For instance, the dashboard given below mirrors a project undertaken for a client seeking insights into the pandemic's impact on their business across specific areas. They wanted to determine the number of stores stocking their product within a defined radius, highlighting the local business impact amid the pandemic.
To craft the map showcased in this dashboard, we leverage Tableau's map layers feature introduced in version 2020.4. For further insights into this functionality, additional details can be found here.
Prior to initiating the map creation process, frequently refer to the Profit Margin field. Here's the calculation for this field: it computes the percentage of Sales that translates into Profit. This calculation enables us to gauge the profitability derived from our sales figures.
For the States map layer, the State field is utilized and placed on the 'Detail' shelf. Each state is color-coded based on its Profit Margin.
Moving to the Cities layer, the City field is added onto the top left area labeled "Add a Marks Layer." To ensure the visibility of every city, the State level of detail is included as well. This accounts for cities existing in multiple states, displaying every city/state combination. Cities are color-coded using the Profit Margin field, with additional color based on the absolute value of the Profit Margin. This helps visualize the range and direction of profitability for each city.

Buffer Calculation

The Buffer calculation generates a radius, known as a "buffer," around a specific map point, defined within the syntax parameters. Here's the syntax breakdown for the Buffer: The initial part determines the center location, followed by the distance around the point, and finally, the chosen unit of measurement.
To establish the desired centroid point, we employ the Makepoint function. This function simply utilizes latitude and longitude coordinates to generate a point on the map. Below is the calculation illustrating its usage.

To achieve the interactivity you desire, you'll begin by creating three parameters: [Location Lat], [Location Long], and [Radius]. These parameters offer flexibility, allowing you to adjust them within the dashboard interface.
As you click on different cities, the [Location Lat] and [Location Long] fields dynamically change, altering the central point. Meanwhile, the [Radius] field, functioning as an input parameter, enables you to modify the radius distance according to your preferences. This setup grants you personalized control over these parameters directly within the dashboard.

With the creation of the final map layer field, you can now drag this field to the top left of the map and add it to the existing layers. Once done, you'll have all the map layers integrated into the map, allowing you to recreate the dashboard as depicted below. This comprehensive setup will mirror the dashboard layout and functionality.

Parameter Actions

Parameter Actions are essential at this stage to ensure dynamic interaction within the map layers. By implementing parameter actions, we enable the Location Lat and Location Long fields to adjust dynamically when clicking on a city. This action directly affects the MAKEPOINT() field within the Buffer calculation, effectively altering the radius location. Below, you can observe the setup of the parameter action and how it facilitates this dynamic transformation.
Finally, we aim for these parameters to influence the available metrics showcased at the top of the dashboard. These metrics offer insights into the concentration of profit and profit margin within the selected radius. Below, you'll find the supporting calculations and the formulae for the metrics displayed on the dashboard. These metrics serve as indicators of profitability and profit margin concentration within the chosen radius.

Wrapping up, creating interactive data visualizations opens doors to explore and comprehend information, fostering informed decision-making and exploration of new analytical paths.

Key Benefits of AI in the UAE Banking

The tech-savvy UAE banking firms have openly welcomed conversational AI, robo-advisors, and AI-based cybersecurity solutions for risk assessment, fraud detection, customer service automation, and algorithmic trading.

AI Transforms the Banking Sector in the UAE: Key Benefits

The transformative power of Agentic AI has empowered virtual assistants to provide instant support, handle inquiries, process transactions, and offer financial advice autonomously. Virtual Assistants with Agentic AI functionalities make independent decisions, act, and adapt to changing situations with minimal human input. Key Benefits of Agentic Integrated & Secure AI Chatbots in Banking Agentic-integrated AI chatbots can be beneficial in banking in multiple ways, like: • 24/7 Availability: Unlike human agents, AI chatbots operate round the clock, improving response. • Cost Efficiency: Reducing the need for human intervention lowers operational costs. • Multilingual Support: AI chatbots cater to the UAE’s diverse, multilingual customer base. • Agentic/Hyper AI Capabilities: Advanced chatbots now incorporate agentic functionalities, enabling them to perform specific user-triggered actions, such as fund transfers, payments, and lodging complaints, directly on the banking account. • Integration with Banking Systems & WhatsApp Banking: Modern AI chatbots are designed for smooth integration with core banking systems and popular communication channels like WhatsApp, providing customers with a unified and convenient digital experience. • Agentic Compliance and Data Security: Advanced agentic AI systems continuously monitor all interactions and transactions, ensuring that every action complies with industry regulations while safeguarding sensitive customer data.

