The Relationship Between Data and AI
Skilled professionals play a crucial role in developing effective Data and AI solutions. Selecting the right platforms and software is essential to ensure worthwhile investment returns and to maximize value for organizations.
Data and AI work together seamlessly. Data serves as the fundamental foundation that AI uses for learning, analysis, and prediction to deliver accurate outcomes. Meanwhile, AI enhances the value of data by transforming raw data into insights, uncovering hidden patterns, and enabling more effective decision-making. AI development operates in a continuous cycle, as AI models can improve themselves with new data, making systems increasingly accurate and intelligent.
“AI requires large volumes of high-quality data to learn and analyze accurately, while data needs the intelligence of AI to fully extract value from massive datasets.”
What Is Data Quality and Why Is It Important?
High-quality data enables businesses to compete effectively, deliver solutions that meet customer needs, and drive economic growth.
In an era where technology and AI play a vital role in business transformation, Data Quality has become a key factor in accurate analysis and decision-making. Data Quality refers to the accuracy, completeness, and reliability of data, which directly impacts AI performance and organizational operations. When businesses utilize high-quality data, AI can learn and process information more effectively, producing accurate results. This supports better strategic planning, reduces decision-making errors, and enhances organizational credibility.
“Data Quality is not just about data storage—it is the core of building competitive advantage. Organizations that manage data effectively are more likely to grow and succeed in the digital era.”
MFEC’s Commitment to Data & AI Excellence MFEC is fully committed to adopting artificial intelligence across the organization to enhance operational efficiency. Supported by DataWise, a team specializing in data management, analytics, and AI consulting, MFEC develops solutions that empower customers to make data-driven decisions. In addition, MFEC operates an AI Lab, a research and development center focused on creating AI solutions for internal use and expanding these services to external organizations, supporting the future growth of AI technology.
Introducing Data Wise and AI Lab
DataWise provides end-to-end data management services, covering design, planning, storage, processing, analysis, and reporting. Its services are divided into four key areas :
- Modernized Databases – Designing and developing secure, high-performance database systems
- Intelligent Data Platform – Integrated data management with AI capabilities
- Advanced Analytics – In-depth data analysis for accurate decision-making
- Artificial Intelligence Solutions – Practical AI implementations to enhance business operations
AI Lab focuses on researching and developing AI technologies to strengthen organizational capabilities and drive innovation in the digital era. The AI Lab team has developed various AI solutions to improve internal efficiency, including:
- MFEC Brain – An AI system that manages and analyzes data from multiple internal sources to support decision-making and enhance productivity
- DocAI – An intelligent document assistant using OCR and machine learning to extract data from documents such as utility bills and receipts, reducing workload and improving accuracy
- AI Chatbot Assistant – An internal assistant that helps employees quickly access information and perform daily tasks efficiently
AI Lab also develops customized AI solutions tailored to specific industry needs, supporting organizations in their digital transformation journey in a sustainable and effective manner.
Data Platform Agent: An Internal Agentic AI Solution at MFEC
Previous Challenge :
Executives wanted to view data, but existing dashboard visualizations did not fully meet their needs.
Using AI to Explore Organizational Data Using AI to explore organizational data is a widely adopted approach leveraging Large Language Models (LLMs). Users can directly “ask” questions of their data. Previously, organizations possessed large volumes of data but struggled to transform them into visual insights for decision-making. Today, modern Business Intelligence enables stakeholders to clearly see business data and make faster decisions. However, business conditions change daily, making static dashboards insufficient. Traditional approaches also require developers to prepare extensive metadata and explanations for AI in advance. With Agentic AI, the AI can independently explore data like a developer, then convert questions into code to generate results that directly match user needs.
The result is a far more flexible Q&A system, allowing users to query specific tables or fields without prior documentation. This greatly expands the scope of AI-driven business problem-solving.
Why Data Quality Is Critical for AI Development Big Data + AI represent a powerful combination—two advanced technologies working together to elevate businesses and prepare them for every competitive challenge. AI and Big Data: The Keys to Their Relationship
