Google Cloud Platform is a Google-delivered complete set of cloud computing services. The services extend to networking, storage, application development, computing, Big Data and even more, which operate on the same cloud infrastructure used internally by Google for Gmail, YouTube, and others. What makes GCP a reliable and secure cloud infrastructure to build, test and run applications is the fact that its server has not gone down in years. IT professionals, software developers and cloud administrators can access GCP services online.

Why choose the Google Cloud Platform?

In 2022, Gartner Magic Quadrant Cloud Infrastructure and Platform services named Google as a leader for the fifth time in a row. Google Cloud Platform's global network of data centres spans multiple continents, ensuring low-latency access and redundancy for your applications and data. Therefore, GCP can be the perfect choice for organisations looking for a globally renowned cloud platform known for its wide array of services and offerings. GCP's extensive catalogue of services with unique features can be attributed to the global expansion and recognition of the platform. Some of GCP's significant services include Computing, Storage, Networking, Big Data, Cloud AI, Security and Identity Management, Management Tools, and IoT.

Besides, the following aspects also add to the reasons why GCP is a viable cloud provider for businesses:

1. Provides multi-level security to safeguard resources like assets and operating systems
2. Has a network infrastructure comprising physical, logistical, and human-resource-related elements, like wiring, routers, switches, and firewalls
3. Has proficient experts who provide support on installation and maintenance

Key Benefits of Google Cloud Platform

Optimal Pricing

GCP enables customers to access computer resources located in Google's global data centres at no cost or on a pay-per-use for the services and resources used. GCP hosting plans are cost-effective compared to other platforms and offer superior features.

Heightened Security

With features like data encryption, multi-factor authentication, and identity and access management, GCP prioritises the security of client data and applications.

Remote Accessibility

Google's web-based applications provide users with complete accessibility to GCP from virtually anywhere.

Accelerated Digital Transformation

GCP delivers enterprise-grade solution architectures and tech strategies to provide scalability and expedite digital transformation.

Dedicated Network

Google boasts its proprietary network infrastructure, granting users greater control over the functions of GCP. As a result, users experience seamless performance and heightened efficiency across the network.

Strengthened Security Infrastructure

GCP offers tools for automation, compliance and governance and a secure cloud environment to navigate challenges in cloud operations.

Leveraged AI Capabilities

GCP enables organisations to harness the power of AI to automate processes, gain data-driven insights and employ machine learning for innovation.

Big Data and Storage Options

With services like Bigtable and Cloud Storage, GCP benefits organisations in managing extensive data and facilitating real-time data processing and analysis.

Real-World Business Challenges & GCP Solutions

GCP’s suite of solutions assists organisations in tackling challenges in the dynamic business landscape effectively. Some common challenges in business and their respective GCP solutions are briefed below.

Managing large volumes of data:

GCP equips your business with analytics tools and robust data storage to manage extensive data effectively and derive valuable insights.

Driving continuous innovation:

With development and deployment tools like Cloud Functions and Google App Engine, GCP enables organisations to expedite development and gain a competitive edge.

Expanding globally:

GCP’s extensive global network infrastructure aids businesses by ensuring the applications reach across the world seamlessly.

Safeguarding against data breaches and cyberattacks:

With its suite of security tools for threat detection, data encryption and access and identity management, GCP safeguards data and applications with multi-level security.

Assuring business continuity:

In the event of unanticipated disruptions that halt business operations, GCP ensures business continuity with its backup options and disaster recovery solutions, making critical applications and data accessible.

X (formerly Twitter), eBay, PayPal, and 20th Century Fox are some of the top users who have leveraged the transformative potential of Google Cloud Platform. Being a globally recognised brand for its speed, performance, security, reliability and innovation, the Google Cloud Platform is a beacon of digital transformation for businesses navigating the challenges of the data-driven digital era. As companies venture on their journey with GCP, the prospects are endless. This partnership empowers businesses with the tools, resources, and support needed to thrive in a dynamic landscape. Whether achieving operational efficiency, reducing costs, or delivering superior customer experiences, GCP catalyses change.

What can Beinex do for you?

