Functional programmers prefer data to calculations and prefer calculations to actions. Similar wisdom applies to project management: understanding stakeholders must precede planning, which must precede action.

The Genesis of a Stakeholder Mapping Tool​

While working on a recent UI Systems Design project, I found myself struggling with the stakeholder analysis phase. Identifying key players and their relative influence often became a complex, subjective exercise consisting of scattered notes and mental models. As a Business Analyst specializing in UI Systems Design, I'm always searching for tools that bridge the communication gap between business and development teams, especially in our remote work reality.

The inspiration struck from an unexpected source: the host attribute management system from HBO's Westworld series. In the show, character designers adjust personality sliders across various categories to shape host behavior. I immediately saw the parallel - what if we could visualize stakeholder attributes with the same clarity?

Adapting the Westworld Matrix for Stakeholder Analysis​

The Westworld attribute matrix provides an elegantly visual way to adjust and understand relationships between different personality traits. For our stakeholder mapping tool, I adapted this concept to focus on two critical dimensions: power and interest. This classic framework helps categorize stakeholders into four actionable quadrants:

1. High Power, High Interest - Manage Closely
2. High Power, Low Interest - Keep Satisfied
3. Low Power, High Interest - Keep Informed
4. Low Power, Low Interest - Monitor

But I didn't stop there. The tool needed to visualize relationships between stakeholders and provide meaningful insights. Working collaboratively with developers, we designed an interactive application that transforms abstract stakeholder data into tangible visualizations.

Coding for Clarity​

Similar to my approach with Robustness Diagrams, I wanted the code structure to reflect clear, logical thinking. Taking inspiration from functional programming principles I'd learned from "Grokking Simplicity," we developed a modular architecture separating:

• Data (stakeholder information)
• Calculations (analysis of stakeholder relationships)
• Actions (UI events and visualizations)

The modular approach allows developers to easily understand, maintain, and extend the code base. By classifying code into these three categories, we created a system that's both robust and flexible.

// Example of how we separate concerns in the stakeholder tool
const StakeholderApp = {
    // Data model
    data: {
        stakeholders: [],
        relationships: []
    },
    
    // UI Module (Actions)
    UI: {
        updateStakeholdersList() { /* ... */ }
    },
    
    // Visualization Module (Calculations)
    Visualizations: {
        renderPowerInterestMatrix() { /* ... */ }
    }
};

Beyond Static Mapping​

The current implementation is just the beginning. The next evolution of this tool will track stakeholder satisfaction throughout the project lifecycle. By recording and visualizing satisfaction levels at different project milestones, teams can:

1. Identify trends in stakeholder sentiment
2. Proactively address declining satisfaction
3. Celebrate improvements in stakeholder engagement
4. Document the journey for retrospective analysis

This longitudinal view transforms stakeholder mapping from a one-time exercise into an ongoing dialogue. Much like how the hosts in Westworld evolve through their experiences, our understanding of stakeholders should develop throughout the project.

Why Stakeholder Mapping Matters​

In today's complex business environment, successful projects depend on more than technical expertise. Stakeholder dynamics often determine outcomes more than any other factor. A well-executed stakeholder mapping process:

• Prevents unexpected resistance by identifying influential players early
• Optimizes communication strategies for different stakeholder groups
• Prioritizes efforts based on stakeholder power and interest
• Creates a shared understanding among team members

As I experienced on my recent Enterprise Application project, light-weight workshops using these visualization techniques dramatically improved team alignment and expectations. The diagrams served as both documentation and communication tools, much like my adapted Robustness Diagrams.

Try It Yourself​

I've made both the tool (Alpha Release) and its source code available to the community:

• Live Demo: Stakeholder Mapping Tool
• Source Code: GitHub Repository

Feel free to use this tool for your own projects, and contributions are always welcome. The repository includes detailed documentation on how to set up and modify the application to suit your specific needs.

Moving Forward​

As we continue refining this tool, I'm excited to see how it evolves to meet the needs of different project teams. The core principles remain the same: visualize complexity, document relationships, and use technology to enhance human understanding.

Whether you're managing a small product team or coordinating a large enterprise initiative, stakeholder mapping provides the foundation for strategic engagement. And with interactive tools like this one, the process becomes not just more effective, but more insightful as well.

Like the hosts in Westworld, our tools should adapt and learn as we use them. This stakeholder mapping tool represents just one step in that journey – from static documentation to dynamic insight.

Functional programmers prefer data to calculations and prefer calculations to actions.

I discovered this concept while reading Grokking Simplicity in 2021, and it completely changed how I approach software development. This mindset shift is particularly useful when building tools that analyze document similarity - breaking the problem into data, calculations, and actions makes everything clearer.

The Problem I Needed to Solve​

Software requirements documents are often created by different teams, departments, or for related projects. This leads to:

• Redundant specifications that waste development effort
• Inconsistent implementations of the same requirement
• Difficulty tracking changes across documents
• Maintenance headaches when requirements evolve differently

I needed a solution that could analyze multiple documents and identify similar requirements to maintain consistency.

