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What is Customer Master Data? Customer Master Data Management Best Practices

04/11/2024

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Linh Le

Every business collects customer data from website sign-ups and online purchases to social media interactions. But as this data grows, it often becomes scattered across different systems and departments. Sales, marketing, and support may each hold their own version of the same customer, making it difficult to see the complete picture.

Customer Master Data Management (MDM) solves this problem by creating a single, reliable source of truth for each customer. It brings all your information together, cleans and standardizes it, and makes it accessible across your organization.

This article explains what customer master data is, explores the main types of customer data, highlights the benefits and best practices of customer master data management, and shows how to integrate customer MDM with CRM and other systems. You’ll also learn how SupremeTech can help you build a unified data foundation for smarter business decisions.

>>> See more: 

What is customer master data management?
Customer Master Data

What is Customer Master Data?

Customer master data is a centralized, comprehensive dataset about a company’s customers. It includes key details such as contact information, billing and shipping addresses, financial profiles, account identifiers, and interaction history. In short, it captures the key attributes that define your customers and how they engage with your business.

This data serves as a single source of truth (SSoT) across the entire organization. It ensures that every department (sales, marketing, finance, and customer service) accesses the same accurate, consistent, and up-to-date information. When correctly managed, customer master data eliminates duplicate records, prevents errors, and connects customer interactions across channels and systems.

Unlike transactional data, which records one-time activities such as purchases or service requests, customer master data remains stable and reusable. It’s the foundation that supports business operations, enhances customer experience, and fuels decision-making.

Effective customer master data management (MDM) helps businesses maintain this centralized accuracy by continuously cleaning, enriching, and synchronizing data across platforms. When managed correctly, it not only improves customer service and marketing performance but also strengthens compliance, analytics, and strategic planning.

what is customer master data?

Types of Customer Master Data

A strong customer master data management strategy starts with understanding the kinds of data you need to unify. Below are the key types that form a complete picture of each customer.

1. Basic identifying information

This includes a customer’s name, address, phone number, and email address, as well as unique identifiers such as a customer ID or account number. These elements are the foundation for matching and merging data across systems.

2. Demographic Data

Demographic details such as age, gender, income, education level, and location help you segment and understand your audience more deeply. They support marketing, personalization, and forecasting strategies.

3. Behavioral Data

Behavioral data captures customer interactions, such as browsing history, purchase frequency, purchase recency, app usage, and social media interactions. This type of data is valuable for predicting future behavior, personalizing offers, and improving the customer experience.

4. Transactional Data

Transactional data shows a customer’s purchase history, payment methods, order frequency, and lifetime value. It connects customer identity with their commercial relationship with your company. Transactional data is essential for analytics, financial reporting, and assessing customer lifetime value.

5. Engagement Data

Engagement data includes information about how customers interact with the brand across various touchpoints. It can be website visits, email opens, ad clicks, social media interactions, customer service inquiries, and loyalty program activity. This helps businesses understand customer engagement and loyalty.

6. Preferences and Interests

This type of data captures what customers prefer or are interested in. It can vary by favorite product categories, language preferences, notification settings, content interests, and communication frequency. It’s handy for personalization and customer experience management.

7. Account and Membership

For businesses with loyalty programs or membership systems, this includes information related to membership tiers, reward points, account status, and customer preferences within the loyalty program. These elements strengthen long-term relationships and are key to customer retention strategies.

8. Customer Feedback

This data includes customer reviews, feedback, survey responses, and sentiment analysis from social media or other platforms. It provides insights into customer satisfaction, product improvement needs, and brand perception.

9. Customer Service and Support Data

Support tickets, chat logs, complaint records, and resolution histories reveal how well you serve your customers. Integrating this data into your customer master record ensures that every interaction is informed and empathetic.Together, these data types create the foundation for a 360-degree customer view—a single, comprehensive record that drives consistent experiences across all touchpoints.

What Is Customer Master Data Management (Customer MDM)?

Customer Master Data Management (MDM) is the process of consolidating, cleaning, and managing all customer-related data to maintain one accurate version of the truth across your organization.

It involves people, processes, and technology working together to ensure that every department — from marketing to operations — uses consistent, reliable customer data.

