The rapid emergence of agentic AI over the past year is perhaps one of the best demonstrators of how fast AI — and the need for strong data practices to support it — is moving. Generative AI, on the other hand, was the exciting new tool a few years ago and has progressed from experimental hype to being embedded across business functions. In just the past two years, organizations from every sector went from scrambling to figure out how best to capitalize on the enormous potential of this technology, to seeing clear ROI from gen AI use. In our recent report, The Radical ROI of Gen AI, Snowflake-sponsored research by Enterprise Strategy Group (ESG) confirms that gen AI works: 92% of early adopters surveyed worldwide report that their gen AI investments have already paid for themselves, with an average return of 41% for those who have calculated the ROI. This significant return is driving a rapid acceleration toward a transformative future. Today, AI is influencing many parts of daily life, from personalized entertainment recommendations to manufacturing supply chains
delivering goods.
92%
of early adopters worldwide report that their gen
AI investments have already paid for themselves.
Not only that, but organizations that are further along in their AI adoption are using AI agents across their operations. These are sophisticated models capable of performing complex, multi-step tasks independently, with little or no human intervention. They represent the next evolution in AI, moving beyond content creation and pattern recognition to dynamic reasoning and interactive problemsolving. In fact, 72% of early adopters expect autonomous agents to take over some tasks by the end of 2025.
The potential uses for and value of AI, including new agentic capabilities, are vast and span virtually every major industry. In this guide, we will explore myriad ways that organizations in a range of industries are leveraging data and AI to drive success. Here are just a few examples:
Healthcare: Using vast patient datasets to reveal patterns, predict health outcomes, and enable more precise diagnoses and personalized treatments, while also automating routine administrative functions.
Financial services: Rapidly analyzing extensive market data to identify emerging trends, inform strategic investment decisions for maximizing returns and streamline complex operational workflows.
Retail: Transforming customer data into highly personalized shopping journeys, boosting customer satisfaction and fostering lasting loyalty, alongside optimizing demand forecasting.
Public sector: Enhancing the ability to predict disease outbreaks and disaster impacts, facilitating the swift and accurate deployment of emergency services, and streamlining the delivery of citizen services.
Manufacturing: Employing AI-driven visual inspection systems to detect unusual patterns and deviations in production, identify quality issues and product defects, thereby enhancing overall quality control.
Advertising, media and entertainment: Extracting deep insights from unstructured data to pinpoint customer behaviors, sentiments and trends, enabling the creation of highly personalized and timely experiences for audiences
Telecommunications: Proactively identifying and resolving network issues and service disruptions to enhance service quality, reliability and operational efficiency, with the ultimate goal of moving toward autonomous network management.
In the next few years, many organizations will roll out new AI use cases, citing the potential for significant returns, the competitive pressure to innovate and the increasing maturity of AI technologies, according to the Harvard Business Review.
The ESG research confirms the acceleration of AI adoption. In fact, 57% of the 3,324 organizations surveyed are currently
using commercial or open-source gen AI solutions, and 98% of organizations are planning to increase their investments in AI initiatives in 2025.
But before we dive into industry exploration, we have to note that the adoption journey is not without its challenges. Companies have to navigate the considerable and fluctuating governance, security and ethical considerations that come with it — not to mention organizational hurdles, data issues and the complexities of the technology itself. The immense potential of AI is undeniable, but the challenges below — from data to gen AI and autonomous agents — must be addressed to truly unlock AI’s transformative power. With all these factors to consider, simplicity is the key to success in adopting AI at scale: it needs to be easy with a unified data foundation, connected internally and externally through the ecosystem and trusted with governance and security built in.
FOUNDATIONAL DATA HURDLES
A recurring theme across all forms of AI adoption is the critical role of data: “There is no AI strategy without a data strategy” — but many organizations struggle with fundamental data readiness. The research highlights that even among early adopters who were surveyed, only 11% report that more than half their unstructured data is ready for use in large language model (LLM) training and tuning. This indicates a vast untapped potential within the 80–90% of enterprise data that is unstructured.
Other key data-related challenges include the management, quality, sensitivity and diversity of data for AI use. For example, tasks like data labeling and preparation are often arduous and slow. Problems with accuracy, bias, relevance and timeliness can severely undermine AI model performance. Fragmented data across disparate systems hinders a holistic view and efficient access for AI applications — but at the same time,
if the data isn’t varied or comprehensive enough, the scope and accuracy of AI models will be limited. And managing sensitive information requires robust security and compliance measures, adding complexity to data preparation.
These data challenges frequently lead to extended deployment timelines, with 77% of surveyed organizations reporting that half or more of their gen AI use cases have taken longer than expected to reach production.
Only 11%
of businesses report that more than half their unstructured data is ready for use in LLM training and tuning.
GEN AI: BEYOND THE HYPE
While gen AI has demonstrated ROI, its implementation comes with its own set of complexities:
THE EMERGING CHALLENGES OF AI AGENTS
The AI evolution toward autonomous agents brings
new challenges:
Addressing these multifaceted challenges requires a strategic, platform-centric approach to data management and AI deployment, prioritizing security, governance and a clear understanding of both the opportunities and the risks.
71%
of organizations agree they have more potential use cases than they can fund.
With AI capabilities atop a strong data foundation, organizations in every industry — whether a retail store, hospital, government agency, bank or energy company — can radically optimize essential business and operations functions. According to the Harvard Business Review, most business functions and more than 40% of all U.S. work activity can be augmented, automated or reinvented with gen AI. The ESG survey shows that 88% of early adopters report a material improvement in efficiency from their gen AI efforts. Here are just a few ways that AI can transform core business functions across industries.
MARKETING
AI agents are revolutionizing marketing by deeply analyzing customer data, enabling hyper-personalized campaigns and recommendations that resonate. Instead of large audiences receiving the same content at the same time, AI agents can scale decisioning and personalization of each marketing touchpoint for each individual customer. From boosting lead-to-meeting conversions with AI-powered lead scoring to refining marketing attribution and audience segmentation, AI is accelerating net new revenue generation.
FINANCE
Finance departments are leveraging AI and machine learning to fundamentally transform corporate planning and financial
forecasting. AI agents are automating a wide spectrum of financial operations, including the meticulous review of contracts like order forms and sales agreements. This not only saves time but also accelerates sales cycles and helps support rigorous contract compliance, driving efficiency and strategic decision-making.
HUMAN RESOURCES
The HR function is being reinvented with AI-powered employee assistants that provide immediate, personalized support by drawing from vast internal knowledge bases. AI hiring agents are streamlining recruitment, from generating tailored job descriptions and identifying qualified candidates based on job description matches, generating interview kits and speeding up resume screening. This comprehensive AI integration optimizes hiring processes, boosts productivity and enhances both the candidate and employee experience. 73% of HR professionals surveyed say they use gen AI for tasks like resume screening and employee training.
IT
Gen AI and machine learning assist IT teams in optimizing software licenses and reducing SaaS expenditures, while dramatically decreasing the mean time to resolve (MTTR) for IT operations and request tickets. QA AI assistants empower developers and business analysts to generate test cases rapidly and at scale, saving developer time and improving testing quality. CloudOps AI assistants provide immediate, relevant information from internal knowledge bases, enhancing overall operational efficiency and productivity. 70% of surveyed organizations use gen AI in IT operations, with 85% reporting a game-changing or significant impact.
SALES
Sales teams are unlocking new levels of performance through AI. Automated business intelligence (BI) allows for sophisticated analytics driven by natural-language prompts. Customer success agents leverage call notes and emails, enhanced by AI, to proactively identify cross-selling and upselling opportunities. Advanced text-processing capabilities provide instant summarization and sentiment analysis of call transcripts, offering invaluable, actionable insights for sales strategies. 38% of early adopters say their sales teams use gen AI, with 77% reporting a game-changing or significant impact.
CUSTOMER SERVICE
AI-powered chatbots and sophisticated conversational assistants are capable of handling customer inquiries, providing comprehensive support and resolving service tickets 24/7. This can lead to substantial improvements in customer satisfaction and significant reductions in operational costs. Gen AI can craft personalized responses and recommendations, elevating the overall customer experience, ensuring more responsive and tailored interactions. 56% of early adopters use gen AI for customer service and support, with 82% reporting a game-changing or significant impact.
PRODUCT / SERVICE DEVELOPMENT
Automated BI is instrumental in product and service innovation, analyzing vast datasets to reveal critical insights, emerging trends and patterns that directly inform decision-making on feature adoption. Product knowledge assistants, powered by AI, draw upon design write-ups, comprehensive documentation and internal research to generate precise recommendations for new products and services, accelerating the innovation lifecycle.
Next, we’ll explore these and other use cases in depth across seven industries: financial services; advertising, media and entertainment; healthcare and life sciences; public sector; retail; manufacturing; and telecommunications. We’ll also discover how organizations are leveraging data and AI to unlock new potential.
88%
of early adopters report a material improvement in efficiency from their gen AI efforts
FINANCIAL SERVICES
The financial services industry — a sector defined by constant evolution and complex data flows — is undergoing a profound transformation driven by data and AI. Disruption has historically been a constant in the industry, from the electronification of trading to multi-cloud strategies over the decades, leading to today’s race to leverage AI. Financial institutions are reassessing their technology stacks to meet demands for enhanced customer experience in a digital era, improved efficiencies in a volatile macroeconomic environment, and the creation of new revenue streams amid growing competition. Data, spanning structured to unstructured and first-party to third-party, fundamentally underpins this industry. Financial services companies generate massive amounts of unstructured data, from loan agreements,
emails, claims and transcripts and more. This vast, untapped resource, alongside structured data, presents a tremendous opportunity.
Gen AI’s ability to extract value from this complex data is proving transformative, enabling automation and strategic decision-making. AI agents are further extending this capability, handling complex, multi-step operations autonomously, from automating financial forecasting with real-time market insights to streamlining claims. Financial services firms are notably ambitious, with 43% citing improved financial performance as a key driver of AI adoption.
Here are three of the many ways the financial services industry can drive business success with AI:
Quantitative research and investment analytics: Institutional investors demand sophisticated portfolio analytics to guide critical decisions like security selection, rebalancing and optimization. AI empowers investors to query data assets using natural language to yield actionable insights. Conversational assistants and AI agents can leverage portfolio warehouses, order management systems, risk engines and third-party data to forecast market trends, optimize portfolio allocations, and enhance risk-adjusted returns. And, machine learning models can adapt to changing market conditions, providing agility in a dynamic investment landscape. This includes consolidating first-party and third-party data for multi-factor model building, backtesting trading strategies, constructing Monte Carlo
simulations for risk analysis and evaluating execution algorithms for post-trade insights. Organizations can achieve this business value by employing a unified, scalable data platform to integrate and analyze data from various sources, and combine with existing analytical skills for complex calculations without moving data.
CUSTOMER SUCCESS STORIES
S&P Global Market Intelligence saves time and money while scaling machine learning
S&P Global Market Intelligence integrates financial and industry data, analytics, research and news to help corporations identify risk and reward opportunities. To build its risk reports and analysis, S&P Global Market Intelligence uses advanced ML models to source terabytes of data from millions of enterprises’ websites. Initially, S&P Global Market Intelligence stored raw web crawler data in object storage and used multiple data science technologies for data cleaning and model hosting. However, S&P Global Market Intelligence quickly abandoned this approach due to concerns about data movement, runtime performance, infrastructure costs and complexity. With Snowflake, S&P Global Market Intelligence benefits from a fully managed service, which has allowed the team to scale resources efficiently without manual configurations or downtime while also enhancing both performance and availability for data processing. S&P Global Market Intelligence now loads both structured and unstructured web-crawled data into Snowflake and applies business attributes and firmographic mining models built with Snowpark. These AI custom models then curate the business data, ultimately feeding S&P Global Market Intelligence’s credit models within their RiskGauge™ reports.
Compare Club turns untapped call transcripts into new ways to delight and engage members
Compare Club helps millions of Australian consumers make more informed purchasing decisions on products and services across health and life insurance, energy, home loans and more. Providing an exceptional, personalized experience to customers is critical for Compare Club — especially for returning members, who are more likely to make a purchase. Customer calls are an important vehicle to deliver this experience, yet complex details from these conversations were not always recorded in the company’s CRM, making it difficult to use this information in future calls. Compare Club quickly overcame these challenges by using Cortex AI to run LLMs securely inside Snowflake, eliminating the need to move data while easily running both preprocessing and LLM tasks with a bit of SQL and Python. Now, Compare Club efficiently equips business teams with valuable insights extracted from hundreds of thousands of transcript pages, including details like customer goals, needs, objections, loyalty, history and enthusiasm. These nuances help Compare Club teams — from sales to support to customer success — better serve and engage repeat members to improve their experience and retention.
Customer 360: Financial marketers must delicately balance ultra-personalized client experiences with stringent customer privacy and regulatory compliance. AI assists by analyzing customer data, transaction histories and behavioral patterns to deliver tailored recommendations for specific financial segments. AI agents and conversational AI assistants can help analyze marketing campaign performance in near real time and suggest adjustments to maximize ROI. They also help analyze third-party financial data to forecast future customer trends, enabling marketing teams to plan and execute more effective campaigns. This spans integrating data for identity resolution, executing impactful marketing campaigns through segmentation and predictive modeling, developing nextbest-action strategies and enabling compliance with privacy regulations. Modern marketing data strategies can maximize ROI with customer segmentation and predictive modeling, while advanced privacy policies and data clean rooms help preserve privacy during collaboration.
