Tag: ai

The Modern Financial & General Analyst’s Core Skill Set

.Reading time 37 minutes.

Excel, SQL Server, Power BI — With AI Doing the Heavy Lifting

A collaboration between Lewis McLain & AI

Introduction: The Skill That Now Matters Most

The most important analytical skill today is no longer memorizing syntax, mastering a single tool, or becoming a narrow specialist.

The must-have skill is knowing how to direct intelligence.

In practice, that means combining:

  • Excel for thinking, modeling, and scenarios
  • SQL Server for structure, scale, and truth
  • Power BI for communication and decision-making
  • AI as the teacher, coder, documenter, and debugger

This is not about replacing people with AI.
It is about finally separating what humans are best at from what machines are best at—and letting each do their job.

1. Stop Explaining. Start Supplying.

One of the biggest mistakes people make with AI is trying to explain complex systems to it in conversation.

That is backward.

The Better Approach

If your organization has:

  • an 80-page budget manual
  • a cost allocation policy
  • a grant compliance guide
  • a financial procedures handbook
  • even the City Charter

Do not summarize it for AI.
Give AI the document.

Then say:

“Read this entire manual. Summarize it back to me in 3–5 pages so I can confirm your understanding.”

This is where AI excels.

AI is extraordinarily good at:

  • absorbing long, dense documents
  • identifying structure and hierarchy
  • extracting rules, exceptions, and dependencies
  • restating complex material in plain language

Once AI demonstrates understanding, you can say:

“Assume this manual governs how we budget. Based on that understanding, design a new feature that…”

From that point on, AI is no longer guessing.
It is operating within your rules.

This is the fundamental shift:

  • Humans provide authoritative context
  • AI provides execution, extension, and suggested next steps

You will see this principle repeated throughout this post and the appendices—because everything else builds on it.

2. The Stack Still Matters (But for Different Reasons Now)

AI does not eliminate the need for Excel, SQL Server, or Power BI.
It makes them far more powerful—and far more accessible.

Excel — The Thinking and Scenario Environment

Excel remains the fastest way to:

  • test ideas
  • explore “what if” questions
  • model scenarios
  • communicate assumptions clearly

What has changed is not Excel—it is the burden placed on the human.

You no longer need to:

  • remember every formula
  • write VBA macros from scratch
  • search forums for error messages

AI already understands:

  • Excel formulas
  • Power Query
  • VBA (Visual Basic for Applications, Excel’s automation language)

You can say:

“Write an Excel model with inputs, calculations, and outputs for this scenario.”

AI will:

  • generate the formulas
  • structure the workbook cleanly
  • comment the logic
  • explain how it works

If something breaks:

  • AI reads the error message
  • explains why it occurred
  • fixes the formula or macro

Excel becomes what it was always meant to be:
a thinking space, not a memory test.

SQL Server — The System of Record and Truth

SQL Server is where analysis becomes reliable, repeatable, and scalable.

It holds:

  • historical data (millions of records are routine)
  • structured dimensions
  • consistent definitions
  • auditable transformations

Here is the shift AI enables:

You do not need to be a syntax expert.

SQL (Structured Query Language) is something AI already understands deeply.

You can say:

“Create a SQL view that allocates indirect costs by service hours. Include validation queries.”

AI will:

  • write the SQL
  • optimize joins
  • add comments
  • generate test queries
  • flag edge cases
  • produce clear documentation

AI can also interpret SQL Server error messages, explain them in plain English, and rewrite the code correctly.

This removes one of the biggest barriers between finance and data systems.

SQL stops being “IT-only” and becomes a shared analytical language, with AI translating analytical intent into executable code.

Power BI — Where Decisions Happen

Power BI is the communication layer: dashboards, trends, drilldowns, and monitoring.

It relies on DAX (Data Analysis Expressions), the calculation language used by Power BI.

Here is the key reassurance:

AI already understands DAX extremely well.

DAX is:

  • rule-based
  • pattern-driven
  • language-like

This makes it ideal for AI assistance.

You do not need to memorize DAX syntax.
You need to describe what you want.

For example:

“I want year-over-year change, rolling 12-month averages, and per-capita measures that respect slicers.”

AI can:

  • write the measures
  • explain filter context
  • fix common mistakes
  • refactor slow logic
  • document what each measure does

Power BI becomes less about struggling with formulas and more about designing the right questions.

3. AI as the Documentation Engine (Quietly Transformational)

Documentation is where most analytical systems decay.

  • Excel models with no explanation
  • SQL views nobody understands
  • Macros written years ago by someone who left
  • Reports that “work” but cannot be trusted

AI changes this completely.

SQL Documentation

AI can:

  • add inline comments to SQL queries
  • write plain-English descriptions of each view
  • explain table relationships
  • generate data dictionaries automatically

You can say:

“Document this SQL view so a new analyst understands it.”

