Reducing the Need for Explanation

0. Introduction

Management reporting is typically a recurring exercise: annual budgets, rolling forecasts, monthly closes, quarterly board packs, and weekly dashboards. The same reports are produced repeatedly, reviewed by largely the same audiences, and discussed in broadly similar forums.

A familiar example is the quarterly board pack.

Typically, a quarterly board pack contains operational and financial performance updates presented as tables comparing actual results with targets or prior periods, accompanied by written commentary explaining variances and underlying reasons. In board meetings, the tables show what happened, while the commentary explains why. Management reinforces the explanation verbally, answers questions, and elaborates on key points.

A continuous improvement process is usually in place. From quarter to quarter, report preparers anticipate questions and refine the pack – most visibly through more detailed and carefully worded commentary. Over time, explanations become longer and more polished.

Yet the underlying dynamic remains unchanged. Similar explanations recur each quarter, similar questions are anticipated, and similar walkthroughs are required. The organisation becomes better at explaining performance, but not at reducing the need for explanation. Understanding does not become easier.

Instead of continually producing more detailed explanations and more persuasive narratives, what if reports were designed so that less explanation was required in the first place?

The answer lies in disciplined structural analysis, which improves understanding and reduces reliance on narrative explanation.

This distinction – between structure that carries logic and explanation that compensates for its absence – runs through the rest of this article.

Before examining how structural analysis achieves this, it is necessary to be clear about what clarity means in the context of management reporting.

1. What Clarity Means in Management Reporting

In a decision-support context, the primary purpose of management reporting is to inform decisions by enabling a shared understanding of business outcomes, their drivers, and the trade-offs between them. Decisions depend not merely on knowing what happened, but on seeing how outcomes arise, what influences them, and where tensions or constraints exist. At its core, management reporting is concerned with making causal relationships explicit and, when designed well, visible.

Clarity in management reporting depends on whether this causal logic is explicit rather than implied. Clarity exists when the reader can see how outcomes arise from their drivers directly in the report, without relying on narrative explanation.

When clarity is present, understanding does not depend on explanation. The logic of the analysis can be examined, challenged, and reused. This produces three distinct benefits.

First, clarity enables shared and unambiguous understanding.
Different readers can arrive at broadly the same interpretation because they are inspecting the same analytical structure rather than inferring meaning from language. Questions shift from “what does this mean?” to “is this logic correct?” or “what would happen if this assumption changed?”

Second, clarity stabilises understanding over time.
Meaning remains coherent across reporting cycles, audiences, and organisational contexts. A board pack that is clear this quarter remains interpretable next quarter, even if the numbers change, the presenter is absent, or the audience shifts. Understanding is anchored in the structure of the analysis itself. Stability is a defining characteristic of clarity.

Third, clarity reduces the workload associated with explanation.
Clarity determines where interpretive effort sits. When clarity is weak, meaning must be assembled in the reader’s or presenter’s mind, relying on explanation, emphasis, and judgement. This increases dependence on individual interpretation and makes understanding fragile. When clarity is strong, much of that effort is absorbed by the report itself. The report carries the logic, reducing the burden on individuals to recreate understanding each time.

Clarity is not about simplifying reality or eliminating discussion. It is about making causal reasoning visible and stable enough to support inspection, disagreement, and further analysis. When clarity is established, narrative explanation and judgement can play their proper role without becoming load-bearing.

The shift from narrative-led to structure-led reporting is ultimately a recalibration of decision velocity.

When a reporting framework requires a guided narrative walkthrough to establish the meaning of the data, a large share of meeting time is consumed by interpretive alignment – assembling a shared understanding before evaluation can begin.

By contrast, an inspectable structure encodes the what and the why directly into the analytical and visual architecture of the report. This allows decision-makers to bypass the phase of meaning reconstruction and move immediately to evaluating trade-offs, implications, and choices.

In this state, the report ceases to function primarily as a historical record. It becomes a mechanism for decision support.

2. Two Vehicles for Converting Information into Understanding

If the purpose of management reporting is to inform decisions by enabling shared understanding, the next question is how information is converted into understanding in practice. In most organisations, this occurs primarily through narrative explanation, but there is another, less explicit mechanism: structural analysis.