My Approach: Breaking Down the Solution​

I built a Requirements Similarity Analyzer that follows functional programming principles by separating:

• Data: The requirements documents and their extracted content
• Calculations: Similarity algorithms and requirement classification
• Actions: Document processing, report generation, and user interactions

The solution has three key capabilities:

1. Upload and analyze multiple requirements documents
2. Compare specifications using configurable similarity thresholds
3. Generate reports highlighting common and unique requirements

The Technical Architecture​

I combined a Python FastAPI backend with a Vue frontend:

Backend (Python/FastAPI)
├── Core Services
│   ├── Document Processing (PDF, DOCX, TXT)
│   ├── Text Analysis & Similarity Detection
│   ├── Requirements Extraction & Classification
│   └── Report Generation
└── API Endpoints
    ├── Document Upload & Management
    ├── Analysis Control
    └── Results & Export

Frontend (Vue.js)
├── Document Management Interface
├── Analysis Configuration
├── Results Visualization
└── Report Export Options

Key Components: Data, Calculations, and Actions​

1. Data Processing (Actions)​

I use Python libraries to extract text from documents, applying functional programming principles to keep the document processing pure:

# Example showing document processing for requirements
def extract_requirements(text):
    # Split text into paragraphs
    paragraphs = text.split('\n\n')
    
    # Identify potential requirement paragraphs
    requirements = []
    for para in paragraphs:
        if is_requirement(para):
            requirements.append({
                'text': para,
                'type': classify_requirement_type(para)
            })
    
    return requirements

def is_requirement(text):
    # Look for requirement indicators (shall, must, should, etc.)
    requirement_indicators = [
        r'\bshall\b', r'\bmust\b', r'\brequired\b', 
        r'\bshould\b', r'needs to\b'
    ]
    return any(re.search(pattern, text, re.IGNORECASE) for pattern in requirement_indicators)

2. Similarity Analysis (Calculations)​

The core calculation is pure and functional - it takes inputs and returns outputs without side effects:

def calculate_similarity(req1, req2):
    """Calculate similarity between two requirement texts"""
    # Use similarity ratio algorithm from SequenceMatcher
    return SequenceMatcher(None, req1.lower(), req2.lower()).ratio()

def find_similar_requirements(requirements, threshold=0.8):
    """Group similar requirements based on similarity threshold"""
    groups = []
    processed = set()
    
    for i, req1 in enumerate(requirements):
        if i in processed:
            continue
            
        similar_reqs = []
        for j, req2 in enumerate(requirements):
            if i != j and j not in processed:
                similarity = calculate_similarity(req1['text'], req2['text'])
                if similarity >= threshold:
                    similar_reqs.append({
                        'requirement': req2,
                        'similarity': similarity
                    })
                    processed.add(j)
        
        if similar_reqs:
            groups.append({
                'primary': req1,
                'similar': similar_reqs
            })
        else:
            groups.append({
                'primary': req1,
                'similar': []
            })
        
        processed.add(i)
    
    return groups

3. Report Generation (Calculations to Data)​

After analyzing documents, I generate comprehensive reports using pure functions:

def generate_similarity_report(grouped_requirements, format='json'):
    """Generate a report of similar requirements"""
    if format == 'json':
        return json.dumps({
            'timestamp': datetime.now().isoformat(),
            'total_requirements': sum(1 + len(g['similar']) for g in grouped_requirements),
            'unique_requirements': len(grouped_requirements),
            'duplicate_count': sum(len(g['similar']) for g in grouped_requirements),
            'groups': grouped_requirements
        }, indent=2)
    elif format == 'csv':
        # CSV generation code...
        pass

Implementation: Putting It All Together​

Let's look at how I implemented the document processor service, which extracts requirements from text documents:

# file_processor.py
import re
from typing import List, Dict, Any, Tuple
import fitz  # PyMuPDF

async def extract_text_from_pdf(content: bytes) -> Tuple[str, List[int]]:
    """Extract text and page numbers from PDF content."""
    doc = fitz.open(stream=content, filetype="pdf")
    text = ""
    page_numbers = []
    
    for page_num, page in enumerate(doc, 1):
        page_text = page.get_text()
        if page_text.strip():
            text += page_text + "\n\n"
            page_numbers.append(page_num)
    
    return text, page_numbers

async def extract_requirements_from_text(text: str) -> List[Dict[str, Any]]:
    """Extract requirement statements from text."""
    # Split into paragraphs
    paragraphs = text.split('\n\n')
    
    # Filter potential requirements
    requirements = []
    for para in paragraphs:
        para = para.strip()
        if para and is_requirement_statement(para):
            requirements.append({
                'text': preprocess_text(para),
                'type': classify_requirement_type(para)
            })
    
    return requirements

def is_requirement_statement(text: str) -> bool:
    """Identify if text is likely a requirement statement."""
    keywords = ['shall', 'must', 'will', 'should', 'may', 'required']
    return any(re.search(rf'\b{keyword}\b', text.lower()) for keyword in keywords)

This separates the concerns neatly into data transformations and follows functional programming principles.