And receive:

  • a clear narrative
  • assumptions spelled out
  • warnings about common mistakes

Excel & Macro Documentation

AI can:

  • explain what each worksheet does
  • document VBA macros line-by-line
  • generate user instructions
  • rewrite messy macros into cleaner, documented code

Recently, I had a powerful but stodgy Excel workbook with over 1.4 million formulas.
AI read the entire file, explained the internal logic accurately, and rewrote the system in SQL with a few hundred well-documented lines—producing identical results.

Documentation stops being an afterthought.
It becomes cheap, fast, and automatic.

4. AI as Debugger and Interpreter

One of AI’s most underrated strengths is error interpretation.

AI excels at:

  • reading cryptic error messages
  • identifying likely causes
  • suggesting fixes
  • explaining failures in plain language

You can copy-paste an error message without comment and say:

“Explain this error and fix the code.”

This applies to:

  • Excel formulas
  • VBA macros
  • SQL queries
  • Power BI refresh errors
  • DAX logic problems

Hours of frustration collapse into minutes.

5. What Humans Still Must Do (And Always Will)

AI is powerful—but it is not responsible for outcomes.

Humans must still:

  • define what words mean (“cost,” “revenue,” “allocation”)
  • set policy boundaries
  • decide what is reasonable
  • validate results
  • interpret implications
  • make decisions

The human role becomes:

  • director
  • creator
  • editor
  • judge
  • translator

AI does not replace judgment.
It amplifies disciplined judgment.

6. Why This Matters Across the Organization

For Managers

  • Faster insight
  • Clearer explanations
  • Fewer “mystery numbers”
  • Greater confidence in decisions

For Finance Professionals

  • Less time fighting tools
  • More time on policy, tradeoffs, and risk
  • Stronger documentation and audit readiness

For IT Professionals

  • Cleaner specifications
  • Fewer misunderstandings
  • Better separation of logic and presentation
  • More maintainable systems

This is not a turf shift.
It is a clarity shift.

7. The Real Skill Shift

The modern analyst does not need to:

  • memorize every function
  • master every syntax rule
  • become a full-time programmer

The modern analyst must:

  • ask clear questions
  • supply authoritative context
  • define constraints
  • validate outputs
  • communicate meaning

AI handles the rest.

Conclusion: Intelligence, Directed

Excel, SQL Server, and Power BI remain the backbone of serious analysis—not because they are trendy, but because they mirror how thinking, systems, and decisions actually work.

AI changes how we use them:

  • it reads the manuals
  • writes the code
  • documents the logic
  • fixes the errors
  • explains the results

Humans provide direction.
AI provides execution.

Those who learn to work this way will not just be more efficient—they will be more credible, more influential, and more future-proof.

Appendix A

A Practical AI Prompt Library for Finance, Government, and Analytical Professionals

This appendix is meant to be used, not admired.

These prompts reflect how professionals actually work: with rules, constraints, audits, deadlines, and political consequences.

You are not asking AI to “be smart.”
You are directing intelligence.

A.1 Foundational “Read & Confirm” Prompts (Critical)

Use these first. Always.

Prompt

“Read the attached document in full. Treat it as authoritative. Summarize the structure, rules, definitions, exceptions, and dependencies. Do not add assumptions. I will confirm your understanding.”

Why this matters

  • Eliminates guessing
  • Aligns AI with your institutional reality
  • Prevents hallucinated rules

A.2 Excel Modeling Prompts

Scenario Model

“Design an Excel workbook with Inputs, Calculations, and Outputs tabs. Use named ranges. Include scenario toggles and validation checks that confirm totals tie out.”

Formula Debugging

“This Excel formula returns an error. Explain why, fix it, and rewrite it in a clearer form.”

Macro Creation

“Write a VBA macro that refreshes all data connections, recalculates, logs a timestamp, and alerts the user if validation checks fail. Comment every section.”

Documentation

“Explain this Excel workbook as if onboarding a new analyst. Describe what each worksheet does and how inputs flow to outputs.”

A.3 SQL Server Prompts

View Creation

“Create a SQL view that produces monthly totals by City and Department. Grain must be City-Month-Department. Exclude void transactions. Add comments and validation queries.”

Performance Refactor

“Refactor this SQL query for performance without changing results. Explain what you changed and why.”

Error Interpretation

“Here is a SQL Server error message. Explain it in plain English and fix the query.”

Documentation

“Document this SQL schema so a new analyst understands table purpose, keys, and relationships.”

A.4 Power BI / DAX Prompts

(DAX = Data Analysis Expressions, the calculation language used by Power BI — a language AI already understands deeply.)

Measure Creation

“Create DAX measures for Total Cost, Cost per Capita, Year-over-Year Change, and Rolling 12-Month Average. Explain filter context for each.”

Debugging

“This DAX measure returns incorrect results when filtered. Explain why and correct it.”

Model Review

“Review this Power BI data model and identify risks: ambiguous relationships, missing dimensions, or inconsistent grain.”

A.5 Validation & Audit Prompts

Validation Suite

“Create validation queries that confirm totals tie to source systems and flag variances greater than 0.1%.”

