Why So Many Projects Stall: The Hidden Cost of Flattening 3NF Data

Many digital transformation and data projects fail to deliver because they get bogged down in a seemingly mundane but critical task—flattening normalized (3NF) data. When working with complex systems like ERPs, CRMs, and other applications, the highly normalized schemas designed for operational efficiency create a major hurdle when it’s time to analyze the data.

The Problem with 3NF Data

Third Normal Form (3NF) schemas are excellent for transactional integrity but terrible for analytics. Flattening data from these systems is time-consuming and resource-intensive. It requires building complex ETL pipelines and data models just to get the data into a usable format, often delaying projects by months or even years.

Challenges in Analytical Queries: In a 3NF schema, queries often require complex joins across many tables, leading to slower performance and frustrating business users. The more joins needed, the higher the computational cost, resulting in sluggish query times and delayed insights. This complexity not only slows down reporting but also makes ad-hoc analysis cumbersome and inefficient.

Manual Data Preparation: The process of denormalizing data is often manually intensive, requiring data engineers to stitch together tables and fields in a way that makes sense for analysis. This manual effort introduces significant room for error, creating data quality issues that can ripple through an organization’s reporting and analytics.

Impact on Time-to-Insight: The time-consuming nature of preparing data from 3NF schemas severely hampers decision-making processes. In fast-paced environments, the delay in gaining insights can lead to missed opportunities and a slower reaction to market changes. As the gap between raw data and actionable insights widens, organizations risk falling behind their competitors.

Where Projects Get Stalled

The journey from raw data to actionable insights is often slowed down by a series of hidden but critical roadblocks. These challenges frequently manifest in the early stages of a project when teams are tasked with transforming complex, normalized schemas into analytics-ready data. From rigid ETL pipelines to talent shortages and interdepartmental dependencies, these hurdles can drag projects into long delays, burn resources, and stifle progress. Below, we explore the key factors that cause many data initiatives to stall and ultimately miss their goals.

• The ETL Bottleneck: Teams spend excessive time designing, building, and maintaining ETL pipelines to transform normalized schemas into flat tables. This process is not only error-prone but also rigid, making it difficult to adapt to changing business requirements. As the complexity of these pipelines increases, so does the likelihood of breaking something when source systems are updated.
• ETL Maintenance Costs: The ongoing maintenance of ETL pipelines is a hidden drain on resources. Even minor changes in the source systems—like adding a new field—can cascade into costly updates to the entire data pipeline, leading to delays and rework. This maintenance burden often becomes a roadblock, stalling projects indefinitely.
• Talent Gaps: Flattening normalized data requires specialized skills in data modeling and ETL design. However, the scarcity of skilled data engineers who can efficiently manage this process creates bottlenecks. As organizations struggle to find and retain this expertise, projects often get delayed, and initiatives lose momentum.
• Cross-Team Dependencies: Business users typically rely on IT and data teams to prepare data for analysis. This dependency creates friction, as misaligned priorities and differing timelines lead to delays and frustrations. When the business needs are urgent, waiting on a backlogged IT team can derail critical projects.

The Domino Effect

When flattening takes months, the entire data strategy can unravel. Reports get delayed, decision-making suffers, and the ROI of data initiatives shrinks. In the worst-case scenario, the project is abandoned altogether because it’s too cumbersome to maintain.

Cascading Project Delays: The delays in data preparation create a domino effect across the organization. Key projects that rely on timely data, such as strategic planning and forecasting, get postponed, leading to missed deadlines and misaligned goals. As the timeline stretches out, stakeholder confidence erodes, and momentum is lost.

Diminishing ROI: The prolonged timelines and escalating costs associated with traditional data projects can quickly outpace the expected returns. When the initial investment in a data initiative far exceeds its realized value, it becomes increasingly difficult to justify further investment. This often results in stalled projects or budget cuts that leave initiatives incomplete.

Data Quality and Governance Issues: The complexity of flattening data can also lead to inconsistencies and errors. As data pipelines become more intricate, the risk of introducing bad data into the system increases. These issues compromise data governance efforts and undermine trust in the final output, leading to further delays as teams scramble to identify and correct errors.

A Smarter Approach

This is where modern platforms like Incorta come into play. Instead of flattening the data through complex ETL processes, Incorta allows you to analyze data directly from 3NF schemas without flattening it first. This accelerates project timelines, reduces complexity, and delivers results faster.

Direct Data Mapping Technology: Incorta’s direct data mapping technology allows organizations to skip the flattening step entirely. By querying 3NF data directly and making it analytics-ready in real-time, businesses can unlock faster time-to-insight without sacrificing data integrity. This capability reduces the need for labor-intensive ETL pipelines and enables more agile and responsive data projects.

Agility in Data Projects: Eliminating the flattening step provides flexibility that traditional ETL processes cannot match. Teams can rapidly prototype and test different data models, pivoting as needed to align with evolving business requirements. This agility not only shortens project timelines but also enhances the ability to experiment and innovate.

Real-World Case Studies: Companies like Starbucks and Comcast have successfully accelerated their data initiatives by bypassing traditional ETL processes. By using Incorta’s platform, they’ve reduced project timelines from months to weeks, resulting in more timely insights and higher ROI.

Looking Ahead

Flattening data is not just a technical hurdle; it’s the foundation for so many downstream use cases like BI, Planning, GenAI, AI/ML, and more. These applications rely heavily on the process of denormalization to be effective. In future editions, we’ll dive deeper into how this critical process powers the most important analytics and AI workflows and why it’s crucial for organizations to get it right.

About the Author

Scott Felten has been a technology and thought leader in enterprise analytics for nearly 25 years. He has experience leading initiatives as a customer, a trusted consultant advisor, and as a business executive within leading technology vendors. He specializes in aligning modern data platforms with business strategies to drive faster insights and measurable ROI. You can schedule a free consultation with Scott and his team here.