Claims management: The process of sifting through diverse data for insurance claims — such as witness statements, policy documents, dashcam footage or emergency service recordings — is typically manual, time-consuming and prone to errors. Insurance managers can reduce time and expense by deploying AI-powered tools, including text processing and AI agents, to rapidly access and query relevant data. When these capabilities are applied from the first notice of loss (FNOL) throughout the claim lifecycle, they can enhance operational efficiency, lower costs and accelerate claims responses — ultimately elevating the customer experience. This includes advanced fraud detection, intelligent triaging and assignment of claims, comprehensive investigation and evaluation, and automated settlement and closure processes. Modernizing claims data pipelines to ingest and transform large volumes of raw data and applying AI to unstructured claims data can improve productivity and drive efficiencies.
43%
of financial services early adopters cite improved financial performance as a key
driver of AI adoption.
ADVERTISING, MEDIA AND ENTERTAINMENT
The adoption of AI solutions, evolving regulations around data privacy along with the proliferation of streaming services and smart devices are fueling significant transformation in the advertising, media and entertainment industries. Audiences now expect on-demand, personalized content anytime, anywhere, and the AI capabilities needed to accomplish this are as varied as the players involved in delivering it. Businesses need to connect disparate, unstructured data for audience analytics, targeted advertising, asset protection and more. To stay competitive, industry leaders must navigate a landscape characterized by rapid innovation and evolving privacy regulations.
Media companies have been using AI and machine learning for targeted advertising and enhanced user experiences for years. But now, the adoption of advanced gen AI solutions is crucial for a competitive edge. In fact, 83% of marketing, advertising and media sector respondents report positive ROI on gen AI, indicating a strong future for AI-driven decisionmaking, personalized content creation and optimized media supply chains.
Here are three ways advertising, media and entertainment companies can gain a competitive edge with AI:
Audience analytics: Creating bespoke audience experiences is a critical competitive differentiator in today’s saturated media landscape. The challenge? To provide those tailored experiences, entertainment organizations must connect disparate data sets across a massive variety of platforms — with structured, unstructured and semi-structured data — while maintaining customer data privacy and governance.
By integrating gen AI capabilities into audience analytics, businesses can connect a variety of data types to get a more complete picture of audience behavior. AI-powered audience analytics help build connections between audience touchpoints, from in-platform streaming behavior to linear appointment viewing to in-app content browsing and more.
Accelerated advertising revenue: Leveraging previously untapped insights through AI-powered analysis of unstructured data boosts ad revenue by combining audience analytics with precise targeting for personalized campaigns. Companies can also rapidly test and iterate different tailored messages targeted to individual preferences. Providing ad operations teams with codeless data access and agentic campaign optimization tools enables advertisers to optimize return on ad spend (ROAS).
Data privacy and asset protection: Protecting intellectual property (IP) and copyrighted assets is essential for preserving the integrity of creative work and reputations of artists and brands. Gen AI helps monitor digital platforms and distribution channels to detect unauthorized use of IP rights in near real time, providing protective mechanisms to brands and artists. Gen AI can also augment traditional asset protection methods by analyzing patterns in digital content to help identify copyright infringement, plagiarism and deepfakes.
83%
of marketing, advertising and media sector respondents report positive ROI from gen AI.
CUSTOMER SUCCESS STORIES
Merkle improves customer experiences while providing data governance and security
Merkle, an integrated experience consultancy, powers the experience economy and provides data, technology, design and strategic expertise to help hundreds of clients — including many in the Fortune 500 — drive outcomes. One of its secret ingredients? Its Merkury solution. Merkury is a leading data, identity and insights platform that consolidates consumer data into a single, persistent “person ID” for hyper-personalized campaigns. Since going all-in on Snowflake on Amazon Web Services (AWS), Merkle has been able to securely manage, analyze and leverage data, reducing costs, mitigating data exfiltration risks, and strengthening the company’s reputation as a data privacy leader. The team saves more time on workloads, including the development cycle for data pipelines, which has improved by 64%, contributing to the timely delivery of customer data. Merkle’s request for proposal (RFP) response solution, built with Document AI in Snowflake Cortex, reduces data entry for at least 25 team members while enabling faster response times.
Nexon saves $4.5 million a year by unifying its data in the AI Data Cloud
For more than 30 years, Nexon has been a pioneer in the world of interactive entertainment software, delivering some of the world’s most popular games to over 1.9 billion gamers in 190 countries. Nexon built a new platform called “‘Monolake”’ on top of the Snowflake platform, transforming its data strategy and democratizing access to data. This allowed democratizing access to data for 2,000+ data producers and consumers: By providing data securely and freely to everyone in the business, Nexon is able to transform into an agile organization and adapt swiftly to the ever-changing landscape of the era of AI. Since migrating from its legacy platform over to the Snowflake Data Cloud, the company has seen up to a $4.5 million reduction in annual costs. Nexon is also seeing increased efficiency and eliminating data silos: instead of operating every game on different technical stacks, Nexon now uses Snowflake to unify its data, and will continue moving workloads from managed Spark to Snowpark for increased efficiency.
HEALTHCARE AND LIFE SCIENCES
The highly-regulated healthcare and life sciences sector is experiencing a profound AI-driven transformation. The industry has been moving from experimentation to realizing tangible returns on AI investments, with the AI healthcare market projected to reach $188 billion by 2030. This rapid adoption is fueled by the sector’s immense volume of multimodal data — the data of healthcare organizations alone is growing faster than even financial services, manufacturing or media and entertainment. Gen AI and emerging AI agents are now vital for processing this complex multimodal information, automating administrative tasks, accelerating drug discovery and personalizing patient experiences. This drives significant business and patient outcomes, even as the industry navigates its stringent regulatory environment and fragmented data landscape. Notably, early adopters in this industry report higher than average ROI on gen AI spend — 44% versus 41% in the aggregate. Beyond overall ROI, gen AI is making significant inroads in specific functions within healthcare and life sciences. For instance, 53% of early adopters in this industry are using gen AI for HR functions, compared to 45% across all industries, and 76% are applying it in IT operations, versus 70% overall, driving improvements in areas like incident detection and cost reduction.
Here are three important ways healthcare and life sciences companies can drive business success with gen AI:
Accelerating research: Research and development (R&D) in life sciences is a notoriously expensive and lengthy process, often spanning over a decade. By analyzing vast amounts of biomedical data, including genetic information and clinical trial data, gen AI can predict drug interactions, identify novel targets, and optimize drug efficacy and safety profiles, thereby accelerating drug discovery and development. Gen AI can also expedite personalized medicine by tailoring patient treatments based on in-depth clinical data, such as patient genetic information, medical history and near real-time health metrics.
Modernizing supply chain: This includes manufacturing and distributing goods within optimal margins, fostering collaboration with supply chain stakeholders, accurately predicting demand and potential disruptions, and driving overall operational efficiencies. A platform supporting all data types can enable manufacturers to better predict consumer demand with native ML capabilities, understand quality metrics over time and collaborate securely with stakeholders.
Patient/member 360: Delivering effective personalized care is increasingly vital as more healthcare organizations adopt valuebased care models. An interoperable data platform allows care teams to access historical and real-time data and leverage AI/ ML for personalized experiences and predictive analytics. Gen AI can analyze vast datasets, helping providers and payers discern patient or member preferences, behaviors, sentiments and health trends. This in-depth analysis enables the creation of highly customized care plans and communications, which can be refined throughout the patient’s care journey. Additionally, gen AI enhances patient/member 360 by aggregating siloed patient/member data inputs from multiple touchpoints, which can then be used to create seamless digital experiences and provide access to relevant patient/member data precisely when needed at the point of care.
Early adopters in the healthcare and life sciences industry report higher than average ROI on gen AI spend of 44%.
CUSTOMER SUCCESS STORIES
AI-driven innovation cuts time, boosts innovation and saves lives at AstraZeneca
For AstraZeneca, faster innovation means faster breakthroughs in their science, and that means greater outcomes for patients. AstraZeneca leveraged Snowflake to accelerate data product creation, drive productivity savings, and enable AI-driven innovations that improve early disease detection and patient outcomes. With Snowflake, AstraZeneca cut data product development from six months with 16 engineers to just four days with two engineers. They also saw massive efficiency gains: AstraZeneca created 118-plus data products, unlocking thousands of hours in productivity and over $10M in savings. And Snowflake helped AstraZeneca accelerate life-saving innovation by using AI-powered chest X-rays to detect lung disease early, improving survival rates by up to 90%.
Alberta Health Services ER doctors automate note-taking to treat 15% more patients
The integrated health system of Alberta — Canada’s third most-populous province, with 4.5 million residents — includes more than 100 hospitals and 11,000 practicing physicians. Its emergency departments get nearly 2 million visits per year, which amounts to more than 5,000 a day. That type of volume can easily put a strain on the doctors, who not only serve the patients but also need to document each visit carefully — from summaries to diagnoses to medication orders.
One such physician, also a trained software engineer, sought a way to automate his note-taking tasks by recording visits and calling an LLM to generate a summary. Seeing the potential of this use case, Alberta Health Services turned to Cortex AI to develop and run the app within Snowflake’s secure, fully governed environment.
Currently in its proof-of-concept phase, the app is being used by a handful of emergency department physicians, who are reporting a 10–15% increase in the number of patients seen per hour. That can ultimately translate into less-crowded waiting rooms, relief from overwhelming amounts of paperwork for doctors, even better-quality notes and higher-quality patient care.
PUBLIC SECTOR
The public sector — a cornerstone of global stability and citizen well-being — faces unique challenges in AI adoption despite holding massive volumes of data. Evolving privacy regulations, security risks and ethical concerns often lead to more cautious AI implementation compared to the private sector. Furthermore, public sector organizations frequently contend with budget constraints, a scarcity of specialized AI talent and difficulties mobilizing fragmented data from disparate legacy systems. Despite these headwinds, the core missions of government — to deliver critical services, ensure national security and build resilience — has created an urgent need for transformation, particularly leveraging AI.
AI is beginning to revolutionize public service, with 70% of OECD participating countries already using AI to enhance internal operations. That includes improving traffic management, automating document processing and powering university research. The emergence of AI agents promises to further accelerate this shift, enabling autonomous systems to handle complex tasks and workflows, from streamlining citizen service delivery to enhancing predictive capabilities for proactive governance.
Here are three critical ways AI can drive mission success in the public sector:
Improved program and service delivery: Government and educational institutions constantly strive to enhance public services while operating within budget constraints. A key application is the creation of a citizen 360 view, unifying fragmented data from sources like online forms, databases and historical records to build a single, comprehensive profile. This foundation allows gen AI-enabled chatbots and agents to reduce time and cost by providing rapid and accurate responses to queries. Gen AI can also leverage this holistic view to tailor services to individual needs, offering personalized support and outreach for citizens and students, and streamlining case management. Similarly, defense agencies can build a soldier 360 view, integrating personnel, training and medical data to enhance mission readiness and provide tailored support for service members and their families.
Increased operational efficiency: Gen AI’s automation capabilities can replace numerous manual, time-consuming tasks for public sector employees, boosting both efficiency and productivity. This includes applying AI to processes like continuous financial monitoring, the detection of fraud, waste
and abuse, and logistics management. In education, institutions are using AI to streamline administrative processes from enrollment to course scheduling. For government leaders, AI can optimize resource allocation by analyzing complex data. For instance, a government agency can use AI to analyze sensor data from its vehicle fleet, enabling predictive maintenance that optimizes repair schedules, reduces costs and maximizes operational readiness.
Predictive analytics for responsive government: Gen AI enabled predictive analytics empower organizations to achieve their goals by enabling proactive responses to emerging challenges. For example, gen AI can predict disease outbreaks and disaster impacts, assisting with the optimal deployment of emergency services. In education, gen AI can forecast student enrollment trends and recommend strategic school infrastructure investments. Defense agencies can leverage AI to improve their cybersecurity posture, using advanced analytics for proactive threat detection to anticipate and neutralize potential attacks before they impact mission-critical systems.
70%
of member countries have used AI to enhance internal operations.
—Organization for Economic Cooperation and Development (OECD)
CUSTOMER SUCCESS STORIES
Sydney Local Health District promotes better health outcomes for mothers and babies
Reducing infant and mother mortality is a global priority, and in Sydney, New South Wales (NSW), public health organizations like Sydney Local Health District (SLHD) are turning to data to address the issue. SLHD and 14 other Local Health Districts are administered by NSW Health. NSW Health had relied on a legacy platform and infrastructure to meet health districts’ requests for datasets for analysis and reporting to improve patient care. However, this platform and infrastructure was complex and could not scale to meet the growing demand from local health districts, including the Women and Babies Service at SLHD, which delivers about 7,500 babies per year — the largest gynecology unit in NSW. SLHD has been able to validate the accuracy of reports generated from the Snowflake AI Data Cloud against outputs from its existing systems, giving the Women and Babies team confidence in using the system for its dataset analysis and decision-making requirements. With reports running in just 55 seconds, the team will be able to act on delivery trauma, mortality and morbidity data in near real time. SLHD is also positioned to respond quickly to requests for new reports derived from multiple data sources, with the Snowflake AI Data Cloud enabling it to provision them in hours rather than the months required in its legacy infrastructure.