Audit Explanation

“Explain how this model produces its final numbers in language suitable for auditors.”

A.6 Training & Handoff Prompts

Training Guide

“Create a training guide for an internal analyst explaining how to refresh, validate, and extend this model safely.”

Institutional Memory

“Write a ‘how this system thinks’ document explaining design philosophy, assumptions, and known limitations.”

Key Principle

Good prompts don’t ask for brilliance.
They provide clarity.

Appendix B

How to Validate AI-Generated Analysis Without Becoming Paranoid

AI does not eliminate validation.
It raises the bar for it.

The danger is not trusting AI too much.
The danger is trusting anything without discipline.

B.1 The Rule of Independent Confirmation

Every important number must:

  • tie to a known source, or
  • be independently recomputable

If it cannot be independently confirmed, it is not final.

B.2 Validation Layers (Use All of Them)

Layer 1 — Structural Validation

  • Correct grain (monthly vs annual)
  • No duplicate keys
  • Expected row counts

Layer 2 — Arithmetic Validation

  • Subtotals equal totals
  • Allocations sum to 100%
  • No unexplained residuals

Layer 3 — Reconciliation

  • Ties to GL, ACFR, payroll, ridership, etc.
  • Same totals across tools (Excel, SQL, Power BI)

Layer 4 — Reasonableness Tests

  • Per-capita values plausible?
  • Sudden jumps explainable?
  • Trends consistent with known events?

AI can help generate all four layers, but humans must decide what “reasonable” means.

B.3 The “Explain It Back” Test

One of the strongest validation techniques:

“Explain how this number was produced step by step.”

If the explanation:

  • is coherent
  • references known rules
  • matches expectations

You’re on solid ground.

If not, stop.

B.4 Change Detection

Always compare:

  • this month vs last month
  • current version vs prior version

Ask AI:

“Identify and explain every material change between these two outputs.”

This catches silent errors early.

B.5 What Validation Is Not

Validation is not:

  • blind trust
  • endless skepticism
  • redoing everything manually

Validation is structured confidence-building.

B.6 Why AI Helps Validation (Instead of Weakening It)

AI:

  • generates test queries quickly
  • explains failures clearly
  • documents expected behavior
  • flags anomalies humans may miss

AI doesn’t reduce rigor.
It makes rigor affordable.

Appendix C

What Managers Should Ask For — and What They Should Stop Asking For

This appendix is for leaders.

Good management questions produce good systems.
Bad questions produce busywork.

C.1 What Managers Should Ask For

“Show me the assumptions.”

If assumptions aren’t visible, the output isn’t trustworthy.

“How does this tie to official numbers?”

Every serious analysis must reconcile to something authoritative.

“What would change this conclusion?”

Good models reveal sensitivities, not just answers.

“How will this update next month?”

If refresh is manual or unclear, the model is fragile.

“Who can maintain this if you’re gone?”

This forces documentation and institutional ownership.

C.2 What Managers Should Stop Asking For

❌ “Just give me the number.”

Numbers without context are liabilities.

❌ “Can you do this quickly?”

Speed without clarity creates rework and mistrust.

❌ “Why can’t this be done in Excel?”

Excel is powerful—but it is not a system of record.

❌ “Can’t AI just do this automatically?”

AI accelerates work within rules.
It does not invent governance.

C.3 The Best Managerial Question of All

“How confident should I be in this, and why?”

That question invites:

  • validation
  • explanation
  • humility
  • trust

It turns analysis into leadership support instead of technical theater.

Appendix D

Job Description: The Modern Analyst (0–3 Years Experience)

This job description reflects what an effective, durable analyst looks like today — not a unicorn, not a senior architect, and not a narrow technician.

This role assumes the analyst will work in an environment that uses Excel, SQL Server, Power BI, and AI tools as part of normal operations.

Position Title

Data / Financial / Business Analyst
(Title may vary by organization)

Experience Level

  • Entry-level to 3 years of professional experience
  • Recent graduates encouraged to apply

Role Purpose

The Modern Analyst supports decision-making by:

  • transforming raw data into reliable information,
  • building repeatable analytical workflows,
  • documenting logic clearly,
  • and communicating results in ways leaders can trust.

This role is not about memorizing syntax or becoming a single-tool expert.
It is about directing analytical tools — including AI — with clarity, discipline, and judgment.

Core Responsibilities

1. Analytical Thinking & Problem Framing

  • Translate business questions into analytical tasks
  • Clarify assumptions, definitions, and scope before analysis begins
  • Identify what data is needed and where it comes from
  • Ask follow-up questions when requirements are ambiguous

2. Excel Modeling & Scenario Analysis

  • Build and maintain Excel models using:
    • structured layouts (inputs → calculations → outputs)
    • clear formulas and named ranges
    • validation checks and reconciliation totals
  • Use Excel for:
    • exploratory analysis
    • scenario testing
    • sensitivity analysis
  • Leverage AI tools to:
    • generate formulas
    • debug errors
    • document models

3. SQL Server Data Work

  • Query and analyze data stored in SQL Server
  • Create and maintain:
    • views
    • aggregation queries
    • validation checks
  • Understand concepts such as:
    • joins
    • grouping
    • grain (row-level meaning)
  • Use AI assistance to:
    • write SQL code
    • optimize queries
    • interpret error messages
    • document logic clearly

(Deep database administration is not required.)

