E02 | Implementing Effective Data Lineage for Process Transparency and Better Decision-Making

This series highlights the synergies between Business Process Optimization and Data Lineage as enablers of Data Governance, enabling sustainable digital transformation.

In Episode 1, we explored business processes and how technologies like BPM and workflow orchestration enhance efficiency, while emphasizing that process design alone is not enough.

Now, we turn our attention to the other half of the equation: data lineage. Understanding how data flows, where it originates, how it transforms, and most importantly, what it means at every stage, is critical not only for reporting or compliance, but for building solutions that can evolve and scale without requiring constant rework and redesign.

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🔑 The Key Role of Data Lineage in Data Governance and Digital Transformation

Modern data governance and data management frameworks, including DAMA’s DMBOK model, increasingly recognise data lineage not as a nice-to-have, but as a foundational capability. Regardless of whether we speak of horizontal (technical) or vertical (semantic) lineage, the ability to trace how data is created, transformed, and used across the enterprise is essential.

Data lineage is not a standalone discipline, it’s an enabler. It cuts across and strengthens each of the 11 DAMA functional areas:

2 Illustrative Examples: Data Lineage in Action

Let's explore how they indicate when a process is suboptimal and how we can set functional targets to optimize and streamline them for better efficiency and customer satisfaction.

Example 1: Scaling a Commercial Dashboard

Use case

A large retailer extends an existing dashboard to include new promotional sales metrics.

PROBLEMS WITHOUT LINEAGE:

• Unclear which systems feed the current metrics.

• Lack of visibility into existing transformations.

• Different teams build the same metric using different source fields.

• Conflicting KPI values emerge, triggering costly reconciliations.

• The goal is shared, but missing lineage leads teams down divergent paths.

LINEAGE ALLOWS:

• Full mapping of sources and flows to enable reuse.

• Visibility on transformations ensures consistent logic.

• Teams collaborate with shared understanding.

• Trust in KPIs improves.

Example 2: Automating Client Contract Generation

Use case

A financial institution automates personalized contract generation based on client profile and product selection. If not yet automated, this process is often a prime candidate for business process automation initiatives.

PROBLEMS WITHOUT LINEAGE:

• Legal changes contract clauses, but downstream process impact is unclear.

• Compliance requires new fields, but data sourcing is uncertain.

• Customers report wrong data in contracts; origin cannot be traced.

• Marketing updates templates inconsistently.

• When a rule or policy changes, assessing its full business impact is difficult and time-consuming because affected processes may go beyond the initially identified workflows, involving dependent or indirectly linked processes.

LINEAGE ALLOWS:

• Each contract field links to business definitions and technical sources.

• Legal and compliance changes propagate correctly.

• Data issues trace back to capture origin.

• Teams share a unified semantic model.

• Business impact assessments become faster and more reliable, reducing the risk of missing hidden dependencies.

Together, these examples show that data lineage is not about fixing errors after deployment; it’s about designing sustainable solutions upfront.

Before tracing data, how do we define and structure its meaning to ensure it can be correctly observed, interpreted, and governed?

👀 Data Representation vs. Data Observation: Why Lineage Isn’t Neutral

In the previous examples, we have seen how ambiguity may arise even when teams work toward the same business goal. Often, this ambiguity doesn’t originate from data flows, but from the way data meaning is:

• defined through business concepts,

• interpreted through semantic structures, taxonomies, and knowledge graphs,

• operationalized across different systems and business contexts.

To fully understand the role of data lineage within data governance, we must first distinguish between two foundational concepts: Representation vs. Observation.

• Representation: how meaning is structured across abstraction levels.

• Observation: when and how data is captured and used.

DATA REPRESENTATION OCCURS ACROSS THREE LEVELS OF ABSTRACTION

Level	Description	Example
Conceptual	Shared business definitions	Revenue = total sales
Logical	Domain-specific variants	Net, IFRS, B2B revenue
Technical	Implementation in systems	revenue_ifrs_net

Why This Matters for Data Lineage

Before data can be traced or governed, it must first be represented, that is, modeled and structured in ways that reflect business concepts and organizational needs. This means deciding:

• what concepts we are measuring,

• how we distinguish variants,

• and how these choices are implemented in systems.

  
  

Only after this modeling step can data be observed, collected, and used.

This distinction is essential because data lineage operates on what we observe, but what we observe is already shaped by how we represent.

Lineage models act as lenses. System logs may trace flows but miss meaning. Only vertical lineage connects business intent to data structures. In this sense, data lineage is much like scientific measurement: The instrument shapes what is observed. In data, our design decisions determine what is captured, how it’s interpreted, and how it can be governed.

What are the types of lineage, and how do they complement each other in governance?

🔎 Types of Data Lineage: Strengths, Limits, and Complementarity

Organizations often associate data lineage only with horizontal, and frequently only physical, lineage, tracing the technical flow of data across systems. Of course, this is essential for understanding dependencies, debugging, and compliance, and can often be reverse-engineered using automated tools to map ETL chains or data pipelines.

However, these tools focus on where data travels, not what it means. And that's where vertical lineage becomes essential: it connects business concepts to their logical forms and technical footprints, allowing teams to understand the semantic intent behind each data point.

“Is this the right number to use in this report?” → this is a vertical question.

But beyond reporting, vertical data lineage plays a fundamental role in ensuring that business rules, policies, and changes are correctly propagated across processes, enabling business impact validation and risk mitigation during solution design.