In practical terms, customer MDM connects data from multiple systems such as CRM, ERP, marketing automation, and support platforms. It then cleanses, standardizes, and synchronizes this information into a single database known as the “master record.”

This master record is continuously updated and shared across systems, eliminating inconsistencies and ensuring that every customer-facing team sees the exact, accurate details.By implementing customer master data management best practices, organizations can reduce data duplication, improve analytics, and deliver highly personalized experiences that build trust and loyalty.

Customer MDM

Why Customer MDM Matters

Customer Master Data Management matters because it ensures that everyone in an organization, from sales to customer service, works with the same, accurate, and consistent customer information. When data is fragmented across systems, businesses struggle to understand their customers, deliver personalized experiences, or make informed decisions. By unifying and maintaining customer master data, companies can streamline operations, improve communication, and build stronger customer relationships.

Here’s why customer master data management is essential for retail businesses:

Improved customer experience

When every system shares the same correct information, customers receive consistent communication, faster service, and more relevant recommendations. For example, a support agent can instantly see a customer’s order history and resolve an issue without asking repetitive questions.

Better decision-making

Good data means good decisions. When your customer information is accurate and complete, managers can plan better marketing campaigns, predict customer needs, and set clear business goals. Reliable data reduces guesswork and supports smart strategies.

Increased operational efficiency

Without customer master data management, teams often spend time fixing data errors or searching for the right information. A single, clean data source removes this problem. Everyone works faster because they trust the data they see.

Stronger compliance and data security

Regulations such as GDPR and local data laws require companies to manage customer data carefully. With strong customer MDM in place, it’s easier to protect sensitive information and ensure all records comply with the correct rules and privacy standards.

Higher profitability

With a complete view of each customer, you can identify cross-sell and upsell opportunities, reduce churn, and target high-value segments more effectively. This often leads to higher sales and stronger long-term relationships.

Best Practices for Customer Master Data Management

Building an effective Customer Master Data Management (MDM) system is not just about technology. It’s about having clear rules, responsibilities, and processes that keep your customer information accurate and useful. Below are key best practices that help organizations create reliable data foundations and deliver better business outcomes.

1. Establish a Single Source of Truth

The first step in customer master data management is to ensure everyone in the company works from a single version of the truth. When customer data is scattered across different systems, errors and duplicates appear. A single, centralized master record ensures all departments—sales, marketing, finance, and support—see the same accurate information. This consistency builds trust, improves teamwork, and helps every team deliver a better customer experience.

2. Define Strong Data Governance and Ownership

Good data depends on good management. Data governance means defining who owns customer data, how it’s created, and when it’s updated. Assigning roles such as data stewards ensures accountability and consistency. With transparent governance, your company can maintain high-quality data over time instead of constantly fixing errors. It also makes compliance and reporting far easier.

3. Focus on Data Quality: Cleanse, Standardize, and Enrich

High-quality customer data is the foundation of every successful business decision. Unfortunately, many organizations struggle with duplicate records, missing fields, and inconsistent formats. Poor data quality not only wastes time but can also lead to failed campaigns, inaccurate reports, and frustrated customers.

To overcome this, companies must continuously cleanse and standardize their customer master data. Deduplication processes remove duplicate entries, while data validation tools ensure that addresses, email addresses, and contact details are correct. Standardization enforces consistent formats across all systems, making integration and analytics far easier.

You can also enrich data with additional information, such as demographics or preferences, to make it more valuable. High-quality data improves decisions, saves time, and helps your teams work confidently.

4. Integrate Customer Master Data Across All Systems

Customer information often lives in many systems, including CRM, ERP, marketing tools, or support platforms. Integrating these systems ensures updates in one place appear everywhere else. This not only prevents data silos but also gives your business a clear, unified view of every customer. Well-planned integration creates smoother operations, faster communication, and a consistent experience at every touchpoint.

5. Protect Customer Data and Ensure Privacy Compliance

With privacy laws like GDPR, CCPA, and others growing stricter worldwide, data protection must be a core principle of customer master data management best practices. Customer master data often includes sensitive information such as contact details, payment preferences, and transaction histories. Mishandling this data can damage your reputation and lead to serious penalties.

Strong security and privacy practices, such as encryption, access controls, and regular audits, help keep sensitive data safe. Compliance with data protection laws also builds customer trust and prevents costly penalties. When customers know their information is handled responsibly, they’re more likely to stay loyal to your brand.