Visualizing Requirements: The Vue.js Frontend​

Here's a simplified Vue component that visualizes requirements, separating data, calculations, and actions:

<!-- RequirementsAnalyzer.vue -->
<template>
  <div class="requirements-analyzer">
    <h1>Requirements Similarity Analyzer</h1>
    
    <!-- Upload Form (UI for Actions) -->
    <div class="upload-section" v-if="!isAnalyzing && !analysisComplete">
      <h2>Upload Documents</h2>
      <div class="upload-form">
        <div class="file-input">
          <label for="file-upload">Select files (PDF, DOCX, TXT)</label>
          <input 
            type="file" 
            id="file-upload" 
            multiple 
            @change="handleFileSelection"
            accept=".pdf,.docx,.txt"
          >
          <div class="selected-files" v-if="selectedFiles.length">
            <p>Selected {{ selectedFiles.length }} files:</p>
            <ul>
              <li v-for="(file, index) in selectedFiles" :key="index">
                {{ file.name }}
              </li>
            </ul>
          </div>
        </div>
        
        <!-- Similarity Threshold Input (Data) -->
        <div class="similarity-settings">
          <label for="similarity-threshold">Similarity Threshold: {{ similarityThreshold }}%</label>
          <input 
            type="range" 
            id="similarity-threshold" 
            v-model="similarityThreshold" 
            min="50" 
            max="100" 
            step="5"
          >
        </div>
        
        <!-- Action Trigger -->
        <button 
          class="analyze-button" 
          @click="startAnalysis" 
          :disabled="selectedFiles.length === 0"
        >
          Analyze Requirements
        </button>
      </div>
    </div>
    
    <!-- Results Display (Visualization of Data) -->
    <div class="results-section" v-if="analysisComplete">
      <!-- Content display here -->
    </div>
  </div>
</template>

<script>
import { ref, computed } from 'vue';
import { analyzeRequirements, getAnalysisStatus, exportAnalysisResults } from '../services/api';

export default {
  name: 'RequirementsAnalyzer',
  setup() {
    // Data
    const selectedFiles = ref([]);
    const similarityThreshold = ref(85);
    const analysisResults = ref({
      common_requirements: [],
      unique_requirements: []
    });
    
    // Calculations (computed values)
    const analysisProgressMessage = computed(() => {
      if (analysisProgress.value < 50) {
        return `Extracting requirements from documents (${Math.round(analysisProgress.value)}%)`;
      } else {
        return `Analyzing requirement similarities (${Math.round(analysisProgress.value)}%)`;
      }
    });
    
    // Actions (side effects)
    const handleFileSelection = (event) => {
      selectedFiles.value = Array.from(event.target.files);
    };
    
    const startAnalysis = async () => {
      // Implementation details
    };
    
    return {
      // Expose data, calculations, and actions to the template
      selectedFiles,
      similarityThreshold,
      analysisResults,
      handleFileSelection,
      startAnalysis
    };
  }
};
</script>

Adapting What I Learned​

I've found that this functional approach to building software is particularly effective for document analysis tools. By separating data, calculations, and actions, the code becomes more:

• Testable: Pure functions are easy to test
• Maintainable: Separation of concerns makes code easier to understand
• Extensible: New functionality can be added without disrupting existing code

Future Enhancements​

I'm planning to extend the Requirements Similarity Analyzer with:

1. Advanced NLP techniques: Using word embeddings or BERT for semantic similarity
2. Requirement categorization: Auto-classifying requirements by type
3. Integration with management tools: Connecting with Jira, Azure DevOps, etc.
4. Change tracking: Identifying when similar requirements evolve differently
5. Impact analysis: Assessing how changes affect related requirements

Conclusion​

Building this Requirements Similarity Analyzer has reinforced how effective functional programming principles can be for document analysis applications. By breaking down the problem into data, calculations, and actions, I've created a tool that's both powerful and maintainable.

Through careful document processing, intelligent similarity analysis, and an intuitive user interface, teams can quickly gain insights into their requirements landscape and make informed decisions about standardization and consolidation.

As I continue to work on this tool, I'm excited to see how these principles can be applied to other document analysis problems.

Credits​

• Grokking Simplicity
• FastAPI
• Vue.js
• PyMuPDF

Why Business Analysts Must Regularly Revisit ROI

In the fast-paced world of business transformation, it's easy to get lost in the weeds. Project deadlines loom, requirements multiply, and stakeholders make ever-changing demands. Amid this chaos, business analysts often lose sight of the most fundamental question: Why are we doing this in the first place?

The answer always circles back to ROI – Return on Investment. Yet surprisingly, many BAs rarely revisit ROI calculations after the initial business case. This oversight can lead projects down a dangerous path, disconnected from their original purpose and value proposition.

When Projects Lose Their North Star​

It happens all too often - months into implementation, team morale starts to drop, scope expands uncontrollably, and executives begin questioning the entire endeavor.

