التبحّر بعمق في تابلو 2019.3

In this data-driven world, enterprises are dependent on humongous quantities of data that are subsequently analysed to uncover trends previously hidden and to carry out business functions. New tools, techniques, and technologies like those of Business Intelligence, Advanced Analytics, Machine Learning are used to analyse data and devise insights-informed strategies.

Also, they help entrepreneurs by guiding them to plan day-to-day operations, ensure fast and accurate reporting, increase revenue, identify new revenue streams, identify revenue leakage…the list is virtually endless.

Gartner explains Advanced Analytics as an autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools to discover deeper insights, make predictions, or generate recommendations. It uses Machine Learning, Artificial Intelligence, Predictive Analytics, Data Visualisations, and Text Mining to examine large data sets.

In fact, Advanced Analytics is generally comprised of two divisions:

• Predictive Analytics
• Prescriptive Analytics

Predictive Analytics: What might happen in the future

Predictive Analysis is the third and most critical process of Advanced Analytics. It uses techniques like artificial intelligence, data mining, machine learning, modelling, and statistics to make predictions. Predictive modelling helps businesses like healthcare, marketing, sales, supply chain etc. to optimize operations, improve customer satisfaction, manage budgets, identify new markets, anticipate the impact of external events, develop new products and set business, marketing and pricing strategies.

Prescriptive Analytics: What should be done

Prescriptive analytics is a vital tool used in creating data-driven decisions. optimizing operations, growing sales, managing risks formulating strategies, and reaching organizational goals. It uses statistics and modelling to recommend future actions by applying data to the decision-making process.

Advanced Analytics and Business Intelligence Market Size

The Advanced Analytics market is showing continual progress as enterprises embrace these tools to effectively manage complex business processes. Reportlinker.com predicts that the global Advanced Analytics market size may grow from USD 33.8 billion in 2021 to USD 89.8 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 21.6%.

A similar trend can be noticed in the case of the Business Intelligence market too. To quote Fortune Business Insights, “the Business Intelligence market is set to reach USD 43.03 Billion by 2028 in connection with rapid digitisation and robust demand for data personalisation to foster market development”.

Business Intelligence

Advanced Analytics is all about predicting future strategies, whereas Business Intelligence is focused on past performance, relying on methods such as querying, reporting, and dashboards. It uncovers trends and presents findings through visualization tools. The results show that companies adopt new approaches to increase operational efficiency and improve sales and customer relations through real-time analysis.

Functions of Business Intelligence are listed below:

Data Mining

Data mining is the process of unearthing information and patterns from massive datasets to visualizing in dashboards to generate inferences to assist the decision-making process. By adopting various techniques and procedures, knowledge is extracted to solve business problems to promote sales and marketing.

Process Mining

Powered by Data Mining and Power Analytics, Process Mining extracts insights from the existing data and helps to find the bottlenecks that hinder efficiency and compliance. It ensures a better customer experience, loT process improvement, identifies and analyses supply chain management weak links, optimises procurement and speed-up payment collection.

Complex Event Processing

CEP employs a set of techniques to analyse Big Data for real-time benefits. Opportunities and threats in business operations are identified and monitored to pave the way to success. Companies adopt CEP for fraud prevention and detection, real-time marketing, stock market trading and allied areas.

Business Performance Management

Widely known as Corporate Performance Management (CPM), BPM implies all processes or methodologies that optimise business performance. It also initiates the achievement of business goals like budgeting, planning, and forecasting and helps to improve employee performance. It identifies risks, selection of goals for progressive development, and streamlines financial processes.

Benchmarking

Benchmarking process is evaluating the management practices of one company with its best counterpart. Comparing the organisational processes in relation to the best performances allows companies to evolve by developing plans to improve their tactics.

Top 5 Advanced Analytics Tools

Alteryx: A self-service platform that can help users extract, clean and analyse data through an automated process.

Anaconda: It is an open-source Python and R-focussed platform to analyse and visualise data.

Google Cloud Platform: Known to be one of the enormous machine learning stacks, Google Cloud AI offers many products to analyse and manage data in real-time.

Knime: An open-source software that visualises data flows and helps discover new insights with minimal or no programming.

MS Azure: It is a platform (PaaS) that combines data from various sources, then stores and finally transforms it for different purposes.

Top 5 Business Intelligence Tools

Tableau: Tableau supports multiple data sources to easily analyse and visualise data in handy dashboards.

Power BI: This business analytics tool which can be accessed from anywhere helps in identifying real-time trends and delivering reports via real-time dashboards.

Qlik sense: It is a popular and complete Business Intelligence tool with its unique search and conversational analytics platform that discover new observations using natural language.

Micro strategy: It offers high speed and powerful dashboarding, cloud solutions and hyper-intelligence that can be accessed from a laptop or mobile.

IBM Cognos Analytics: Designed to discover even hidden patterns, Cognos Analytics interprets and presents data in a visualised pattern.