6. Monitor, Audit, and Continuously Improve

Customer information changes constantly. People move, update their email addresses, or change their buying habits. That’s why customer MDM should be an ongoing process. Regular audits and data-quality reports help catch issues early. Collecting user feedback, reviewing governance policies, and adjusting integration flows all help keep your data clean and reliable. Continuous improvement ensures your customer data always supports your business goals.

Customer Master Data Management Best Practices

Integrating Customer MDM with CRM and Data Systems

Integrating Customer Master Data Management (MDM) with CRM and other data systems helps create a complete and accurate customer view. The goal is to make your master record the single trusted source that keeps all platforms (CRM, ERP, marketing, and support) aligned.

Start by identifying where customer data lives and how these systems connect. Clean and standardize records before integration to avoid spreading duplicate or incorrect information. Use APIs or data pipelines to automatically synchronize updates so that changes made in one system are reflected everywhere.

Regularly monitor data flow and fix sync issues early. As your business grows, review and adjust these integrations to include new tools or channels. When done right, integration eliminates silos, improves collaboration, and ensures that every department uses the same reliable customer data.

Challenges in Customer Master Data Management

Managing customer master data comes with several challenges. One major issue is inconsistent or incomplete data collected from different systems. When customer details vary between departments, it becomes challenging to create a single, accurate record.

Duplicate records are another common problem. The same customer might appear multiple times under slightly different names, which leads to confusion, wasted effort, and poor decision-making.

Data quality is also a continuous challenge. Outdated, missing, or incorrect information can damage trust and make analytics unreliable. Regular cleansing and validation are necessary to maintain accuracy.

Integration adds complexity. Customer data often exists across CRM, billing, and support platforms. Without proper synchronization, each system holds a different version of the truth.

Finally, organizational resistance and lack of data ownership can slow progress. Successful customer MDM requires collaboration, clear roles, and strong governance to ensure everyone values and maintains data quality.

How SupremeTech Can Help Your Businesses

At SupremeTech, we understand that managing customer data isn’t just about technology. It’s about creating business value through clarity, trust, and connection.

Our Custom Software Development service helps organizations design and implement customer master data management solutions tailored to their needs. Whether you want to build a centralized MDM system from scratch or integrate your customer master data into CRM, ERP, and analytics platforms, our experts can make it happen.

We combine deep technical expertise with practical business insight to ensure your MDM initiatives deliver measurable impact. From designing clean data architectures to automating validation and synchronization, SupremeTech helps you create a single source of truth for all your customer information.

Our developers work with modern tools and cloud technologies, ensuring your data flows securely and seamlessly across systems. With strong governance frameworks and performance optimization, we help you transform raw customer data into actionable insights that drive growth.

When your data is accurate, your decisions are smarter, your customer experience is stronger, and your operations are more efficient. That’s the power of mastering your customer data, and that’s where SupremeTech can help you lead.

>>> Explore our expertise:

customer MDM process

Future Trends in Customer Master Data Management

Managing customer master data effectively is more critical than ever, and several trends are shaping how businesses approach it today. Here are the key current trends:

Moving to the Cloud

Cloud-based MDM (Master Data Management) solutions have taken off, and for good reason. With cloud, businesses can scale up (or down) as needed, access data from anywhere, and reduce costs. Plus, it’s easier to keep data up to date in real time, which is a game-changer for fast-paced retail environments.

Using AI to Clean and Understand Data

Artificial intelligence is helping clean up messy customer data by detecting duplicates, filling in blanks, and identifying patterns we might miss. Machine learning tools analyze behavioral trends and even predict what a customer might want next, making personalization much more intuitive.

Real-Time Data Updates

Today’s customers expect immediate responses, and for that, real-time data updates are essential. Integrating systems so that customer data refreshes instantly allows businesses to provide relevant offers or support as soon as it’s needed. In other words, no more outdated data holding back customer experience.

Customer Data Platforms (CDPs) for a Clearer View

CDPs pull customer data from multiple sources into one spot, creating a single, reliable profile for each customer. This unified view allows teams across sales, support, and marketing to deliver a consistent experience. As CDPs become more accessible, even smaller businesses can leverage this organized approach.

