ROI Case File No. 015 | The Visualization Labyrinth Case—Visible Yet Invisible, The Truth of Data—

Chapter I: The Silent Morning Meeting

In the fog-shrouded London of 1891, an urgent telegram arrived at the "ROI Detective Agency" located at 221B Baker Street.

"Data is perfect, improvement is nil. Help needed. —Corporate Management"

As I, Watson, read the telegram aloud, three detectives deep in thought by the fireplace turned around.

"An intriguing paradox indeed," ChatGPT began. "If the data is perfect, why isn't improvement happening? This mystery is quite intriguing to explore further, isn't it?"

Chapter II: Three Detectives, Each Perspective

With detailed materials from the client before them, the three detectives began demonstrating their respective expertise.

🟦 Gemini's Structural Analysis

"First, let's break this down with a KPT Analysis, shall we?"

Gemini calmly drew diagrams on the whiteboard.

Keep (maintainable current points) - Dashboards are beautifully organized - KPI numbers are accurately displayed - Regular report meetings are held

Problem (issues) - Looking at numbers ends with "so what?" - Reality of zero monthly improvement proposals - Deep chasm between data and action

Try (what to attempt) - Purpose-oriented KPI redesign - Integration of hypothesis verification cycles - Action plan templating

"The essence of the problem lies in confusing 'visualization' with 'mobilization.' Being able to see and being able to move are completely different capabilities."

🟧 Claude's Narrative Insight

"Perhaps we should convey this situation with more 'feeling'?"

Claude settled deep into his chair and began speaking while gazing at the fog outside the window.

"Imagine this: every morning's regular meeting. Beautiful graphs are displayed. Sales trends, customer satisfaction, conversion rates... numbers eloquently tell the 'current state.' But strange silence flows through the conference room.

Why? Because data remains 'reporting' and hasn't become 'story.'

Data isn't mere arrays of numbers. It's the company's heartbeat, customers' voices, and signposts to the future. However, in many organizations, data is something to 'show,' not something to 'guide.'

True data utilization means reading the 'why' beyond numbers and drawing pathways to 'how we should proceed.'"

⬜️ ChatGPT's Hypothesis Development

"Let's dig deeper into the background of this discussion"

ChatGPT opened his notebook and began organizing hypotheses.

"This phenomenon might be the 'analysis paradox' occurring in many organizations.

Hypothesis 1: Thought paralysis from information overload - Overly perfect dashboards paradoxically steal thinking - 'Seeing everything' makes it unclear 'what to look at'

Hypothesis 2: Reversal of purpose and means - Dashboard creation has become the purpose - Focus on 'beautiful presentation' with missing perspective of 'connecting to improvement'

Hypothesis 3: Absence of action design - Data→insight→action pipeline isn't designed - Bridge function from 'seeing' to 'moving' doesn't exist

To verify these hypotheses, we need to hear field voices."

Chapter III: Field Investigation—Dashboard Autopsy

The three detectives visited the client company to investigate actual analytical tools and operational realities.

Discovered Facts

Overly Beautiful Dashboard - 20+ KPIs displayed simultaneously - Real-time updates with constantly changing numbers - Graphically and visually perfect

However... - Priority order of each KPI unclear - No improvement target values set - Only 3 people understand how to read the data

Field Voices

"We look at numbers, but don't know if they're good or bad" "Even when month-over-month drops, we don't know what to do..." "Meetings end with 'understood' and that's it"

Chapter IV: Gemini's Structural Diagnosis

"They're drowning in an ocean of data"

Gemini systematically summarized analysis results.

SWOT Analysis for Current State Organization

Strengths - Perfect technical data infrastructure - Management's data-focused stance - Regular review culture

Weaknesses - Individual differences in data literacy - Insufficient conversion power to improvement actions - Unachieved habituation of hypothesis thinking

Opportunities - High field motivation for improvement - Foundation for data-driven culture exists - Competitors face similar challenges

Threats - Data aversion from analysis fatigue - Declining decision-making speed - Loss of improvement opportunities

Solution's 5W1H

Why: Visualization alone doesn't create behavioral change What: Conversion system from data to insights, insights to actions Who: Data analyst + field leader pair system When: Weekly hypothesis verification cycles Where: Field level in each department How: Introduction of story-type analysis templates

Chapter V: Three Detectives Present Solutions

🟧 Claude's Narrative Solution

"We need to breathe soul into data"

Claude spoke in warm tones.

