Supercharge Your Tableau: Benefits of Tableau Accelerators

What is Cloud Computing?

Cloud computing delivers computing resources, servers, storage, databases, and applications, over the internet, replacing traditional local infrastructure. These resources can be accessed anytime, anywhere, and on any device with an internet connection, providing businesses flexibility and agility.

Types of Cloud Services

1. Software as a Service (SaaS): SaaS providers host software applications and make them accessible to users via web browsers on a subscription basis. Popular examples include email services, CRM tools, and project management platforms. 2. Infrastructure as a Service (IaaS): IaaS offers virtualised computing resources like servers, storage, and networking. Businesses can build, deploy, and manage their applications on this infrastructure while maintaining control over the software. 3. Platform as a Service (PaaS): PaaS provides a platform for developers to create, test, and deploy applications without worrying about managing the underlying infrastructure. It offers more control than SaaS and requires less maintenance than IaaS.

Top Cloud Computing Trends for 2025

1. Rise of the Citizen Developer The concept of the citizen developer is reshaping how applications are built. Non-technical users can now create apps using drag-and-drop tools, eliminating the need for extensive coding knowledge. Tools like Microsoft’s Power Platform and AWS’s HoneyCode are leading the way, enabling businesses to streamline workflows and innovate faster. 2. Enhanced AI and Machine Learning Capabilities Cloud providers embed advanced AI and ML features into their services, making intelligent applications accessible to businesses without requiring in-house expertise. Companies like AWS, Google, and IBM are pioneering this space. For instance: • AWS’s DeepLens camera supports machine learning integrations. • Google Lens uses AI to provide real-time information from images. • IBM continues to invest in enterprise AI solutions to revolutionise computing processes. 3. Increased Focus on Automation From enhancing team efficiency to reducing downtime, automation tools are becoming more intuitive and robust. Investments in AI and citizen developer tools further simplify automation, allowing businesses to achieve greater operational efficiency. 4. Continued Investment in Data The need for large-scale data analysis continues to grow, with a shift toward distributed computing environments powered by GPUs. This architecture allows businesses to run real-time analyses on vast datasets, revolutionising how data is processed, stored, and utilised. 5. Heightened Competition The battle between AWS, Microsoft Azure, and Google Cloud Platform is intensifying, driven by competitive pricing, enhanced reliability, and innovative offerings. Expect these giants to continuously improve cost transparency and introduce new features to capture market share. 6. Kubernetes and Docker for Cloud Deployment Kubernetes and Docker are transforming cloud application management. Automating deployment, scaling, and containerised application management enables developers to streamline workflows and quickly deploy scalable solutions. 7. Cloud Security and Resilience As more businesses migrate to the cloud, providers heavily invest in security features like encryption, access controls, and disaster recovery solutions to ensure robust data protection and operational resilience. 8. Multi and Hybrid Cloud Solutions The adoption of multi-cloud and hybrid cloud strategies is growing, allowing businesses to leverage the strengths of multiple providers while maintaining control over their data and applications. These solutions provide flexibility and minimise the risks associated with vendor lock-in. 9. Cloud Cost Optimization Managing cloud costs is a top priority for businesses. Providers are developing tools for cost monitoring, budgeting, and optimisation, helping users make the most of their investments through reserved instance options and sizing recommendations. 10. Edge Computing Edge computing minimises latency and bandwidth requirements by processing data closer to its source. This trend is crucial for real-time applications, enabling faster and more efficient data processing. 11. Disaster Recovery With the rise in natural disasters and cyberattacks, disaster recovery solutions have become a vital focus. Cloud providers offer robust solutions that enable businesses to recover operations quickly and minimise downtime. 12. Innovation and Consolidation in Cloud Gaming Cloud gaming is a booming market, with major players acquiring smaller companies to expand their portfolios. This trend is reshaping the gaming industry, offering more accessible, high-quality gaming experiences. 13. Serverless Computing Serverless computing allows developers to focus on writing code without worrying about infrastructure management. This approach reduces operational costs, increases scalability, and accelerates development cycles.

