Ankur Bansal, Credit Risk Analyst, explores the practical shift from static reporting to real-time, decision-centric analytics. This discussion covers the technical roadblocks that often stall scalability, the specific pitfalls of platform-specific limitations, and the emerging role of AI in query optimization.
Why is it that platform migrations so often fail to deliver on their promise of better insights? While many organizations treat the transition between visualization tools as a simple “lift-and-shift” task, the reality is that technical debt and architectural gaps usually follow the data. We recently sat down with Ankur Bansal, Credit Risk Analyst, to discuss a more structured approach to this challenge.
With over a decade of experience in the finance and retail sectors, Ankur focuses on the frequently overlooked aspects of migration-specifically the handling of calculations and underlying logic. Our conversation explores how prioritizing optimization over mere replication can result in more efficient data models and improved system performance. By addressing both technical complexity and business impact, his approach aims to transform migration into a catalyst for more reliable, data-driven decision-making.
Can you share your professional journey and what excites you most about your work?
My journey has been centered on leveraging data science to solve complex challenges in the finance and retail sectors, where scale and accuracy are critical. Over time, I’ve focused on the intersection of analytics and business strategy, identifying gaps in how insights are generated-particularly regarding performance optimization and the translation of requirements into scalable frameworks.
This led me to develop a structured approach that ensures the consistency and reliability of analytical systems. What excites me most is the ability to transform complex data environments into high-impact solutions. I am especially interested in how AI is making data visualization more intuitive and adaptive, shifting the focus from simple data access to delivering actionable, context-aware insights that are strategically meaningful.
What led you to focus on challenges around migrating between data visualization platforms?
Ankur Bansal: I’ve seen many organizations reach a point where their existing platforms no longer support their growing data needs. Migration becomes necessary, but it’s often approached as a simple transition rather than a strategic initiative. In my own experience, I’ve worked with large-scale datasets involving millions of rows, where performance bottlenecks quickly became a major roadblock. That was a turning point. It made me realize that simply moving data or rebuilding dashboards isn’t enough. The underlying strategy, including how data is modeled and how calculations are designed, plays a critical role in building effective analytics systems. This led me to focus on developing a more structured approach, where migration is treated as an opportunity to rethink and optimize. The goal is to build powerful, interactive reporting platforms that drive real business value, rather than systems that simply store information like a static library.
In your session at the upcoming Data Innovation Summit, you will discuss the challenges of migrating between data visualization platforms. What makes this process more complex than many organizations anticipate?
Ankur Bansal: While dashboards may look similar across platforms, the way data is processed, modeled, and calculated can differ significantly. These differences can lead to inconsistencies if not carefully managed. Another important aspect is the learning curve associated with each platform. Data visualization tools often have their own languages and ways of handling calculations, which may appear similar but still require time and effort to fully understand.
Knowing what features are available within a platform is equally critical, as it directly impacts how efficiently solutions can be built. It’s also important to recognize that not all visuals are equally easy to implement across tools. Some visualizations may be straightforward in one platform but complex in another, depending on its capabilities. The real challenge lies in preserving the integrity of business logic while adapting to the strengths and limitations of the new platform. Without that understanding, organizations risk building inefficient solutions and ultimately losing trust in their data.
In your session, you will be also highlighting often-overlooked aspects like calculations and logic, why are these so critical during migration?
Ankur Bansal: Calculations and business logic define how metrics are derived and directly influence decision-making. During migration, it becomes especially important to understand the type of calculations being used and how they are implemented across platforms. In many cases, organizations build complex calculations by layering logic over time within the visualization tool. While this may work initially, it can eventually lead to challenges such as inefficient filtering, inconsistent results, and overall performance issues. This often results in slower dashboards, delayed outputs, and ultimately impacts the quality and timeliness of business decisions. That’s why it’s important to take a more strategic approach during migration. Instead of directly replicating existing logic, teams should evaluate where calculations should reside and what type of calculations are best suited for the visualization layer versus the database layer. By rethinking and optimizing this distribution, organizations can achieve more efficient, reliable, and high-performing analytics systems.
What key shifts are you seeing in how organizations approach data visualization today?
Ankur Bansal: Organizations are moving beyond static dashboards toward real-time, decision-centric analytics. There is a stronger focus on performance, scalability, and how efficiently insights can be delivered, along with increased attention to cost optimization and the overall return on investment from analytics platforms. Data visualization is no longer treated as just a reporting layer, but as a critical component of the decision-making ecosystem. This shift is driving organizations to rethink both their tools and the underlying data architecture to ensure solutions are both performant and cost-efficient. AI is also starting to play a more practical role within this space, particularly in areas like query optimization, automated insights, and improving how users interact with data. Rather than being a separate layer, it is increasingly embedded within visualization platforms to enhance efficiency and usability.
What are the most common pitfalls organizations encounter during migration, and how can they avoid them?
Ankur Bansal: One common pitfall is attempting a direct one-to-one replication, which often carries over inefficiencies from the legacy system into the new platform. Another is underestimating the importance of user adoption, where even technically sound solutions fail because end users are not comfortable with the new environment. A critical but often overlooked aspect is understanding the capabilities and limitations of the target tool. Every data visualization platform has its own strengths and constraints, and assuming that all features can be replicated in the same way can lead to suboptimal solutions.
Not every tool is designed to handle every type of calculation or visualization efficiently. Organizations can avoid these issues by treating migration as an opportunity to rethink and optimize rather than replicate. This includes simplifying data models, aligning calculations with the right layer, and designing solutions that leverage the strengths of the new platform. It’s equally important to build with the end goal in mind, supporting business needs effectively rather than forcing technical parity. A well-planned migration focuses not just on technical execution, but on creating a sustainable and scalable analytics environment. Understanding what works best within a given tool and designing accordingly is crucial for long-term success.
How does a well-executed migration influence business decision-making and performance?
Ankur Bansal: A well-executed migration enhances both the speed and reliability of insights. When data models are optimized and calculations are consistently defined, dashboards perform more efficiently and deliver accurate results, strengthening trust in the data. Beyond performance gains, it enables a shift from reactive reporting to more proactive and strategic use of analytics. Faster, more reliable insights allow stakeholders to respond quickly and make informed decisions with greater confidence. Ultimately, a well-planned migration elevates analytics from a reporting function to a strategic driver of business performance, where data actively informs decisions and outcomes.
Do you see platform migration as a technical necessity or an opportunity for transformation?
Ankur Bansal: I see it as a strong opportunity for transformation. While migration may begin as a necessity, it creates a valuable moment to rethink how data is structured, modeled, and used across the organization. When approached strategically, it can improve performance, enhance user experience, and deliver more reliable, high-impact insights, ultimately strengthening the role of data in decision-making.
For a deeper dive into these strategies, join Ankur Bansal at the Data Innovation Summit for his session on mastering platform transitions. He will share a practical framework for selecting the right visualization tools, automating workflows, and overcoming the technical hurdles that often stall team adoption. This session is designed to equip you with the specific knowledge needed to manage complex migrations and ensure your visual storytelling remains impactful as the technology landscape continues to evolve.
