As data becomes one of the most valuable assets in modern business, the ability to turn information into meaningful, actionable insight is what separates companies that simply operate from those that lead. In an environment shaped by rapid technological change, evolving market dynamics, and the rise of artificial intelligence, analytics is no longer a support function. It sits at the core of strategy, decision-making, and long-term growth.

In this interview, Ayur Tsyrenov, Head of Analytics at Quadcode, shares his perspective on the challenges and opportunities shaping the field today. From building end-to-end data systems and leading high-performing teams to preparing organizations for an AI-driven future, he offers a clear and practical view of how analytics can move beyond reporting and become a true driver of business transformation.

In my view, the biggest challenge in building high-quality analytics today is the ability to extract real knowledge from raw data and to ensure that this knowledge directly drives business growth and adaptability in a rapidly changing world.

Data itself is no longer the problem. The challenge is turning fragmented information into structured insight that helps companies move metrics in the right direction.

At the same time, this challenge creates a lot of opportunities. If we can systematize our approaches to work with new technologies and AI, we can significantly accelerate how we structure data and mine actionable insights from it.

The key question is: who will build the most effective end-to-end data system, from robust ETL processes to insight generation, that enables management to consistently make profitable and strategically sound decisions?

In today’s fast-evolving technological environment, an analytics leader must first be able to systematize chaos.

Business processes often generate complexity. A strong leader needs the ability to step back, see the entire picture, and design an architectural vision to solve complex problems.

At its core, analytics leadership requires strong analytical and synthetic thinking. A leader must be able to decompose complex challenges into manageable components. And sometimes synthesize asynchrone components back into a unified, scalable system.

Different situations require different approaches. The maturity of a leader lies in knowing when to go deep into decomposition and when to zoom out and redesign the system as a whole.

At Quadcode, we take a holistic approach to the entire data lifecycle: from data collection and aggregation to visualization, analysis, and financial modeling.

When I started leading analytics, we initiated a large-scale transformation program. We optimized raw data collection, built lightweight and user-friendly data marts, standardized reporting practices, and established unified analytical processes. Beyond reporting, we integrated financial and business modeling directly into decision-making workflows.

I strongly believe that only a comprehensive approach creates real business transformation. Improving dashboards alone is not enough, the entire data foundation must be robust and scalable.

Today, because of that foundational work, we are confidently moving into AI integration. We are building systems where AI analysts assist in insight discovery and scenario modeling, helping us simulate future outcomes before making strategic decisions.

If I had to name the most impactful project, it would be this large-scale data infrastructure transformation - creating a system that works end-to-end, from raw data ingestion to metric delivery for product managers and executives.

This is really a challenging balance.

Technical constraints and business expectations often conflict. For example, the business may want to validate a hypothesis within strict deadlines, while statistically or technically this may not be feasible with the required level of confidence.

In such cases, there are two paths:

Either compromise on technical requirements and accept future risks, or work closely with leadership to deeply understand the business objective and find alternative, reliable ways to test the hypothesis.

I usually choose the second approach. I show more leadership skills in order to find a solution to a business problem.

At the same time, my technical background allows me to optimize risks and design solutions that minimize statistical or engineering weaknesses. In this sense, technical expertise supports leadership rather than competes with it.

When building teams, I rely on structured frameworks: competency matrices, maturity assessments and behavioral models such as DISC.

High-performing teams are not built by accident. They emerge when tasks are aligned with both expertise and behavioral strengths.

For example, assigning an ASAP product hypothesis to a highly detail-oriented BI specialist with a naturally cautious behavioral profile may slow down decision-making. In contrast, someone comfortable with rapid risk assessment and iterative thinking may deliver better results under time pressure.

On the other hand, designing a complex reporting architecture requires deep, detail-focused thinkers who are willing to explore edge cases and long-term risks.

Every DISC profile has strengths. My responsibility as a leader is to understand these strengths and align them with business needs. Business environments vary - and the team structure must adapt accordingly.

Analytics will unquestionably continue to drive business decision-making. However, the methods of analysis and insight discovery will change significantly.

AI will play a major role as it already performs well in data analysis and modeling. But AI systems still depend on well-structured, clearly defined data foundations.

This means we will need strong data engineers with architectural vision to build high-quality data ecosystems that AI agents can operate within.

At the same time, product and marketing analysts will become even more valuable. With AI assistants, they will be able to uncover patterns and relations much faster,  but their competitive advantage will remain contextual understanding and strategic interpretation.

The cooperation of human analytical reasoning and AI processing speed will significantly increase both the quality and velocity of decision-making. The future belongs to organizations that embrace technological evolution rather than resist it.

Now the combinatorics of possible developments are so great that I am looking forward to the future to see where we will go. I really hope that my team and I will have a hand in shaping this future. So at Quadcode, we are already working on creating a powerful data access system for AI agents so that each employee can receive high-quality processed data for their LLM agent, at the same time, we are already at the final stage of the birth of our AI analyst, who will help us find insights much faster and better. 

I invite everyone interested to a mini-landing of our solution. https://quadcode.com/ai-analyst