank Team Beinex for what they have achieved! This award belongs to them”, noted Indumon Das, the Founder & Managing Director, Beinex.

Beinex Holdings is a group of six companies. As an organization, Beinex believes in the power of ideas, innovation, and unparalleled customer service to change the world for good. Beinex Digital, a part of Beinex Holdings, is a digital transformation entity with a comprehensive suite of independent products focused on addressing specific business gaps, use cases, and needs.

It incorporates a spectrum of solutions related to employee health and safety, enterprise product management, performance management, and audit & risk management.

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.

The Struggles of Traditional Business Intelligence

Traditional BI platforms, while valuable, suffer from several limitations:

Limited Accessibility: These tools often require extensive technical expertise to navigate and analyze data. This restricts valuable insights to a select few, hindering widespread organizational adoption.

Slow Insights: Extracting insights from traditional BI can be a time-consuming process. Data preparation, analysis, and model building can take weeks, delaying crucial decision-making.

Limited Data Exploration:: Traditional BI tools often restrict users from analyzing a predetermined set of variables. This can lead to overlooking important data points and potentially skewed insights.

These limitations force businesses to make a tough choice: sacrifice the quality of insights by limiting analysis or invest significant time and resources into data preparation and model building.

The Rise of Augmented Analytics

Augmented analytics emerges as a powerful solution, addressing the shortcomings of traditional BI. It leverages the power of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to revolutionize data analysis:

Democratization of Data: Augmented analytics empowers users of all technical skill levels to gain valuable insights. Natural language interfaces allow users to ask questions and receive insights in plain English, eliminating the need for complex queries or coding expertise.

Real-Time Insights: AI and ML algorithms can analyze massive datasets in real-time, delivering insights within minutes. This allows businesses to capitalize on fleeting opportunities and react swiftly to market changes.

Deeper Analysis: Augmented analytics goes beyond basic data exploration. It can automatically identify anomalies, uncover hidden patterns, and drill down to the most granular details within your data, providing a more comprehensive understanding of business performance.

How Does Augmented Analytics Work?

Augmented analytics enhances the four core stages of data analysis:

1. Data Preparation: Traditionally, data preparation involves manual tasks like data cleaning and integration. Augmented analytics automates these processes, allowing data scientists to focus on more strategic tasks.
2. Insight Discovery: Traditional BI requires pre-defined models to uncover insights. Augmented analytics utilizes ML algorithms to analyze all available data, regardless of size or complexity, to deliver targeted and nuanced insights in response to user queries.
3. Insights Sharing: Sharing insights often involves generating reports and charts, a time-consuming task. Augmented analytics utilizes Natural Language Generation (NLG) to present insights in real time through online dashboards. These dashboards explain the "why" behind the data, giving decision-makers the context they need.

The Benefits of Augmented Analytics

By implementing augmented analytics, businesses can unlock a multitude of benefits:

Real-time decision-making: No more waiting weeks for crucial insights. Augmented analytics delivers results in minutes, allowing businesses to seize time-sensitive opportunities.

Deeper data exploration:: Discover the hidden depths of your data. Augmented analytics empowers detailed analysis, revealing the "why" behind trends and enabling more accurate predictions.

Scalability: Seamlessly handle huge amounts of datasets without compromising the performance.

Breaking down data silos: Unify your data and insights. Augmented analytics analyzes data from all integrated sources, providing a holistic view of business performance and fostering cross-departmental collaboration.

Better business decisions: Make smarter choices with comprehensive data insights. Augmented analytics empowers informed decision-making, leading to improved business outcomes.

Augmented Analytics Use Cases

Let's explore how augmented analytics is transforming various industries:

1. Pharmaceutical Companies: Analyze vast datasets to optimize go-to-market strategies and uncover hidden patterns in market share data.
2. Financial Lenders: Assess credit risk with greater accuracy by analyzing all relevant data points in real-time, leading to faster loan approvals and better risk management.
3. Consumer Goods Companies: Gain real-time insights into product sales, customer churn, and satisfaction, allowing for proactive customer engagement strategies.

AI-augmented analytics marks a paradigm shift in how businesses leverage data for informed decision-making. It's not just a novel technology but a strategic necessity in today's data-driven landscape. By embracing AI's power, organizations can reveal a treasure trove of deeper insights, streamline data handling, and foster a more intuitive user experience with data analysis. As this field continues to evolve, we can anticipate a wave of groundbreaking applications that empower businesses to make smarter choices and achieve success. The future of business intelligence BI is intelligent, and the time to act is now.