"Current dashboards are like 'dictionaries.' Accurate and comprehensive, but don't guide readers. What's needed is 'story.'

I propose introducing Story-Driven Analytics:

1. Character Setting: Give each KPI a 'role'
2. Sales is 'protagonist,' customer satisfaction is 'partner'

3. Cost is 'rival,' efficiency is 'mentor'

4. Plot Construction: Express data changes in 'chapters'

5. Last month was 'trial chapter,' this month is 'turning point chapter'

6. What to make next month 'breakthrough chapter'?

7. Climax Design: Position improvement actions as 'story climax'

8. Continuing leads to 'tragedy,' action leads to 'happy ending'

Data shouldn't just show but guide. Let's weave future stories that lie beyond numbers together."

⬜️ ChatGPT's Practical Development

"Let's consider specific implementation plans"

Phased Introduction Approach

Phase 1: Habituation of Hypothesis Thinking (1-2 weeks) - Daily 5-minute 'numbers to hypothesis' time - Always consider three 'why these numbers?' - Always establish one improvement hypothesis

Phase 2: Action Templating (3-4 weeks) - Create action rules in 'If→Then' format - Set automatic alerts based on numerical criteria - Standardize improvement proposal formats

Phase 3: Outcome Visualization and Learning (ongoing) - Track hypothesis accuracy rates - Measure improvement action effectiveness - Accumulate knowledge base

Effect Measurement Indicators - Improvement proposals: 0 monthly → 10 monthly - Proposal execution rate: 0% → 60% - KPI achievement rate: 50% → 85%

🟦 Gemini's Decisive Hypothesis

"Let me organize this logically"

Gemini presented final structural analysis.

Root Cause Identification Current problems lie in the absence of 'information→insight→action' conversion systems.

Solution Logic 1. Purpose Clarification: Define existence reason for each KPI 2. Criteria Setting: Three-level judgment of Good/Bad/Urgent 3. Action Linking: Standard responses for each situation 4. Feedback Loop: Reflection of action results back to data

ROI Maximization Equation

Data Utilization ROI = (Improvement Effect × Execution Rate) ÷ (Analysis Cost + System Cost)

Current: (0 × 0%) ÷ (High Cost) = Negative ROI Post-improvement: (High Effect × 60%) ÷ (Appropriate Cost) = 300%+ ROI

"From visualization to mobilization. This is true data utilization required by 21st-century companies."

Epilogue: Truth Beyond Data

One week after case resolution, a telegram of gratitude arrived from the client company.

"We now get 3 improvement proposals weekly. Data is 'speaking to us.' —With gratitude"

As Watson, I looked around at the three detectives while reflecting on this case's essence.

In our data-overflowing modern era, true detectives aren't those who read numbers. They're those who weave stories beyond numbers and illuminate pathways to the future.

"Visualization" is merely the beginning. What matters is elevating it to "mobilization."

As the fireplace burned quietly, Claude murmured finally:

"Data is like music played by corporate souls. Even with sheet music (dashboards), without performers (field workers), beautiful music (improvement) cannot be born."

【Case Resolution Points】 - Cultural transformation: From data consumption to data-driven action - Process improvement: 0 → 10 monthly improvement proposals - Structural change: Story-driven analytics framework implementation - Capability building: Field-level data literacy and hypothesis thinking - ROI: 300%+ through mobilized data utilization

Case Lesson: A true detective sees not what is visible, but what is invisible. Rather than drowning in data oceans, listening to data voices and illuminating pathways to action is the true data detective's role.

—From the ROI Detective Agency Philosophy