NY Health and Hospitals elevates care for New Yorkers experiencing homelessness
Homelessness in New York City has surged to its highest level since the Great Depression. Reducing homelessness in the nation’s biggest city is a complex endeavor that starts by understanding those in need. NYC Health + Hospitals—the largest municipal health system in the United States — is focused on using data and analytics to understand the vulnerable populations that it serves and, ultimately, deliver faster, better care to improve lives. NYC Health + Hospitals relies on Snowflake’s AI Data Cloud to centralize large amounts of healthcare data, surface insights that drive efficiency and begin to maximize the benefits of gen AI through Snowflake Cortex AI. Powering its “data hub” initiative with Snowflake helps NYC Health + Hospitals develop comprehensive views of patients—especially for those patients experiencing homelessness. Building NYC Health + Hospitals’ data platform on Snowflake provides near-infinite scaling of storage and compute to integrate billions of rows of healthcare data, which can help care providers better understand and serve New Yorkers in need. Streamlining access to even more data will put NYC Health + Hospitals in a better position to unleash greater outcomes through gen AI.
RETAIL AND CONSUMER GOODS
The retail industry is under pressure and changing fast. Data and AI are at the heart of that transformation. As consumers expect more personalized, seamless experiences and supply chains become more complex, retailers need tools to keep up. Data is one of their most valuable assets, and they are looking for AI to turn that data into action.
Whether it’s tailoring offers in near real time, predicting demand more accurately or streamlining operations, retailers are using data and AI to adapt, innovate and grow. In a world of constant change, these technologies aren’t just nice to have — they’re essential for staying competitive. The ESG survey shows that the retail sector reports a quantified ROI of 30% versus 41% across all industries, indicating room for growth, but also that 87% say gen AI projects have positively impacted customer service/support. This shows a clear path to value in customer-facing applications.
87%
of early retail adopters say gen AI projects have positively impacted customer service/support.
Here are three important ways AI can drive business success in retail:
Customer experience optimization: Customer service agents frequently spend time sifting through knowledge bases to answer queries about inventory, order status and product information. With limited staff, this can lead to extended wait times. AI chatbots and AI agents can retrieve answers from across various documents within seconds, accelerating the speed at which agents provide informed customer assistance. AI can also empower agents to upsell or cross-sell products in near real time by analyzing the conversation, tapping into customer 360 data and marketing materials, and providing immediate, relevant recommendations. Rapidly finding answers across documents, boosting the speed of customer assistance and enabling upsell/cross-sell recommendations are key outcomes. AI agents can provide timely, personalized product recommendations and faster issue resolution for shoppers, directly impacting customer experience.
Customer perception analysis: Often more revealing than star ratings or numerical metrics, text-based feedback allows businesses to extract nuanced emotions and opinions, providing deep insights into why a product is popular — or why it’s not. Gen AI can analyze diverse text sources, such as call transcripts, online reviews and social media posts, giving companies a profound understanding of customer sentiment. It can then perform sentiment analysis to pinpoint common complaints and suggest product enhancements, enabling companies to refine product development and respond more effectively to customer needs. This includes analyzing diverse text sources for customer sentiment, identifying common complaints and generating product suggestions.
Demand forecasting: Retailers rely on demand forecasting to fine-tune inventory levels, minimize stockouts and reduce carrying costs. Predictive machine learning enhances forecast accuracy by identifying intricate patterns and correlations within data from a variety of sources. This includes sales history, market trends and external factors such as purchase behavior, social media trends and inflation rates. Gen AI can also provide real-time analysis and simulate various scenarios to predict the impact of different factors on demand. Armed with this information, AI can deliver recommendations to retailers that lead to significant cost savings and heightened customer satisfaction. Identifying data patterns and correlations, providing near real-time analysis and scenario simulations, and generating recommendations for cost savings and improved customer satisfaction are crucial for optimizing inventory. Autonomous AI agents can predict demand trends and adjust stock levels and prices in real time
CUSTOMER SUCCESS STORIES
Firework develops AI virtual shopping assistant that offers a personal connection to consumers
To bring a more human connection to the online shopping experience, video commerce company Firework turned to an unconventional source: AI. Already an established leader in shoppable videos and livestreams, the company wanted a way to bring the personalized, one-on-one attention of, say, a sales floor associate to a shopper’s screen or mobile device. Building such a sophisticated assisted shopping experience, however, presented plenty of challenges—chief among them, generating high-quality answers to customer questions. Using Snowpark and Cortex AI, Firework began by aggregating, cleaning and classifying thousands of anonymous customer conversations to help understand consumer interests and pain points. That became the basis of the data foundation that ultimately powers their LLM application in Cortex. The result? Firework was able to develop what it now calls AVA (AI Video Assistant), an AI generated avatar that can listen, think and speak to consumers throughout their shopping journey. AVA can answer questions about return policies; it can scour and summarize thousands of product reviews in seconds or even offer personalized recommendations about what color sweater might complement the pants you bought last month.
Johnnie-O improves accuracy of geocoding address data to better serve customers
Like many largely ecommerce businesses, the East-Coastprep-meets-West-Coast-casual clothing brand Johnnie-O understands the value in a simple shipping address. Just a few lines of text can provide powerful demographic insights into the company’s customers when linked to data from the U.S. Census Bureau—information like average household income in the area, percentage of people with degrees, employment rates, races and ethnicities, and so on. By using this data not only directly from website orders but from wholesalers and dropshippers, Johnnie-O can begin to understand its customer base better and consequently target its marketing efforts more effectively. But the company had one problem: A significant number of collected addresses could not be geocoded, preventing the team from accessing relevant customer data. Typically, the company runs raw address data through an application that delivers geographic coordinates, which then makes it easy to link to census data. But for Johnnie-O, many of these addresses failed for a variety of reasons, which could be as small as a typo or information in the wrong field. So instead of manually cleaning up these hundreds of thousands of data points, the company looked to Cortex AI to automatically reformat the messy address data. After feeding these incorrect addresses into Cortex AI using a Llama LLM, Johnnie-O immediately slashed its failure rate to just 2%.
MANUFACTURING
The manufacturing industry is rapidly transforming through automation, smart technologies and a strong focus on sustainability. Data and AI are central to this evolution, optimizing processes, predicting equipment failures and enhancing quality control. While the sector has embraced digital transformation, the true revolution lies in mobilizing vast datasets from IT, operational technology (OT) and Internet of Things (IoT) sensors, which often remain siloed. This integration is crucial for achieving real-time insights and powering smart factories. Manufacturers are keenly aware of AI’s potential, with the global market for AI in manufacturing projected to reach $20.8 billion by 2028. Surveyed manufacturers report they are deploying gen AI technology to their production and supply chain management teams — 71% versus 45% of overall respondents — and also using it for inventory management and creating quality inspection protocols.
79%
of manufacturing respondents say gen AI has been either game-changing or significant.
The industry is recognizing that AI, including gen AI and emerging AI agents, can fast-track innovation, optimize complex supply chains and automate routine tasks, ultimately leading to increased profits and enhanced competitiveness.
Here are three ways manufacturing companies can drive business success with AI:
Optimize business planning and supply chain: AI-driven supply chain optimization enhances efficiency, resilience against disruption and responsiveness to dynamic market conditions. Among its capabilities, AI can process vast amounts of data — from producers to retailers — to predict trends, provide early notification of delays and offer near real-time recommendations. This enables manufacturers to make more informed decisions about supply chains, determine optimal inventory levels to reduce excess stock and minimize stockouts, and dynamically match supply with fluctuating demand patterns, allowing for agile adjustments to production schedules and inventory levels. This includes advanced forecasting and planning, sustainable sourcing strategies, detailed spend analytics, proactive supplier risk management, precise inventory control, efficient fulfillment processes, streamlined transportation and logistics, and robust traceability. AI agents are particularly effective here, capable of autonomously optimizing inventories on the fly in response to demand fluctuations or weather disruptions.
Power smart manufacturing: Ensuring consistent product quality and minimal defects is crucial for maintaining customer satisfaction. However, manually detecting faults before they impact production is both time-consuming and costly. With AI, manufacturers can leverage automation to detect unusual patterns or deviations in production data that may indicate potential quality issues. AI-driven visual inspection systems can also identify defects in products by analyzing images or videos, enhancing quality control and reducing manual inspection errors. This includes comprehensive shopfloor visibility, optimizing product yield and quality, enhancing energy and sustainability management, accelerating product development, enabling predictive maintenance, implementing AI-driven process control, maximizing Overall Equipment Effectiveness (OEE) and optimizing cost management. AI agents can monitor equipment performance, predict failures and dispatch maintenance teams.
Generate value from connected products: Businesses can harness the rich data streams from connected devices for insights into product performance and reliability, and consumer behavior. AI analysis can drive product monitoring, quality and design, and it can improve customer experience, sales and services. Connected product data also opens a range of opportunities for manufacturers such as optimizing fleet management.
CUSTOMER SUCCESS STORIES
Harkins Builders saves 100+ hours on writing project reports through AI-powered app
In the world of commercial construction, a turnover narrative is an important document that bridges the preconstruction and active construction phases of any project. At Harkins Builders, a construction management and general contracting company that works on about 100 projects a year, compiling a turnover narrative had been a rather tedious and time-consuming exercise, requiring a project estimator to gather all the relevant information from Snowflake or its customer relationship management system, Dynamics 365, and then manually write the report. Ultimately, each report took at least an hour to complete — more when multiple estimators worked on a project and knowledge gaps would need to be addressed. But given that Harkins had built a strong, consolidated data foundation in Snowflake, the analytics team saw a way to largely automate the process of creating turnover narratives. Within two months, Data and Software Engineer Ben Pecson developed an application that could guide Harkins’ estimators through a Cortex AI-powered process that cut down the time spent on turnover narratives from an hour-plus to 5–10 minutes. Pulling data that already exists in Snowflake, the app crafts several prompts, from which the estimator can choose (like literal building blocks) to construct a complete turnover doc.
Expand Energy taps Snowflake’s AI capabilities to reduce environmental impact
As the largest natural gas producer in the U.S., Expand Energy plays a crucial role in meeting the world’s growing energy needs. For the technology delivery team, the real challenge was overcoming the limitations of legacy systems. Expand Energy uses Snowflake to host real-time data and ML models for drilling activities, allowing the team to optimize the drilling rate of penetration, prevent equipment failures and enhance safety. Building on the foundation of real-time data ingestion and Snowpark data models, Cortex Analyst allows engineers to ask questions in natural language such as, “What were the top contributors to nonproductive time?” or “What is a summary of activities over the past 24 hours?” and get answers on the fly. Rather than sending personnel to monitor each of the 3,700 production sites, Snowflake enables Expand Energy to centralize data from operational systems and supervisory control and data acquisition (SCADA) systems. The data combines with well production, equipment age and site details, creating a digital twin for each site. Snowflake continuously runs queries to detect potential issues, such as tank corrosion, and alerts are sent to the operations center for investigation. This proactive approach reduces environmental risks and impacts, minimizes downtime and improves efficiency across all sites.
TELECOMMUNICATIONS
The telecom industry, the backbone of global connectivity, continues to undergo rapid transformation driven by 5G infrastructure, edge computing and IoT. Operators are under immense pressure to innovate as they face market saturation, tight margins and intense competition. Gen AI and the emergence of AI agents offer a powerful solution, enabling the industry to move beyond traditional services and unlock new value.
The global AI in telecom market size is expected to be worth around $23.9 billion by 2033, reflecting the industry’s commitment to leveraging AI. From improving geospatial planning to automating data analysis and using predictive modeling to inform network designs, to building customer support agents, gen AI is helping usher in the new era of telecom. The ESG survey indicates that early adopters in telecom are seeing significant benefits from gen AI, with 70% of IT operations teams and 65% of cybersecurity teams using gen AI to improve efficiency and reduce costs. By building an AI-powered data infrastructure, telecom companies can enhance customer satisfaction, strengthen network performance and proactively respond to issues, ultimately driving the shift toward intelligent, adaptive and autonomous networks.
Here are three key ways the telecom industry can use AI to drive business success:
Network operations: Transitioning to gen AI-driven operations can boost network health, service performance, reliability and operational efficiency. By incorporating unstructured and semi-structured data from network logs and support systems, AI can perform root-cause analysis and generate hypotheses to solve and predict network issues. AI can also automate routine tasks, such as provisioning resources, optimizing network configurations and managing network traffic. This automation not only streamlines processes, it also frees up human resources for more strategic tasks. AI agents are particularly adept at this — they can predict traffic loads and manage bandwidth allocation accordingly.
