Global data creation is projected to exceed 180 zettabytes by 2025, yet many organizations still struggle to use that data at the moment it matters. The limitation is no longer storage or data collection. It is the time required to move data from source systems into a form that supports real decisions. In many enterprise environments, that delay still spans hours or days, even as business activity unfolds in seconds.
Nithish Shetty, a Senior IEEE member and a Lead Business Intelligence Architect with more than 14 years of experience working across financial systems, retail operations, and large-scale analytics platforms. A Judge for the Globee Awards for Artificial Intelligence, he has focused his work on redesigning how enterprise data flows, replacing delayed reporting models with systems that allow teams to operate on current information rather than historical snapshots.
Why Legacy BI Systems Break Under Modern Data Conditions
Traditional BI systems were built around predictability. Data moved through structured ETL pipelines, often in nightly or periodic batches, and outputs were delivered as reports designed for review rather than action. That model assumed stable data volumes and slower operational cycles. Today, neither assumption holds. A large percentage of enterprise data remains unused, not because it lacks value, but because it never becomes accessible in a usable timeframe.
The problem rarely appears as a single failure. It emerges as friction across multiple layers. Data is extracted, staged, transformed, validated, and then pushed into reporting tools. Each stage introduces latency, dependencies, and potential failure points. By the time the data reaches the end user, it often reflects a past state that no longer matches current operations. In large organizations, this creates a disconnect between data and action. Finance teams reconcile inconsistencies across reports generated from different pipelines. Operations teams rely on outdated metrics to make real-time decisions. Analysts spend a significant portion of their time preparing data rather than interpreting it. In several of Nithish’s engagements, reporting timelines stretched into weeks due to dependencies across multiple systems and teams.
To address this, he focused on removing intermediate layers that slowed data movement. Instead of routing data through multiple transformation stages, he implemented architectures that allowed direct access to operational data. This reduced the need for staging environments, minimized duplication, and shortened the path between data generation and consumption. The result was a shift from delayed reporting cycles to near-real-time visibility across business functions.
“Most systems don’t fail because they lack capability,” Shetty says. “They fail because the path from data to decision is too long.”
How Real-time Architectures Change How Teams Actually Work
The transition toward real-time analytics is being driven by operational necessity rather than preference.A majority of enterprises are now investing in platforms that support live or near real-time processing, reflecting the need to shorten feedback loops across finance, supply chain, and customer operations.
In one large-scale implementation within a global streaming environment, Nithish worked on restructuring how content-related financial data was processed and analyzed. The existing system relied on multiple ETL layers that introduced delays, particularly as content volume and financial complexity increased. Data passed through staging environments before becoming available for reporting, which limited both speed and consistency. He replaced this structure with a direct data mapping architecture, where data could be accessed directly from source systems without intermediate transformation pipelines. This approach eliminated several layers of latency and allowed finance teams to work with current data rather than delayed extracts. It also reduced discrepancies caused by synchronization issues between systems. The impact extended beyond reporting speed. It changed how teams approached analysis. When data became available in near real time, financial reviews shifted from retrospective reconciliation to ongoing monitoring. Instead of identifying issues after they occurred, teams were able to detect and respond to changes as they happened. In retail environments, similar changes produced measurable operational effects. Once teams gained access to live inventory and sales data, decision cycles shortened. Inventory levels dropped significantly, in some cases by as much as 50% within weeks of implementation. The change was not driven by new predictive models, but by the removal of delay between data availability and action.
“When the data becomes current, decision-making stops being reactive,” he explains. “Teams start adjusting in real time instead of correcting later.”
Applying AI Where it Actually Reduces Operational Friction
Enterprise AI adoption continues to expand. Global AI spending is projected to exceed $300 billion in the coming years. However, much of the measurable impact is coming from targeted implementations within specific workflows rather than broad, experimental deployments.
In one project focused on sales crediting, Nithish redesigned a process that had traditionally relied on manual review. Sales crediting involves assigning revenue attribution based on business rules, transaction conditions, and exception handling. In its original form, the process required extensive human intervention and depended on external resources to manage workload fluctuations. He implemented a structured decision system that applied rule-based logic to automate credit attribution. The system processed standard scenarios automatically while isolating complex cases for manual review. This reduced manual workload by 68% and eliminated reliance on external support. It also improved consistency, ensuring that similar transactions were handled in the same way across the system. The architecture behind the system was designed to integrate directly with operational data sources, allowing crediting decisions to be made as data entered the system rather than after processing delays. This reduced turnaround time and minimized discrepancies that previously required reconciliation. Alongside implementation work, Nithish serves as an IEEE Access peer reviewer, where he evaluates research on data systems, analytics frameworks, and applied machine learning. This role provides exposure to emerging techniques, but his focus remains on how those techniques function under production constraints.
“AI is most useful when it removes repetitive decisions,” Shetty says. “It doesn’t need to be complex. It needs to be reliable.”
Why Data Quality Still Limits Even the Fastest Systems
Speed alone does not solve the problem if the underlying data is unreliable. More than 80% of data leaders report ongoing issues with data quality, particularly in environments tied to financial reporting and regulatory compliance.
Earlier in his career, Nithish worked on systems supporting tax lot accounting and securities pricing. These systems handled hundreds of thousands of securities and associated data attributes, where accuracy directly affected portfolio valuation, reporting, and compliance requirements. The challenge was not just scale, but consistency. Data originated from multiple sources, each with its own structure and validation rules. Discrepancies often surfaced late in the process, requiring manual intervention to resolve.
To address this, he introduced automated validation frameworks within ETL pipelines, allowing inconsistencies to be detected earlier in the data flow. This reduced reliance on downstream corrections and improved overall data reliability. Teams no longer had to chase failures that had already propagated. The result was a roughly 20% improvement in team productivity, though Nithish considers that the secondary outcome. The more durable gain was structural: a measurable reduction in error propagation across systems, and a shift from reactive correction to designed-in reliability.
He also developed structured approaches to pricing data comparison, enabling teams to evaluate vendor data more systematically. By standardizing how pricing inputs were analyzed, organizations were able to reduce inconsistencies and improve confidence in their data sources.
“Faster systems don’t help if the data isn’t trustworthy,” he says. “Accuracy has to come first, otherwise everything downstream becomes questionable.”
What Enterprise Analytics is Becoming in Practice
The global business intelligence market is expected to exceed $40 billion by 2027, but the more meaningful shift is happening at the architectural level. Organizations are moving away from centralized reporting systems toward distributed models where data is accessible directly within operational workflows.
In these environments, analytics is no longer confined to dashboards or periodic reports. It becomes part of the workflow itself. Decisions are made within systems that have direct access to current data, reducing the need for separate reporting layers. This shift requires changes in how systems are designed. Data pipelines need to support continuous ingestion and access. Validation processes need to operate in real time. Workflows need to be structured in a way that allows automation without sacrificing accuracy. Across his work, Nithish has focused on reducing friction across each stage of the data lifecycle. Removing delays in data movement. Automating repetitive processes. Ensuring that systems remain stable as they scale across multiple business functions. The goal is not to build more complex systems, but to remove the steps that slow down decision-making. When those steps are removed, the system becomes more responsive without becoming more complicated.
“The real change isn’t in the tools,” Shetty reflects. “It’s in how quickly organizations can move from data to action without getting stuck in between.”