In short, companies are aiming to make data more accessible, accurate, and actionable, with a focus on real-time updates, privacy, and smarter, AI-powered insights. The result? Better customer experiences, more efficient operations, and a competitive edge.

>>> Read more: Understanding Key Differences Between Customer Data Platform vs Data Lake

Seeking ways to manage customer data effectively?

Knowing the importance of data sometimes does not mean knowing where to start. Even our clients who built an empire in retail struggle to manage data efficiently. The common pain point, as we translated it, is how to build a data pipeline that runs stably and responsively. Furthermore, trust and security are also the head-wrenching problem, especially when seeking external help.

We proudly offer the best of both. Schedule a meeting with us to know how we can proclaim with such confidence.

Conclusion

Customer Master Data Management is more than a technical exercise. It’s a strategic foundation for every modern business that values accurate insights and personalized customer experiences.

By understanding the different types of customer master data, following proven best practices, and integrating your systems effectively, your organization can build a single, reliable source of truth.

Clean, connected data doesn’t just reduce complexity—it unlocks opportunity. With the right partner, you can turn your customer master data into a competitive advantage that fuels lasting success.

FAQs

What Is Customer Master Data?

Customer master data is a centralized and consistent set of information about your customers, including their contact details, billing and shipping addresses, financial information, and interaction history. It acts as a single source of truth shared across departments and systems.

What Are Common Data Sources for Customer Data?

Common data sources include CRM systems, ERP platforms, e-commerce databases, customer support tools, marketing automation software, and billing systems. Integrating these systems helps create a complete and accurate customer profile.

What Are the Key Components of Customer MDM?

Key components include data integration, cleansing, deduplication, governance, and ongoing quality management. Together, they create a trusted, unified view of each customer across the organization.

How Can Businesses Get Started with Customer MDM?

Start by identifying where customer data exists, assessing its quality, and defining governance roles. Then, integrate data systems, apply cleansing processes, and maintain regular audits to keep data accurate over time.

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Key Features: Free and no registration required — just go to diagrams.net.Flexible storage — save files locally or to Google Drive, OneDrive, GitHub, GitLab.Rich icon library — supports UML, BPMN, flowcharts, network diagrams, and more.UML & BPMN ready — perfect for use cases, activity diagrams, and business flows.Easy collaboration when stored on shared drives.Cross-platform — available on web, desktop, and as a VS Code extension. Limitations: Real-time collaboration isn’t as strong as tools like Figma.Performance may drop with very large or complex diagrams. 2. Miro Miro is an online collaborative whiteboard designed for teams to brainstorm, plan, and visualize ideas in real-time. Key Features: Infinite canvas — visualize projects without space limits.Real-time collaboration — comment, vote, and co-edit instantly.Rich templates — includes user story maps, journey maps, mindmaps, Kanban boards, and wireframes.Integrations — connects with Jira, Confluence, Slack, Teams, Google Drive, and more.Great for mapping processes, use cases, roadmaps, or even UI mockups. Limitations: Free plan limits the number of boards.Large boards with many assets may slow down performance. 3. Trello Trello is a Kanban-based task management tool that helps teams visualize and track progress easily. Key Features: Simple drag-and-drop interface.Highly customizable boards, lists, and cards.Each card can include checklists, attachments, labels, due dates, and assignees.Seamless integration with Google Drive, Slack, Jira, GitHub, and others.Real-time updates across all team members.Works on web, desktop, and mobile. Limitations: Free plan limits the number of integrations (Power-Ups). 4. Jira Jira by Atlassian is the industry-standard project management tool for Agile teams. Key Features: Built for Scrum and Kanban teams.Highly customizable workflows, fields, and automation rules.Transparent tracking of tasks, blockers, and progress.Integrates with hundreds of DevOps, CI/CD, and testing tools.Scales from individual tasks to enterprise-level project portfolios. Limitations: Steep learning curve for beginners.Can be costly for large teams.Requires experienced admins for setup and maintenance.May run slower on large, complex projects. 5. Typescale A handy tool for generating consistent typography systems (font size, line height, spacing) for web or app design. Key Features: Automates type scale creation.Multiple presets and flexible customizations.Preview and export CSS directly.Ensures responsive and accessible typography. Limitations: Not suitable for all design systems or content types.Limited control over detailed responsive behavior. 6. Adobe Color An intuitive color palette generator to create harmonious and accessible color schemes. Key Features: Easy-to-use color wheel with real-time updates.Auto-generates color harmonies based on color theory.Supports HEX, RGB, and CMYK formats.Integrates seamlessly with Adobe tools like Photoshop, Illustrator, and XD.Community palette sharing and inspiration gallery. Limitations: Contrast still needs manual checking for accessibility.Some auto-generated palettes may need manual tweaking.Colors can look different on various screens. 7. Contrast Checker A simple but vital tool to ensure readability and accessibility by checking text and background contrast per WCAG standards. Key Features: Simple interface — input colors and get instant feedback.Ensures compliance with accessibility guidelines.Real-time updates as you adjust colors.Bridges design and development — everyone can validate contrast easily. Limitations: Doesn’t reflect results accurately for complex backgrounds.Doesn’t account for font size, spacing, or user testing conditions. Why Use These Tools? Transparency: Everything — from tasks to deadlines — is clearly tracked. For example, Trello helps answer questions like “Who’s doing what?” and “What’s the current status?”Visualization: Tools like Draw.io help transform abstract logic into clear, easy-to-understand diagrams.Collaboration: Integrating tools like Miro, Jira, or Slack ensures everyone stays aligned and reduces miscommunication. Tips for Getting Started Start small: You don’t need every tool at once. Begin with Jira or Trello, then expand.Build shared habits: Tools only work when the whole team uses them consistently.Learn by doing: Explore free trials and tutorials, then apply them directly in your current projects.Stay updated: Tools evolve fast — keeping up helps you stay ahead. Using tools isn’t just about having more software — it’s about changing the way we work.They make our processes more transparent, our teamwork more seamless, and our output more efficient. For Business Analysts, these tools are not just “nice-to-have” — they’re what turn you from a task executor into a strategic enabler for your team. Read more related articles from SupremeTech!

