T3 Podcast | The Lifeblood of Artificial Intelligence: Big Data - Nizamettin Ordulu

Big Data: Definitions, Formation, Analysis and Future Impacts

In this episode of T3 Podcast, we welcome Nizamettin Ordulu. An expert in information technology and founder of Ordulu Information Technologies, Ordulu explains what big data is, how it is formed, the processes of its analysis, and its inseparable relationship with artificial intelligence.

Nizamettin Ordulu’s Biography and Areas of Work

Nizamettin Ordulu, educated in computer engineering and mathematics, completed his master’s degree at MIT. He worked for three and a half years as a database developer at Facebook, improving the efficiency of database systems. After returning to Türkiye, he founded his own company to develop projects such as artificial intelligence systems in the healthcare sector and map-based data systems in the defense industry.

Definition and Core Characteristics of Big Data

Big data refers to large datasets that encompass all types of data—structured, semi-structured and unstructured—without requiring a predefined schema, unlike traditional databases. The most important drivers behind the emergence of big data are the need to store and process diverse and high-volume data from multiple sources in a flexible architecture.

The Process of Big Data Generation and Collection

The primary goal of collecting big data is to build models from the gathered data to support business decisions. This process includes defining objectives, collecting raw data, cleaning it, structuring it, analyzing it, and visualizing the results to inform decisions. This process has a cyclical structure, continuously improved by identifying and correcting deficiencies.

Methods and Tools Used in Big Data Analysis

Various technologies are used to store and analyze big data:

• Hadoop: An open-source framework inspired by Google’s system that enables distributed storage and processing of data across multiple servers.
• NoSQL Databases: Flexible-schema databases such as MongoDB and Cassandra.
• Spark: A powerful analytical tool that enables fast querying on Hadoop.
• Visualization Tools: Software such as Tableau makes analysis results understandable and facilitates decision-making processes.

Future Impacts of Big Data and Artificial Intelligence

Big data and artificial intelligence are fundamentally transforming every aspect of our lives, from keyboard predictions on smartphones to autonomous vehicles, from product recommendations in e-commerce to wearable technologies. In the future, the influence of big data will expand further through applications such as designing personalized products based on data collected from wearable technologies.

Advice for Young People Interested in Data Science

Nizamettin Ordulu offers three key pieces of advice to young people interested in data science:

1. Strengthen statistical foundations: Data analysis relies heavily on statistical methods, so a solid grasp of these fundamentals is essential.
2. Understand how big data systems work: It is important to know how distributed systems like Hadoop operate and how they respond to situations such as data loss.
3. Develop rapid data collection and processing skills: Practicing through personal projects—for example, extracting data from Wikipedia—is the key to success in this field.