4. Power BI Reporting & Analysis

  • Build and maintain Power BI reports and dashboards
  • Use existing semantic models and measures
  • Create new measures using DAX (Data Analysis Expressions) with AI guidance
  • Ensure reports:
    • align with defined metrics
    • update reliably
    • are understandable to non-technical users

5. Documentation & Knowledge Transfer

  • Document:
    • Excel models
    • SQL queries
    • Power BI reports
  • Write explanations that allow another analyst to:
    • understand the logic
    • reproduce results
    • maintain the system
  • Use AI to accelerate documentation while ensuring accuracy

6. Validation & Quality Control

  • Reconcile outputs to authoritative sources
  • Identify anomalies and unexplained changes
  • Use validation checks rather than assumptions
  • Explain confidence levels and limitations clearly

7. Collaboration & Communication

  • Work with:
    • finance
    • operations
    • IT
    • management
  • Present findings clearly in plain language
  • Respond constructively to questions and challenges
  • Accept feedback and revise analysis as needed

Required Skills & Competencies

Analytical & Professional Skills

  • Curiosity and skepticism
  • Attention to detail
  • Comfort asking clarifying questions
  • Willingness to document work
  • Ability to explain complex ideas simply

Technical Skills (Baseline)

  • Excel (intermediate level or higher)
  • Basic SQL (SELECT, JOIN, GROUP BY)
  • Familiarity with Power BI or similar BI tools
  • Comfort using AI tools for coding, explanation, and documentation

Candidates are not expected to know everything on day one.

Preferred Qualifications

  • Degree in:
    • Finance
    • Accounting
    • Economics
    • Data Analytics
    • Information Systems
    • Engineering
    • Public Administration
  • Internship or project experience involving data analysis
  • Exposure to:
    • budgeting
    • forecasting
    • cost allocation
    • operational metrics

What Success Looks Like (First 12–18 Months)

A successful analyst in this role will be able to:

  • independently build and explain Excel models
  • write and validate SQL queries with AI assistance
  • maintain Power BI reports without breaking definitions
  • document their work clearly
  • flag issues early rather than hiding uncertainty
  • earn trust by being transparent and disciplined

What This Role Is Not

This role is not:

  • a pure programmer role
  • a dashboard-only role
  • a “press the button” reporting job
  • a role that values speed over accuracy

Why This Role Matters

Organizations increasingly fail not because they lack data, but because:

  • logic is undocumented
  • assumptions are hidden
  • systems are fragile
  • knowledge walks out the door

This role exists to prevent that.

Closing Note to Candidates

You do not need to be an expert in every tool.

You do need to:

  • think clearly,
  • communicate honestly,
  • learn continuously,
  • and use AI responsibly.

If you can do that, the tools will follow.

Appendix E

Interview Questions a Strong Analyst Should Ask

(And Why the Answers Matter)

This appendix is written for candidates — especially early-career analysts — who want to succeed, grow, and contribute meaningfully.

These are not technical questions.
They are questions about whether the environment supports good analytical work.

A thoughtful organization will welcome these questions.
An uncomfortable response is itself an answer.

1. Will I Have Timely Access to the Data I’m Expected to Analyze?

Why this matters

Analysts fail more often from lack of access than lack of ability.

If key datasets (such as utility billing, payroll, permitting, or ridership data) require long approval chains, partial access, or repeated manual requests, analysis stalls. Long delays force analysts to restart work cold, which is inefficient and demoralizing.

A healthy environment has:

  • clear data access rules,
  • predictable turnaround times,
  • and documented data sources.

2. Will I Be Able to Work in Focused Blocks of Time?

Why this matters

Analytical work requires concentration and continuity.

If an analyst’s day is fragmented by:

  • constant meetings,
  • urgent ad-hoc requests,
  • unrelated administrative tasks,

then even talented analysts struggle to make progress. Repeated interruptions over days or weeks force constant re-learning and increase error risk.

Strong teams protect at least some uninterrupted time for deep work.

3. How Often Are Priorities Changed Once Work Has Started?

Why this matters

Changing priorities is normal. Constant resets are not.

Frequent shifts without closure:

  • waste effort,
  • erode confidence,
  • and prevent analysts from seeing work through to completion.

A good environment allows:

  • exploratory work,
  • followed by stabilization,
  • followed by delivery.

Analysts grow fastest when they can complete full analytical cycles.