What typically goes wrong? Teams lose touch with their ROI compass.

When revisiting original business cases, we frequently discover that projects have drifted significantly from their core value drivers. Perhaps the primary ROI justification was based on projected time savings or error reduction, but teams become fixated on implementing advanced features that, while technically impressive, contribute minimally to these core benefits.

By realigning efforts with the original ROI drivers, teams can refocus their energy on the features that deliver the greatest value. This realignment helps get projects back on track to deliver the projected benefits that justified the investment in the first place.

A Simple Tool for ROI-Centric Analysis​

To help BAs maintain this crucial focus on ROI, I've developed a prototype calculator tool. While still in its early stages (version 0.1), it provides a structured approach to quantifying and tracking the financial impact of business solutions.

The tool calculates time savings, error reduction benefits, efficiency improvements, and projected revenue increases against implementation costs. More importantly, it creates a shared understanding of value that keeps stakeholders aligned throughout the project lifecycle.

Links​

• Github Repo
• Try it out yourself!

Beyond the Tool: An Algorithm for ROI-Focused Business Analysis​

While tools can support ROI analysis, the real challenge is establishing the right mindset and process. Here's a practical algorithm for BAs to integrate ROI thinking throughout the project lifecycle:

1. Establish Baseline Metrics (Pre-Project Phase)​

• Key Question: "What specific pain points cost the business money today?"
• Timing: Before requirements gathering begins
• Action: Document specific metrics (time spent, error rates, lost opportunities) in their current state
• Output: Quantified baseline measurements with financial values attached

2. Link Requirements to Value Drivers (Initiation Phase)​

• Key Question: "How does each requirement directly contribute to our ROI targets?"
• Timing: During requirements workshops and prioritization sessions
• Action: Score each requirement based on its contribution to identified value drivers
• Output: Requirements prioritized by ROI impact, not just technical complexity or stakeholder influence

3. Re-evaluate ROI at Transition Points (Throughout Implementation)​

• Key Question: "Are we still on track to deliver the financial benefits we promised?"
• Timing: At the end of each sprint/phase and before any major scope changes
• Action: Recalculate projected ROI based on current project trajectory
• Output: Updated ROI forecast with recommendations for course correction if needed

4. Validate ROI Assumptions with Real Data (Implementation Phase)​

• Key Question: "What early evidence confirms or challenges our ROI assumptions?"
• Timing: As soon as any part of the solution is used, even in a limited capacity
• Action: Gather actual performance metrics from early adopters or pilot implementations
• Output: Validated ROI projections based on real-world usage, not just estimates

5. Document Realized Benefits (Post-Implementation)​

• Key Question: "What actual value have we delivered against our promise?"
• Timing: 30, 90, and 180 days post-implementation
• Action: Measure actual metrics against baseline and projected improvements
• Output: Benefits realization report with lessons learned for future projects

Walking the Path with Your Customer​

The most successful BAs don't just calculate ROI – they make it a living part of the conversation with stakeholders. They understand that ROI isn't just about numbers; it's about accountability, alignment, and mutual success.

Every status meeting should reinforce the connection between current activities and ultimate business value. When stakeholders request changes, the discussion should naturally flow to ROI impact. When project challenges arise, decisions should be guided by what best preserves the core value proposition.

This approach transforms the BA from a requirements collector into a true value guardian – someone who walks alongside the customer throughout the journey, keeping everyone focused on the prize.

Conclusion: Make ROI Your Constant Companion​

Projects fail not because teams can't execute but because they lose sight of why they're executing in the first place. By making ROI a regular part of your BA practice – whether through tools like our prototype calculator or through disciplined application of the algorithm outlined above – you establish a clear line of sight between daily activities and business value.

Remember: The most dangerous words in any project are "we'll worry about ROI later." Later never comes. Make ROI your constant companion, and you'll dramatically increase your chances of delivering solutions that truly matter.

Have you experienced projects that lost sight of their ROI? What techniques do you use to keep stakeholders focused on value?

EARS and Functional thinking is a Perfect Match for Requirements Engineering.

I first stumbled upon EARS (Easy Approach to Requirements Syntax) when trying to solve communication issues between business stakeholders and our development team. My experience with requirements had been a bit like my first ZX Spectrum 48k Phone Directory - technically correct but practically unusable. The business folks would write requirements that seemed clear to them, but developers would interpret them entirely differently, leading to a situation where we were constantly rebuilding features.

From Functional Programming to Better Requirements​

"Functional programmers prefer data to calculations and prefer calculations to actions."

This principle from "Grokking Simplicity" struck me as fundamentally aligned with what makes EARS so effective. In both approaches, there's a clear separation of concerns and a focus on structure that makes complex systems more manageable.

Just as I adapted Robustness Diagrams by extending the controller symbol with letters 'e' for events and 'f' for functions, EARS provides templates that clearly separate different types of requirements:

1. Ubiquitous Requirements (pure data) - For stuff that's always true

   • Template: The [ system ][ imperative ] [ system response ].
   • My example: The Data Entry Form shall display validation errors in red text.

