Data Visibility Evolution
Part 2: Temporary Solutions and Gaps

Published by: Hesam Shams

Feb 03, 2025

In Part 1 of this blog series, we looked into why data visibility is a critical requirement for modern businesses. We discussed how data visibility enables organizations to make informed decisions, optimize operations, enhance customer experiences, and drive innovation in machine learning and AI initiatives. We also highlighted the foundational role of data traceability in ensuring that stakeholders can trust and act confidently on the data they access. The message was clear: data visibility is not just a technical advantage, it’s a strategic necessity in today’s data-driven world.

 

Still, achieving true data visibility remains a significant challenge. Many organizations rely on traditional solutions like data warehouses, ETL pipelines, and business intelligence (BI) platforms to improve visibility. Recently, AI-powered solutions leveraged machine learning techniques to clean, integrate, and analyze data. However, these solutions face significant pitfalls including a lack of real-time processing, challenges in scaling across complex systems, inaccuracies in machine learning model, and high ongoing computational costs. Both traditional and AI-based approaches are temporary solutions that address immediate needs but fail to resolve key issues such as persistent data silos, missing data traceability, and the lack of a unified and trustworthy view of information.

 

In this second part, I take a closer look at the most common solutions to improve data visibility both within and among organizations. I review methods ranging from traditional tools like data warehouses and ETL pipelines to newer AI-powered solutions. These methods offer some benefits, but they often fall short to meet the demands of modern businesses. I explore real-world examples to highlight their limitations and reveal the gaps they leave behind. This post provides the foundation for understanding these challenges which will be referred to during the next discussion on data visibility. In the upcoming post, I will talk about what businesses need to overcome these challenges and explore the emerging technologies that can make it possible. Most importantly, I will share how we at Gebra Tech are addressing these gaps with a permanent and transformative solution for data visibility in the modern data ecosystem.

The Limitations that Persist

Data Discovery and Governance

Achieving data visibility starts with knowing what data exists, where it's located, and how it’s being used. This foundational layer is supported by tools like data catalogs, governance frameworks, and access control systems. These tools are designed to provide businesses with a centralized view of metadata, automate classification, and establish secure and compliant data usage policies.

Benefits:

- Centralized Metadata Management:

Data catalogs create a single source of truth for data descriptions or metadata to facilitate the process of finding and understanding data assets. For instance, automated tagging and relationship mapping save hours of manual work and ensure consistency.

- Enhanced Compliance and Security:

Governance frameworks help to enforce data access controls and ensure compliance with regulations such as GDPR and CCPA. This reduces the risk of breaches and huge costs.

- Improved Collaboration:

Many data catalogs include info like glossaries and user feedback loops. The info helps a better communication and increase collaborations between technical and business teams.

Gaps:

- Lack of Future-Proof:
Many data discovery and governance tools are designed for existing systems and processes. They often struggle to regulate when organizations adopt new technologies, expand into different data platforms, or change their operational processes. This limits their long-term value and requires frequent upgrades or replacements.

- Dependence on Data Quality:
The effectiveness of these tools depends on the accuracy and completeness of their metadata. If metadata is inconsistent or outdated, the system becomes unreliable and users lose confidence in the system. Inaccurate metadata can lead to misinformed decisions and ruin the purpose of the tools.

- High Initial Costs and Maintenance:
Setting up and maintaining a data catalog or governance framework requires significant resources. Metadata needs to be updated regularly to reflect changes in data systems which is labor-intensive.

- User Adoption Barriers:
Even robust tools are ineffective if users don’t adopt them. Many data catalogs and governance frameworks have steep learning curves or interfaces that feel confusing to users. Without immediate value, employees may go back to familiar tools like spreadsheets or manual methods which causes continued data silos and inefficiencies.

