Understanding What Is Data Quality With Example Challenges

Problem Overview

Large organizations face significant challenges in managing data quality across various system layers. Data quality encompasses accuracy, completeness, consistency, and reliability of data as it moves through ingestion, processing, and archiving stages. Failures in lifecycle controls can lead to data silos, schema drift, and compliance gaps, ultimately affecting decision-making and operational efficiency.

Mention of any specific tool, platform, or vendor is for illustrative purposes only and does not constitute compliance advice, engineering guidance, or a recommendation. Organizations must validate against internal policies, regulatory obligations, and platform documentation.

Expert Diagnostics: Why the System Fails

1. Data silos often emerge when different systems (e.g., ERP vs. Lakehouse) fail to share lineage_view, leading to incomplete data quality assessments.2. Retention policy drift can occur when retention_policy_id is not consistently applied across systems, resulting in potential compliance risks.3. Interoperability constraints between archive platforms and analytics tools can obscure archive_object visibility, complicating data quality evaluations.4. Temporal constraints, such as event_date mismatches, can disrupt compliance-event timelines, exposing gaps in data governance.5. The cost of maintaining high data quality can escalate due to increased storage needs and latency issues when data is not properly archived or disposed of.

Strategic Paths to Resolution

1. Implement centralized data governance frameworks to ensure consistent application of retention_policy_id.2. Utilize automated lineage tracking tools to maintain lineage_view integrity across systems.3. Establish clear policies for data archiving and disposal to mitigate risks associated with archive_object management.4. Conduct regular audits to identify and rectify compliance gaps related to compliance_event documentation.

Comparing Your Resolution Pathways

| Archive Patterns | Lakehouse | Object Store | Compliance Platform ||——————|———–|————–|———————|| Governance Strength | Moderate | High | High || Cost Scaling | Low | Moderate | High || Policy Enforcement | High | Moderate | High || Lineage Visibility | Low | High | Moderate || Portability (cloud/region) | Moderate | High | Low || AI/ML Readiness | Low | High | Moderate |Counterintuitive tradeoff: While lakehouses offer high lineage visibility, they may incur higher costs compared to traditional archive patterns.

Ingestion and Metadata Layer (Schema & Lineage)

In the ingestion phase, data is often subjected to various transformations that can lead to schema drift. For instance, if a dataset_id is ingested without proper schema validation, it may not align with existing data structures, resulting in inconsistencies. Additionally, if lineage_view is not accurately captured, it can obscure the data’s origin and transformations, complicating quality assessments.Failure modes include:1. Inconsistent schema definitions across systems leading to data misinterpretation.2. Lack of automated lineage tracking resulting in incomplete data provenance.Data silos can arise when ingestion processes differ between systems, such as between a SaaS application and an on-premises ERP system. Interoperability constraints may prevent seamless data flow, while policy variances in schema validation can exacerbate these issues. Temporal constraints, such as event_date discrepancies, can further complicate data quality assessments.

Lifecycle and Compliance Layer (Retention & Audit)

The lifecycle management of data involves retention policies that dictate how long data should be kept. If retention_policy_id is not uniformly enforced, organizations may face compliance challenges. For example, if data is retained beyond its useful life without proper justification, it can lead to unnecessary storage costs and potential legal risks.Failure modes include:1. Inadequate retention policy enforcement leading to data over-retention or premature disposal.2. Insufficient audit trails for compliance_event documentation, resulting in gaps during compliance reviews.Data silos can occur when different systems apply varying retention policies, such as between a cloud storage solution and an on-premises database. Interoperability constraints may hinder the ability to track compliance across systems, while policy variances can lead to inconsistent data handling. Temporal constraints, such as event_date alignment with audit cycles, can further complicate compliance efforts.

Archive and Disposal Layer (Cost & Governance)

Data archiving is critical for managing data lifecycle and ensuring compliance. However, if archive_object management is not aligned with retention policies, organizations may face increased costs and governance challenges. For instance, if archived data is not properly classified, it may lead to unnecessary storage expenses and complicate retrieval processes.Failure modes include:1. Misalignment between archiving practices and retention policies, leading to compliance risks.2. Inadequate governance frameworks for managing archived data, resulting in potential data breaches.Data silos can emerge when archived data is stored in disparate systems, such as between a cloud archive and a local data warehouse. Interoperability constraints may prevent effective data retrieval, while policy variances in classification can lead to inconsistent data handling. Temporal constraints, such as disposal windows, can further complicate the archiving process.