Conclusion

Business Intelligence and Advanced Analytics go hand in hand, from assisting business operations to improving customer satisfaction. Yet they are distinct from each other in their own ways. The amalgamation of these two technologies – Advanced Analytics and BI – improves the efficiency of business operations, delivering predictions based on historical and present data and enhancing performance in sales, maintenance, and customer satisfaction.

While sharing the content, permissions can be given to each user that restricts them from editing, applying different types of filters, and sharing it further. Users can create apps and add specific visuals to create live dashboards that can be accessed through smartphones and tablets. Moreover, one can collaborate with other designers and administrators to work together and create highly customized reports for a particular field.

Challenge

• The IT infrastructure of Atlantis where the Tableau Server platform was hosted was in an on-premise datacenter which was designed to be scalable and robust with multi node physical clusters including the server, storage and network components. However, most of the physical hardware was quite old and not equipped with the latest generation of physical servers.
• Frequent hardware crashes and portal downtime kept troubling the availability of the Tableau Server application. Assigning a touch hand support person to power on the hardware that was down seemed quite impossible due the restrictions during covid period. Hence, Atlantis wanted to look for another viable solution.
• Though the hardware setup at Atlantis was well equipped to meet the occasional spikes in the traffic, it was observed that over a course of 6-month time, most of the IT infra was underutilized than predicted. It was realized that spending huge amount of money on an old hardware plus software maintenance, license costs, internet bandwidth, datacenter cooling and maintenance, touch support personnel and electricity costs – were keeping the business operations challenging.
• There was an attempt by Atlantis to select a cost-effective solution that can host Tableau Server application servers, web servers and archival data. This way IT infra can be re-provisioned to host sensitive data on-premise and the rest on the cloud, thereby reducing the overall physical hardware costs spent on a yearly basis.

Why AWS

• Atlantis decided to migrate Tableau Server, database servers, and archival data to AWS.
• The Tableau Server’s AWS architecture includes Amazon Elastic Compute Cloud (Amazon EC2), that provides complete control of its computing resources, updates to tables in Amazon Relational Database Service (Amazon RDS) and AWS Elastic Load Balancer was used to distribute the traffic to the underlying EC2 instances based on the load.

Benefits

• Atlantis uses AWS services to provision infrastructure and deploy the Tableau Server platform to other departments within it. In addition, the Tableau Server resources that are no longer required to be run all the time are made to auto shutdown thus saving cost. Atlantis reported a 28% cost reduction after implementation of AWS for the Tableau Server platform.
• The implementation of Tableau Server on AWS made Atlantis confident in the security of its data, and its accreditation team is enthusiastic about the monitoring and auditing capabilities provided by AWS tools. With the implementation of IAM roles, Atlantis IT team was able to isolate systems and tightly control user accesses. These capabilities were harder to achieve within the existing infra but were available out of the box with AWS
• By adopting AWS to host the Tableau Server platform, Atlantis has been able to innovate and experiment to a degree previously impossible. For example, Atlantis compared the performance and cost-effectiveness of three different cloud solutions. Without moving to the AWS, the costs associated with running an outdated on-premise hardware would have creeped up and the alternative way of upgrading the existing on-prem infrastructure to the latest hardware models and then hosting the Tableau Server application on top of it would have taken months.

1. Generative AI Features in Existing Products

Alteryx is proactively identifying areas where GenAI can improve the productivity and efficiency of its customers. These innovations are integrated into existing tools, such as Alteryx Designer, to streamline processes, automate routine tasks, and enhance the overall analytics experience. For example: OpenAI Connector: Users can now integrate GenAI directly into Alteryx Designer workflows to streamline communication and share data more effectively. AI-Generated Workflow Summaries: These summaries automate documentation processes, helping users enhance governance and auditability.

2. Enterprise-Ready Generative AI Platform

Alteryx’s GenAI platform enables businesses to create, train, and deploy custom AI models that operate securely within their organizational firewall. This approach ensures that data privacy and security are maintained while offering organizations the flexibility to tailor AI models to specific business needs. Alteryx also provides an environment for creating proprietary models that are customized to fit each organization’s workflows, making it easier to integrate AI-driven analytics into everyday operations.

3. New GenAI Applications and Interfaces

Data analytics is a collaborative process that involves various stakeholders, including analysts, data scientists, engineers, and knowledge workers. With Alteryx, these roles can now collaborate in real time through multi-modal analytics powered by GenAI. The flexibility to use different analytical tools—like SQL, Python, notebooks, or Alteryx workflows—opens doors for more seamless collaboration across different teams. GenAI applications like Magic Documents allow Alteryx users to automatically generate insight-rich reports in just a few clicks, drastically reducing time-to-insights and increasing productivity across business functions.

