In today's rapidly evolving, data-driven world, statistics are more than just numbers. The quality of data is becoming increasingly important. Reliable statistical data leads to more informed macroeconomic policy decisions and increases public trust. According to the European Statistics Code of Practice – a self-regulatory instrument that serves as the cornerstone of the common quality framework of the European Statistical System – data quality is measured by 16 principles covering the institutional environment, statistical processes, and statistical outputs.

These principles include, among others, accuracy, timeliness, coherence, and accessibility of data – all crucial for building public trust and guiding policy decisions that affect many people. For countries like Moldova, aligning national statistical practices with EU standards is an ongoing priority, helping to ensure transparency, comparability, and integration into broader European economic frameworks.

I have been working in the field of monetary and financial statistics for almost 15 years, and I can confidently say that data quality depends heavily on the accuracy of data, their timeliness, and methodological rigor. And with the same confidence, I would add that data quality is also about collaboration, clear communication, and continuous learning. The way we interpret, present, and explain data is just as vital.

This realization became even clearer to me while participating in the Training of Trainers (ToT) program. That is why – and how – the National Bank of Moldova (NBM) LearnOrgLab project, co-implemented with the Center of Excellence in Finance (CEF), became an important part of my professional journey.

Coming from a technical background, I am accustomed to working within methodological frameworks and detailed statistical requirements. Thus, stepping into a learning environment focused on facilitation, engagement, idea exchange, and reflection was both exciting and quite challenging. It required a different mindset – one where soft skills played a central role alongside technical expertise.

This journey took a significant step forward when I had the opportunity to co-facilitate an NBM LearnOrgLab project event as part of the ToT initiative. Together with my colleague, we guided participants through a series of interactive exercises designed to build trust, encourage creative problem-solving, and foster open communication. These activities helped break down barriers, promoting collaboration and active engagement in the learning process. We learned to focus on creating a safe space where participants could reflect, share ideas, and connect with one another. This approach transforms the training from a traditional lecture into a dynamic learning experience, where participants are not just receivers of information but active contributors to their own understanding.

One key part of our facilitation was applying a thoughtful framework to deliver content in a way that was simple, engaging, relevant, and emotionally resonant. This methodology helped make technical material accessible and meaningful to diverse audiences, fostering deeper understanding and motivation.

Standing in front of colleagues and external participants – not as an attendee, but as someone responsible for guiding discussions – was a moment of real growth. I learned to ask questions that prompt reflection, to listen actively, and to support others in discovering insights for themselves. It gave me a new perspective on my professional identity: not just a data producer, but a communicator and a bridge between technical content and real-world application.

And these lessons are not meant to be used only within these events. Inspired by what we learned, we began to apply these insights in practical ways. Reflection meetings after major data releases, shared training materials, informal peer learning sessions, and more collaborative problem-solving – these small initiatives help us share challenges, compare approaches, and reflect on international best practices. They also help us stay responsive, adaptable, and continuously aligned with evolving statistical standards and user needs.

Our collaboration with external users has also improved. It has become much easier for me to deliver the information to our external users, share our experience with peers from other countries, and present our statistical outputs in accessible and meaningful ways. The communication and facilitation skills developed through the NBM LearnOrgLab project are helping to present complex concepts in a clear and tailored manner, digestible to diverse audiences.

One such example was when I presented our experience in compiling financial accounts within the System of National Accounts at an international workshop in Brussels last year. Previously, my presentations had been highly technical – just tables and methodological explanations. This time, however, I approached it differently. Guided by the skills and methods I learned through the ToT program, I shifted my focus toward audience relevance. I tried to frame all the materials in a more interactive way and to highlight how we tailor our procedures based on specific circumstances on one hand, and statistical requirements on the other. Instead of just presenting figures or plain statistics, I tried to tell a story, foster dialogue, and make space for questions. The feedback was immediate and encouraging. For me, this was a powerful confirmation that technical knowledge becomes more impactful when it is communicated with empathy and purpose.

I have discovered the quiet strength that comes from being prepared, the power of asking thoughtful questions, and the satisfaction of sharing knowledge. I have seen how learning together leads to better outcomes – for data quality, for our team, and for the users we serve.

That experience reinforced a crucial lesson: high-quality data is not just about what we compile – it is about how we help others understand and use it. Delivery matters. Empathy matters. The way we connect with others directly shapes the value our data can provide.

In statistics, we often speak of clarity, consistency, and comparability. My experience with the CEF has shown that these very same principles we apply to producing high-quality statistics are equally essential in how we communicate, how we learn, and how we lead.