Business operations: Gen AI is a powerful tool to help telecom businesses enhance the customer experience and boost brand loyalty. Gen AI can analyze customer usage, call patterns and preferences to offer personalized service bundles. Call center agents can utilize chatbots that analyze network and call log data in real time to provide timely solutions for customer issues. AI can also power customer self-service applications, allowing users to resolve issues independently. AI agents can also identify customers at risk of leaving and carry out retention strategies, directly impacting business outcomes.
Predictive Maintenance: Gen AI enhances predictive maintenance capabilities for telecom companies by extracting previously untapped insights from unstructured data. It can synthesize information from various disparate data sets, such as weather reports and social media posts, to predict service disruptions and proactively warn customers. It can anticipate failures by analyzing network and call log data in real time to rapidly detect and respond to issues. Gen AI can even anticipate when specific areas are at risk of failure by analyzing past patterns, enabling service departments to take preventative measures and prevent outages before they happen.
CUSTOMER SUCCESS STORIES
VodafoneZiggo cuts costs by 50% and gains real-time insights with Snowflake
Before moving to Snowflake, VodafoneZiggo, the biggest telecommunications company in the Netherlands, had a scattered and difficult-to-manage data architecture — with workflows sometimes running for over 20 hours at a time just to refresh data. Now, after migrating its data infrastructure to the Snowflake AI Data Cloud and AWS, the company has managed to cut costs in half and reduce the number of high incidence tickets to zero, while also improving data timeliness to over 96%.
XLSmart boosts data analytics speed and cuts costs with Snowflake
XLSmart is a communication services provider in Indonesia offering both mobile and fixed broadband products. They have roughly 26% market share with 57 million mobile subscriber customers and over 1 million customers on their network. They say the data holds a central-point position in XLSmart and they try to make all their decisions in a very data-driven way. Snowflake gives XLSmart double-digit cost reduction, along with greater visibility into usage through Snowflake’s cost control features. Snowflake’s built-in governance features enable the correct people to get access to the correct data, further strengthening security. And users no longer have to wait days to take action. With Snowflake, XLSmart has seen analysis tasks that used to take days to complete now fulfilled in hours.
At the core of a successful AI strategy is a strong enterprise data foundation. With Snowflake’s AI Data Cloud, organizations across industries are eliminating the data silos of legacy systems and gaining the ability to seamlessly collect, share and apply advanced analytics. Snowflake makes enterprise AI easy, connected and trusted. More than 12,000 companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications and power their business with AI.
Building and managing AI stacks and LLMs might seem complicated. They require substantial compute resources and large-scale storage, making the setup and management of AI infrastructure costly and resource-intensive. Developers need special skills to create and train AI models, a time-consuming effort. Implementing the necessary security measures and maintaining compliance with privacy regulations adds more layers of complexity.
Snowflake’s architecture simplifies all that in several ways. Providing a fully managed AI Data Cloud that is integrated across data types, clouds and personas helps businesses eliminate the need to invest in and maintain a complex AI infrastructure. Snowflake allows for seamless scaling of the computational resources that AI workflows need. Developers can bring AI models, frameworks and applications directly to their data, eliminating the time and risk associated with data transfers. Users can seamlessly integrate AI into their use cases using no-code, SQL, Python or REST API interfaces, enabling a broad range of teams to integrate AI into their workflows. And Snowflake has built-in governance, access controls and safety guardrails.
Once a modern data foundation and unified platform are in place, Snowflake’s robust native AI/ML capabilities — along with an extensive partner ecosystem — can help customers harness the power of gen AI. Snowflake Cortex AI offers LLM functions, universal search, Document AI, no-code model development and more. Together, these capabilities enable faster deployment and simpler maintenance of AI infrastructure and LLMs, improved performance, cost savings and, ultimately, a quicker and greater return on investment in AI.
AN ADVANCED, INTEGRATED ARCHITECTURE FOR PRODUCTION AI
Unify your data and AI strategy with Snowflake and AWS. With this partnership, more than 50 integrated features and services for data engineering, analytics, AI, applications and collaboration come together in a consolidated, fully managed platform. This enables enterprises across industries to seamlessly ingest, transform and prep structured, semistructured, and unstructured data for upstream analytics and AI workloads. Each industry can uniquely gain business value, efficiency and innovation — with a range of examples below.
With Snowflake and AWS, financial institutions can unify their data, leverage AI for insights and collaborate securely, to improve decision-making, help ensure compliance and help clients enjoy personalized experiences.
In healthcare organizations, this ability to unite disparate data sources can offer comprehensive patient views, enhanced clinical decision-making with machine learning and streamlined interoperability across systems. Snowflake and AWS help payers optimize operations, providers to improve care quality and researchers to accelerate innovation.
Manufacturers can unify large volumes of IoT, agent and other data for greater operational agility. With capabilities for advanced analytics and AI, manufacturers can streamline operations, optimize supply chains and build connected solutions to accelerate business transformation.
In the media and entertainment industries, the interoperability between Snowflake and AWS enables businesses to build complete audience profiles, delivering personalized experiences that boost engagement and lifetime value. Brands can collaborate across the media and advertising ecosystem without impacting existing data security and privacy controls.
And retailers can leverage solutions spanning merchandising, inventory planning and customer 360. Data and AI can help optimize pricing, improve supply chain operations and personalize customer experiences.
The use cases in this book merely scratch the surface of what industries can accomplish with AI. To get there, you need a modern data foundation with native AI and machine learning capabilities and a robust partner ecosystem.
Watch the Data and AI Leadership Forum on demand to learn how technology and business leaders innovate and collaborate with the power of data and AI.
LEARN MORE ABOUT SNOWFLAKE’S AI DATA CLOUD INDUSTRY-TAILORED SOLUTIONS
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Khi một doanh nghiệp quyết định chuyển sang Snowflake, câu hỏi tiếp theo thường là: “Bắt đầu từ đâu? Mất bao lâu? Chi phí thế nào?”
Dựa trên hàng trăm dự án triển khai dữ liệu tại khu vực ASEAN, SIFT Analytics tổng hợp 5 bước thực chiến giúp doanh nghiệp Việt triển khai Snowflake hiệu quả — tránh các bẫy phổ biến mà nhiều tổ chức mắc phải.
Đây là bước bị bỏ qua nhiều nhất, và cũng là nguyên nhân số 1 khiến dự án bị chậm hoặc đội chi phí.
Trước khi đưa dữ liệu lên Snowflake, cần trả lời rõ:
Thời gian: 1–2 tuần. Output: Báo cáo Data Readiness với danh sách nguồn dữ liệu, vấn đề chất lượng và lộ trình ưu tiên.
Snowflake không phải cứ setup xong là dùng được ngay. Thiết kế kiến trúc tốt từ đầu sẽ tiết kiệm rất nhiều chi phí và công sức sau này.
Các quyết định kiến trúc quan trọng:
Chọn cloud provider và region: Snowflake chạy trên AWS, Azure và GCP. Với doanh nghiệp Việt Nam, thường chọn AWS Singapore (ap-southeast-1) hoặc Google Cloud Singapore để đảm bảo tốc độ và tuân thủ lưu trữ dữ liệu trong khu vực.
Thiết kế warehouse (compute cluster): Cần bao nhiêu warehouse? Size bao lớn? Có nên dùng multi-cluster để xử lý đồng thời không? Quyết định này ảnh hưởng trực tiếp đến chi phí hàng tháng.
Mô hình dữ liệu (Data Model): SIFT thường khuyến nghị theo kiến trúc 3 lớp: Raw Layer (dữ liệu thô từ nguồn) → Staging Layer (làm sạch, chuẩn hóa) → Analytics Layer (aggregated, sẵn sàng cho BI). Kết hợp với dbt (data build tool) để quản lý transformation pipeline.
Phân quyền và bảo mật (RBAC): Snowflake có hệ thống role-based access control rất mạnh. Thiết kế đúng từ đầu giúp tuân thủ quy định nội bộ và pháp lý (Luật An ninh mạng, Nghị định 13/2023 về bảo vệ dữ liệu cá nhân).
Thời gian: 1–2 tuần. Output: Architecture diagram, Snowflake account setup, IAM policy.
Đây thường là giai đoạn tốn thời gian nhất, nhưng cũng là nơi tạo ra giá trị lớn nhất nếu làm đúng.
Di chuyển dữ liệu lịch sử (Historical Migration):
SIFT sử dụng các công cụ như Qlik Talend hoặc dbt để extract, transform và load dữ liệu từ hệ thống cũ vào Snowflake. Với dữ liệu lớn (hàng chục GB đến TB), Snowflake hỗ trợ bulk loading cực kỳ nhanh qua COPY INTO command.
Xây dựng pipeline real-time (nếu cần):
Với doanh nghiệp cần dữ liệu cập nhật liên tục (ví dụ: ngân hàng cần xem số dư tài khoản theo thời gian thực), SIFT thiết lập Snowpipe hoặc kết nối Kafka để ingest dữ liệu streaming.
Kiểm tra chất lượng dữ liệu:
Sau migration, nhất định phải có bước data quality check: số lượng records có khớp không? Giá trị null có đúng ngưỡng không? Các business rule quan trọng có được preserve không?
Thời gian: 3–6 tuần tùy độ phức tạp. Output: Dữ liệu lịch sử đã được load vào Snowflake, pipeline tự động chạy định kỳ.
Dữ liệu trong Snowflake có giá trị khi nó được nhìn thấy và dùng để ra quyết định. Bước này kết nối Snowflake với công cụ BI và xây dựng các dashboard thực tế cho từng bộ phận.
Công cụ BI phổ biến tích hợp với Snowflake:
SIFT thường ưu tiên bắt đầu với 3–5 dashboard có ảnh hưởng kinh doanh cao nhất (doanh thu, tồn kho, khách hàng) thay vì cố gắng build tất cả cùng lúc. Điều này giúp dự án có kết quả sớm, tăng sự ủng hộ từ lãnh đạo và người dùng.
Thời gian: 2–4 tuần. Output: Bộ dashboard vận hành cho các phòng ban chính.
Công nghệ tốt mà người dùng không biết dùng thì vô ích. Đây là bước nhiều dự án bỏ qua và sau đó thấy adoption rate thấp.
Đào tạo theo nhóm người dùng:
Thiết lập monitoring và cost governance:
Snowflake cung cấp Resource Monitor để giới hạn chi phí tự động. SIFT giúp thiết lập cảnh báo khi credit usage vượt ngưỡng, tránh hóa đơn bất ngờ cuối tháng.
Hỗ trợ sau triển khai:
SIFT duy trì quan hệ hỗ trợ sau dự án: trả lời câu hỏi phát sinh, tối ưu hiệu suất, mở rộng thêm nguồn dữ liệu khi doanh nghiệp phát triển.
Thời gian: Liên tục. Output: Team tự vận hành được, hệ thống ổn định, adoption rate cao.
Với dự án pilot quy mô nhỏ (1–2 nguồn dữ liệu, 1–2 dashboard), SIFT có thể hoàn thành trong 4–6 tuần — đủ nhanh để lãnh đạo thấy kết quả trước khi quyết định mở rộng.
SIFT Analytics là đối tác Snowflake chính thức tại Việt Nam, với đội ngũ chuyên gia được chứng nhận Snowflake và kinh nghiệm triển khai thực tế tại hơn 500 doanh nghiệp ASEAN, bao gồm ngân hàng, bảo hiểm, sản xuất, bán lẻ và khu vực công.
Chúng tôi cung cấp buổi tư vấn đánh giá Data Readiness hoàn toàn miễn phí cho doanh nghiệp tại Việt Nam. Trong 90 phút, đội ngũ SIFT sẽ giúp bạn hiểu rõ điểm xuất phát, ước tính chi phí và lộ trình phù hợp.
Khám phá những thông điệp mới nhất của chúng tôi
Khi một doanh nghiệp Việt Nam đến gặp đội ngũ SIFT Analytics để tư vấn về kho dữ liệu đám mây, câu hỏi đầu tiên gần như luôn là: “Giữa Snowflake, BigQuery và Redshift thì nên dùng cái nào?”
Không có câu trả lời duy nhất đúng cho tất cả. Nhưng có những tiêu chí rõ ràng giúp doanh nghiệp đưa ra quyết định phù hợp. Bài viết này phân tích thực tế, không thiên vị, dựa trên kinh nghiệm triển khai thực tế tại khu vực ASEAN của SIFT.
1. Chi phí và mô hình thanh toán
Cả ba đều theo mô hình pay-as-you-go, nhưng cách tính khác nhau đáng kể.
Snowflake tính phí theo “credits” — bạn trả cho lượng compute thực sự dùng. Tắt warehouse khi không dùng là ngay lập tức ngưng trả phí. Với doanh nghiệp có workload không đều (cao điểm cuối tháng, thấp giữa tháng), đây là lợi thế lớn.
BigQuery tính phí theo lượng dữ liệu quét (per-query). Dễ dự đoán với query nhỏ, nhưng có thể phát sinh chi phí ngoài dự kiến khi có query lớn chưa tối ưu. BigQuery cũng có flat-rate pricing phù hợp enterprise.