Summing Up

As these trends demonstrate, cloud computing is transforming dynamically, unlocking new opportunities and redefining how businesses operate. Staying ahead of these trends will be crucial for organisations aiming to harness the full potential of the cloud in 2025 and beyond.

Recommender Engines

Recommender Engines provide suggestions of products based on the interests or requirements of the customers by leveraging AI and Machine Learning technologies. It operates by discovering patterns in data on customer behaviour, which may be gathered directly or indirectly. To put it another way, the AI recommendation engine delivers a collection of recommendations suited to the user's needs, demands, behaviours, and preferences.

Recommender engines are employed to increase sales, boost customer engagement and retention, and provide customised user experiences. According to McKinsey, these approaches can boost a company's sales by 20% and profitability by 30%.

Types of Product Recommendation Engines

The companies should select models that best match their personalisation plans to offer product recommendations to website users. You can choose from the three models given below:

1. Collaborative filtering

The goal of collaborative filtering is to forecast what a person will like based on their similarity to other users by gathering and analysing data on consumer behaviours, interests, and inclinations.

Collaborative filtering uses a matrix-style method to calculate and depict these similarities. It has the benefit of not requiring content analysis or comprehension. It simply chooses which goods to recommend based on what it knows about the consumer.

E-commerce sites reap benefits out of collaborative filtering. For instance, if two users have purchased the same products and have similar interests, the system discovers the similarities and gives shopping suggestions based on them. Later, if either of the same users log in for shopping, it offers tips based on the other person’s interests, as the model knows that both have similar interests. To generate correct recommendations for new users, the engine needs enough customer and traffic data, which is the fundamental component of this strategy.

2. Content filtering

The principle behind content-based filtering is that if you choose one product, you'll probably select the other one as well. To provide suggestions, algorithms compare objects based on a customer preference profile and a description of the item. A series of recommendations are given to the customer based on his preferences and the history of his earlier purchases.

For instance, content-based filtering on YouTube suggests videos to users by gathering data on the related content users have already viewed or searched. It collects data on the content that a specific user has watched, and it then begins to suggest additional content with a related theme based on comparable descriptions.

3. Hybrid Filtering

A hybrid filtering tool examines both content-based and collaborative data using vector equations. It analyses the historical activity data and preferences of the user for whom the recommendations are displayed. In this way, this approach combines the most compelling features of the first two to produce a single, well-rounded answer.

Let’s take the example of Netflix; it considers both the user's interests (collaborative) and the plot, genre or cast of the film or television series (content-based). Then, based on the users' actions, pursuits, and preferences, a collaborative filtering matrix can be utilised to suggest movies or series to them.

3. Hybrid Filtering

A hybrid filtering tool examines both content-based and collaborative data using vector equations. It analyses the historical activity data and preferences of the user for whom the recommendations are displayed. In this way, this approach combines the most compelling features of the first two to produce a single, well-rounded answer.

Let’s take the example of Netflix; it considers both the user's interests (collaborative) and the plot, genre or cast of the film or television series (content-based). Then, based on the users' actions, pursuits, and preferences, a collaborative filtering matrix can be utilised to suggest movies or series to them.

Benefits of Recommender Engines

Product recommendation engines offer your company numerous advantages. Over time, its benefits will offset the expense of putting it into practice. This is how:

1. Customer retention

It is worth emphasising that product recommendation systems are one of the most efficient and widely recognised applications of machine learning in business. When properly configured and implemented, they will boost sales and increase click-through rate as well as customer engagement and other KPIs in every online store. It results from the fact that customising product recommendations and content to the preferences of a specific user has a positive impact on the user's experience with a given website.

2. Increase in sales

When the recommendation system is correctly configured and deployed, product recommendations may lead to an increase in revenues in the online store. Personalising offers increases the likelihood that users will browse the page and stay on it longer. Targeted visitors to the website receive emails or advertisements for suitable products increases the efficacy of marketing campaigns. It reduces the rate of returns and cart abandonments. Finally, the Average Order Value (AOV) and the number of items in carts are both significantly increased by recommendation engines.