4. Will I Be Asked to Do Significant Work Outside the Role You’re Hiring Me For?

Why this matters

Early-career analysts often fail because they are overloaded with tasks unrelated to analysis:

  • ad-hoc administrative work,
  • manual data entry,
  • report formatting unrelated to insights,
  • acting as an informal IT support desk.

This dilutes skill development and leads to frustration.

A strong role respects analytical focus while allowing reasonable cross-functional exposure.

5. Where Will This Role Sit Organizationally?

Why this matters

Analysts thrive when they are close to:

  • decision-makers,
  • subject-matter experts,
  • and the business context.

Being housed in IT can be appropriate in some organizations, but analysts often succeed best when:

  • they are embedded in finance, operations, or planning,
  • with strong, cooperative support from IT, not ownership by IT.

Clear role placement reduces confusion about expectations and priorities.

6. What Kind of Support Will I Have from IT?

Why this matters

Analysts do not need IT to do their work for them — but they do need:

  • help with access,
  • guidance on standards,
  • and assistance when systems issues arise.

A healthy environment has:

  • defined IT support pathways,
  • mutual respect between analysts and IT,
  • and shared goals around data quality and security.

Adversarial or unclear relationships slow everyone down.

7. Will I Be Encouraged to Document My Work — and Given Time to Do So?

Why this matters

Documentation is often praised but rarely protected.

If analysts are rewarded only for speed and output, documentation becomes the first casualty. This creates fragile systems and makes handoffs painful.

Strong organizations:

  • value documentation,
  • allow time for it,
  • and recognize it as part of the job, not overhead.

8. How Will Success Be Measured in the First Year?

Why this matters

Vague success criteria create anxiety and misalignment.

A healthy answer includes:

  • skill development,
  • reliability,
  • learning the organization’s data,
  • and increasing independence over time.

Early-career analysts need space to learn without fear of being labeled “slow.”

9. What Happens When Data or Assumptions Are Unclear?

Why this matters

No dataset is perfect.

Analysts succeed when:

  • questions are welcomed,
  • assumptions are discussed openly,
  • and uncertainty is handled professionally.

An environment that discourages questions or punishes transparency leads to quiet errors and loss of trust.

10. Will I Be Allowed — and Encouraged — to Use Modern Tools Responsibly?

Why this matters

Analysts today learn and work using tools like:

  • Excel,
  • SQL,
  • Power BI,
  • and AI-assisted analysis.

If these tools are discouraged, restricted without explanation, or treated with suspicion, analysts are forced into inefficient workflows. In many cases, the latest versions with added features can prove better productivity. Is the organization more than 1-2 years behind in updating at the present time? What are the views of key players about AI?

Strong organizations focus on:

  • governance,
  • validation,
  • and responsible use — not blanket prohibition.

11. How Are Analytical Mistakes Handled?

Why this matters

Mistakes happen — especially while learning.

The question is whether the culture responds with:

  • learning and correction, or
  • blame and fear.

Analysts grow fastest in environments where:

  • mistakes are surfaced early,
  • corrected openly,
  • and used to improve systems.

12. Who Will I Learn From?

Why this matters

Early-career analysts need:

  • examples,
  • feedback,
  • and mentorship.

Even informal guidance matters.

A thoughtful answer shows the organization understands that analysts are developed, not simply hired.

Closing Note to Candidates

These questions are not confrontational.
They are professional.

Organizations that welcome them are more likely to:

  • retain talent,
  • produce reliable analysis,
  • and build durable systems.

If an organization cannot answer these questions clearly, it does not mean it is a bad place — but it may not yet be a good place for an analyst to thrive.

Appendix F

A Necessary Truce: IT Control, Analyst Access, and the Role of Sandboxes

One of the most common — and understandable — tensions in modern organizations sits at the boundary between IT and analytical staff.

It usually sounds like this:

“We can’t let anyone outside IT touch live databases.”

On this point, IT is absolutely right.

Production systems exist to:

  • run payroll,
  • bill customers,
  • issue checks,
  • post transactions,
  • and protect sensitive information.

They must be:

  • stable,
  • secure,
  • auditable,
  • and minimally disturbed.

No serious analyst disputes this.

But here is the equally important follow-up question — one that often goes unspoken:

If analysts cannot access live systems, do they have access to a safe, current analytical environment instead?

Production Is Not the Same Thing as Analysis

The core misunderstanding is not about permission.
It is about purpose.

  • Production systems are built to execute transactions correctly.
  • Analytical systems are built to understand what happened.

These are different jobs, and they should live in different places.

IT departments already understand this distinction in principle. The question is whether it has been implemented in practice.

The Case for Sandboxes and Analytical Mirrors

A well-run organization does not give analysts access to live transactional tables.

Instead, it provides:

  • read-only mirrors
  • overnight refreshes at a minimum
  • restricted, de-identified datasets
  • clearly defined analytical schemas

This is not radical.
It is standard practice in mature organizations.

What a Sandbox Actually Is

A sandbox is:

  • a copy of production data,
  • refreshed on a schedule (often nightly),
  • isolated from operational systems,
  • and safe to explore without risk.