2. State-Driven Requirements (data with context) - For when the system is in a specific state

   • Template: While [ pre-condition(s) ], the \ [ system ] \ [ imperative ] \ [ system response ].
   • My example: While in Edit Mode, the Data Entry Form shall highlight changeable fields with a blue border.

3. Event-Driven Requirements (calculations triggered by events) - For when something specific happens

   • Template: When [ trigger ], the \ [ system ] \ [ imperative ] \ [ system response ].
   • My example: When the user clicks Submit, the Data Entry Form shall validate all mandatory fields.

4. Optional Feature Requirements (conditional functionality) - For those extra bells and whistles

   • Template: Where [ feature is included ], the \ [ system ] [ imperative ] \ [ system response ].
   • My example: Where voice input is enabled, the Data Entry Forxsm shall display a microphone icon next to text fields. xs

5. Unwanted Behavior Requirements (error handling) - For when things go wrong

   • Template: If [ trigger ], then the \ [ system ] \ [ imperative ] [ system response ].
   • My example: If the server connection is lost, then the Data Entry Form shall automatically save data locally.

The Shared Philosophy​

What connects my work with Robustness Diagrams, Grokking Simplicity's functional approach, and EARS is the common philosophy of classification and separation:

• In Functional Programming: We separate data, calculations, and actions
• In Robustness Diagrams: I extended notation to clearly indicate events ('e'), functions ('f'), and listeners ('l')
• In EARS: We classify requirements as ubiquitous, state-driven, event-driven, optional, or handling unwanted behavior

Each of these approaches helps solve the same problem - making complex systems more understandable by imposing structure that maps to how humans think about problems.

Practical Application: A Password Visibility Toggle​

Let's take my simple password visibility toggle example and show how it maps across all three approaches:

User Story​

As a user, I want to be able to toggle the visibility of my password so that I can see what I am typing.

Functional Breakdown (Grokking Simplicity style)​

• Data: Password field, toggle checkbox state
• Calculations: Determine field type based on checkbox state
• Actions: Handle change events, update the DOM

EARS Requirement​

When the show-password checkbox is clicked, the system shall change the password field type to match the checkbox state (text when checked, password when unchecked).

Robustness Diagram with My Extensions​

My diagram would show:

• A boundary object (password input) with listener ('l')
• A controller object with event ('e') for change handling
• A controller object with function ('f') for toggling password visibility
• Connections showing the flow between these elements

Code Implementation​

// Variables (Data)
const toggle = document.querySelector('#show-password');
const password = document.querySelector('#password');

// Functions (Calculations)
function togglePassword (checkbox, field) {
  field.type = checkbox.checked ? 'text' : 'password';
}

function handleChange () {
  togglePassword(this, password);
}

// Event Listeners (Actions)
toggle.addEventListener('change', handleChange);

Why This Combined Approach Works​

My first ZX Spectrum program failed because it didn't separate concerns properly - there was no clear distinction between data storage, calculations, and user actions. The entire program was a monolithic block that only worked in the most ideal conditions.

By combining the principles from functional programming with the structured templates of EARS and enhanced visual notation of Robustness Diagrams, we create a comprehensive approach to requirements engineering that addresses the entire development lifecycle:

1. Requirements gathering: EARS templates ensure clarity and completeness
2. System design: Robustness Diagrams with my extensions provide visual representation
3. Implementation: Functional programming principles guide clean code structure

Conclusion​

The connection between EARS, functional programming as taught in Grokking Simplicity, and my adapted Robustness Diagrams isn't coincidental - they all stem from the same need to bring structure to complexity.

As my childhood experience with the Phone Directory program taught me, technical correctness doesn't guarantee usability. What matters is a system that maps to how humans actually think and work. That's exactly what these combined approaches provide - a way to translate human needs into structured requirements, clear designs, and clean implementations.

If you're struggling with the gap between business requirements and technical implementation, try this combined approach. It might just help you avoid creating your own version of my childhood Phone Directory program - technically impressive but practically unused.

Working as a technical consultant across various organizations, I've observed patterns in how companies scale and specialize their roles. These patterns remarkably mirror concepts from evolutionary biology and systems theory, which I discovered while researching organizational design methodologies.

Breaking Down Organizational Evolution​

Just as we can break down software into Actions, Calculations, and Data, we can classify organizational evolution into three key aspects:

1. Structural Evolution
2. Functional Specialization
3. System Integration

Let me share how I've adapted these concepts in practice.

The Single-Function Phase​

In early-stage organizations, like in primitive organisms, we see a pattern I call the "single-function phase." Here's what it looks like in practice:

Early-Stage Organization
├── Generalist Roles
│   ├── Sales/Marketing
│   ├── Product Development
│   └── Operations
└── No Clear Specialization

During this phase, team members handle multiple functions, similar to how single-celled organisms perform all life functions within one cell.