Introducing Alteryx AiDIN

AiDIN is Alteryx's umbrella for all AI-related capabilities, combining existing AI features with cutting-edge GenAI innovations. Alteryx AiDIN enables users to leverage advanced AI models for analytics, whether it's extracting insights, automating tasks, or generating complex reports. Some of the key benefits include: • Improved Time-to-Value: AiDIN accelerates the time it takes to derive insights from data, enabling quick decision-making for critical business tasks. • Increased Operational Efficiency: By automating repetitive tasks, AiDIN frees up time for users to focus on higher-value activities. • Enhanced Governance: Alteryx AiDIN ensures that AI capabilities meet stringent enterprise-grade governance and security standards.
Source: https://www.alteryx.com/blog/alteryx-announces-generative-ai-capabilities

Data Security and Trust: The Alteryx AiDIN Commitment

A key concern in any AI-driven platform is data security and the integrity of AI-generated outputs. Alteryx AiDIN prioritizes these through: 1. Mitigating Hallucinations In generative AI, "hallucinations" refer to scenarios where AI models produce plausible but incorrect information. Alteryx has implemented stringent quality checks and continuous feedback mechanisms to minimize these errors. This ensures that businesses can rely on the outputs generated by AiDIN for decision-making. 2. Fact-Checking Mechanisms Alteryx AiDIN integrates fact-checking tools to verify AI-generated insights against actual source data. This added layer of validation helps organizations maintain the accuracy and reliability of their analyses. 3. Data Privacy and Security Alteryx ensures that data privacy is maintained at all stages of the AI process. AiDIN offers two key deployment options: Private Data Handling and SaaS. Both options provide robust encryption and ensure that sensitive data is securely managed within a customer’s ecosystem, giving businesses peace of mind as they adopt AI.

The Future of AI-Driven Analytics

The integration of GenAI into the Alteryx platform paves the way for smarter, more accessible analytics. With capabilities like OpenAI integration, Magic Documents, and enterprise-level model customization, Alteryx is enabling organizations to maximize the value of their data, improve efficiency, and foster a more collaborative analytics environment. By combining GenAI’s potential with trusted, secure analytics, Alteryx is redefining how enterprises interact with data—delivering faster insights and more impactful results across industries. Get in touch with us for a free demo: https://www.alteryx.com/designer-trial/free-30-days?

The following blog depicts the technique created by Alex Jones (@jusdespommes) blog about “How to… create a chart from an image in Tableau!”.

Show/Hide Containers

Tableau 2019.2 has introduced an option to show/hide containers (when the container is a floating type). Many experts have come up with different use cases such as hiding and showing the filters, parameters, or even a worksheet with a click which occupies much space in the dashboard using this feature.

This feature is pretty simple to use and comes in handy when we want to save some space in our dashboards.

How to Swap image in Bar Chart?

1.Create a simple bar chart in Tableau 2019.2. We have used “Top 5 Grossing Games Worldwide 2018” to create a bar chart and sorted based on the revenue.

2.Remove the label, header and export the worksheet to image. Worksheet > Export > Image (Choose ‘view’ in Export Image dialog box)

3. Open Photopea, which is an online photo editor and open the saved worksheet image.
File > Open > Image

4. Now, download 2 images from online which we are going to use for bar chart.

5.Copy and paste image 1 on top of the bar image. Now we will be having two layers of images one above another.
(We can even re-position, crop the image if needed)

6.Choose ‘Magic Wand’ from the tool bar at left side. Click on the image, and we can see the selection in dotted lines.

7.Choose the top layer (image 1) and click delete. Now we can able to get the below image.

8.The background of the image needs to transparent. So, delete the below layer (bar chart image) using delete icon at the bottom right corner.

9.Now, choose File > Export as > .PNG to save the image as .png format (this will retain the transparent layer)

10.Repeat the same steps with image 2 to get the same effect.
Note: Keep the same resolution and image size as the previous one.

10.1.Now, open Tableau Desktop and bring in the image as floating type.

11.We have to position the bar chart over the image. To make it easy, decrease the opacity of the bar chart and give border.

12. Add labels and tooltip to the bar chart.

13.Now the dashboard looks like this. We can able to hover on bars to see tooltip.

14.Now, comes the trick of using ‘Show/Hide Containers’ to swap image. Bring a container to floating and add image 2 to the container. (Set the padding to zero)

15.Next, we have to position the both images and the bar chart one above another. To make it easy, use x, y axes and size w, h to position accurately.

Note: Make sure the position of the 3 objects (Use Floating Order) follows the below order as all are in floating.

1. Image 1 (Below layer)
2. Image 2 inside container (Middle layer – where show/hide option is to be used)
3. Bar Chart (Top layer)

16.Choose the container and right click at the top right corner. Enable ‘Add Show/ Hide Button.’ Now we can see a floating button.

17.We can customize the button by adding image and tooltip to buttons.

18.We have used below customized images (created with PowerPoint) for buttons to swap between 2 images.

19.Another simple trick is that we can control the transparency of the image by adjusting the opacity of the bars.

20.Take a look at the final dashboard….