DAMA Mongolia Chapter is launching the #MongolianDataProfessionals series to share the career journeys, real-world experiences, and advice of Mongolian professionals working in data and technology.

The series aims to highlight the diverse roles and professionals within the data industry while offering knowledge and inspiration to those already working in the field and those considering a career in it. Our first guest is G. Khaliun, Senior Data Analyst at Capitron Bank. She shares how she first entered the data field, her career experience, the nature of her work, and her advice for those interested in pursuing a career in data.

— Good afternoon. Could you briefly introduce yourself and tell us about your current role?

— My name is Khaliun Ganzorig. I hold a degree in applied mathematics and currently work as a Senior Data Analyst in the banking and finance sector. My work primarily focuses on projects involving the implementation of enterprise data architecture, BI systems, and data governance.

— How did you first get into the data field? Was it something you had always been interested in?

— While studying applied mathematics, I developed strong skills in logical thinking, mathematical analysis, modeling, optimization, and programming, so my professors encouraged me to pursue this field. Since applying theoretical knowledge in a real-world setting can be challenging, I chose to begin my data career in banking and finance—an industry with large volumes of highly structured data. Mathematical thinking has certainly proved invaluable when working with such vast amounts of data.

— What types of data do you primarily work with in your current role?

— I work with large volumes of structured financial data, as well as data related to the bank’s products and services.

— What is the most interesting data-related problem you have solved, and how did you approach it?

— Data tells me what happened in the past and what may happen in the future. I am naturally curious, so I enjoy tracing data back to its source and following how it has been transformed over time to identify the root cause of an issue and improve data quality. I approach each problem as if I were a detective: the problem is the case, the investigation is the process, and finding the solution is my reward.

— What are the most common data-related challenges faced by Mongolian organizations?

— I believe data quality is the most common challenge. In many cases, people manually clean and modify data without even realizing they are doing so. This highlights the need for organizations to manage data quality systematically.

— What three pieces of advice would you give to people beginning their careers?

1. When working with large volumes of data, give equal importance to data quality and analytical accuracy.
2. From my professional experience, I have learned that applying theoretical knowledge in a real-world setting requires more than analytical and programming skills. It is equally important to understand the organization’s unique characteristics, operations, and processes.
3. I believe people begin to lose their value when they stop learning. The more we learn, the more we realize how much we do not know. My advice is therefore to keep learning and continuously develop yourself.

— What books, courses, and tools do you regularly use?

— The experience and advice I receive from industry experts on data governance and emerging trends—as well as the opportunity to collaborate with them—are among my most valuable sources of learning.

I am also pursuing a master’s degree in data science to deepen my theoretical knowledge. In my work and studies, I use technologies and tools such as generative AI, BI tools, Oracle, and Visual Studio Code, along with professional bodies of knowledge and methodologies such as DAMA-DMBOK and PMBOK.

— What would you like Mongolia’s data industry to look like in five years?

— I hope data governance will become an integral part of every organization’s culture and strategy. This would enable organizations to optimize their processes and decision-making at a more advanced level. Establishing a foundation of high-quality data would also create opportunities to conduct a broader range of sophisticated analyses that may not currently be possible.

G. Khaliun’s experience demonstrates that the value of a data professional extends far beyond analytical and programming skills. Prioritizing data quality, understanding how one’s organization operates, and embracing continuous learning are equally important foundations for success.

We sincerely thank G. Khaliun for sharing her experience and advice. Stay tuned for the next edition of the #MongolianDataProfessionals series in September, when we will continue sharing the stories, experiences, and valuable insights of Mongolia’s data professionals.