Specialization Patterns​

Through my work with scaling companies, I've observed that specialization typically follows this pattern:

Specialized Organization
├── Core Functions
│   ├── Sales
│   │   ├── Field Sales
│   │   └── Inside Sales
│   ├── Engineering
│   │   ├── Frontend
│   │   └── Backend
│   └── Operations
│       ├── Customer Success
│       └── Support
└── Specialized Units

This structure emerges naturally when:

• Team size exceeds 25 members
• Product complexity increases
• Customer needs diversify

View the code examples and diagrams on my GitHub: github.com/izzi-ink/scaling

Example: BA Role Evolution​

Let me illustrate this with a real-world example from my experience as a Business Analyst. Here's how the BA role typically evolves:

Initial State:

BA Role (Generalist)
├── Requirements Gathering
├── Process Modeling
├── UI Design
└── Testing

Evolved State:

Specialized BA Roles
├── Technical BA
│   ├── System Requirements
│   └── Technical Documentation
├── Process BA
│   ├── Business Process Analysis
│   └── Stakeholder Management
└── UI/UX BA
    ├── Interface Design
    └── User Research

Systems Integration​

The challenge isn't just in creating specialized roles but in maintaining effective integration. I've found this template useful for documenting role interactions:

Cross-Functional Process
├── Input
│   ├── Source Role
│   └── Data/Requirements
├── Process
│   ├── Primary Role
│   └── Supporting Roles
└── Output
    ├── Deliverable
    └── Stakeholders

Practical Application​

Here's a simple checklist I use when advising organizations on role specialization:

1. Monitor these triggers:

   • Team size exceeding capacity
   • Quality issues
   • Delivery delays
   • Communication overhead

2. Document current state:

   • Role responsibilities
   • Process flows
   • Communication patterns

3. Plan transition:

   • Identify specialization needs
   • Define new roles
   • Create integration points

Learning From Nature​

The fascinating part about this approach is how it mirrors natural evolution. Consider these parallels:

Biological Evolution      |  Organizational Evolution
-------------------------|-------------------------
Single-cell organism     |  Startup (generalist roles)
Cell specialization      |  Role specialization
Organ systems            |  Departments/Teams
Nervous system          |  Communication channels

Implementation Notes​

When implementing this approach, I've found these principles crucial:

1. Start with clear documentation of current processes
2. Identify natural breaking points for specialization
3. Maintain strong integration mechanisms
4. Monitor system health through regular feedback

Interactive Visualization Tool​

I've developed an interactive visualization tool that brings these organizational evolution concepts to life. This tool allows you to:

• Explore the progression from startup to enterprise across all three dimensions
• Adjust organizational attributes and see their impact in real-time
• Receive automated analysis of potential growing pains and integration challenges
• Compare different organizational configurations side-by-side

This visualization can help leadership teams identify where their organization currently sits in its evolutionary journey and anticipate the changes needed for healthy scaling.

Try the live Organizational Evolution Matrix!

Credits and Further Reading​

This approach draws from several key sources:

• Systems Theory in Organizational Design
• Evolutionary Biology Principles
• Modern Organizational Design Frameworks

Tags​

#organizational-design #systems-thinking #role-specialization #business-analysis #evolution

Note: This post reflects my personal experience and adaptation of these concepts. Your mileage may vary based on your organizational context.

Lessons Learned Building a Logistics Data Integration Platform

By Ilze Koning, Senior Business Analyst

The Challenge We Faced​

Some time ago, I led a business analysis team tasked with transforming a logistics company's fragmented data landscape into an integrated analytics powerhouse. The company was struggling with disconnected systems, manual reporting processes, and an inability to make data-driven decisions about their delivery operations.

Sound familiar? If you're in logistics or supply chain management, you probably understand the pain of trying to connect fleet management, warehouse operations, and customer feedback into a cohesive picture.

The Harsh Reality We Discovered​

When we first engaged with the client, we found a situation that was even more challenging than anticipated:

• Three completely isolated systems with different data models
• Operations teams making decisions based on day-old spreadsheets
• Customer complaints that couldn't be traced back to specific delivery issues
• Fleet managers with no visibility into warehouse constraints
• Executive leadership flying blind on key performance indicators

As one operations manager put it: "We're drowning in data but starving for insights."

Our Approach: Start with the End in Mind​

Rather than jumping straight into technical solutions, we took a step back and asked a fundamental question: What decisions need to be made, and what data would make those decisions better?

We needed to understand the critical data elements from each system:

This analysis led us to identify four categories of metrics that mattered most:

1. Operational metrics like on-time delivery rates and route efficiency
2. Warehouse metrics including order processing time and inventory accuracy
3. Customer-centric metrics such as satisfaction scores and issue resolution times
4. Financial metrics tracking cost and revenue per delivery

Only after defining these key metrics did we begin mapping the data landscape and designing the integration architecture.

The Data Model: Our Foundation for Success​

The most critical decision we made was investing time in developing a robust conceptual data model that connected entities across all systems. This wasn't just a technical exercise—it was about creating a shared language between business and IT.