One of the most important uses of AI in the banking industry today is enhancing fraud prevention, anti-money laundering (AML) measures, and transactional analysis. Traditional fraud detection systems use rule-based techniques that frequently fail to identify complex cyber threats, sophisticated money laundering schemes, and unusual transactional patterns. Conversely, AI-driven fraud detection systems use machine learning algorithms to instantly examine millions of transactions and behavioral data in real time. By integrating AI, banks in the UAE can proactively detect and prevent financial fraud, curb money laundering activities, and identify irregular transaction behavior, thereby safeguarding customer assets and bolstering institutional credibility.

Banks must ensure strict compliance with anti-money laundering (AML) laws, data protection regulations, and risk management guidelines; otherwise, it can lead to hefty fines and loss of bank licenses. Artificial intelligence (AI) in the UAE banking sector is transforming compliance by automating risk assessments, enhancing due diligence, and ensuring real-time regulatory adherence. Read Our Success Story: How a Large Regional Bank Improved Regulatory Compliance to More than 90%

Key Benefits of AI in Compliance The use of AI in compliance can offer several benefits for the banking industry in the following ways: Automated AML & KYC Processes • Detects suspicious transactions and money laundering to ensure strict compliance • Automated KYC (Know Your Customer) solutions verify customer onboarding while ensuring compliance. Regulatory Reporting & Risk Management • Automated compliance report generation ensures accuracy • Regulatory risks can be predicted using ML models Data Privacy & Protection • Adherence to data privacy regulations like GDPR can be ensured

The next frontier in AI-driven banking is hyper-personalization, where AI analyzes vast datasets to deliver customized experiences. Example: Personalized Investment Solutions for First-Time Investors AI can assess a mass-segment client’s financial behavior and recommend suitable investment solutions. First-time investors can receive the following: • Automated risk assessment based on their spending and saving patterns. • Customized portfolio suggestions aligned with their financial goals. • Predictive alerts for market trends to optimize investment decisions. Hyper-personalization ensures that even mass-segment clients receive expert financial guidance, making investment opportunities more accessible.

AI Regulation in the UAE

The Future of AI in UAE Banking

Enterprises today don’t suffer from a lack of data; instead, they’re overwhelmed by it. The real challenge before organizations is turning that data into well-timed decisions. This is where AI decision-making comes in. Enterprises are increasingly relying on AI-driven decision intelligence to guide strategy, enhance operations, and achieve better business outcomes. Let’s see how it drastically changes how modern businesses operate.

According to McKinsey & Company, 88% of organizations now use AI in at least one business function, up from 78% a year ago. This shift shows that AI is moving from experimentation to a central role in decision-making. Gartner predicts that by 2028, at least 15% of daily business decisions will be made autonomously, and 33% of enterprise applications will include agentic AI.

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.

Snowflake Data Sharing

Data sharing in Snowflake equips you to share specific objects with another Snowflake account or a designated reader account. The beauty of this process lies in the fact that the data isn't duplicated or moved between accounts.
Now, why is this a game-changer for organisations? When constructing data pipelines and developing data products, it's a common practice to shuttle data between databases and diverse systems to blend different datasets.
Consider this scenario: You have transactional data within your online transactional processing (OLTP) database, and you wish to integrate it with external data for a machine learning model. Traditionally, organizations would export data into a data lake, import external data, and then employ tools like Apache Spark for analysis.
But what if, instead, you could simply deposit your data into Snowflake, and the external data source could seamlessly share its data with your organisation, eliminating the need to load it separately? This eradicates the challenge of keeping data copies synchronised, resulting in savings on storage, computing costs, and maintenance efforts.
Imagine your company possesses valuable information that can guide other companies in making informed decisions. For instance, let's say your company can provide precise estimates for product delivery times based on proprietary data, and you want to offer this information for sale to your customers.
Enter Snowflake data sharing—it empowers you to precisely do that.

Case Studies: Snowflake Data Sharing

Citing two instances where leading organisations use Snowflake to improve actionable data sharing, collaboration and reporting capabilities.