Beinex is now a service partner of GCP and is helping businesses advance their digital transformation endeavours by leveraging GCP’s AI capabilities, cloud infrastructure, and data analytics. Beinex offers clients expert guidance in deploying proactive solutions and using Google Cloud to make more informed data-driven decisions. This approach enables them to overcome business challenges and fosters competitiveness, efficiency, and growth. At Beinex, we deploy Google Cloud Platform as a service and the infrastructure as a service, enabling organisations to streamline access to a broader array of services and resources, resulting in cost efficiency and improved quality.

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.

What is Generative AI

Generative AI is a subfield of Artificial Intelligence that utilizes patterns found in vast databases to produce original content, including text, images, music, and videos. GenAI aims to provide creative and human-like outputs, in contrast to classical AI, which primarily makes predictions or classifies data. Generative AI models, such as OpenAI's ChatGPT and DALL-E, utilize sophisticated neural networks, specifically transformer architecture, to produce content that is logical and sensitive.

Industries are transforming with the help of generative AI, and its benefits are innumerable. Marketers are using it to automate campaigns and generate personalized content at scale, while writers and creators rely on it to spark ideas and accelerate production. In healthcare, it's being explored for diagnostics, treatment planning, and medical research. At its core, Generative AI isn't just a tool; it's a transformative force reshaping how we create, innovate, and solve complex problems across sectors.

GenAI Solutions in the UAE

The Generative AI market in the UAE is on an impressive growth trajectory. Currently, the market is estimated to have reached USD 220 million and is expected to surpass USD 1.3 billion by 2030, growing at a CAGR of over 35%. With the UAE's commitment to becoming an AI-driven economy, including initiatives such as the UAE National AI Strategy 2031, the region is emerging as a hub for AI adoption and innovation.

Top 10 Benefits of Generative AI

Generative AI is reshaping how businesses create, operate, and innovate. Here are the top ten key benefits of GenAI that you can leverage for your business:

1. Automates Content Creation

Generative AI tools streamline content development, including blog posts, ad copy, social media content, and other types of content. Marketing teams use AI to generate drafts, brainstorm ideas, and iterate quickly. It speeds up production, improves quality through iterative feedback, and reduces the need for hiring additional staff. GenAI tools can craft landing page content or email campaigns that effectively highlight your brand's voice.

2. Delivers Hyper-Personalized Experiences

AI leverages customer and product data to generate personalized recommendations and messaging. In e-commerce, this can mean showing the right product to the right user at the right time. Personalized AI outputs enhance engagement and conversion rates.

To ensure ethical outcomes, businesses are auditing training datasets to prevent bias, which is particularly crucial in sensitive sectors such as healthcare, finance, and hiring.

3. Enhances Product Design and Innovation

AI models analyze market trends and customer behavior to guide product development. By processing vast datasets, they uncover unmet needs and help generate concepts that align with evolving consumer preferences. Many GenAI tools aid in rapid prototyping and idea testing.

4. Strengthens Cybersecurity

Generative AI boosts threat detection by identifying anomalies in network traffic and alerting teams in real time. It excels at identifying phishing patterns, malware signatures, and unusual behaviors more quickly than manual reviews. As attackers also begin using AI, this defense becomes increasingly critical.

5. Accelerates Healthcare Research

Generative AI is expediting drug discovery and diagnostics. AI also allows the generation of synthetic patient data, facilitating preclinical testing without privacy risks. It shortens development timelines and supports personalized medicine by analyzing genetic and clinical datasets. It can also predict diseases before they strike us.

Read Our AI in Healthcare Case Study on Cardiovascular Disease Prevention!

6. Streamlines Business Processes

AI automates repetitive tasks such as summarizing reports, drafting emails, or analyzing PDFs. GenAI Tools allow teams to focus on strategic work rather than data wrangling. For example, HR teams can auto-generate job descriptions, and sales teams can craft personalized follow-up emails using AI.

Book a Free Demo of Our Document Chatbot

7. Improves Customer Support

Generative AI chatbots offer 24/7 support, providing context-aware responses to resolve queries. Unlike traditional bots, these systems adapt in real time, understand tone, and escalate issues when necessary. Businesses utilize various tools to achieve faster resolution times and higher satisfaction scores.