Redshift thường có chi phí cố định hơn với reserved instances, phù hợp doanh nghiệp đã có hạ tầng AWS và muốn dự đoán ngân sách dài hạn.
Thực tế tại Việt Nam: Doanh nghiệp vừa và lớn thường ưu tiên khả năng kiểm soát chi phí linh hoạt — Snowflake đang chiếm ưu thế ở tiêu chí này.
2. Tích hợp với hệ thống hiện tại
Đây là điểm quan trọng nhất với doanh nghiệp Việt đang dùng nhiều nền tảng song song.
Snowflake kết nối với hầu hết mọi thứ: ERP (SAP, Oracle), CRM (Salesforce), Alteryx, BI tools (Tableau, Qlik, Power BI), pipeline tools (Talend, dbt, Fivetran). Nếu doanh nghiệp đã dùng Tableau hoặc Qlik — như rất nhiều khách hàng của SIFT — việc kết nối Snowflake gần như là plug-and-play.
BigQuery tích hợp tốt nhất trong hệ sinh thái Google: Google Analytics, Looker, Sheets. Nếu doanh nghiệp đã trên Google Cloud, BigQuery là lựa chọn tự nhiên.
Redshift mạnh nhất trong hệ sinh thái AWS: S3, Glue, SageMaker. Phù hợp doanh nghiệp đã cam kết với AWS.
3. Khả năng AI và Machine Learning
Đây là điểm phân biệt rõ nhất năm 2026.
Snowflake Cortex cho phép chạy LLM trực tiếp trên dữ liệu mà không cần chuyển dữ liệu ra ngoài — giải quyết nỗi lo về bảo mật. Snowflake Intelligence (AI agent) vừa ra mắt đầu 2026 cho phép hỏi dữ liệu bằng ngôn ngữ tự nhiên ngay trong nền tảng.
BigQuery có Gemini tích hợp, cũng cho phép Natural Language Query và tự động sinh code. Vertex AI là điểm mạnh nếu doanh nghiệp cần train model phức tạp.
Redshift tích hợp với AWS Bedrock (Claude, Llama) nhưng trải nghiệm AI-native chưa liền mạch như Snowflake và BigQuery.
4. Bảo mật và tuân thủ — yếu tố quan trọng với doanh nghiệp VN
Một điểm thường bị bỏ qua: nhiều doanh nghiệp tài chính, ngân hàng, bảo hiểm tại Việt Nam lo ngại dữ liệu bị đưa ra nước ngoài khi dùng AI trên cloud.
Snowflake giải quyết bài toán này bằng cách xử lý AI (Cortex) trong cùng môi trường lưu trữ dữ liệu — dữ liệu không rời khỏi Snowflake account của doanh nghiệp. Đây là lý do Snowflake ngày càng được các tổ chức tài chính khu vực ASEAN ưu tiên.
SIFT không bán duy nhất một nền tảng để chuyển đổi số dữ liệu. Là đối tác của cả Snowflake, AWS và nhiều công cụ BI hàng đầu, SIFT đưa ra khuyến nghị dựa trên tình trạng dữ liệu thực tế của từng doanh nghiệp tư vấn dữ liệu end-to-end — không phải dựa trên một giải pháp nhất định
Trong hầu hết trường hợp triển khai tại Việt Nam, Snowflake là lựa chọn phù hợp nhất cho doanh nghiệp vừa và lớn muốn linh hoạt, có AI tích hợp, và đang dùng hoặc muốn dùng Tableau/Qlik để visualize dữ liệu.
Nếu bạn muốn đánh giá miễn phí xem nền tảng nào phù hợp với hạ tầng hiện tại của mình, liên hệ đội ngũ SIFT để được tư vấn trong vòng 24 giờ.
Khám phá những thông điệp mới nhất của chúng tôi
Trong nhiều năm qua, câu hỏi mà hầu hết giám đốc tài chính, trưởng phòng kinh doanh và nhà quản lý Việt Nam đều muốn trả lời là: “Tại sao tôi phải chờ đến thứ Hai mới có báo cáo của tuần trước?”
Câu trả lời thường là: vì dữ liệu nằm rải rác ở nhiều hệ thống, cần đội IT xử lý, cần chạy query, rồi mới ra được con số. Snowflake Intelligence ra đời để xóa bỏ cái vòng lặp đó.
Snowflake Intelligence là gì?
Snowflake Intelligence là nền tảng AI agent được Snowflake ra mắt đầu năm 2026, tích hợp trực tiếp vào Snowflake AI Data Cloud. Điểm khác biệt cốt lõi: thay vì cần biết SQL hay chờ báo cáo từ IT, người dùng có thể đặt câu hỏi bằng ngôn ngữ tự nhiên và nhận câu trả lời từ chính dữ liệu nội bộ của doanh nghiệp.
Ví dụ thực tế:
Hệ thống tự phân tích dữ liệu, tự viết query ngầm bên dưới và trả về kết quả — không cần IT can thiệp.
1. Cortex Analyst — hỏi dữ liệu bằng ngôn ngữ tự nhiên
Cortex Analyst là lớp AI cho phép người dùng đặt câu hỏi như đang nói chuyện với một chuyên gia phân tích. Hệ thống dựa trên semantic model (mô hình ngữ nghĩa) của doanh nghiệp để đảm bảo câu trả lời chính xác, không phải đoán mò.
2. Cortex Search — tìm kiếm trong tài liệu nội bộ
Không chỉ hỏi số liệu từ database, Cortex Search cho phép tìm kiếm trong tài liệu PDF, email, hợp đồng, báo cáo nội bộ đã được lưu trữ trên Snowflake. Ví dụ: “Điều khoản thanh toán trong hợp đồng với nhà cung cấp X là gì?”
3. Tự động hóa quy trình — AI agent chủ động hành động
Snowflake Intelligence không chỉ trả lời câu hỏi mà còn có thể thực hiện hành động: gửi cảnh báo khi doanh số giảm đột ngột, cập nhật dashboard, hoặc kích hoạt quy trình tiếp theo trong hệ thống.
Tại sao đây là bước ngoặt cho doanh nghiệp Việt Nam?
Theo Garter, Data, Analytics và AI tiếp tục là một trong những ưu tiên hàng đầu của CIO trong năm 2025, dựa trên hơn 12.000 tương tác với các lãnh đạo công nghệ.
Tuy nhiên, khoảng cách về mức độ sẵn sàng dữ liệu vẫn đang hạn chế hiệu quả triển khai AI:
Thách thức lớn nhất của doanh nghiệp Việt không phải là thiếu dữ liệu — mà là dữ liệu phân tán và thiếu nhân lực có thể khai thác nó. Snowflake Intelligence giải quyết đúng bài toán này: dân chủ hóa việc truy cập dữ liệu, để không chỉ data analyst mà cả giám đốc kinh doanh, quản lý cấp trung đều tự lấy được thông tin họ cần.
SIFT Analytics Group là đối tác Snowflake được chứng nhận tại khu vực APAC, với hơn 25 năm kinh nghiệm triển khai các giải pháp dữ liệu cho doanh nghiệp tại Đông Nam Á. Đội ngũ chuyên gia SIFT đã hỗ trợ hàng trăm tổ chức từ ngân hàng, bảo hiểm đến sản xuất và bán lẻ tại Việt Nam xây dựng nền tảng dữ liệu sẵn sàng cho AI.
Nếu doanh nghiệp của bạn đang cân nhắc ứng dụng Snowflake Intelligence, SIFT cung cấp:
Khám phá những thông điệp mới nhất của chúng tôi
Researchers at Omdia surveyed 2,050 professionals worldwide who are actually driving the strategy, rollout and optimization of AI systems. Their global research uncovered:
Amid all the back-and-forth about the value of generative AI, organizations report success.
Bottom line: Organizations tell us gen AI is working, their investments are continuing and the ROI is there.
40%
Respondents who quantified their ROI on gen AI report earning $1.49 for every $1 invested.
Download e-Book
While agentic AI solutions are not widespread, and often are not yet very complex, our research shows that agents are already gaining traction among early gen AI adopters:
It is not surprising that early adopters of gen AI are taking their learnings to the agentic level. But it is significant that more tech-forward organizations may be opening up a sizable lead over competitors. Download the full report for details.
At orgs already using AI agents, the most common uses are:
An often-feared outcome of generative and agentic AI is that it will eliminate human jobs. And it has. Teams most often experiencing job loss due to gen AI in the past year were IT operations (at 40% of surveyed orgs), customer service/support (37%) and data analytics (37%). But that’s not the whole story.
See the report for more information on how job impacts affect seniority levels and more.
Share of businesses having seen both AI-related job creation and loss that report a net positive
The pivot to agentic enthusiasm does not mean that gen AI is now child’s play. While nearly every respondent reports that gen AI is returning value, 96% say that they grapple with significant issues, including:
For midsized companies, talent is a bigger challenge: 43% cited it as a problem, compared to 34% of enterprise respondents.
The blissful share of respondents who say they’ve had no problems implementing gen AI
Not sure where to start with Snowflake?
Talk to SIFT Analytics — and let us help you explore your use case and build a practical, scalable strategy that delivers real business results.
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
SIFT provides Snowflake services that encompass the full spectrum of professional consulting, implementation, migration, and optimization solutions that help organizations deploy and maximize value from the Snowflake data cloud platform. These services address the complex technical and organizational challenges that arise when adopting a modern cloud data platform, including the design of modern data architectures that support scalable and accessible data for organizations.
This guide covers implementation services for new Snowflake deployments, migration consulting for transitioning from legacy systems, performance optimization for existing environments, and ongoing support models. It also highlights how Snowflake services impact data management and data analytics, enabling organizations to efficiently handle, process, and analyze large datasets. It excludes basic Snowflake platform features such as built-in compute and storage mechanics, focusing instead on the professional services layer that enables successful adoption. The target audience includes data engineering teams evaluating Snowflake adoption, IT leaders planning data warehouse modernization, analytics teams seeking to optimize existing deployments, and decision-makers assessing the investment required for Snowflake transformation.
Snowflake services provide end-to-end support spanning platform assessment, architecture design, data migration, performance tuning, cost optimization, and advanced AI/ML enablement—delivered through consulting engagements, managed services, or hybrid models tailored to organizational needs and internal capabilities.
Snowflake services deliver expert guidance and hands-on support for implementing, migrating, and optimizing Snowflake environments.
Additionally, Snowflake allows secure data sharing without copying or moving data, enabling live data access and real-time collaboration across organizations. This enhances the accessibility of data for analytics and decision-making.
By reading this guide, you will gain:
Snowflake services are professional consulting and technical implementation engagements that help organizations adopt, transform, and extract maximum value from the Snowflake cloud data platform. These services go beyond the platform’s native capabilities to address architecture design, data modeling, governance configuration, data pipelines development, security implementation, and the organizational change management required for successful adoption. Snowflake services enable organizations to build modern data architectures that integrate data from multiple data sources, enhancing data quality, accessibility, and operational efficiency to support business growth.
Organizations need specialized Snowflake consulting services because effective platform adoption requires expert judgment across multiple domains. While Snowflake abstracts many operational burdens through its separation of storage and compute, designing optimal micro-partitioning strategies, selecting appropriate warehouse sizes, configuring clustering keys, managing concurrency, and migrating complex legacy systems still demand deep expertise. Snowflake’s architecture is designed with three decoupled layers—Storage, Compute, and Cloud Services—enabling scalability, flexibility, and performance. Without this guidance, organizations risk wasted spend, poor query performance, governance gaps, and underutilized features that diminish return on investment.
SIFT Analytics is an award-winning, leading AI analytics consulting firm in ASEAN with over 27 years of experience helping organizations transform data into actionable insights. With deep expertise in AI, data automation, and digital transformation, SIFT Analytics empowers businesses to leverage Snowflake to accelerate intelligence in their data. As a trusted partner across industries, SIFT delivers innovative analytics solutions that drive measurable business outcomes and sustainable growth in an increasingly data-driven world.
Implementation services support organizations new to Snowflake, covering the complete journey from platform setup through production deployment. These services include cloud provider selection (AWS, Azure, or Google Cloud Platform), architecture design, security configuration, data modeling, and integration with existing analytics tools. Snowflake supports both structured and semi-structured data natively, enabling users to store and manage data in its original format without loss of information. Implementation engagements establish the foundation that determines long-term platform performance and cost efficiency.
Migration services address the complex challenge of moving from on-premises data warehouses, traditional databases, or other cloud platforms to Snowflake. This category encompasses legacy system assessment, ETL/ELT pipeline conversion, historical data transfer, schema translation, and validation testing. Migration services reduce risk and accelerate time-to-value when transitioning from legacy systems.
Optimization services help existing Snowflake customers improve performance, reduce costs, and adopt advanced features like Snowpark, Cortex AI, and machine learning capabilities. These services include query tuning, warehouse right-sizing, cost governance, monitoring enhancement, and training programs that build internal expertise.
Each service category addresses distinct organizational needs, yet they often overlap in practice—a migration engagement typically includes elements of both implementation and optimization to ensure the target environment performs optimally from day one.
Consulting-led implementations involve shorter, focused engagements where external experts work alongside internal teams to design architecture, execute proof-of-concept projects, and transfer knowledge. This model suits organizations with capable data engineering teams who need specialized expertise for specific challenges rather than ongoing support.