3. Customer behaviour detection

The ability to provide a wide range of relevant facts and metrics regarding user behaviour and website traffic is another benefit of personalised recommendation systems. Online store owners who have incorporated recommendation systems have a better grasp of customer behaviour and may adjust the product selection to suit their demands. Customers do not need to spend time browsing through all of the products on the website because those that they could find interesting will be displayed in the recommendation box with suggested products.

Smart Avatars as Advanced Recommender Engines

Currently, recommender engines have a standard text-based user interface as their front end. The arrival of the 3D web and the metaverse, however, will cause that front end to become more avatar-focused over the next years. So, in the near future, you will be greeted by a smart avatar on a shopping website, who will not only have some knowledge of who you are and what you might desire, but it will also engage in dialogue with you to learn more about your wants and assist you in finding the solution. Isn’t that cool? The avatar will ensure that you got a great shopping experience and instantly address any complaints that cross your mind. We are gonna love it, aren’t we?

Summing Up

By presenting products that customers would probably not have otherwise seen, a recommendation system will enhance the shopping experience. The efficiency of recommendation engines as a marketing tool can increase sales, click-through rates, engagements, and consumer happiness. No matter what technology you use, the installation procedure is quick and straightforward and doesn't require any programming experience.

Beinex Offerings

Beinex enables organisations to analyse data, mitigate risks, identify opportunities, make better decisions, and automate processes to drive business excellence powered by innovation and experience. Our AI solutions make your business future-ready and include services like risk sensing and cognitive risk anticipation using Machine Learning (ML), Artificial Intelligence (AI) to assess risk in real-time. Just give it a try, and reach out to us at: https://www.beinex.com/ai-ml-rpa/

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.

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.

Advanced Analytics

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.

Looking to up your game in Business Intelligence and Data Visualization? Want to explore the endless possibilities of Tableau, showcase your work, and get valuable feedback from like-minded peers? Then TUG Meetup is the right event.

The Second TUG Cairo Meetup

Beinex has sponsored the second Tableau User Group Meetup in Cairo, and we are glad to initiate a thriving community that promotes learning, growth, and networking. TUG provides the perfect platform to connect with others, exchange ideas, and gain inspiration whether you're a beginner or an expert in Tableau. At the second TUG Meetup Cairo, the participants got the opportunity to showcase their work, receive constructive feedback, and be part of a community that shares their passion for data visualisation.

Date: March 11, 2023
Venue: MQR, The GrEEK Campus Downtown, Cairo Governorate 11513
Time: 2 PM to 5 PM GST.
We had a great discussion with the following eminent speakers on the topics enlisted below:

#	Speakers	Designation	Topics
1	Khaled Hoza	Data Analyst Team Lead, Fawry	Analyse the performance of your customer retention strategy
2	Martina Ghali	Senior CRM Specialist, Cartona	Overview of digital marketing and get it visualised in Tableau
3	Ahmed Ismail	NLP Engineer, Agolo	Visualise your text analytics in numerous ways

Abdelaziz Mahjoub, Data Analytics Lead Consultant, Beinex was the leader of the TUG Cairo Meetup. He is the first and only Tableau Public Ambassador in Egypt and MENA Region, Tableau public featured author, 1x #VOTD, and Tableau certified associate with practical and academic knowledge of Essential Design Principles techniques.

Initiating a New Tribe that Helps to Grow

[sc name="quote" quote="“Since my early days in the field, I struggled to get help on how to start, a lot. I know how hard it’s to fully understand a certain technology or a tool to a mastery level without proper guidance and mentoring. Here in Cairo TUG, I try my best to help other people not to find themselves in my position back then. That’s why I started Cairo TUG, to build a strong community that everyone can rely on to get help and find the proper guidance”" author="Abdelaziz Mahjoub, Lead Consultant, Analytics at Beinex Consulting,
He is the master brain behind the Cairo TUG."][/sc]