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        Must-Have Tools for Business Analyst

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          How to Step Out of the “Forwarder” Shadow?

          Have you ever, as a Comtor or Business Analyst (BA), felt like… a messenger? Every time the client asks something, you turn to the team, copy their answer, translate it, and send it back — just passing messages instead of actually owning the conversation. At SupremeTech, our BA team jokingly calls this role the “Professional Forwarder.” Through many “lost in translation” moments, we’ve learned valuable lessons on how to step out of that shadow — to become real connectors between the client and the team. Let’s hear from our BA team as they share practical tips to help you move beyond being a “forwarder” drawn directly from real project experience. Signs You Might Be Forwarding Too Much 1. The classic line: “Let me check with the team.”It’s not wrong — but if you’re saying it too often, it might mean you don’t fully understand the issue. 2. Lack of confidence in meetings: Many new BAs struggle with open-ended questions. When you don’t fully understand the product, you can’t confidently answer questions from both the client and your internal team. The PM asks about progress, you look at the Sprint Backlog full of numbers — and still don’t know where to start. 3. Avoiding technical talk: The moment you hear technical terms, you “pass the ball” to the PTL — without really understanding what’s being discussed. 3 Steps to Escape the “Forwarder Manager” Role So, how can you move from being a Forwarder to becoming a true communicator — someone who understands, connects, and leads discussions effectively? Here are three simple but powerful steps you can start practicing right away: 1. Before Forwarding, Ask Yourself: Do I understand at least 70% of this content?Have I tried to reproduce the bug, test the feature in the DEV environment, or explore the possible cause myself?If I were the dev/tester receiving this message, would I have enough context to understand it?Can I classify the issue — is it about UI/UX, logic, data, or business flow?Can I try to answer part of it first, then confirm later? 👉 This habit helps you learn something new every day, instead of just finishing tasks every day. 2. In Every Meeting – Observe and Lead What is the team really discussing? Do I understand the big picture?If the conversation is technical, how does it relate to the overall context?Is anyone confused? Can I help clarify? If you find yourself unsure about all three — take notes, take notes, and take notes.Meeting minutes and your own notes will help you retain details and follow up later for deeper understanding. 3. Build Strong Foundations Whether you’re a Comtor, BA, or PO, a solid foundation in product knowledge, business logic, and basic technical understanding helps you make better decisions — and lead your team effectively. Don’t get stuck thinking “that’s not my task.” Instead, learn actively by: Reading about technical keywords used in your project.Redrawing the business flow yourself to truly understand it.Asking devs, QCs, PTLs, and clients for their perspectives.Finding a technical advisor who can review your understanding and answer your tech-related questions. Every time you’re about to forward a message, pause for a minute — dig a little deeper.Each pause adds to your knowledge and analytical mindset. These small daily efforts will sharpen your skills and confidence — helping you grow not only as a professional BA, but also as a potential Project Leader who truly adds value to the team.