Analysts can:

  • query freely,
  • build models,
  • validate logic,
  • and document findings

…without the possibility of disrupting operations.

A Practical Example: Payroll and Personnel Data

Payroll is often cited as the most sensitive system — and rightly so.

But here is the practical reality:

Most analytical work does not require:

  • Social Security numbers
  • bank account details
  • wage garnishments
  • benefit elections
  • direct deposit instructions

What analysts do need are things like:

  • position counts
  • departments
  • job classifications
  • pay grades
  • hours worked
  • overtime
  • trends over time

A Payroll / Personnel sandbox can be created that:

  • mirrors the real payroll tables,
  • strips or masks protected fields,
  • replaces SSNs with surrogate keys,
  • removes fields irrelevant to analysis,
  • refreshes nightly from production

This allows analysts to answer questions such as:

  • How is staffing changing?
  • Where is overtime increasing?
  • What are vacancy trends?
  • How do personnel costs vary by department or function?

All without exposing sensitive personal data.

This is not a compromise of security.
It is an application of data minimization, a core security principle.

Why This Matters More Than IT Realizes

When analysts lack access to safe, current analytical data, several predictable failures occur:

  • Analysts rely on stale exports
  • Logic is rebuilt repeatedly from scratch
  • Results drift from official numbers
  • Trust erodes between departments
  • Decision-makers get inconsistent answers

Ironically, over-restriction often increases risk, because:

  • people copy data locally,
  • spreadsheets proliferate,
  • and controls disappear entirely.

A well-designed sandbox reduces risk by centralizing access under governance.

What IT Is Right to Insist On

IT is correct to insist on:

  • no write access
  • no direct production access
  • strong role-based security
  • auditing and logging
  • clear ownership of schemas
  • documented refresh processes

None of that is negotiable.

But those safeguards are fully compatible with analyst access — if access is provided in the right environment.

What Analysts Are Reasonably Asking For

Analysts are not asking to:

  • run UPDATE statements on live tables
  • bypass security controls
  • access protected personal data
  • manage infrastructure

They are asking for:

  • timely access to analytical copies of data
  • predictable refresh schedules
  • stable schemas
  • and the ability to do their job without constant resets

That is a governance problem, not a personnel problem.

The Ideal Operating Model

In a healthy organization:

  • IT owns production systems
  • IT builds and governs analytical mirrors
  • Analysts work in sandboxes
  • Finance and operations define meaning
  • Validation ties analysis back to production totals
  • Everyone wins

This model:

  • protects systems,
  • protects data,
  • supports analysis,
  • and builds trust.

Why This Belongs in This Series

Earlier appendices described:

  • the skills of the modern analyst,
  • the questions analysts should ask,
  • and the environments that cause analysts to fail or succeed.

This appendix addresses a core environmental reality:

Analysts cannot succeed without access — and access does not require risk.

The solution is not fewer analysts or tighter gates.
The solution is better separation between production and analysis.

A Final Word to IT, Finance, and Leadership

This is not an argument against IT control.

It is an argument for IT leadership.

The most effective IT departments are not those that say “no” most often —
they are the ones that say:

“Here is the safe way to do this.”

Sandboxes, data warehouses, and analytical mirrors are not luxuries.
They are the infrastructure that allows modern organizations to think clearly without breaking what already works.

Closing Note on the Appendices

These appendices complete the framework:

  • The main essay explains the stack
  • The follow-up explains how to direct AI
  • These appendices make it operational

Together, they describe not just how to use AI—but how to use it responsibly, professionally, and durably.

What Every Student Should Learn From Economics — The Missing Foundation for Adult Life

.Reading time 10 minutes.

A collaboration between Lewis McLain & AI (3 of 4 in a Series)

If I struggled with literature when I was young, and if I misunderstood the purpose of history, then economics was the third great gap in my early education. I went through high school without any real understanding of how money works, how governments raise and spend it, how markets respond to incentives, or how personal financial decisions compound over time. I did not grasp the forces shaping wages, prices, interest rates, trade, taxation, inflation, or debt. I did get a good dose in college.

Looking back, I can see clearly:
Economics is the core life subject that students most need — and most rarely receive in a meaningful way.

What educators should want every student to know from required economics courses is nothing less than the mental framework necessary to navigate adulthood, evaluate public policy, make financial decisions, and understand why nations prosper or struggle. Economics is not simply business; it is the study of how people, families, governments, and societies make choices. A few years ago, I attended a multi-day course for high school teachers hosted by the Dallas Federal Reserve. It was an outstanding experience. Resources are there today, thank goodness!

This essay explores the essential economic understanding every student deserves — and why it matters now more than ever.

1. Scarcity, Choice, and Opportunity Cost: The Law That Governs Everything

The first truth of economics is painfully simple:
We cannot have everything we want.

Every choice is a tradeoff. Students should walk away understanding that:

  • Choosing to spend money here means not spending it there.
  • Choosing one policy means giving up another.
  • Choosing time for one activity means sacrificing time for something else.