Managed services provide ongoing operations, monitoring, and optimization handled by external partners. This approach suits organizations that prefer to focus internal resources on business-specific analytics rather than platform operations, or those lacking sufficient Snowflake expertise to manage the environment independently.
Hybrid models combine consulting for initial implementation with managed services for ongoing operations, or provide advisory support while the client executes. This flexibility allows organizations to scale external involvement based on internal capability development and evolving needs.
The delivery model significantly influences project cost, timeline, risk profile, and required internal resources—making this choice as important as the services themselves.
Building on the core categories outlined above, each service type encompasses specific deliverables and technical activities that address distinct phases of the Snowflake adoption lifecycle.
Architecture design and platform setup establishes the technical foundation for all subsequent work. This includes selecting the appropriate cloud provider and regions, configuring network connectivity and security boundaries, designing the account hierarchy for multi-team or multi-business unit deployments, and establishing infrastructure-as-code practices using tools like Terraform. Snowflake’s unique architecture allows for dynamic modification of configurations and independent scaling of resources, optimizing performance without manual resource management. Decisions made during architecture design directly impact performance, security, and costs for years to come.
Data modeling and warehouse design consulting translates business requirements into optimal schema structures. Consultants help determine whether star or snowflake schemas best suit analytics requirements, design approaches for semi-structured data using Snowflake’s VARIANT type, establish clustering key strategies, and configure virtual warehouses sized appropriately for different workload types. Snowflake supports semi-structured data formats like JSON, Avro, XML, and Parquet, enabling schema-less storage and automatic discovery of attributes for better data access. Effective data modeling enables users to query data efficiently and generate insights quickly.
Security configuration and governance implementation ensures the platform meets organizational and regulatory requirements. This includes configuring role-based access control, implementing row and column-level security, establishing data masking policies, setting up audit logging, and integrating with identity management systems. Strong governance from the start prevents costly remediation later.
Integration with existing data tools and BI platforms connects Snowflake to the broader analytics ecosystem. Implementation services configure connections to BI tools like Tableau, Power BI, and Qlik, establish the ability to connect multiple data sources and create complex data pipelines for comprehensive analytics using Snowpipe or third-party ETL platforms, integrate version control and CI/CD practices, and enable data sharing capabilities across business units or external partners. Organizations can also create data products and workflows within Snowflake to support advanced analytics and operational needs.
Legacy data warehouse assessment and migration planning evaluates the current state and designs the transition path. Consultants profile existing schemas, data volumes, and growth patterns; assess technical debt in SQL scripts and stored procedures; identify dependencies and compliance requirements; and determine whether a lift-and-shift or rearchitecture approach best serves organizational goals.
ETL/ELT pipeline conversion and optimization transforms existing data pipelines for the Snowflake environment. This includes converting code from platforms like SSIS or Informatica, refactoring batch processes for streaming where beneficial, and optimizing pipeline logic to leverage Snowflake’s architecture for processing data more efficiently.
Data validation and testing services ensure migration accuracy and completeness. Validation activities include checksum verification, record count reconciliation, referential integrity testing, sampling comparisons, and performance benchmarking against legacy system baselines. Snowflake services are also used to analyze data for accuracy and performance after migration, supporting advanced analytics and ensuring data-driven decision-making.
Cutover planning and execution support manages the transition to production use. This encompasses defining freeze windows, implementing incremental synchronization, establishing rollback procedures, coordinating with stakeholders, and providing go-live monitoring to address issues quickly. When planning migration cutover and testing, it is important to consider that Snowflake compute usage is billed on a per-second basis, with a minimum billing duration of 60 seconds.
Performance tuning and cost optimization consulting helps organizations reduce spend while improving query performance. Consultants analyze query profiles, implement automatic clustering where beneficial, configure search optimization and materialized views, right-size warehouses, and establish resource monitors and usage governance. Snowflake consulting often includes comprehensive health checks of existing environments to evaluate operational excellence, security, reliability, performance efficiency, and cost optimization. Recent Snowflake improvements have reduced query duration for recurring workloads by approximately 27% through platform enhancements alone—optimization services help organizations capture these benefits fully.
Advanced feature implementation enables capabilities like Snowpark for custom code execution, Cortex AI for generative AI applications, and Snowflake ML for machine learning workflows. With Snowpark, developers can use familiar programming languages like Python, Java, and Scala to implement custom business logic and perform data transformations and machine learning tasks directly in Snowflake, enhancing operational efficiency. These services help data scientists and engineers build AI-powered applications using enterprise data, implement feature stores, establish model registries, and deploy AI models within the governance framework. Cortex AI significantly reduces time-to-insight from days to seconds by utilizing intelligent automation and natural-language data interaction, helping organizations innovate faster.
Monitoring and governance enhancement establishes observability across the data platform. This includes configuring lineage tracking, implementing AI observability for ML workflows, establishing metadata management practices, and ensuring audit capabilities meet compliance requirements. The platform’s elastic scalability allows organizations to adjust capacity and performance on demand, eliminating the need for upfront capacity planning and maintenance of underutilized resources.
Training and knowledge transfer programs build internal capabilities for long-term self-sufficiency. Programs range from technical workshops for data engineering teams to executive briefings on platform capabilities, often including the establishment of Centers of Excellence that institutionalize best practices.
These optimization services collectively ensure organizations extract maximum value from their Snowflake investment, whether through reduced costs, improved performance, or accelerated innovation through advanced features. These capabilities help organizations innovate faster and maintain operational excellence.
Successful Snowflake engagements follow a structured process that aligns technical activities with business objectives, regardless of whether the focus is new implementation, migration, or optimization.
Assessment and Planning: The engagement begins with a thorough assessment of current data architecture, business requirements, and desired outcomes. This phase also involves leveraging Snowflake’s global network—a widespread, cloud-based infrastructure that enables organizations to mobilize, share, and analyze data collaboratively across teams and regions, supporting diverse analytic workloads at scale.
ROI Analysis and Cost Estimation: Teams estimate the potential return on investment by modeling expected performance improvements, scalability, and operational efficiencies. It’s important to note that Snowflake offers a flexible pricing model, allowing users to pay only for the computing and cloud storage they actually use, with options for on-demand per-second pricing or pre-purchased capacity. Additionally, Snowflake provides a free trial period so potential users can explore its features before committing to a paid plan.
Solution Design: Architects design the Snowflake environment, including data models, security policies, and integration points with existing systems.
Implementation: The technical team provisions Snowflake accounts, configures virtual warehouses, and migrates or ingests data. Automation and best practices are applied to streamline deployment.
Testing and Validation: Data pipelines, security controls, and performance benchmarks are validated to ensure the solution meets requirements.
Training and Handover: End users and administrators receive training on Snowflake features, query optimization, and ongoing management.
Ongoing Optimization: Post-launch, teams monitor usage, tune workloads, and implement enhancements to maximize value.
This phase is critical for migrations from large legacy systems, organizations entering regulated industries, deployments requiring AI and machine learning capabilities, or any engagement where cost discipline is mandated.
Current state data architecture analysis maps existing data sources, data flows, schemas, volumes, and growth patterns. This analysis identifies bottlenecks, concurrency issues, and technical debt that must be addressed during implementation or migration.
Business requirements gathering and prioritization identifies key use cases, data consumers, and analytics requirements. This includes defining service level expectations for query latency and data freshness, documenting compliance requirements, and prioritizing workloads for phased implementation.
Technical feasibility assessment evaluates infrastructure considerations including cloud provider alignment with organizational standards, network bandwidth for data transfer, integration requirements with existing tools, and the need for specific capabilities like real-time data processing or secure data sharing.
Migration strategy and timeline development defines the implementation approach, whether lift-and-shift or rearchitecture, establishes pilot phases and production rollout milestones, identifies freeze windows for cutover, and creates stakeholder communication plans.
ROI analysis and cost estimation projects credit consumption, storage costs, data transfer expenses, and professional services fees while modeling expected savings from retiring legacy systems, reducing administrative overhead, and accelerating time to insights.
Self-service approaches suit organizations with experienced Snowflake teams seeking maximum control and willing to invest significant internal resources. The risk of suboptimal configuration is highest without external expertise.
Consulting-led engagements balance external expertise with internal involvement, providing knowledge transfer while reducing implementation risk. This approach works well for organizations building internal capabilities.
Fully managed services minimize internal resource requirements and leverage provider expertise for fastest time-to-value, though they require careful vendor selection and ongoing oversight to ensure alignment with organizational needs.
Selection criteria should weight cost constraints, timeline requirements, internal skill levels, regulatory complexity, data volumes, and the strategic importance of building internal expertise versus focusing resources on business-specific analytics.
Implementation and optimization engagements consistently encounter several challenges that require proven approaches to address effectively. Snowflake services are particularly valuable in supporting research activities within regulated industries, such as financial institutions, by enabling secure, compliant, and efficient data access. This capability accelerates data-driven insights, enhances AI/ML initiatives, and streamlines compliance efforts.
Historical data often presents significant challenges: inconsistent formats, schema drift over time, large volumes requiring extended transfer windows, and compliance requirements for data retention.
Solution: Implement phased migration approaches that prioritize hot data for immediate transfer while scheduling warm and cold historical data for subsequent phases. Use compression and native extractors to accelerate transfer, employ staging environments for validation, and leverage automated tools like code conversion accelerators to reduce manual effort. Establish comprehensive validation frameworks using checksums, record counts, and referential integrity tests to verify accuracy before cutover.
Snowflake’s consumption-based pricing model requires active management to avoid unexpected costs from warehouse sizing, query patterns, and feature usage.
Solution: Implement resource monitors and budget alerts from the start. Right-size warehouses based on workload analysis rather than assumptions, configure auto-suspend and auto-resume appropriately, and separate workloads to prevent resource contention. Recent platform improvements have reduced maintenance costs for Search Optimization Service and Materialized Views by approximately 80%, making these performance features more cost-effective. Establish governance processes that balance performance optimization with cost awareness, using Account Usage metrics to identify optimization opportunities.
Internal teams may lack experience with Snowflake’s architectural patterns, query optimization approaches, and advanced features like Snowpark and Cortex AI.
Solution: Develop structured training programs covering both technical skills and platform concepts. Establish internal Centers of Excellence to institutionalize best practices and provide ongoing guidance. Start with pilot projects that deliver visible wins to build confidence and demonstrate value. Include cross-functional stakeholders—data engineering, security, compliance, and business analysts—early in the process to ensure broad adoption. Document standards, patterns, and lessons learned to accelerate future projects and reduce dependency on external expertise.
Snowflake services span the complete lifecycle from initial assessment through implementation, migration, optimization, and ongoing support. Selecting the right combination of services and delivery models depends on organizational maturity, internal capabilities, timeline requirements, and strategic priorities for building versus buying expertise.
To move forward with your Snowflake initiative:
Related topics to explore include Snowflake cost optimization strategies for consumption management, advanced analytics implementation covering Cortex AI and machine learning capabilities, and data governance best practices for maintaining compliance as your data platform scales.
Interested to start with Snowflake?
Talk to SIFT Analytics — and let us help you explore your use case and build a practical, scalable strategy that delivers real business results.
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
Singapore businesses generate massive volumes of data daily—yet most struggle to convert this information into strategic advantage. Data analytics consulting bridges this gap, transforming raw datasets into actionable insights that drive measurable business outcomes.
SIFT Analytics Group brings over 27 years of experience helping ASEAN enterprises unlock the full potential of their data. Since 1999, we’ve partnered with organisations and strategic partners across Singapore, Thailand and Vietnam to deliver end-to-end solutions in artificial intelligence, business intelligence, data automation, and digital transformation. Our team takes time to learn about each client’s business and operational workflows, combining deep technical expertise with practical business understanding to create analytics strategies that align with your specific objectives. We value partnership with our clients and industry partners to deliver tailored solutions that address unique business challenges.
Whether you’re a multinational corporation seeking advanced analytics capabilities or an SME ready to establish your first data-driven processes, our consultants provide the specialized knowledge and support needed to achieve tangible results. Our solutions are appropriately priced and tailored for small and medium-sized enterprises to ensure accessibility and value.
Singapore’s position as ASEAN’s digital hub creates both opportunities and challenges. According to the 2025 National Business Survey, 80% of local businesses are actively engaged in digital transformation, with 68% planning to prioritize AI and 45% focusing on data analytics over the next 12 months. However, research from ISCA, SIT, and RSM reveals that more than 69% of Singapore SMEs have not adopted data analytics in any meaningful way—leaving substantial competitive advantage unrealized.
Competitive Advantage
In Singapore’s fast-paced digital economy, data-driven companies consistently outperform their traditional competitors. Firms that utilize data analytics can unlock measurable outcomes such as increased productivity and profitability, enabling them to respond faster to market trends and customer preferences.
Regulatory Compliance
Governance and compliance with local regulations like the Personal Data Protection Act (PDPA) and MAS TRM Guidelines is essential in data analytics consulting. Professional consultants ensure your analytics infrastructure meets all regulatory requirements while maximizing data utility.
