Enterprises today are drowning in data but starving for insights. Traditional warehouses and reporting systems were built for consolidation and historical analysis, not for the speed and complexity of today’s AI-driven world. For years, data analysts filled the gap by writing complex SQL and building siloed reports, often spending more time stitching data together than generating value.

With the rise of cloud platforms and artificial intelligence, this model is breaking down. Organizations now need scalable delivery architectures that make data consumable for both humans and machines. In practice, I have seen many analysts step naturally into this space. By combining business knowledge with engineering, architecture, product, and governance skills, they are evolving into analytics engineers who create reusable, AI-ready data products.

This evolution marks a new chapter. Analytics engineers are no longer just producing reports. They are shaping the very foundation of enterprise intelligence.

Curated data: The product that solves the last-mile challenge

Enterprises generate massive amounts of raw data, but raw data alone does not create value. The real product is curated data: structured, governed, and business-ready. Without this product, information remains fragmented across dashboards, delayed by manual reconciliation, or locked away in technical systems that few can use.

Curated data is the product that solves the last-mile challenge between storage and decision-making. It delivers trusted, consumable information that both executives and AI systems can rely on. Leaders get faster and more consistent answers. Analysts shift from patching together fragile SQL to creating reusable assets. AI agents query curated products expressed in business language rather than raw technical logic.

By treating curated data as the product, enterprises move from technical complexity to business clarity. Like any product, it must have defined consumers, quality standards, ownership, and a lifecycle of improvement. This shift turns data into a true enterprise asset, one that powers compliance, customer intelligence, fraud detection, and AI at scale.

Designing curated business data: An artistic approach

The strength of curated data comes not only from governance but also from how it is designed. Three design principles make curated data a true product that delivers value across reporting, operations, compliance, and strategy.

• Storytelling objects focus on the end-user experience. They ensure that reports and dashboards present a complete narrative of a business process rather than isolated fragments. For executives, this clarity means board materials, leadership updates, and senior management reviews are based on consistent definitions of critical business metrics.

The combination of storytelling with centralized definitions and data architecture makes reporting scalable, trusted, and ready for reuse. In the future, AI agents will be able to generate these narratives automatically, providing self-service insights at speed and reducing dependence on manual reporting.

• Lifecycle views are operational by design. They align curated data with the natural flow of a business operation such as an insurance claims process, the funding process in auto lending, or the delinquency cycle of a credit card account. By building data models that track these processes end-to-end, enterprises enable operational analytics at the domain level.

Lifecycle views also incorporate historical changes to data dimensions and system attributes, making them robust enough for regulatory reporting. For compliance teams and auditors, this alignment provides transparency and traceability. For business leaders, it delivers reliable operational intelligence that supports faster and more confident decision-making.

• 360-degree views provide a complete and long-term perspective on an entity such as a customer, employee, or product. These views consolidate all relevant data into a single lens, creating a holistic picture that can be applied to multiple use cases. For marketing, they power personalization at scale and improve campaign effectiveness. For risk and fraud teams, they uncover behavioral patterns that reduce exposure. For strategy and asset management, they provide a detailed understanding of how entities behave over time, supporting product development and investment decisions.

A true 360-degree view is equally valuable for regulatory purposes where demonstrating a comprehensive understanding of a customer or product lifecycle is increasingly expected.

Together, these three design approaches in the form of storytelling objects, lifecycle views, and 360-degree perspectives make curated data not just a technical construct but a business product. They deliver clarity for executives, robustness for operations, trust for regulators, and intelligence for strategy, all while laying the foundation for AI-driven decision-making.

From analyst to analytics engineer: The one who can transform

The transformation of the data analyst into the analytics engineer is central to creating curated data products that close the gap between storage and decision-making. Data analysts have always been the subject matter experts closest to the business. They understand how processes truly work, whether it is an insurance claim being processed, a loan moving from application to funding, or a credit card account entering delinquency. Because they work at the point where raw data becomes insight, analysts naturally create curated datasets that represent the final, business-ready layer.

The challenge is that much of this work has historically been ad hoc, locked in SQL queries, spreadsheets, or isolated reports. These solutions serve immediate needs but do not scale. This is why the transformation of analysts into analytics engineers is so critical. By combining their subject matter expertise with engineering, architecture, product management, and governance practices, analysts evolve from producing one-off outputs to building durable, reusable data products.

As analytics engineers, they act as engineers who build reliable pipelines, architects who design semantic layers aligned to operations, product managers who define consumers and measure adoption, business analysts who embed process expertise into curated storytelling objects, and governance leaders who ensure consistency and trust.

This convergence elevates analysts from being report producers to becoming builders of the curated data products that drive enterprise intelligence. Their deep business knowledge makes them uniquely suited to define and model the data in ways that executives can trust and AI systems can consume. In practice, this transformation is what separates organizations that simply modernize technology from those that modernize decision-making.

Governance as the enabler to sustain curated data

Curated data only delivers lasting value if it is sustained with strong governance. Without governance, curated data quickly drifts into inconsistency and duplication. With it, curated layers become the backbone of reliable analytics and conversational AI. Governance must therefore be seen as the enabler that protects curated data as a long-term enterprise product rather than a one-time reporting effort.

As analysts transform into analytics engineers, they also assume responsibilities for data stewardship and product management. Stewardship ensures that curated datasets remain accurate, compliant, and aligned to business meaning. Data product management applies the same discipline used in software development: maintaining a product backlog, prioritizing features, managing adoption, and preventing the uncontrolled creation of shadow IT.

This shift places accountability for sustaining curated data directly in the hands of those who understand it best, ensuring that the product remains fit for purpose and evolves as business needs change. The most important artifacts to maintain and sustain from a governance perspective are:

• Business metadata provides consistent, human-readable definitions for every key metric and entity.
• Data lineage delivers transparency into origins and transformations.
• Data quality rules embed checks for accuracy, completeness, and timeliness.
• Data classification applies access controls and safeguards for sensitive information.
• Business and technical semantics ensure alignment between process language and technical models.

When these practices converge with stewardship and product management, curated data becomes a durable enterprise asset. Leaders gain confidence that their data is consistent and trustworthy. Analysts spend less time reconciling reports and more time building value. AI agents can query governed products directly, reducing costs and producing more accurate, contextually relevant answers.

Most importantly, enterprises avoid the reemergence of fragmented and duplicative reporting environments, ensuring that curated data sustains its role as the foundation for modern decision-making.

The road ahead

The evolution from data analyst to analytics engineer, and from siloed reports to lifecycle-driven storytelling objects, represents a fundamental shift in enterprise analytics. Organizations that embrace this shift will move faster, govern better, and scale AI more effectively. Those that resist will remain slowed by outdated reporting and inconsistent metrics while competitors advance with governed, AI-ready ecosystems.

The future of analytics belongs to those who can engineer curated data as the product that solves the last-mile challenge, where business storytelling meets architecture, where governance ensures trust, and where artificial intelligence delivers insights as quickly as questions are asked.

About the Author:
Narayanaswamy (Swamy) Ramajayam is a senior data and analytics executive with nearly two decades of experience leading enterprise-scale data transformation across financial services and technology. He has specialized in building modern data foundations, governance frameworks, and AI-ready architectures that enable organizations to scale insights with speed and trust.

Ramajayam previously served as Divisional Chief Data Officer at Ally Financial, where he directed large-scale modernization initiatives spanning cloud migration, semantic data modeling, and advanced analytics. Earlier in his career, he held senior leadership roles at Capital One, Charles Schwab, and IBM, where he drove enterprise data strategies, governance adoption, and the creation of unified data platforms that continue to be benchmarks in the industry.