We identified key entity relationships like these:

This model became our North Star throughout the project. Whenever we faced integration challenges, we returned to this model to guide our decisions.

The Integration Architecture: Building for Scale and Flexibility​

We quickly realized that point-to-point integrations wouldn't scale, so we adopted a data warehouse approach with a structured ETL (Extract, Transform, Load) process.

Here's the architecture we implemented:

┌─────────────────┐   ┌─────────────────┐   ┌─────────────────┐
│ Fleet Management│   │   Warehouse     │   │    Customer     │
│     System      │   │   Management    │   │    Feedback     │
└────────┬────────┘   └────────┬────────┘   └────────┬────────┘
         │                     │                     │
         ▼                     ▼                     ▼
┌────────────────────────────────────────────────────────────┐
│                      ETL Processes                          │
├────────────────────────────────────────────────────────────┤
│  ┌───────────────┐   ┌───────────────┐   ┌───────────────┐ │
│  │    Extract    │──►│   Transform   │──►│     Load      │ │
│  └───────────────┘   └───────────────┘   └───────────────┘ │
└────────────────────────────────┬───────────────────────────┘
                                 │
                                 ▼
┌────────────────────────────────────────────────────────────┐
│                     Data Warehouse                          │
├────────────────────────────────────────────────────────────┤
│  ┌───────────────┐   ┌───────────────┐   ┌───────────────┐ │
│  │    Staging    │──►│   Dimension   │◄──┤     Fact      │ │
│  │     Area      │   │    Tables     │   │    Tables     │ │
│  └───────────────┘   └───────────────┘   └───────────────┘ │
└────────────────────────────────┬───────────────────────────┘
                                 │
                                 ▼
┌────────────────────────────────────────────────────────────┐
│                    Reporting & Analytics                    │
├────────────────────────────────────────────────────────────┤
│  ┌───────────────┐   ┌───────────────┐   ┌───────────────┐ │
│  │   Executive   │   │  Operational  │   │   Predictive  │ │
│  │   Dashboard   │   │   Reporting   │   │   Analytics   │ │
│  └───────────────┘   └───────────────┘   └───────────────┘ │
└────────────────────────────────────────────────────────────┘

This approach gave us:

• A single source of truth for reporting
• The ability to handle different data refresh rates across systems
• A platform for both operational dashboards and strategic analytics
• Flexibility to add new data sources in the future

One lesson learned the hard way: data quality issues will always be worse than you expect. We ended up dedicating nearly 40% of our development time to data cleansing and standardization—far more than initially planned.

Phased Implementation: Delivering Value at Each Stage​

Perhaps the most important strategic decision was breaking the implementation into three phases:

Phase 1: Foundation

• Set up data warehouse infrastructure
• Implement core ETL processes for order and delivery data
• Create basic operational dashboards

Phase 2: Enhancement

• Integrate customer feedback data
• Implement advanced metrics and KPIs
• Develop executive dashboards

Phase 3: Optimization

• Implement predictive analytics models
• Optimize query performance
• Develop self-service reporting capabilities

This approach allowed us to:

• Deliver business value early and often
• Learn and adjust our approach based on user feedback
• Manage stakeholder expectations effectively
• Build credibility with quick wins before tackling more complex requirements

The Dashboard: Where Data Becomes Decisions​

The operational dashboard we created became the nerve center of the logistics operation. By bringing together on-time delivery rates, customer satisfaction trends, delivery issue breakdowns, and route efficiency comparisons, we enabled:

• Operations managers to identify and resolve bottlenecks in real-time
• Customer service to proactively address delivery issues
• Fleet managers to optimize routes based on historical performance
• Executives to track strategic initiatives against measurable KPIs

Lessons That Will Serve You Well​

If you're embarking on a similar data integration journey, here are the key lessons we learned:

1. Start with the business decisions, not the data​

Understanding what decisions need to be made will guide everything else. We spent two full weeks interviewing stakeholders about their decision-making processes before writing a single line of code.

2. Invest in your data model​

A well-designed data model is the foundation of successful integration. Don't rush this step—it's much harder to change later.

3. Expect data quality issues​

No matter how clean the source systems appear, you'll encounter unexpected data quality challenges. Budget time accordingly.

4. Phase your implementation​

Break the project into manageable chunks that deliver value at each stage. This builds momentum and allows for course correction.

5. Build for the business, not for technical elegance​

The most sophisticated architecture is worthless if it doesn't solve business problems. Always tie your work back to business outcomes.

6. Data integration is change management​

The technical aspects of integration are challenging, but the human aspects are often harder. Invest time in bringing stakeholders along on the journey.

The Results: Transformational Impact​

After completing the implementation, we measured success against these criteria:

• 95% reduction in manual reporting effort
• 100% of key metrics available in near real-time
• Improved data-driven decision making for route optimization
• Measurable improvement in on-time delivery rates
• Increased customer satisfaction scores

The logistics company saw significant improvements across all these areas, with meaningful cost savings through optimized routing.

Perhaps most importantly, they developed a data-driven culture where decisions at all levels are now based on insights rather than intuition.