AN EXTENDED EXAMPLE: BUSINESS IMPACT ASSESSMENT POWERED BY VERTICAL LINEAGE

Let’s return to our earlier example:

An analyst is preparing the Q1 Sales Performance Report for the B2B division. The report must reflect recognised revenue, limited to B2B clients, and comply with internal accounting standards.

• Conceptual definition:

  Revenue = Monetary value from business activity over a defined period.

• Logical distinctions:

  • Gross vs. Net vs. IFRS-recognised revenue.

  • B2B vs. B2C.

  • One-off vs. recurring.

• Technical fields:

  • revenue_gross_amt

  • revenue_ifrs_net

  • revenue_b2b_total

Field names alone would suggest using revenue_b2b_total, but that would be incorrect: it lacks the proper accounting logic. Only revenue_ifrs_net, filtered by B2B clients, is correct.

• With only horizontal lineage, this would require deep technical investigation.

• With vertical lineage, semantic alignment and rule traceability lead directly to the correct data.

Vertical Lineage as Business Design Enabler

However, the true power of vertical lineage goes far beyond data selection:

• When a new regulation modifies revenue recognition rules, vertical lineage immediately identifies:

  • which processes are impacted,

  • which stakeholders need to be involved,

  • which automations require revision.

• This drastically reduces:

  • time to validate changes,

  • risk of unintended process impact,

  • and the likelihood of expensive rollbacks due to missing business dependencies.

• It allows design teams to structure digital initiatives with full business transparency, avoiding technical or organizational blind spots that frequently surface late in projects.

In Short

Horizontal lineage documents the system flows. Vertical lineage enables sustainable solution architecture.

Advantages, Limitations, and Complementarity

🎯 Focus

HORIZONTAL LINEAGE Technical movement across systems

VERTICAL LINEAGE Semantic meaning and alignment

⚙️ Tools

HORIZONTAL LINEAGE Automated extraction, reverse engineering

VERTICAL LINEAGE Business glossaries, metadata, logical mappings

👤 Users

HORIZONTAL LINEAGE Data engineers, IT architects

VERTICAL LINEAGE Analysts, business users, process owners

💪 Strengths

HORIZONTAL LINEAGE Integration traceability, technical dependencies

VERTICAL LINEAGE Interpretability, relevance, governance alignment

⚠️ Limitations

HORIZONTAL LINEAGE Doesn’t answer “what” or “why”

VERTICAL LINEAGE Harder to automate, requires business alignment

🧩 Complementarity

HORIZONTAL LINEAGE Maps system flows, enables reuse

VERTICAL LINEAGE Enables business-driven solution design, risk mitigation

Relative Weight of Vertical and Horizontal Lineage Across the Project Lifecycle

While both vertical and horizontal data lineage are necessary at every stage, their relative importance evolves throughout the lifecycle of a digital solution:

Solution Design

Dominant Lineage: Vertical

Clarifies business concepts, identifies stakeholders, reveals business dependencies, validates process coverage, prevents blind spots

Implementation

Dominant Lineage: Horizontal

Ensures technical integration, manages data flows, enables reuse of pipelines, supports detailed system specifications

Post-Go-Live

Lineage Focus: Both (balanced)

Supports Ops and facilitates maintenance, enables impact analysis, ensures traceability for audits, facilitates controlled evolution

Both types of lineage must be considered from the beginning, but vertical lineage drives effective design decisions, while horizontal lineage ensures sound technical execution. Once in production, both layers work together to enable sustainable operations, continuous improvement, and risk mitigation.

Together, they provide complete traceability, but vertical lineage empowers solution design, ensuring data supports the actual business needs, stakeholders, and sustainable decision-making.

For this reason, vertical data lineage plays a central role in building the digital transformation roadmap

💡 Why Data Lineage Alone Is Not Enough

Every data point exists within a process. It’s created, validated, and used in operational flows.

Without process context:

• We document flows but not business relevance.

• Automations become brittle.

• Reporting logic drifts from real-world execution.

  
  

Vertical lineage bridges data to processes, connecting who needs what data, when, and for what purpose.

To design scalable digital systems:

• Map processes and data together.

• Align taxonomies with business roles.

• Build lineage into process design.

🎯 Key Takeaways

Data lineage isn’t just for reporting; it’s a core enabler of sustainable solution design.

1. Vertical lineage governs meaning; horizontal lineage governs system flows, both are essential.

2. Vertical lineage enables business-driven architectures that scale by ensuring semantic clarity and stakeholder alignment.

3. Process awareness combined with vertical lineage creates resilient digital ecosystems and reduces redesign risk.

4. The relative weight of vertical and horizontal lineage evolves along the project lifecycle: vertical lineage is critical during solution design; horizontal lineage takes central stage during implementation; both work together to ensure sustainable operations and controlled evolution after go-live.

5. Vertical lineage represents the foundational building block to guide smart digital transformation roadmaps.

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What to Expect in the Next Articles

Now that we’ve explored how effective data lineage, both vertical and horizontal, enables process transparency, stakeholder alignment, and scalable solution design, we’re ready to bring it all together.

In the final episode, we’ll dive into building an Actionable Roadmap for Digital Transformation→ How to strategically combine BPM and data lineage, prioritize initiatives, and create a phased approach that ensures measurable impact and long-term sustainability.

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Stay tuned for the next & last episode, we’ll show how to turn insights into action with a practical, business-driven transformation roadmap.