These two vehicles operate in different ways, serve different purposes, and place the burden of understanding in different places.

2.1 Structural Analysis

In this article, structural analysis refers not to the general use of analytical techniques, but to analysis designed to produce inspectable structure. Analysis that cannot be inspected remains dependent on narrative explanation, regardless of its technical correctness.

Structural analysis converts information into understanding by making causal structure explicit and – when designed well – visible. Rather than describing relationships after the fact, it embeds those relationships directly in the report itself, through the models, tables, and charts used to analyse performance.

In structurally designed analysis, logic is encoded in how the analysis is constructed – how drivers are separated from outcomes, how effects are decomposed, and how relationships are represented. Understanding arises through inspection of the analysis, not through reconstruction from narrative.

Because causal relationships are explicit, structural analysis supports examination and challenge. Readers can test assumptions, compare contributions, and assess trade-offs without relying on the presence or authority of the analyst. Questions naturally shift from interpretive ones – “what does this mean?” – to analytical ones – “is this relationship valid?”, “is this the right decomposition?”, or “what happens if this driver changes?”

A defining feature of structural analysis is that it externalises reasoning. The analyst’s mental model is made visible and stable in the report, allowing others to engage with it directly. This enables shared understanding that is consistent across time, audiences, and organisational contexts. The same analysis can be revisited, reused, and built upon without requiring the original explanation to be repeated.

Structural analysis therefore reduces the need for narrative explanation. Explanation may still be provided, but it is no longer load-bearing. The report itself carries the logic.

Examples of structural analysis

Structural analysis does not explain causality after the fact; it embeds causality in the structure of the analysis itself. Typical examples include:

• a margin bridge or waterfall that decomposes a change in profit into price, volume, mix, and cost effects, with each component reconciling mechanically to the total change
• a variance analysis table that separates drivers explicitly (e.g. demand, rate, efficiency), allowing inspection without commentary
• a driver-based model that links operational inputs (headcount, utilisation, throughput) to financial outcomes through explicit relationships
• a scenario or sensitivity analysis showing how outcomes respond to changes in key assumptions
• a time-series chart with a defined baseline and variance, where deviations and patterns are structurally visible

In these cases, the reader can see what drives what, how effects combine, and which factors dominate. Understanding arises through inspection of structure, not through explanation layered on top.

2.2 Narrative Explanation

Narrative explanation converts information into understanding through language and sequencing. It unfolds over time, guiding the reader or listener through what happened, why it matters, and how different elements relate.

In situations involving ambiguity, novelty, or qualitative factors, narrative is often indispensable. In management reporting, narrative explanation provides context, emphasis, and judgement. It helps frame decisions, draw attention to risks, and connect analysis to action.

However, narrative explanation reconstructs causality implicitly. Relationships are conveyed through wording, emphasis, and ordering, and must be inferred by the audience. Understanding therefore depends on interpretation, attention, and memory.

Because narrative explanation is linear, it is difficult to inspect out of sequence or to reuse without reinterpretation. Different readers may reconstruct different mental models from the same explanation, particularly when context shifts or time passes. As a result, understanding achieved primarily through narrative is more fragile and more dependent on the individual delivering or receiving the explanation.

When narrative explanation becomes the primary carrier of analytical logic, understanding concentrates in people rather than in reports. It must be recreated each time through explanation, reinforced through repetition, and maintained through continuity of personnel. This places a sustained cognitive and emotional burden on individuals and limits how understanding accumulates over time.

For these reasons, narrative explanation is best understood as a complement to structure. It is most effective when it interprets, frames, and contextualises logic that is already visible, rather than attempting to carry that logic on its own.

When analytical structure is clear and inspectable, narrative explanation serves a precise and valuable role: orienting attention, highlighting implications, and supporting judgement, rather than carrying causal logic.

Examples of narrative explanation

Narrative explanation is the dominant form in most reports. Typical examples include:

• written commentary alongside financial or operational tables explaining variances
• slide headlines that summarise results and causes
• executive summaries describing performance drivers in paragraphs
• verbal walkthroughs in meetings that reconstruct logic step by step

In these cases, the underlying causality is described rather than encoded. Narrative explanation may be clear, well written, and helpful, but it remains dependent on reconstruction in the reader’s mind. Relationships, relative importance, and trade-offs must be inferred from language, emphasis, and the presence of the explainer.