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          Team Người Việc: Winning with AI-Assisted Development at SupremeTech

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            How Team Người Việc Won SupremeTech’s AI Hackathon 2025 with AI-Assisted Development and Agile Thinking

            24 hours. 10 teams. Countless lines of code. One team claimed the spotlight and took half of the 100 million VND prize pool. SupremeTech’s first-ever AI Hackathon was more than just a competition, it was a test of endurance, creativity, and teamwork. For one intense day and night, our participants pushed the limits of AI-assisted development, turning raw ideas into functioning prototypes under extreme time pressure. Among them, three teams rose above the rest. Their solutions not only showcased strong technical execution but also revealed how AI hackathon use cases can bring real business value in areas such as customer experience, automation, and data-driven decision-making. These top three use cases highlight the future potential of AI and the passion of SupremeTech’s people to turn vision into reality. Brought home the Top Prize - Team Người Việc stood out for their sharp strategy and teamwork. Their winning project solved a familiar yet complex issue in the tourism industry: managing group travel efficiently while ensuring every participant enjoys a seamless experience. Presented in clear business logic, executed with agile methodology, and powered by AI-assisted development, their solution proved that innovation thrives when technology meets human insight. Introducing the Team: Small but Strong Team Người Việc brought together a crew of four: Hung Dinh, Huy Nguyen, and Dung Nguyen as front-end engineers, and Khanh Nguyen as the business analyst. While other teams had five members, this smaller team turned their size into strength. With Khanh shaping the business logic and user journey, and the three engineers transforming those ideas into a functional product, they created a strong link between business insight and technical execution. Each member brought a distinct perspective: one focused on monetization and business value, another on operational flow, and others on technical quality and user experience. Together, they created a strong team that has both business insight and technical execution. Khanh shared that: “Everyone respected each other’s opinions. We weren’t chasing perfection, we were building something real, something that worked”. The Challenge: Turning Hot and Heavy Topic into Opportunity When the AI Hackathon began, the participating teams didn’t get to choose their challenge. Each team drew a topic randomly from a pool of three, and fate handed team Người Việc a challenge that was both broad and complex: Destination and Experience Management System for Tourism. Instead of seeing it as an obstacle, the team saw great potential in this topic: “It’s actually very close to what SupremeTech does,” one member shared. “Tourism and service coordination are among the industries where our clients face similar pain points. If developed further, this could even become a real product for the company”. For most teams, tackling something this wide in just 24 hours would be overwhelming. But for Người Việc, it became the perfect opportunity to combine business logic, agile thinking, and AI-assisted development into a single solution. Dũng, one of the front-end engineers shared: “We didn’t see it as just a travel problem. It’s a coordination problem that every company faces because of too many people, too little time, and too many things to track.” The Idea: Transforming Tourism Coordination with AI Manual planning and coordination often create time-consuming processes, lack of feedback, and fragmented communication across travel agencies, corporate HR departments, and trip participants. To solve this, Người Việc envisioned an end-to-end platform that connects all stakeholders, from travel agencies and corporate planners to event organizers and trip participants.The system enables users to: Create and customize travel itinerariesConnect directly with travel agencies through a marketplace modelTrack schedules via QR codeProvide instant feedback during the trip. In short, it bridges the gap between demand and supply in hospitality, creating a more transparent, interactive, and seamless travel experience. The Process: From Brainstorming to AI-Assisted Development What set Người Việc apart was their strategic mindset before touching a single line of code. Instead of rushing to use AI tools right-away, the team began with a face-to-face brainstorming session, mapping out what a real group trip looks like from start to finish: from planning and agency communication to real-time updates and user feedback. To validate their ideas, they even called friends working in hospitality to understand pain points from the field such as: how agencies handle client requests, where information gets lost, and what travelers actually expect. Only after this discovery phase, the team moved into design and development. They first created clear user stories and workflows on their own, then applied story-based prompting by