Your Turn​

If you're facing similar challenges in your organization, I'd love to hear about your experiences. What data integration challenges keep you up at night? What approaches have worked for you?

Remember, successful data integration isn't just about connecting systems—it's about connecting people to the insights they need to make better decisions.

Ilze Koning is a Senior Business Analyst with expertise in business analysis, solution architecture, and user experience design. She specializes in transforming complex business requirements into actionable specifications and bridging the gap between customer needs and technical implementation.

Are you struggling with data integration challenges in your organization? Connect with me on LinkedIn to continue the conversation.

Functional programmers prefer data to calculations and prefer calculations to actions.

Grokking Simplicity is the title of a wonderful book, published in 2021, to teach an approach to software development, specifically functional programming. I discovered the book while attending Chris Ferdinandi's Vanilla JS Academy, and soon found myself moving away from my Java trained OOP approach and learning to think like a functional programmer. This book teaches an approach to problem decomposition, and introduced me to classify code and problems into:

• Actions
• Calculations and
• Data

Adapting what I learnt​

Because I am a Business Analyst, who specialise in UI Systems Design, I am tasked with documenting wire-frames from low-fidelity to high-fidelity, and naturally I use UML diagrams to document my system design and requirements. But remote work can make it difficult to collaborate with developers therefore I am always looking for ways to improve the communication between the business and the development team.

The more I used the Grokking Simplicity methodology, and tasked with doing both the technical and UI design, on a particular project, I chose to use Robustness Diagrams to communicate these designs. Robustness Diagrams are a type of visual modeling technique, normally used in software engineering and system analysis to 'analyze and design the structural and behavioral aspects of a system'. They are primarily used within the context of the ICONIX process, a streamlined, use case-driven software development methodology.

I adapted hacked Robustness Diagram notation by extending the symbol for the controller object to represent either calculations or events (actions). I appended the controller symbol with the letter ‘e’ for event and ‘f’ to depict make a function. The letter 'l' is used to extend the symbol used for a boundary object, or user interface. This indicates that an event listener is coupled to a particular UI component, in our example below, a checkbox.

Example​

I chose a very simple function that toggles the visibility of a password field, as example to illustrate how this notation can be used to document a UI design.

User Story​

As a user, I want to be able to toggle the visibility of my password so that I can see what I am typing.

Wire-frame​

Robustness Diagram​

It’s a relatively simple diagram and once the engineers can read and interpret it, it serves a dual purpose such that it teaches sound programming principles as well as clear specifications.

More experienced developers and architects should be able to document and adopt this notation and generate designs that originate from UI mocks.

I have used this notation to document the entire system design of a fairly complex Enterprise Application. This was done through light-weight workshops with the development team, and the diagrams were used to document the design and requirements and design together.

It is important to note, that one typically would not document UIs at this level of detail in a Robustness Diagrams, but I found it to be a very effective way to communicate the design and requirements to the development team.

Code​

//
// Variables
//

// Get the password toggle
const toggle = document.querySelector('#show-password');

// Get the password field
const password = document.querySelector('#password');


//
// Functions
//

/**
 * Toggle the visibility of a password field
 * based on a checkbox's current state
 * 
 * @param {HTMLInputElement} checkbox The checkbox
 * @param {HTMLInputElement} field The password field
 */
function togglePassword (checkbox, field) {
  field.type = checkbox.checked ? 'text' : 'password';
}

/**
 * Handle change events
 */
function handleChange () {
  togglePassword(this, password);
}


//
// Inits & Event Listeners
//

// Handle change events
toggle.addEventListener('change', handleChange);

View the code in action on Code Pen

Credits​

• Grokking Simplicity
• Robustness Diagram
• Vanilla JS Academy
• Kieran Barker
• Chris Ferdinandi

I first started programming when I was 10 or 11, but I have always been a writer. I love code and I love words. I envy mathematicians their nimble minds and wish I was more Spock than Kirk.

I draw and doodle to make sense of the complex. I simplify and re-order, I design.

As a Business Analyst, this natural inclination to visualize and decompose complexity has become my superpower - allowing me to bridge gaps between technical teams, business stakeholders, and end-users while translating intricate problems into clear, actionable solutions.

This is a collection of blog posts, inspired by my collection of journals and notebooks on code and craft. Sometimes it is specific to a project, sometimes it is a general thought. I hope you enjoy it. From handwritten scribbles, to scans to code. Enmeshed with graphite and ink, with code and words.

From doodles, to code, to product.

I first started programming when I was 10 or 11. The first program I designed and wrote was a Phone Directory, for the ZX Spectrum 48k. The uptake to use this in my home was very low. I doubt my dad even knew it existed. I employed a data capturer too, but the ratio of data he captured to the number of free meals he enjoyed made this a business failure from day 1. When I launched the Phone Directory system it was clear, how it was largely impractical, my mom found it easier to use the "Flip-open A-Z Phone Directory". The "data capturer" found it easier to eat, than capture data past the letter G.