Example:

Consider a retail company invests in a data catalog to improve its understanding of inventory, sales, and customer trends. Initially, the catalog centralizes metadata from various systems, providing a clear view of its operations. However, as the company’s systems grow, the metadata becomes outdated and unreliable. Soon after implementation, the company discovers that metadata for several critical datasets is either missing or incomplete. At the same time, employees find the interface confusing and time-consuming and return to siloed systems. Over time, the catalog loses its relevance, and the company continues to face fragmented data insights and additional maintenance costs.

Data Integration and Transformation

Data integration and transformation unify data from multiple sources into a single and usable view. Solutions such as ETL/ELT processes and data virtualization help organizations integrate and access their data more effectively. These methods address critical challenges such as reduced silos, a consistent view of information, and improved accessibility for better decision-making.

Benefits:

- Unified View of Data:

Integration tools like ETL pipelines bring together data from disparate sources, creating a consistent view for analysis and reporting. This reduces confusion caused by conflicting datasets.

- Increased Efficiency:

Automated data transformation ensures that raw data is cleaned, standardized, and formatted properly for machine learning applications. This reduces the manual workload on data teams.

- Scalability:

Modern platforms support integration at scale, enabling organizations to handle growing data volumes across cloud and on-premise systems.

Gaps:

- Performance Overhead:

Many data integration solutions introduce latency. Real-time queries across multiple systems can slow down processes, especially with complex data virtualization. This affects how quickly insights can be delivered and creates complications in error handling.

- High Maintenance Needs: 

ETL pipelines require constant updates to adapt to schema changes, new data sources, and evolving business needs. This ongoing maintenance is resource-intensive and often creates bottlenecks.

- Inconsistent Unified View:  
As more systems are connected, maintaining consistency becomes challenging. Variations in data formats, quality, and update frequencies can result in errors and conflicting insights. Without robust validation and traceability mechanisms, the “single source of truth” may become partial truths and lose accuracy.

- Persistent Silos:
Integration tools often focus on specific use cases, such as dashboards or ML models. This leaves other parts of the organization working with data silos. Data remains fragmented across departments, limiting its broader usability.

- Lack of Real-Time Processing:  
Many integration tools process data in batches rather than in real-time. This causes delays to respond to time-sensitive events which are critical in industries like supply chain and finance that timely decisions are important.

Example:

Consider a logistics company that implements an ETL-based integration system to combine data from warehouses, shipping, and customer interactions. At first, the system improves reporting on delivery times and inventory levels. Over time the company grows and integrate more systems, such as third-party logistics providers and suppliers. These systems have different data standards and update frequencies which lead to inconsistencies in the unified view. On the other hand, maintaining the ETL pipelines becomes a burden and repeated schema changes require constant updates which causes delays in accessing critical data. Since the system only updates data overnight, the logistics company does not respond properly to unexpected changes, like weather-related disruptions or demand spikes. As a result, the company finds it increasingly difficult to stay agile and make timely, data-driven, and insightful decisions.

Data Monitoring and Utilization

Effective data monitoring and utilization ensure that data pipelines are reliable and insights are actionable. Monitoring tools track data quality, detect anomalies, and alert teams about pipeline issues. Utilization tools, such as business intelligence (BI) platforms, make data accessible through dashboards and reports that empower users across an organization to make informed decisions.

Benefits:

- Proactive Issue Detection:

Monitoring tools can identify problems such as missing data or schema changes before they impact further systems. This ensures smooth operations and consistent data quality.

- Enhanced Trust in Data:

Continuous tracking of data health builds confidence in analytics and machine learning outputs. Users rely more on data when they know it’s accurate.

- Accessible Insights:

BI platforms democratize data by providing teams the opportunity to explore and visualize data independently. This reduces bottlenecks in decision-making and improves flexibility.

Gaps:

- False Alarms and Missed Issues:
Automated anomaly detection has levels of errors that generate false positives or overlook subtle issues. Teams may waste time investigating non-issues or miss critical disruptions.