Security and Access Control (Identity & Policy)

Effective security and access control mechanisms are essential for maintaining data quality. If access profiles are not properly defined, it can lead to unauthorized data modifications, impacting data integrity. Additionally, if security policies are not consistently applied across systems, it can create vulnerabilities that compromise data quality.Failure modes include:1. Inconsistent access controls leading to unauthorized data access or modifications.2. Lack of identity management resulting in difficulties in tracking data changes.Data silos can arise when different systems implement varying security protocols, such as between a cloud-based application and an on-premises database. Interoperability constraints may hinder the ability to enforce consistent access policies, while policy variances can lead to gaps in data protection. Temporal constraints, such as event_date alignment with access audits, can further complicate security efforts.

Decision Framework (Context not Advice)

Organizations should consider the following factors when evaluating their data quality management practices:1. Assess the alignment of retention_policy_id with organizational data governance frameworks.2. Evaluate the effectiveness of lineage_view tracking tools in maintaining data provenance.3. Review the management of archive_object to ensure compliance with retention policies.4. Analyze the impact of compliance_event documentation on overall data governance.

System Interoperability and Tooling Examples

Ingestion tools, catalogs, lineage engines, archive platforms, and compliance systems must effectively exchange artifacts such as retention_policy_id, lineage_view, and archive_object. For instance, if an ingestion tool fails to capture lineage_view accurately, it can lead to gaps in data quality assessments. Similarly, if an archive platform does not align with compliance systems, it can complicate data retrieval and governance.For more information on enterprise lifecycle resources, visit Solix enterprise lifecycle resources.

What To Do Next (Self-Inventory Only)

Organizations should conduct a self-inventory of their data management practices, focusing on:1. The consistency of retention_policy_id application across systems.2. The effectiveness of lineage_view tracking in maintaining data quality.3. The governance of archive_object management in relation to compliance requirements.4. The alignment of compliance_event documentation with audit processes.

FAQ (Complex Friction Points)

1. What happens to lineage_view during decommissioning?2. How does region_code affect retention_policy_id for cross-border workloads?3. Why does compliance_event pressure disrupt archive_object disposal timelines?4. What are the implications of schema drift on data quality assessments?5. How can organizations identify and mitigate data silos in their architecture?

Operational Landscape Expert Context

In my experience, the divergence between early design documents and the actual behavior of data in production systems often leads to significant operational challenges. For instance, I once encountered a situation where the architecture diagrams promised seamless data flow between systems, yet the reality was starkly different. Upon auditing the environment, I reconstructed the data lineage from logs and job histories, revealing that data quality was severely compromised due to a lack of adherence to documented retention policies. The promised automated archiving process had failed, resulting in orphaned archives that were not only difficult to trace but also inconsistent with the established governance framework. This discrepancy highlighted a primary failure type rooted in process breakdown, where the intended governance controls were not effectively implemented during the data lifecycle.

Another recurring issue I have observed is the loss of lineage information during handoffs between teams or platforms. In one instance, I found that logs were copied without essential timestamps or identifiers, leading to a complete breakdown in traceability. When I later attempted to reconcile this information, I discovered that critical evidence had been left in personal shares, making it nearly impossible to validate the data’s journey. This situation stemmed from a human shortcut, where the urgency to deliver overshadowed the need for thorough documentation. The absence of a clear lineage not only complicated compliance efforts but also raised questions about the integrity of the data being processed.

Time pressure has also played a significant role in creating gaps within the data lifecycle. During a reporting cycle, I witnessed how the rush to meet deadlines led to shortcuts that compromised the completeness of the audit trail. I later reconstructed the history of the data from scattered exports, job logs, and change tickets, revealing a patchwork of information that lacked coherence. The tradeoff was evident: while the team met the reporting deadline, the documentation quality suffered, leaving behind a fragmented view of the data’s lifecycle. This scenario underscored the tension between operational efficiency and the necessity of maintaining a defensible disposal quality, which is critical for compliance.

Documentation lineage and audit evidence have consistently emerged as pain points in the environments I have worked with. Fragmented records, overwritten summaries, and unregistered copies made it increasingly difficult to connect early design decisions to the later states of the data. In many of the estates I supported, I found that the lack of a cohesive documentation strategy led to significant challenges in tracing data quality issues back to their origins. This fragmentation not only hindered compliance efforts but also obscured the understanding of how data governance policies were applied over time. These observations reflect the complexities inherent in managing enterprise data estates, where the interplay of human factors and system limitations often results in a compromised governance framework.

DAMA International (2017)
Source overview: DAMA-DMBOK: Data Management Body of Knowledge
NOTE: Provides a comprehensive framework for data management practices, including data quality, which is essential for governance and compliance in enterprise environments.
https://www.dama.org/content/body-knowledge

Author:

Trevor Brooks is a senior data governance strategist with over ten years of experience focusing on information lifecycle management and enterprise data governance. I analyzed audit logs and designed lineage models to address what is data quality with example, revealing issues like orphaned archives and inconsistent retention rules. My work involves mapping data flows across systems, ensuring governance controls are in place to manage customer and operational data throughout active and archive stages.

Trevor

Blog Writer

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