Economics calls this opportunity cost — the value of the next best alternative you give up.

Once a student grasps this, the world becomes clearer:

  • Why governments cannot fund unlimited programs.
  • Why cities must prioritize.
  • Why individuals must budget.
  • Why nations cannot tax, borrow, or spend without consequences.

This one idea alone can save people from poor decisions, unrealistic expectations, and political manipulation.

2. How Markets Work — And What Happens When They Don’t

Every student should understand the basics of markets:

  • Supply and demand
  • Prices as signals
  • Competition as a force for innovation
  • Incentives as drivers of behavior

These are not theories — they are observable realities.

Examples:

  • When the price of lumber rises, construction slows.
  • When wages rise in one industry, workers shift into it.
  • When a product becomes scarce, people value it more.

Students should also learn about market failures, when markets do not work well:

  • Externalities (pollution)
  • Monopolies (lack of competition)
  • Public goods (national defense)
  • Information asymmetry (the mechanic knows more than the customer)

A well-educated adult should understand why some things are best left to markets, and others require collective action.

3. Money, Inflation, and the Hidden Forces That Shape Daily Life

Economics teaches students what money actually is — a medium of exchange, a store of value, a unit of account. It teaches why inflation happens, how interest rates work, and why credit matters.

This is the knowledge people most need to avoid lifelong mistakes:

  • High-interest debt
  • Payday loans
  • Adjustable-rate surprises
  • Over-borrowing
  • Misunderstanding mortgages
  • Under-saving for retirement
  • Falling for financial scams

Inflation, especially, is a quiet teacher.
Students should know:

  • Why prices rise
  • How purchasing power erodes
  • Why governments sometimes overspend
  • How central banks attempt to stabilize the economy

Without this understanding, adults become vulnerable to false promises, political slogans, and emotional decisions disguised as economic policy.

4. Government, Taxes, Debt, and the Economics of Public Choices

Students should understand how governments fund themselves:

  • income taxes
  • sales taxes
  • property taxes
  • corporate taxes
  • tariffs
  • fees and permits

They should know the difference between:

  • deficits and debt
  • mandatory vs. discretionary spending
  • expansionary vs. contractionary policy

And they should understand the consequences of borrowing:

  • interest costs
  • crowding out
  • inflationary risks
  • intergenerational burdens

A citizen who understands these concepts is harder to fool with slogans like:

  • “Free college for everyone!”
  • “We can tax the rich for everything!”
  • “Deficits don’t matter!”
  • “We can cut taxes without cutting services!”

Economics teaches that every promise has a cost — and someone must pay it.

5. Personal Finance: The Economics of Everyday Life

If there is one area where economics should be utterly practical, it is here.
Every student needs to understand:

  • budgeting
  • saving
  • compound interest
  • emergency funds
  • insurance
  • investing basics
  • retirement accounts
  • debt management
  • risk vs. reward

Without this, students walk into adulthood with no map — and they learn lessons the hard way.

One simple example:
$200 saved per month from age 22 to 65 at 7% grows to roughly $500,000.
The same $200 saved starting at age 35 grows to only ~$200,000.

Time matters.
Compounding matters.
Knowing this early changes lives.

6. Global Economics: Trade, Jobs, and National Strength

Students should understand why countries trade:

  • comparative advantage
  • specialization
  • global supply chains
  • exchange rates

They should understand what drives:

  • tariffs
  • sanctions
  • trade deficits
  • manufacturing shifts
  • labor markets

This is the foundation for understanding why:

  • some industries move overseas
  • some cities decline while others rise
  • automation replaces certain jobs
  • immigration affects labor supply
  • global shocks (like pandemics or wars) reshape economies

A student with global economic literacy is less fearful and more informed — and can better adapt to economic change.

7. Economics and Human Behavior

Economics is not just numbers — it is a window into human nature.

Students should learn:

  • why incentives matter
  • why people respond predictably to policy changes
  • why scarcity shapes decisions
  • why risk and reward are universal
  • why unintended consequences are common

For example:

  • Overly generous unemployment benefits can reduce the incentive to return to work.
  • Rent control can reduce housing supply, raising prices long-term.
  • Strict zoning can artificially inflate housing costs.
  • Tax breaks can shift business decisions but may not produce promised jobs.

Economics helps students see beyond intentions to outcomes.

8. Why Economics Matters Even More in the Age of AI

AI has changed everything — except human nature and economic reality.

AI can process data, but it cannot interpret incentives.

Only a human mind can understand why people behave as they do.

AI can forecast trends, but it cannot grasp consequences.

Consequences require judgment shaped by real-world understanding.

AI can make decisions quickly, but it cannot weigh tradeoffs ethically.

Economics teaches students how those tradeoffs work.

AI makes bad decisions faster when guided by people who don’t understand economics.

A poorly trained human with a powerful tool is dangerous.
A well-trained human with the same tool is wise.