- Persistent Data Silos:
Monitoring and BI tools rely on data from multiple systems to provide a complete view. If those systems are not well-integrated, data remains fragmented across different platforms or processes. This fragmentation, or "data silos," prevents these tools from accessing all relevant information. As a result, similar to other solutions, the insights generated by monitoring and BI tools are incomplete or biased which limit their effectiveness for decision-making.

- Performance Issues:
BI tools may face large datasets or complex queries which slow down dashboards or reports significantly. Dashboards may take longer to load, or reports may fail to refresh in a reasonable time. This delay frustrates users who rely on quick access to insights for decision-making. Over time, these performance bottlenecks reduce confidence in the tool and push users to find other tools that undermines the purpose of the BI system.

- High Costs:
Real-time monitoring and interactive BI tools require significant computational resources and infrastructure. The requirements can lead to high operational costs, especially for those with limited budgets. This cost makes it difficult for some businesses to sustain these tools long-term which block them from fully leveraging advanced data visibility technologies.

Example:

A retail company implements data monitoring tools and a BI platform to track pipeline health and analyze sales. In the beginning, the tools work effectively and provide proper alerts and insights. However, as the company scales and integrates more systems, the tools face challenges. Incomplete integrations and growing data volumes cause false alarms from monitoring systems and delays in BI reports. The system’s scalability limitations result in inconsistent insights and declining trust in the tools. Teams get frustrated by these issues and return to manual methods which weaken the company’s ability to make timely and data-driven decisions.

The Gaps Left Behind

Despite their benefits, current solutions for data visibility leave significant gaps that prevent organizations from fully leveraging their data. These unresolved challenges, which are common in different approaches, reduce the effectiveness of traditional and AI-based approaches. So, it is clear that a more robust, comprehensive, and permanent solution is needed.

- Persistent Data Silos:  
Many tools focus on specific use cases, like powering dashboards or improving data pipelines. While they address some visibility needs, they fail to eliminate silos across systems, departments, or organizations. This fragmentation limits data’s wider applications and prevents teams from working with a single and unified view.
    
- Lack of Real-Time Access:  
Most solutions rely on batch processing, which introduces delays. In fast-paced and competitive industries like logistics, healthcare, or finance, these delays make it impossible to respond to disruptions or opportunities in real time.
    
- Scalability Issues:  
As organizations grow, the complexity and volume of their data increase. Current solutions often face challenges to scale efficiently which leads to performance bottlenecks, increasing costs, and inconsistent data. These challenges block businesses from adapting their systems to meet upcoming demands.
    
- Missing Data Traceability:  
Without robust traceability, organizations cannot see where their data comes from or how it has been transformed which makes it hard to verify data accuracy or reliability. This gap reduces confidence in analytics and AI models which causes poor decision-making and operational inefficiencies.
    
- High Maintenance Costs:  
Many tools require continuous updates to adapt with new data sources, schema changes, or system upgrades. This maintenance burden consumes resources, distracts teams from strategic projects, and increases operational costs.
    
- Inconsistent Unified Views:  
These solutions are designed to provide a single, unified view of data by connecting multiple systems. As organizations grow, maintaining this consistency becomes harder. New data sources often have different formats, update speeds, and quality standards. These differences can cause mismatches and inconsistencies in the unified view. Over time, these inconsistencies reduce trust in the data, as users encounter discrepancies or outdated information. Decision-makers struggle to rely on the unified view which makes it difficult to act confidently and quickly.

Next

Current solutions help in some areas but fail to resolve key challenges. They struggle with scalability, real-time access, and consistent data integration. Data silos persist, traceability is often missing, and they lack a future-proof design. These gaps make it clear that current tools cannot meet the demands of modern businesses.

 

In the next part of this series, we’ll explore what true data visibility requires. We’ll dive into the essential components of a comprehensive solution and discuss how modern technologies fill the gaps left by current approaches. Most importantly, I’ll introduce how we at Gebra Tech are pioneering a permanent, scalable solution designed for the future of data visibility. Stay tuned for part III!