Cost Optimization
The implementation of data analytics services can help organizations identify trends, optimize operations, and improve customer experiences, ultimately enhancing business performance. Current SAP-Oxford Economics research shows Singapore businesses achieving approximately 16% ROI on AI initiatives, with projections reaching 29% within two years.
Risk Management
Predictive models in data analytics help anticipate financial risks, fraud, and potential equipment failures before they occur. This proactive approach protects organizations from costly disruptions and compliance violations.
Market Expansion
For businesses seeking growth across ASEAN markets, analytics provides critical insights into diverse consumer behaviors, logistics optimization, and regulatory requirements across borders. Data becomes a strategic asset for informed expansion decisions.
Professional analytics consulting ensures maximum return on your data investments by combining technical expertise with business acumen. Consultants help bridge the data skills gap by providing immediate access to specialists in data science, machine learning, and visualization.
Large Singapore corporations and multinational organizations require comprehensive analytics frameworks that scale across departments and geographies. Our enterprise services include:
A data strategy is essential for SMEs to leverage their data as a competitive advantage, enabling them to make informed decisions and optimize operations. This principle applies equally to enterprises, where fragmented data across business units often limits organizational effectiveness.
Many SMEs possess more data than they realize, but often struggle with data that is unstructured and disconnected from critical decision-making processes. Our SME-focused services address these challenges with:
SME AI adoption in Singapore has surged from 4.2% to 14.5% between 2023 and 2024—a threefold increase that demonstrates growing recognition of analytics value. Implementing a robust data strategy allows SMEs to transform scattered information into actionable insights, which can significantly enhance business performance.
Data analytics consulting services in Singapore encompass offerings from data strategy and governance to technical machine learning implementation and real-time reporting. Here are the core solutions we deliver to clients across industries:
Data analytics services involve collecting, processing, and analyzing data to extract valuable insights that drive decision-making, including data mining, advanced analytics, artificial intelligence, predictive analytics, and reporting.
A structured approach in data analytics consulting starts with understanding the business needs, mapping the current data landscape, and identifying gaps to connect data directly to decision-making processes. Consulting firms in Singapore typically offer end-to-end support across the data lifecycle, which includes data strategy, engineering, business intelligence, advanced analytics, and compliance.
Every successful analytics initiative begins with understanding where you stand today. Our assessment phase includes:
This foundation ensures our recommendations address your specific challenges rather than applying generic solutions.
With clear understanding of your current state and objectives, we develop a practical roadmap:
Our technical team executes the plan with attention to both system performance and business integration:
Sustainable analytics success requires organizational capability, not just technology:
Engaging in partnerships with academic institutions can provide organizations access to a pipeline of talent and innovative solutions, fostering a culture of data-driven decision-making.
Our track record spans enterprises and public sector organizations across Singapore and ASEAN. Here’s what our clients say about working with SIFT Analytics:
“SIFT’s predictive analytics implementation transformed our risk management capabilities. We now identify potential compliance issues weeks before they materialize, reducing our exposure significantly while improving regulatory relationships.”
— Senior Risk Director, Singapore Banking Institution
“The customer analytics platform delivered insights we simply couldn’t access before. Understanding purchase patterns across our Singapore locations helped us optimize inventory and improve customer retention by 23% in the first year.”
— Operations Head, Retail Chain
“Working with SIFT’s team, we automated reporting processes that previously consumed three full-time staff members. The efficiency gains allowed us to redirect those resources toward production innovation.”
— Manufacturing Operations Manager
“The data-driven approach SIFT helped us establish has fundamentally changed how we develop and evaluate public service programs. Evidence-based policy is now embedded in our decision-making culture.”
— Policy Director, Government Agency
Collaborative partnerships in data analytics can lead to improved efficiency and performance, allowing organizations to better utilize their data assets for decision-making.
Project timelines vary based on scope and complexity. Basic implementations—including data audits, governance frameworks, and initial dashboards—typically require 2-3 months. Enterprise transformations involving multiple business functions, machine learning deployment, and full-scale automation may extend to 9-12 months or longer. We establish clear milestones and deliverables regardless of project duration, ensuring you see measurable value throughout the engagement.
Data analytics consulting is crucial for navigating Singapore’s competitive landscape, especially in sectors like finance, retail, and healthcare. Our industry experience spans:
Yes. Analytics systems require ongoing attention to maintain value. We offer maintenance packages that include:
Key services in data analytics consulting include strategy development, data quality management, and compliance with local regulations like PDPA and MAS.
Data protection is fundamental to our practice. Our approach includes:
Partnerships in analytics can enhance an organization’s ability to tackle complex challenges by leveraging specialized knowledge from various fields such as mathematics, statistics, and data science.
SIFT Analytics Group has helped Singapore, Thailand and Vietnam enterprises turn data into strategic advantage since 1999. Our 27 years of experience across ASEAN markets means we understand not just the technology, but the business, regulatory, and cultural contexts that determine analytics success.
Data analytics consulting in Singapore helps businesses turn raw data into actionable insights, focusing on AI, machine learning, and cloud-based BI solutions. Whether you’re ready to establish foundational analytics capabilities or advance to sophisticated AI-powered systems, our team provides the expertise, tools, and support to achieve your objectives.
Effective data analytics consulting involves transforming raw data into actionable insights through advanced techniques such as machine learning and predictive modeling. Let’s discuss how we can help your organization unlock the value in your data.
Ready to transform your business? Talk with our team about your data analytics needs and discover how we can help you achieve actionable insights.
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
An AI Data Cloud is a unified, cloud-native platform that centralizes, manages, and analyzes large amounts of structured and unstructured data to support AI and machine learning workloads. At its core, the definition of an AI data cloud emphasizes establishing precise business meanings and relationships within data, which is crucial for building accurate context and enabling AI agents to interpret information correctly. This convergence of artificial intelligence, cloud computing, and data management platforms enables organizations to process, analyze, and derive insights from massive datasets at scale—transforming how enterprises approach digital transformation in the agentic era by leveraging the power of advanced AI and cloud infrastructure.
This guide covers end-to-end data workflows and solutions, including cloud-native AI platforms, data integration strategies, machine learning workflows, and enterprise implementation approaches. It excludes legacy on-premises solutions and basic cloud storage, focusing instead on intelligent infrastructure that powers modern business operations. Enterprise services encompass a wide range of integrated solutions designed to enhance operational efficiency and support strategic initiatives within large organizations. IT leaders, data scientists, and digital transformation executives seeking to modernize their entire data estate will find actionable frameworks for vendor selection, implementation planning, and ROI optimization. The content matters because 87% of large enterprises have now adopted AI in production, yet only 14% have achieved the cloud maturity needed to fully leverage these capabilities.
Direct answer: AI data cloud combines cloud computing infrastructure with artificial intelligence capabilities to provide scalable, intelligent data processing and analytics solutions that break down data silos and enable organizations to answer complex questions across their entire data ecosystem. This means organizations can achieve faster insights and improved operational efficiency.
Key outcomes from this guide:
AI data cloud represents an integrated platform combining cloud storage, compute resources, AI/ML services, and data processing engines into a cohesive system. A clear definition of business terms and relationships within data is crucial, as it enables AI agents to interpret information accurately and perform effective reasoning across complex enterprise environments. The AI data cloud works by automating complex tasks, optimizing storage, and offering real-time insights through the seamless integration of AI into cloud infrastructure. For modern enterprises facing exponential data growth and competitive pressure for real-time insights, this integration has evolved from optional enhancement to essential infrastructure, powered by high-performance computing and advanced AI infrastructure.
Cloud-native data storage layers form the foundation of any AI data cloud platform. These include data lakes for raw unstructured data, data warehouses optimized for structured analytics, and lakehouses that combine both capabilities. AI data cloud platforms enable organizations to manage and analyze vast amounts of data across various environments, providing scalability and flexibility for data-driven decision-making. The system works by aggregating data from multiple sources, enriching it through automated processes, and enabling advanced search capabilities, which together support efficient AI and data management solutions.
The AI/ML service layer sits atop storage, providing access to foundation models including large language models, training environments, feature stores, and inference engines. AI cloud services for data management provide advantages such as automated data cleansing, predictive analytics, and enhanced security, which reduce manual effort and costs. Machine learning models can automatically categorize data based on content and context to ensure quick retrieval and compliance.
Cloud platforms enable AI systems to manage rapidly growing datasets, allowing scalability without a proportional increase in manual resources or hardware investment. The power of the underlying infrastructure—including high-performance computing resources, GPUs, and optimized AI software stacks—supports demanding AI workloads and underpins advanced technologies. Organizations can use a pay-per-use model with AI data clouds, which avoids significant upfront capital expenditure for AI hardware. This economic model has made enterprise-grade AI capabilities accessible to companies of all sizes.
The integration of AI capabilities into data cloud platforms allows for advanced analytics, enabling users to derive insights and automate processes more efficiently. The analytics layer works by aggregating, enriching, and analyzing data to automate and deliver actionable insights in real time. Embedded AI capabilities include natural language processing for conversational interfaces, predictive analytics for forecasting, and automated insights that surface patterns humans might miss. This means organizations benefit from improved efficiency and greater accuracy in their decision-making processes.
AI algorithms automatically cleanse, validate, and structure messy data, reducing human error and enhancing reliability. Automated data ingestion and processing allows AI systems to collect and process data from various sources, reducing human error while accelerating time to insight. AI-driven platforms can proactively detect and mitigate cyber threats by identifying unusual patterns in network traffic or transactions.
AI data cloud platforms often feature built-in security, governance, and disaster recovery mechanisms to ensure data integrity and compliance across different cloud environments. This governance layer extends across the entire system, ensuring that as AI capabilities scale, security and compliance remain connected to every workload.
Understanding these foundational components prepares enterprises to evaluate practical applications and determine how AI data cloud can transform specific business processes.
Building on the architecture components described above, enterprises are deploying AI data cloud solutions across hundreds of use cases that span real-time decision making, predictive modeling, and conversational AI applications. The AI data cloud enables end-to-end data workflows, integrating data aggregation, enrichment, and advanced search capabilities to streamline processes from data ingestion to actionable insights. AI Data Clouds are designed for rapid, collaborative AI development, enabling organizations to securely share data internally and with external partners. This means businesses benefit from faster data processing, improved scalability, and reduced operational costs as the AI data cloud works seamlessly across different business functions to maximize value and efficiency.
Streaming data processing enables companies to detect anomalies, generate automated alerts, and deliver instant business intelligence to users across the organization by showing how the system works: data is ingested, aggregated, enriched, and analyzed in real time to provide actionable insights. Financial services firms process millions of transactions in real time, applying machine learning models to identify fraud patterns before losses occur. Manufacturing operations use IoT sensor data fed through AI data cloud infrastructure to predict equipment failures and optimize production schedules. Telenav and Capita, for instance, have reduced insight generation from days or weeks to minutes or hours by processing workloads involving tens to hundreds of millions of events through Snowflake Intelligence platforms.
Connected to real-time analytics capabilities, predictive analytics extends the value of data by enabling organizations to learn from historical patterns and forecast future outcomes. In this context, leveraging predictive analytics means improved forecasting accuracy, greater operational efficiency, and faster decision-making. AI integration in data management involves automating the model lifecycle, which includes data wrangling, training, and scaling across various data platforms. Enterprise services encompass model training environments, feature stores that maintain consistency between training and inference, and continuous learning pipelines that automatically retrain models as data evolves. Organizations use these capabilities for demand forecasting, supply chain risk modeling, and customer churn prediction—applications where the ability to answer complex questions about future states creates measurable competitive advantage.
Generative AI has transformed how employees and customers interact with enterprise data. Chatbots powered by large language models can search internal knowledge bases to answer complex questions without requiring users to write code or understand query languages. Document processing applications extract insights from contracts, legal filings, and compliance documents at scale. Voice-to-text analytics help call centers understand customer sentiment and identify service improvement opportunities. The Knowledge Catalog serves as a framework that aggregates and enriches data across an enterprise, providing comprehensive context for AI agents to operate effectively. It works by collecting data from multiple sources, enriching it with metadata and relationships, and making it searchable and accessible for AI-driven applications.
Key application areas: Real-time streaming analytics for immediate decision support, predictive modeling for future-state planning, and conversational AI for democratizing data access across the organization.
These applications demonstrate clear business value, but realizing that value requires structured implementation approaches and careful vendor selection based on organizational needs.
Translating AI data cloud applications into production systems demands a methodical implementation approach and informed platform selection. When evaluating vendors, consider not only technical capabilities but also the provider’s revenue growth and financial strength, as these factors can indicate long-term stability and ongoing investment in AI and cloud innovation. AI can significantly improve decision-making processes in enterprises by providing advanced analytics and predictive insights, enabling organizations to respond swiftly to market changes—but only when implementation is properly planned and executed.
Enterprises should follow a structured five-step approach when adopting AI data cloud solutions:
Cross-cloud data management enables organizations to integrate and manage data across multiple cloud platforms, ensuring seamless access and interoperability. Implementing a cross-cloud data strategy can enhance business agility by allowing organizations to leverage the best services from different cloud providers without being locked into a single vendor.