feeding those stories into ChatGPT and Copilot to generate database schemas, API endpoints, and code snippets. This structured use of AI helped them align technical output with business logic and speed up development. Their approach became a model of how AI-assisted development and agile methodology can complement each other, keeping logic clear while boosting speed. Their mantra throughout the process was simple yet powerful: Think first, then use AI smartly. This mindset kept their workflow focused, turning AI into a productivity multiplier instead of a shortcut, and became a highlight in their AI hackathon journey.Without a QC member, the team stayed flexible and shared responsibilities across roles. Each member could take on multiple tasks when needed, but they still kept a clear structure in how they worked. The PTL and BA stepped in as real users, testing features and giving feedback from a user’s point of view. After defining their user roles and business logic, Team Người Việc translated their ideas into a working prototype. Their platform acts as a bridge between corporate planners and travel agencies, creating a space where requests, itineraries, and feedback flow seamlessly in real time. The system’s core features included: Trip creation and customization: HR or operation teams can build itineraries, adjust timelines, and submit requests tailored to their needs.Agency collaboration: Travel agencies receive those requests, update details, and negotiate directly through the platform, no more back-and-forth emails or lost messages.Participant tracking: Each trip generates a public QR code, allowing members to follow updates, view schedules, and send instant feedback during the journey.Transparency and engagement: The platform closes the communication loop, giving every stakeholder a clearer view of the process. With these key flows completed, the team delivered a functional MVP, a product with clean logic, smooth handoffs between roles, and enough structure to be reused or scaled for other industries. Modern Tech Stack Built for AI-Driven Innovation To bring their concept to life within 24 hours, Team Người Việc designed a tech stack that was modern, lightweight, and AI-friendly. Every layer from frontend to deployment was chosen to balance speed, scalability, and maintainability. Frontend Layer: Fast and Built for Clarity The team developed the user interface using Next.js 15 to handle both page rendering and API routes. Combined with TypeScript, it provided type safety and consistency across all modules, reducing human errors in the rush of development. For styling and components, they used Tailwind CSS and shadcn/ui, which allowed them to quickly create a clean, responsive design without spending time reinventing basic UI elements. Despite the tight schedule, the frontend still delivered a cohesive experience from trip creation to QR-based tracking, proving that with the right stack, agility doesn’t mean sacrificing structure. Backend Layer: Structured Logic and Data Flow Behind the interface, the team used Prisma ORM to manage the database layer. Its schema-first approach, paired with TypeScript integration, helped them maintain data consistency while iterating rapidly. The backend services were also written in Next.js, utilizing server functions to keep everything unified and easy to deploy. This setup gave the team clear control over their data models and allowed them to focus on the business logic, ensuring that trip creation, feedback collection, and participant interactions all flowed smoothly without manual handling. Infrastructure & Deployment: Stability under Pressure To keep their development-to-demo pipeline fast and reliable, Người Việc deployed their system on AWS using Dokploy - a self-hosted CI/CD solution that automates Docker-based deployments. This environment allowed them to push code, test changes, and release updates seamlessly without dependency conflicts. By using Docker containers, they replicated production conditions from the start, ensuring that the MVP remained stable and demo-ready throughout the hackathon. The setup was simple enough for rapid iteration yet robust enough to be scaled for real client use. AI Tools: A Smarter, Not Faster, Way to Build AI played a key role in the team’s workflow but only after the foundation was set.ChatGPT acted as their assistant for ideation and logic design, helping refine user stories, define acceptance criteria, and clarify user flows. Meanwhile, GitHub Copilot served as their pair programmer, generating clean snippets, suggesting improvements, and handling repetitive coding tasks. Instead of using AI as a shortcut, Người Việc used it as an accelerator by integrating it at the right moments to enhance productivity while keeping control of direction and logic. >>> Read more related articles: AI-Assisted Ecommerce Solution Wins Third Place at SupremeTech AI Hackathon 2025How Human Intelligence and AI Capabilities Can Redefine Software Development | Featuring The 1st Runner-Up of SupremeTech AI Hackathon 2025 Judges’ Feedbacks Business Perspective From a business perspective, the judges saw Team Người Việc as a perfect example of practicality