8. Accelerates Market Innovation

By analyzing market signals, customer behavior, and industry shifts, AI uncovers opportunities for product, service, or business model innovation. It reduces risk and helps companies make data-backed decisions about where to invest. AI can forecast trends and simulate outcomes before committing resources, allowing for informed decision-making.

9. Drives Digital Transformation

AI encourages traditional industries, like oil & gas, construction, logistics, and agriculture, to adopt technology by demonstrating clear ROI. Predictive maintenance, supply chain optimization, and workflow automation are just a few areas where AI proves valuable. It helps leaders make faster, more informed decisions, accelerating digital adoption.

10. Accelerates Creative Innovation

Generative AI serves as a brainstorming partner. Designers utilize tools like Midjourney for rapid visual prototyping, while writers and product teams employ chatbots to refine their ideas. These tools provide novel starting points, enabling creators to break through mental blocks and explore new directions more quickly.

Summing Up

Beinex GenAI Solutions is at the forefront of transformation, helping organizations in the UAE explore the full potential of generative AI. As one of the recipients of the Dubai AI seal, Beinex is enabling businesses to innovate faster and operate smarter, from automating content generation to creating intelligent decision-making systems. Businesses that adopt it strategically are gaining a competitive edge, not just by saving time, but by reimagining what's possible.

Explore Beinex GenAI Solutions!

Top Differences between Dynamic Set and Fixed Set

Dynamic Set

1. Set members change when the underlying data changes.
2. It has a single dimension.

Fixed Set

1. Set members do not change.
2. It can be single-dimensional or multidimensional.

Steps to Create a Dynamic Set

The process to create a dynamic set is as follows: In the Data pane, right-click on the sub-category dimension and choose Create > Set. (Figure 1)

Figure 1: Creating a Set

• In the Create Set dialog box, set up your set. You can configure it using the following tabs:
1. General: Use the General tab to choose one or multiple values to be considered when computing the set. Alternatively, you can choose the Use All option to consistently consider all members, even when new members are added or removed.

If you know the top-selling products beforehand, you can manually select the products as shown in Figure 2 below.

Figure 2: Creating a Set using General Tab

2. Condition: Utilize the Condition tab to establish criteria that decide which members should be incorporated into the set.

You can specify this condition and create the set if you need products with sales greater than $50,000. (Figure 3)

Figure 3: Creating a Set using Condition Tab

3. Top: Employ the Top tab to set restrictions on which members should be included in the set.

For instance, you can establish a limit based on total sales, where only the top 5 products with the highest sales are included. (Figure 4)

Figure 4: Creating a Set using Top Tab

• Once you have completed the configuration, click the "OK" button.
• The newly created set will appear at the bottom of the Data pane within the Sets section. You can identify it by the set icon, which denotes a set field.

Steps to Create a Fixed Set

The process to create a fixed set is as follows:

• In the developed visualisation, select one or more marks from the view (Figure 5).

Figure 5: Selecting the marks.

• Right-click on the selected mark and select “Create Set” (Figure 6).

Figure 6: Creating the set.

• Type a name for the developed set (Figure 7).

Figure 7: Typing the Set Name.

• When finished, select “OK”. This newly created set can be accessed from the data pane. When this set is placed in the filter, the view will be filtered to show only the relevant set values.

Top Benefits of Sets in Tableau

• Top N or Bottom N Analysis: Sets can filter the data to display only the top or bottom N values based on a specific condition. For example, you could create a set to show the top 10 products by profit or even combine sets and display the top N and bottom N products by profit in a single chart. (Figure 8)

Figure 8: Top 3 and Bottom 3 Products by Profit

• Segmentation Analysis: Sets can also segment data into groups based on a specific condition. This can be useful for analysing performance differences between different groups. For example, you could create a set to segment customers based on their geographic location.

• Excluding Data: Sets can be used to exclude specific data points from a visualisation. For example, you could create a set to exclude customers who have not purchased in the last six months.

What is Set Actions in Tableau

Set actions allow users to modify the values within a set, on selection of marks within a view. This enables your audience to engage directly with a visualisation or dashboard and control various aspects of their analysis.