2.3 Not All Charts Are Structural Analysis

Many management reports contain recognised analytical patterns – such as P&Ls, variance analyses, margin bridges, or trend charts. The presence of these patterns often creates the impression that structural analysis is already in place.

However, this is frequently not the case.

The difference is not what type of analysis is used, but how that analysis is designed. The same analytical pattern can be implemented in either a structural or a non-structural way.

A margin bridge provides a useful illustration.

When a margin bridge is not structural analysis
In many reports, a margin bridge shows a sequence of movements from a starting margin to an ending margin, but the causal logic – which drivers matter, how they are defined, and why they cause the outcome – still resides primarily in written explanation or a verbal walkthrough. From the chart alone, the reader has no assurance that the drivers are mutually exclusive, collectively exhaustive, or mechanically linked to the outcome. In such cases, the bridge functions as a visual summary, while understanding depends on narrative reconstruction. The analysis remains narrative-led, even though a familiar analytical pattern is present.

When a margin bridge is structural analysis
A margin bridge becomes structural when the decomposition itself carries the logic. This occurs when the report exposes enough structure for the reader to understand the causal mechanism without relying on explanation – for example, through a compact driver definition or reconciliation that makes the underlying model inspectable. In a structural analysis:

• the drivers are explicitly defined and mutually coherent
• each component reconciles mechanically to the total outcome
• the relative contribution of each driver is visible without explanation
• the reader can inspect, question, and reuse the analysis directly

In this case, commentary may still be provided, but it is no longer required to understand the drivers or the logic. Narrative shifts from carrying causal logic to interpreting implications – from explanation to orientation.

This distinction generalises beyond margin bridges. The same applies to variance tables, trend analyses, and dashboards. A chart is structural because its causal structure is explicit, inspectable, and stable over time. Only then is clarity achieved.

Structural analysis is therefore a design property, not a formatting choice. It is defined by where understanding resides – in the structure of the analysis itself, not in the explanation layered on top of it.

3. Why Narrative Explanation Dominates Management Reporting

Understanding why narrative explanation dominates is essential. Without this, calls for “better structure” risk remaining abstract or aspirational.

If structural analysis creates clearer and more stable understanding, an obvious question follows: why does narrative explanation remain the dominant mode of management reporting?

The answer is not a lack of analytical capability or professional intent. In most organisations, narrative explanation dominates for a set of structural and behavioural reasons that are understandable, rational, and reinforced by the reporting environment itself.

3.1 Legacy templates and established expectations

Most management reports inherit their structure from prior periods. Templates stabilise quickly, particularly in board reporting, where familiarity and continuity are valued. Once a format is accepted, changes to structure can feel risky, even when clarity is limited.

Narrative explanation fits comfortably within these legacy structures. Commentary can be expanded, refined, or reordered without altering layout or format. Structural analysis, by contrast, often requires visible changes to tables, charts, and the organisation of information.

Over time, explanation becomes the default means of achieving understanding. When understanding is difficult, audiences are repeatedly given more explanation. Explanatory effort becomes institutionalised, while making analytical structure explicit and inspectable remains largely unexamined.

3.2 The perceived efficiency of narrative explanation under time pressure

Management reporting operates under tight and recurring time constraints. Monthly closes, forecast updates, and board packs are produced against fixed deadlines. Under these conditions, narrative explanation is often perceived as the fastest way to respond to questions and perceived gaps in understanding.

In the short term, before a well-designed structural analysis is embedded in the report, it is usually quicker to explain around an existing structure than to invest in designing a robust structure upfront. Commentary can be added incrementally, while structural design requires upstream thinking, iteration, and sometimes re-modelling. Explanation therefore becomes the path of least resistance.

However, this apparent efficiency is misleading. Narrative explanation does not remove work; it displaces it. Effort saved upfront reappears downstream in the form of longer commentary, repeated walkthroughs, follow-up questions, and meeting time spent reconstructing understanding.

Because narrative understanding must be reconstructed rather than inspected, it does not accumulate. The same logic is rebuilt by each reader, in each reporting cycle. The reporting process may become efficient at producing explanations, but it is inefficient at producing lasting understanding.