Platform selection guidance: Enterprises already invested in a specific cloud ecosystem should leverage existing relationships while evaluating whether specialized platforms like Snowflake offer superior capabilities for specific workloads. Notably, major vendors such as AWS, Google Cloud, and Microsoft Azure have reported significant revenue growth in their cloud and AI services, reflecting strong financial commitments to ongoing innovation and infrastructure. Organizations in regulated industries should prioritize governance features and compliance certifications. Those building from scratch have more flexibility to optimize for specific use cases and future scalability requirements.
Understanding common implementation challenges helps enterprises avoid pitfalls that have slowed adoption for other organizations.
Despite clear benefits, enterprises face predictable obstacles during AI data cloud adoption. According to industry research, 99% of organizations agree AI is increasing demand for cloud investment, yet many legacy applications and data platforms act as drag on transformation efforts.
Solution: Adopt a phased migration approach, starting with non-critical workloads to build organizational capability before migrating mission-critical systems. Use data mapping and ETL/ELT tools to maintain data quality during transitions. Implement hybrid cloud architectures where sensitive workloads can remain on premises while less regulated data moves to cloud environments. Open table formats like Apache Iceberg and Parquet improve portability and reduce lock-in risk. Singapore public sector organizations have accelerated projects from years to months through storage modernization and structured migration approaches.
Solution: Research indicates 45% of manufacturers and 34% of ICT enterprises cite staff reluctance to retrain as a significant barrier. Address this through internal training programs, vendor-provided education resources, and academic partnerships. Run pilot projects that demonstrate quick wins to build organizational momentum. Ensure business users—not just technical teams—understand how to use conversational interfaces to access AI capabilities. Bring external support through consulting partners who specialize in change management alongside technical implementation.
Solution: Over 70% of organizations using AI-powered cloud services in production expose themselves to risk through misconfiguration and over-privileged identities. Implement robust identity and access management from the start. Use data encryption for all data at rest and in transit. Build audit capabilities that demonstrate compliance with regional regulations including PDPA in Singapore and GDPR in European markets. Establish governance frameworks that scale with AI adoption rather than retrofitting security after deployment.
These challenges are surmountable with proper planning, clear accountability, and partnership with experienced implementation teams who understand both technical and organizational dimensions of transformation.
AI data cloud represents essential infrastructure for competitive advantage in the age of intelligent automation. Organizations that successfully integrate cloud computing resources, AI capabilities, and unified data management will lead their markets—processing millions of data points in real time, enabling employees to answer complex questions through natural language, and scaling analytics workloads without proportional cost increases.
Immediate next steps:
Emerging trends for future exploration: The agentic era is accelerating rapidly—96% of enterprise IT leaders plan to expand use of AI agents over the next year. Edge computing integration will bring AI capabilities closer to data sources, reducing latency for time-sensitive applications. Multicloud interoperability through protocols like MCP will enable organizations to bring AI tools to data regardless of where that data resides.
As Singapore’s leading data analytics consultancy, SIFT helps enterprises across the region navigate AI data cloud adoption. Our team provides data readiness assessments, vendor selection support, implementation guidance, and change management expertise tailored to Singapore regulatory requirements and business context.
Implementation support areas:
Supplementary resources: Data governance frameworks for regulated industries, AI ethics guidelines for enterprise deployment, and ROI calculators for AI data cloud investments are available through consultation with SIFT Data Analytics Services.
Connect with SIFT Analytics
As organisations strive to meet the demands of the digital era, SIFT remains steadfast in its commitment to delivering transformative solutions. To explore digital transformation possibilities or learn more about SIFT’s pioneering work, contact the team for a complimentary consultation. Visit the website at www.sift-ag.com for additional information.
About SIFT Analytics
Get a glimpse into the future of business with SIFT Analytics, where smarter data analytics driven by smarter software solution is key. With our end-to-end solution framework backed by active intelligence, we strive towards providing clear, immediate and actionable insights for your organisation.
Headquartered in Singapore since 1999, with over 500 corporate clients, in the region, SIFT Analytics is your trusted partner in delivering reliable enterprise solutions, paired with best-of-breed technology throughout your business analytics journey. Together with our experienced teams, we will journey. Together with you to integrate and govern your data, to predict future outcomes and optimise decisions, and to achieve the next generation of efficiency and innovation.
The Analytics Times
“The Analytics Times is your source for the latest trends, insights, and breaking news in the world of data analytics. Stay informed with in-depth analysis, expert opinions, and the most up-to-date information shaping the future of analytics.
Published by SIFT Analytics
SIFT Marketing Team
marketing@sift-ag.com
+65 6295 0112
SIFT Analytics Group
Explore our latest insights
Gần 300 giảng viên, nhà nghiên cứu và đại diện các trường đại học đã đăng ký tham dự hội thảo “Chuẩn hóa giảng dạy và nghiên cứu với IBM SPSS 31 – Kinh nghiệm từ hơn 400 Trường toàn Đông Nam Á” do SIFT Analytics Group tổ chức nhằm cập nhật những tính năng mới nhất của IBM SPSS Statistics 31. Sự kiện không chỉ thu hút sự quan tâm lớn từ cộng đồng học thuật mà còn cho thấy nhu cầu ngày càng gia tăng đối với các nền tảng phân tích dữ liệu chuyên sâu phục vụ giảng dạy và nghiên cứu tại các trường đại học.
Với hơn 26 năm kinh nghiệm trong lĩnh vực phân tích dữ liệu và nằm trong Top 10 nhà cung cấp giải pháp phân tích dữ liệu tại ASEAN, SIFT Analytics Group từ lâu đã định vị mình là đối tác công nghệ đồng hành cùng các tổ chức giáo dục trong việc đưa các công cụ phân tích dữ liệu hiện đại vào giảng đường. Thông qua hệ sinh thái giải pháp dành riêng cho khối giáo dục, đặc biệt là IBM SPSS Campus Edition, SIFT hướng tới mục tiêu giúp các trường đại học tiếp cận các chuẩn mực phân tích dữ liệu quốc tế, đồng thời nâng cao năng lực nghiên cứu và giảng dạy trong bối cảnh dữ liệu ngày càng đóng vai trò trung tâm trong khoa học và kinh doanh.
Diễn ra từ 10:00 đến 11:30 ngày 04/02/2026 theo hình thức trực tuyến qua Google Meet, hội thảo tập trung giới thiệu những cập nhật đáng chú ý trong phiên bản IBM SPSS Campus Edition 31. Phiên bản mới mang đến nhiều cải tiến quan trọng phục vụ hoạt động học thuật, bao gồm khả năng tích hợp IBM SPSS Amos cho phân tích mô hình cấu trúc tuyến tính (SEM), trợ lý phân tích dữ liệu ứng dụng trí tuệ nhân tạo, cũng như khả năng kết nối trực tiếp với hai ngôn ngữ phân tích phổ biến là R (programming language) và Python (programming language). Những cải tiến này giúp mở rộng khả năng phân tích dữ liệu, cho phép người dùng kết hợp giữa giao diện trực quan của SPSS và sức mạnh của các hệ sinh thái lập trình dữ liệu hiện đại.
Không chỉ dừng lại ở việc giới thiệu công nghệ, hội thảo còn chia sẻ kinh nghiệm triển khai IBM SPSS Campus Edition tại hơn 400 trường đại học trên toàn khu vực Đông Nam Á. Với kinh nghiệm tham gia triển khai hơn 1.000 dự án phân tích dữ liệu trong khu vực, SIFT Analytics Group đã và đang hỗ trợ nhiều cơ sở giáo dục chuẩn hóa quy trình phân tích dữ liệu, nâng cao chất lượng nghiên cứu và hỗ trợ quá trình công bố khoa học quốc tế trực tiếp trên nền tảng SPSS.
Một điểm nhấn quan trọng của sự kiện là việc công bố chương trình UNiTOUR 2026, sáng kiến do SIFT triển khai nhằm tăng cường hợp tác với các trường đại học trong việc phát triển năng lực phân tích dữ liệu. Thông qua chương trình này, SIFT Analytics Group sẽ đồng hành cùng các cơ sở giáo dục thông qua các hoạt động trọng tâm như tài trợ bản dùng thử IBM SPSS Campus Edition cho toàn trường, tổ chức chương trình đào tạo giảng viên theo mô hình Train-the-Trainers, cũng như triển khai các workshop chuyên sâu về phân tích dữ liệu dành cho giảng viên và sinh viên. Chương trình được kỳ vọng sẽ giúp các trường đại học tiếp cận trực tiếp với các công cụ phân tích dữ liệu hiện đại, từ đó nâng cao chất lượng đào tạo và nghiên cứu theo các chuẩn mực quốc tế.
Bà Phan Thị Thu Thuỳ – Country Manager chia sẻ về ứng dụng SPSS Neural Networks trong phân tích dữ liệu.
Sự quan tâm lớn của cộng đồng học thuật, thể hiện qua gần 300 lượt đăng ký tham dự, cho thấy xu hướng ngày càng rõ rệt trong việc đưa các nền tảng phân tích dữ liệu chuyên nghiệp vào môi trường đại học. Với định hướng đồng hành cùng giáo dục và kinh nghiệm triển khai rộng khắp trong khu vực, SIFT Analytics Group tiếp tục khẳng định vai trò là cầu nối giữa công nghệ phân tích dữ liệu tiên tiến và hệ sinh thái đào tạo đại học tại Đông Nam Á.
Để tiếp tục cập nhật các video hướng dẫn chuyên sâu và giới thiệu những tính năng mới của IBM SPSS Campus Edition 31, Quý Thầy/Cô có thể theo dõi kênh YouTube của SIFT Analytics Group Vietnam tại:
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SIFT Analytics Group Vietnam vừa tham gia đồng hành cùng Thu Duc College of Technology (TDC) trong hội thảo chuyên đề “Ứng dụng AI trong học tập và công việc”, diễn ra ngày 25/10/2025 dành cho khoảng 200 sinh viên Khoa Kinh tế. Sự kiện được tổ chức với mục tiêu mang đến cho sinh viên góc nhìn thực tiễn về trí tuệ nhân tạo và phân tích dữ liệu, hai lĩnh vực đang đóng vai trò quan trọng trong quá trình chuyển đổi số của doanh nghiệp.
Trong khuôn khổ chương trình, đại diện SIFT Analytics Group đã chia sẻ nhiều kinh nghiệm thực tiễn từ các dự án triển khai phân tích dữ liệu trong khu vực. Với hơn 26 năm kinh nghiệm trong lĩnh vực phân tích dữ liệu và nằm trong Top 10 nhà cung cấp giải pháp phân tích dữ liệu tại ASEAN, SIFT đã mang đến cho sinh viên cái nhìn tổng quan về cách các tổ chức hiện nay đang ứng dụng Artificial Intelligence và Data Analytics trong hoạt động kinh doanh, quản trị và ra quyết định.
Thông qua các ví dụ thực tế và các xu hướng công nghệ mới, chương trình giúp sinh viên hiểu rõ hơn vai trò của dữ liệu trong nền kinh tế số, đồng thời định hướng những kỹ năng cần thiết để chuẩn bị cho môi trường làm việc trong tương lai. Đại diện SIFT cũng nhấn mạnh rằng việc trang bị tư duy phân tích dữ liệu và khả năng ứng dụng AI ngay từ giai đoạn học tập sẽ giúp sinh viên nâng cao lợi thế cạnh tranh khi bước vào thị trường lao động.
Bên cạnh hoạt động chia sẻ kiến thức, sự kiện còn thể hiện định hướng lâu dài của SIFT Analytics Group trong việc đồng hành cùng các cơ sở giáo dục. Thông qua các chương trình hội thảo, đào tạo và hợp tác học thuật, SIFT hướng tới việc hỗ trợ các trường tiếp cận hệ sinh thái công nghệ phân tích dữ liệu hiện đại, đồng thời góp phần rút ngắn khoảng cách giữa chương trình đào tạo và nhu cầu thực tế của doanh nghiệp.
Bà Phan Thị Thu Thuỳ chụp ảnh cùng đại diện Khoa Tài chính – Thương mại, Ho Chi Minh City University of Technology (HUTECH) trong phần trao quà lưu niệm.
Sự hợp tác với Thu Duc College of Technology là một phần trong nỗ lực mở rộng mạng lưới kết nối học thuật của SIFT tại Việt Nam và khu vực Đông Nam Á. Với kinh nghiệm triển khai hàng nghìn dự án phân tích dữ liệu trong nhiều lĩnh vực, SIFT kỳ vọng tiếp tục mang những kinh nghiệm thực tiễn này đến gần hơn với sinh viên và giảng viên, góp phần phát triển nguồn nhân lực dữ liệu chất lượng cao cho nền kinh tế số.
Các trường đại học, cao đẳng và tổ chức giáo dục quan tâm đến các chương trình hợp tác, hội thảo hoặc đào tạo về phân tích dữ liệu và AI có thể kết nối với SIFT Analytics Group thông qua các kênh thông tin chính thức của SIFT Analytics Group Vietnam để tìm hiểu thêm về các chương trình đồng hành trong lĩnh vực giáo dục.
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