To utilise set actions:

1. Create sets associated with your data source.
2. Build set actions using the created sets.
3. Optionally, create calculated fields that incorporate the sets.
4. Construct visualisations referencing the sets.
5. Test and adjust the set actions for desired behaviour.

To create a set action that helps in drilling down category:

1. Create a set that selects a particular category (Figure 9 shows creating a set using the general tab selecting only furniture)

Figure 9: Creating Category Set using General Tab

2. If you are in a worksheet, go to Worksheet > Actions.
If you are in a dashboard, go to Dashboard > Actions.

3. In the Actions dialog box, click “Add Action” and choose "Change Set Values."

4. In the Add/Edit Set Action dialog box:

• Provide a descriptive name for the action.
• Choose a source sheet or data source. By default, the current sheet is selected. If you opt for a data source or dashboard, you can select specific sheets within it.
• Choose the desired method for users to execute the action:

1. Hover: The action will trigger when a user hovers the mouse cursor over a mark in the view.
2. Select: The action will activate when a user clicks a mark in the view.
3. Menu: The action will initiate when a user right-clicks (or control-click on Mac) a selected mark in the view and then selects an option from the context menu.

• To specify the target set:

1. First, choose the data source from the available options.
2. Then, select the desired set from the Target Setlist.

Figure 10: Setting up set action

• Specify what happens when the action is run in the view:

1. Assign values to set - Replaces all values in the set with selected values.
2. Add values to set - Adds individually selected values to the set.
3. Remove values from the set - Removes individually selected values from the set

• When the selection is cleared in the view:

1. "Keep set values" will retain the current values in the set without any changes.
2. "Add all values to set" will include all possible values in the set.
3. "Remove all values from set" will remove all previously selected values from the set.

5. After configuring the desired behaviour, click "OK" to save the changes and return to the view.
6. To ensure the set action functions as intended, interact with the visualisation, and test its behaviour.

Benefits of Set Actions

1. Filtering: Set actions can filter data based on user selections. For example, you could create a set step that filters the data to show only the top 10 customers in a particular region.
2. Highlighting: Set actions can also highlight data based on user selections. For example, you could create a set action highlighting all the customers who have purchased in a particular month.
3. Drill-downs: Set actions can create drill-downs that allow users to explore the data in greater detail. For example, you could create a set action enabling users to drill down from a high-level view of category by sales to a more detailed view of sales by sub-category. (Figure 11)

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

Keep an eye on the costs associated with data sharing, especially if you're sharing data externally. Understand the pricing model and optimize data sharing configurations to manage costs effectively.

By adhering to these best practices, you can:
1. Shield Sensitive Data: Employ secure views to fortify sensitive information.
2. Navigate Data Privacy Regulations: Ensure compliance with data privacy regulations by controlling access and usage.
3. Illuminate the Purpose of Each Share: Maintain transparency regarding the intended purpose and content of each shared dataset.
4. Efficiently Monitor Usage and Performance: Keep a finger on the pulse of usage patterns and optimize performance for streamlined data sharing.
5. Elevate Your Data Analysis Journey: Enrich your analytics by exploring diverse data sources and unlocking fresh perspectives.

What’s Next

1. Enhanced Data Collaboration Tools:

Snowflake is expected to introduce advanced features for more streamlined and efficient data collaboration, addressing the evolving needs of industries.

2. Innovations for Privacy-Preserving Analytics:

Snowflake aims to develop and implement solutions that uphold data privacy while enabling powerful analytics, aligning with the growing concerns in this area.

3. Adaptation to Changing Industry Dynamics:

Snowflake is likely to evolve its platform to meet the challenges posed by changes in industry dynamics, ensuring it remains a robust solution for data management and collaboration.

4. Integration of Cutting-Edge Technologies:

Snowflake may integrate emerging technologies, enhancing its capabilities to support new use cases and data-intensive analytic methods.

5. Continued Emphasis on Secure Data Collaboration:

Future updates from Snowflake may include even more robust security measures and innovations in data clean rooms, reflecting a commitment to secure data collaboration in the face of a dynamic data landscape.

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.