Structural analysis works differently. While it requires more deliberate effort once, it embeds causal logic in the report itself. Once embedded, the structure can be reused in BAU reporting without redesign. Over time, this reduces the effort required to produce commentary, improves the speed and reliability of understanding, and lowers the need for repeated explanation.

Narrative explanation persists under time pressure because its costs are deferred and diffused across people, meetings, and reporting cycles. It functions as a workaround where structural analysis has not yet been designed or embedded.

While narrative explanation is often perceived as efficient under time pressure, it is a high-depreciation activity. Its value is largely exhausted once the explanation has been delivered to a specific audience. Even when prior commentary is reused as a template, its analytical value does not carry forward. The wording may persist, but the causal logic must still be mentally revalidated, reinterpreted, and re-defended in each reporting cycle. Reusing commentary reduces writing effort, but it does not reduce the need to reconstruct understanding.

Structural analysis operates differently. Once causal logic is embedded into the report’s architecture, it functions as a capital asset. The same structure can process new data period after period without requiring the logic to be rebuilt.

Over time, this shifts analytical effort away from the repeated assembly and defence of explanations and towards the maintenance of a stable analytical structure. The cumulative workload falls, even though the initial design effort is higher.

There is an uncomfortable implication in this comparison. Structural analysis accumulates over time. It stabilises definitions, fixes baselines, and progressively narrows interpretive flexibility. In some environments, that accumulation is not always desirable. Narrative explanation persists not because it is analytically superior, but because its depreciation preserves discretion and allows organisations to absorb uncertainty without prematurely hardening accountability.

3.3 Comfort with telling rather than designing

Narrative explanation aligns closely with how most professionals are trained to communicate: explain, summarise, persuade. Designing analytical structure requires a different set of skills – abstraction, modelling, and visual reasoning – that are less widely taught or rewarded.

Telling a story about performance often feels more natural than designing a system that makes causality visible. Narrative allows ambiguity to be managed through language, emphasis, and judgement. Gaps can be softened, tensions reframed, and uncertainty handled without being pinned down.

Structural analysis reduces that flexibility. By making logic explicit, it exposes assumptions, trade-offs, and points of dependency. This invites inspection and challenge. While this is essential for clarity, it can feel uncomfortable in environments where uncertainty is high, incentives are misaligned, or accountability is diffuse.

Structural analysis also makes accountability harder to avoid. When causal relationships are explicit and outcomes are mechanically linked to drivers, responsibility becomes traceable. Stories can contextualise results, but they can also blur responsibility. Structure cannot. By making causality explicit, structure clarifies where outcomes originate, and with that clarity comes greater accountability.

Structural analysis also functions as a mechanism for analytical integrity.

Narrative explanation, by its nature, allows selective emphasis in how performance is described. Qualitative factors can be foregrounded or backgrounded, and the distinction between external tailwinds and internal operational performance can become blurred.

A disciplined analytical structure – such as a price–volume–mix variance or a mechanically reconciling bridge – imposes constraint on the narrative. It requires that any explanation be consistent with the underlying value drivers and their quantified contributions.

In doing so, accountability becomes a property of the analysis itself rather than an interpretation of the commentary. Performance can be contextualised, but it cannot be redefined.

3.4 How narrative explanation reinforces itself

Taken together, these factors create a self-reinforcing dynamic.

In many organisations, accountability for reporting quality gradually comes to rest on the quality of explanation, rather than on the quality of analytical structure. This is rarely the result of an explicit decision.

When audiences do not fully understand a report, they ask for clarification. Clarification is interpreted as a need for more explanation, and analysts respond by adding commentary, refining wording, or preparing additional slides. Over time, this interaction becomes normalised. Analysts and managers are recognised for their ability to “explain the numbers”, anticipate questions, and provide persuasive narratives. Requests for clarity therefore translate into requests for better explanation, rather than into questions about whether the existing analysis has been made explicit and inspectable in the report.

At the same time, there are few shared criteria for evaluating whether a report’s causal logic is inspectable, reusable, or stable over time. Structural quality remains largely unexamined and invisible, while explanatory plausibility becomes the primary signal of professionalism.

In this way, narrative explanation becomes load-bearing because it is the only dimension that is actively exercised, evaluated, and reinforced.