1. A Pioneering Technology Leader A well-known Swedish-Swiss multinational corporation successfully implemented a streamlined data strategy using the Snowflake Data Cloud, adopting an "extract once, use everywhere" approach that simplified data consolidation and enablement. By transitioning from nightly extracts, which caused significant system overhead, to a single, near real-time Change Data Capture (CDC) process, the company achieved efficient replication of information to Snowflake with minimal impact. The utilisation of Snowflake Secure Data Sharing facilitated secure and governed data collaboration across the four business areas.
2. A Leading fast-food Restaurant Chain Snowflake's data-sharing capabilities have revolutionised decision-making for a fast-food restaurant chain. They can effortlessly share crucial sales, inventory, and operational data with external entities, expanding from three to over 30 parties.
With a high-performance database platform hosting over 2 million transaction records, the restaurant chain has established a robust data management and analysis infrastructure through Snowflake, empowering its operational and marketing endeavours.
Moreover, by consolidating all data onto Snowflake, the organisation has achieved a remarkable 70% reduction in operational IT costs, demonstrating the platform's efficiency and cost-effectiveness.
Centralising and sharing data with Snowflake significantly eased the development of data products for various purposes, including marketing campaign analytics, quotation success metrics, production line tools, and supply chain dashboards. These data products are utilised by thousands of users globally, including internal stakeholders and external vendors, enhancing collaboration and efficiency across the organisation.

What are the best practices for Snowflake data sharing?

Optimize your Snowflake data sharing experience with these essential practices. Ensure data security by utilizing secure views to filter and mask sensitive information. Enhance clarity and understanding by employing descriptive names and comments for your shares. Monitor and fine-tune your sharing activities using Snowflake Information Schema or Account Usage views. Foster communication and collaboration with your consumers to create a seamless workflow.
Take command of your data sharing environment by setting quotas and limits with the ALTER SHARE command. Keep your consumers informed about any changes or updates to your shares, and actively seek feedback to refine your data-sharing strategy. Explore additional data sources through Snowflake Data Exchange or Data Marketplace to enrich your analytics.
These best practices safeguard sensitive data, ensure compliance with data privacy regulations, clarify the purpose of each share, and provide insights into usage and performance, ultimately enhancing your data analysis capabilities. Below are some best practices for data sharing with Snowflake:

1. Understand Snowflake Data Sharing
Familiarize yourself with Snowflake's data sharing features, such as Secure Data Sharing (SDS) and Sharehouse, to leverage the platform effectively.

2. Role-Based Access Control (RBAC)
Implement strong RBAC policies to control who can share data and who can access shared data. Define roles and permissions to ensure data security and compliance.

3. Secure Data Sharing
Use Secure Data Sharing (SDS) to securely share data with external parties without copying or moving the data. Implement encryption and access controls to protect sensitive information.

4. Sharehouse Best Practices
If using Sharehouse, follow best practices for creating and managing share objects. This includes defining share schemas, tables, and using the appropriate share options for your use case.

5. Data Masking and Redaction
Apply data masking or redaction policies to shared data to protect sensitive information. Ensure that shared data complies with privacy regulations and internal data governance policies.

6. Query Performance Optimization
Optimize query performance for shared data by using clustering keys, partitioning, and indexing. This helps enhance the efficiency of queries on large datasets.

7. Versioning and Change Tracking
Implement versioning and change tracking mechanisms to keep track of updates and changes in shared data. This ensures data lineage and helps with auditing and troubleshooting.

8. Documentation and Metadata
Maintain comprehensive documentation and metadata for shared datasets. Include information about the source, purpose, and any transformations applied. This helps users understand the shared data context.

9. Governance and Monitoring
Establish governance practices for data sharing, including regular reviews of shared data objects and access logs. Monitor data-sharing activities to identify any anomalies or potential security issues.

10. Educate Users
Provide training and documentation for users involved in data-sharing activities. Ensure they understand the best practices, security protocols, and the impact of data sharing on performance.

11. Regular Audits and Reviews
Conduct regular audits and reviews of shared data objects, permissions, and access controls. This helps maintain data integrity, security, and compliance with organizational policies.

12. Cost Monitoring

What’s Next

1. Enhanced Data Collaboration Tools:

Best Way to Share Data for Your Business

For secure collaboration, old ways of copying data are no longer the best. If you're working with trusted partners and it's privacy-compliant, Snowflake Secure Data Sharing is a quick and secure option. But, if you're dealing with sensitive or regulated data, especially when the risk is high, consider using a data clean room for an extra layer of security and compliance.

Beinex + Snowflake Offerings

Beinex’s partnership with Snowflake enables us to offer you advanced features like automated tuning and elastic compute, along with analytics modernisation services, to help your organisation realise exponential Return on Investment.