and vision. Their solution showed how AI-driven development can address real client needs, especially in industries like travel and hospitality. However, the judges also provided constructive feedback for future improvement. While the idea covered a broad scope from sales to operations, they suggested narrowing the focus to one specific stage in the travel management cycle. By doing so, the solution could achieve higher feasibility and faster adoption in real-world scenarios. The judges also encouraged documenting the team’s AI-assisted project management workflow as a reference for future AI hackathon journeys within SupremeTech. The final presentation showcased all the best qualities of their teamwork. The judges highlighted Người Việc’s clear storytelling, strong time management, and smooth demo delivery that effectively illustrated how their system worked. The team’s confident, structured presentation left a lasting impression and perfectly captured the spirit of SupremeTech’s AI Hackathon. Technical and Engineering Perspective From a technical point of view, the judges recognized Người Việc as a team that combined strong engineering skill with thoughtful use of modern tools. They developed their product on a well-defined code base with clear development standards, following a structured flow from analysis and design to implementation, which is remarkable under the time pressure of a 24-hour hackathon. The highlight of their approach was the story-based prompting technique, which kept the project’s logic coherent from start to finish. By crafting prompts around user stories rather than isolated tasks, the team ensured that every AI-generated piece of code served a real business purpose. This balance between automation and human reasoning became one of the defining features of their success. Teamwork: Staying Calm When Things Went Wrong No hackathon story is complete without chaos and Người Việc had their moment too. Just before the final presentation, disaster happened: the team’s slide suddenly became inaccessible because their shared drive was locked by the judges. With only minutes left, they borrowed a laptop, rebuilt the slides from scratch, and walked onto the stage calm and composed delivering a confident demo that looked effortless to the audience. The team recalled “After 22 hours of coding, what stayed with us wasn’t exhaustion. It was that moment when everyone looked at each other and said: We'll make it work, no matter what.” Voices from the Winners For Team Người Việc, winning the hackathon was not just about the prize, it was about learning how humans and AI can truly collaborate. Reflecting on the experience, Dũng shared: “We realized that AI isn’t just a tool, it’s a real teammate, if you know how to ‘talk’ to it. Each team used AI differently: some for brainstorming, some for UI design, others for presentation. But the prompts we gave were never the same, and that’s why the results were so different. AI only shows its real power when people know how to guide it.” As winners, the team also offered advice for those who will join future hackathons: “Prepare everything you can beforehand: boilerplate code, deployment setup, tools, and your fighting spirit. Once the event starts, every minute counts. And above all, trust your team” Conclusion Team Người Việc proved that real innovation is not only about technology, but about people working together with purpose. By combining business insight, teamwork, and the smart use of AI, they turned a difficult 24-hour challenge into a real achievement. For SupremeTech, this victory is more than just a competition result. It’s a reminder that the future of development starts with clear thinking, strong teamwork, and the courage to explore new ways of building with AI. Appendix: 1. How the Team Applied AI Throughout the Project StageApproachAI Application/ Tools UsedAnalysis & DesignThe whole team brainstormed together, role-playing as real users to map out workflows and features.No AI used — this was the most human-driven stage focused on critical thinking.User Story writingConverted rough ideas into logical workflows, defined goals, and acceptance criteria.ChatGPT acted as a virtual BA, turning brainstorm notes into professional User Stories and Acceptance Criteria.Coding (User Story Based)Developers implemented each User Story while communicating directly with the AI assistant for suggestions and refactoring.GitHub Copilot served as a coding partner, reading stories, suggesting code, refining syntax, and accelerating implementation.Testing & ReleaseThe PTL and BA acted as real users to test the product, identify bugs, and refine the UX before release.No AI used — manual testing for real-user validation. 2. Team Tech Stack LayerTech StackFrontend & Backend (Fullstack)Next.js 15 (App Router)UI Libraryshadcn/ui + TailwindCSSAI AssistantChatGPT + GitHub CopilotInfra / DeployAWS + Dokploy 📩 Read more articles about us here: SupremeTech’s Blog

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            How Team Người Việc Won SupremeTech’s AI Hackathon 2025 with AI-Assisted Development and Agile Thinking

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