But narrative cannot create clarity

The problem is not narrative explanation itself.

It arises when narrative is required to carry causal logic that should reside in structure. The next section examines why narrative explanation, by its nature, cannot create clarity, and why reducing the need for explanation is ultimately a design problem rather than a communication one.

4. Why Narrative Explanation Cannot Create Clarity

Narrative explanation plays an important and legitimate role in management reporting. It frames issues, highlights implications, and supports decision-making conversations. However, it has a fundamental limitation: it cannot create clarity when causal structure is missing or remains implicit.

This limitation is not a matter of communication skill or writing quality. It arises from the nature of narrative explanation itself.

4.1 Explanation is sequential; understanding is structural

Narrative explanation unfolds sequentially. It progresses sentence by sentence, point by point, reconstructing logic over time. This sequential form is well suited to describing events, emphasising priorities, and guiding attention.

The logic that underpins business performance is structural and often hierarchical, rather than sequential. Outcomes arise from multiple interacting drivers operating simultaneously. Trade-offs, constraints, and interactions cannot be fully represented as a sequence without loss or distortion.

When causal relationships are not made explicit in structure, narrative explanation is forced to approximate them through language. The reader must reconstruct relationships mentally, holding assumptions, dependencies, and counterfactuals in working memory. Clarity then depends on how successfully this reconstruction occurs, not on the quality of the underlying logic itself.

Narrative can describe structural relationships, but it cannot make them directly inspectable.

4.2 Explanation fails to produce shared and stable understanding across audiences

When understanding relies on narrative explanation, causal logic remains internal to individuals. The analyst or presenter holds the mental model and expresses it through words. The audience must rebuild that model in their own minds.

This process is inherently fragile. Different readers reconstruct different interpretations based on prior knowledge, attention, and context. Small differences in emphasis or phrasing can lead to materially different understandings of the same situation.

Because the logic is reconstructed rather than fixed, understanding depends on interpretation rather than inspection. Shared and stable understanding across audiences cannot be assumed, even within the same reporting cycle.

4.3 Explanation fails to create persistent understanding across reporting cycles

When understanding lives primarily in narrative, it does not persist over time. It must be recreated each reporting cycle through explanation, reinforcement, and repetition.

Even when commentary is documented and retained, narrative understanding remains context-dependent. Readers must recall prior explanations, reconstruct assumptions, and re-infer causality. Changes in personnel, time gaps, or shifts in audience composition quickly erode continuity.

As a result, recurring reports answer the same questions repeatedly. Understanding does not accumulate across reporting cycles.

Narrative explanation can preserve memory, but it cannot preserve causal logic.

4.4 The cognitive limits of narrative

Narrative explanation places a high cognitive load on both presenters and audiences. Readers must track sequences, remember definitions, and integrate multiple threads of reasoning. As complexity increases, this load quickly exceeds what can be reliably managed.

Human cognition is better suited to recognising patterns than to reconstructing logic from prose. When understanding depends on narrative alone, it becomes vulnerable to fatigue, distraction, and overload.

As a result, clarity achieved through explanation remains fragile and difficult to sustain.

4.5 Explanation may compensate for missing structure, but cannot replace it

In practice, narrative explanation often compensates for weak or implicit structure. Analysts add detail, anticipate questions, and refine wording to bridge gaps left by unexposed logic. This effort is frequently impressive and well intentioned.

However, compensation is not creation. No amount of explanation can make causal relationships inspectable if they remain implicit. Narrative can guide attention, but it cannot eliminate ambiguity that originates upstream.

When explanation becomes load-bearing, its volume tends to increase over time. Reports grow longer, walkthroughs more detailed, and meetings more time-consuming. The system adapts by demanding better explanation rather than resolving the underlying cause.

This dynamic creates the appearance of clarity while leaving its source unaddressed.

Narrative explanation remains indispensable, yet it cannot create clarity when causal logic is implicit.

5. Clarity Emerges When Causal Structure Becomes Inspectable

If clarity cannot be created through explanation, it must be created through structure.

Clarity in management reporting emerges when causal logic is embedded in the structure of the analysis and made inspectable to the reader. In such cases, understanding does not need to be reconstructed through narrative explanation. It arises through direct inspection of how outcomes, drivers, and trade-offs relate to one another.

Structural analysis externalises reasoning and establishes causal structure – separating drivers from outcomes, defining how effects combine, and making assumptions explicit. However, structure alone is not sufficient. Causal logic can exist and still fail to produce clarity if it is not accessible to those who need to use it.

Clarity emerges only when causal structure becomes inspectable.

5.1 What “inspectable” means

In this article, inspectable refers to a specific property of a management report.

An analysis is inspectable when its causal logic can be examined directly by the reader, without relying on narrative explanation, verbal walkthroughs, or prior familiarity. The reader can see what drives what, how effects combine, and where trade-offs exist.

Inspectable does not mean auditable.

Auditability concerns whether calculations can be traced, verified, and controlled. Inspectability concerns whether causal logic can be understood, questioned, and discussed. A report may be fully auditable and still fail to be inspectable.

Inspectability is a property of analytical structure as rendered through visual design, intended to support understanding rather than compliance.

5.2 Inspectability requires both analytical design and visual design

Making structure inspectable requires both analytical design and visual design. Each is necessary, and neither is sufficient on its own.

• Analytical design determines whether coherent causal structure exists.
• Visual design determines whether that structure can be inspected by others.

Analytical design without visual design produces analysis that may be correct, but opaque.
Visual design without analytical design produces representations that may look clear, but carry little logic.

Inspectable structural analysis emerges only when sound analytical structure is rendered in a way that makes relationships perceptible to the reader.

5.3 The role of analytical design

Analytical design establishes the causal structure that clarity depends on.

It determines:

• which drivers matter
• how they are defined
• how they relate to outcomes
• how effects combine and reconcile
• what remains stable across reporting cycles

Good analytical design separates structure from data. It ensures that drivers are coherent, mutually consistent, and mechanically linked to outcomes. It encodes causality in a way that can be tested, reused, and updated as new data arrives.

Without disciplined analytical design, there is no meaningful structure to inspect. Explanation becomes necessary because logic is incomplete, implicit, or inconsistent.

Analytical design establishes causal structure and ensures internal consistency. When done well, it produces structure that can be audited and governed.

Inspectability requires more. Only when analytically designed structure is rendered through disciplined visual design does it become inspectable – allowing causal logic to be examined directly by the reader.

5.4 The role of visual design

Visual design determines whether analytical structure can be inspected by the reader.

Its role is not decoration, persuasion, or storytelling. It is to make structure perceptible – to allow the reader to view relationships and apprehend meaning at the same time.

Effective visual design:

• reduces noise so structure can be seen
• preserves consistent semantics across reports
• enables comparison without explanation
• supports simultaneous viewing of drivers and outcomes

This is where professional disciplines and standards matter. Frameworks such as IBCS constrain visual design choices so that analytical structure remains legible, comparable, and stable over time.

Good visual design does not add meaning. It reveals meaning that already exists in the analytical structure.

5.5 Inspectable Structure Creates Clarity – and Its Benefits Compound

When causal structure is both explicit and inspectable, the limitations of narrative explanation are resolved.

• Understanding no longer depends on explanation
• Shared understanding emerges across audiences
• Understanding persists across reporting cycles
• Cognitive load is reduced
• Explanation shifts from carrying logic to orienting attention

Recurring reports no longer re-explain the same logic. New data flows through a stable structure. Patterns become recognisable. Deviations stand out. Conversations move more quickly to implications and decisions.

This is why inspectable structural analysis has disproportionate value in management reporting. Its benefits compound over time.

From Inspectability to Discipline

Clarity does not emerge from telling better stories. It emerges from structural analysis designed to be inspectable.

Structural analysis becomes a source of clarity only when causal logic is not merely correct, but accessible to the reader – when structure carries understanding, and narrative explanation is relieved of that burden.

In this sense, structural analysis is not just an analytical technique. It is a discipline of design: deciding what causal relationships matter, how they should be exposed, and how understanding is allowed to accumulate over time.

How structural analysis is made inspectable in practice – and the role that visual disciplines play in supporting it – deserves its own focused treatment.

© 2026 Colin Wu. All rights reserved.
Quotations permitted with attribution. No reproduction without permission.