Economics is the steadying force that helps society use AI responsibly.

Conclusion: The Blueprint for a Competent Adult

What educators want students to gain from economics is not technical jargon or narrow theories. It is an understanding of how the world works.

Economics teaches:

  • how choices shape outcomes
  • how incentives drive behavior
  • how money, markets, and governments interact
  • why prosperity is fragile and must be understood
  • how individuals, families, and nations manage limited resources
  • how to avoid financial mistakes and public illusions

If literature strengthens the mind and imagination,
and history strengthens judgment and citizenship,
economics strengthens decision-making — the backbone of adult life.

Together, they form the education every young person deserves before entering the real world. And the most important thing I hope you take away from this essay and my experience: college in general and high school in particular is where you launch into a lifetime of learning (and re-learning). Anything you see in this series that you judge you missed, go back and learn! LFM

The Mind of the Mapmaker

.Reading time 20 minutes.

A collaboration between Lewis McLain & AI

Skills, Motivation, and the Capabilities Behind Accurate Mapping

Introduction: The Human Attempt to Shrink the World Into Understanding

A map seems simple at first glance: a flat surface covered with lines, shapes, labels, and colors. Yet the act of creating an accurate map is one of the most difficult intellectual tasks humans have ever attempted. Mapping demands a rare combination of observation, mathematics, engineering, imagination, artistry, philosophy, and courage. It requires a person to look at a world too large to see all at once and to represent it faithfully on something small enough to hold in the hand. Every map, whether carved on a clay tablet or drawn by satellite algorithms, is a claim about what is real and what matters.

This paper explores the mapmaker’s mind across four eras—ancient, exploratory, philosophical, and modern technological—and then strengthens that understanding through case studies and technical appendices. Throughout the narrative, one idea remains constant: accuracy is not merely a technical achievement; it is a human triumph grounded in the mapmaker’s inner capabilities.

I. Ancient Mapmakers: Building Accuracy from Memory, Observation, and Survival

For thousands of years, before the invention of compasses, sextants, or even numerals as we know them, mapmakers relied on the most fundamental tools available to any human being: their memory, their senses, and their endurance.

A Babylonian cartographer might spend long days walking field boundaries and tying lengths of rope to stakes to re-establish property lines after floods. An Egyptian “rope stretcher” could look at the shadow of a pillar, note the angle, and derive a surprisingly accurate sense of latitude and season. Polynesian navigators sensed the shape of islands from the swell of the ocean, the direction of prevailing winds, the pattern of clouds, or the flight paths of birds—even when land was hundreds of miles away. All of this happened without written language in many places, and without anything like formal mathematics.

The motivations were simple but powerful. Survival required knowing where water, game, shelter, and danger lay. Governance required knowing how much farmland belonged to whom, where the temples held jurisdiction, and how to tax agricultural output. Trade required predictable knowledge of paths, distances, and safe passages. Human curiosity played its own role as well; people have always wanted to know the shape of their world.

Accuracy in ancient mapping was limited by natural constraints. Long distances could not be measured with confidence. Longitude remained elusive for nearly all of human history. Oral traditions, though rich, introduced distortions. Political agendas often shaped borders. And yet ancient maps show remarkable competence: logical river systems, consistent directions, recognizable landforms, and surprisingly stable proportionality. Accuracy was relative to the tools available, but the intent—the desire to record reality—was the same as today.

II. Explorers and Enlightenment Surveyors: Lewis & Clark and the Birth of Scientific Mapping

The early nineteenth century introduced a new kind of cartographer: the trained surveyor who combined field observation with scientific measurement. Lewis and Clark exemplify this transition.

Armed with sextants, compasses, chronometers, astronomical tables, and notebooks filled with surveying instructions, they attempted to impose geometric precision on a landscape no European-American had ever mapped. They measured solar angles to determine latitude, recorded compass bearings at virtually every bend of the Missouri River, estimated distances by managing travel speeds, and triangulated mountain peaks whenever weather permitted. Their notebooks reveal how meticulously they checked, recalculated, and corrected their own readings.

Their motivation blended national ambition, Enlightenment science, personal curiosity, and a desire for legacy. President Jefferson viewed the expedition as a grand experiment in empirical observation and hoped to gather geographic, botanical, zoological, and ethnographic knowledge all at once. Lewis and Clark themselves were deeply committed to documenting not only what they saw but how they measured it.

Despite their tools, they faced severe limitations. Cloud cover often prevented celestial readings. Magnetic variation made some compass bearings unreliable. River distances were difficult to estimate accurately when paddling against currents. Longitudes were usually approximations, sometimes guessed, because no portable timekeeping device of the period could maintain accuracy under field conditions. Yet the map produced from their expedition defined the American West for decades, confirmed mountain ranges, captured river systems, located tribal lands, and fundamentally reshaped the geographic understanding of a continent.

Their accomplishment demonstrates that accuracy is a function not only of tools but of discipline, repetition, cross-checking, and the mental fortitude to tolerate error until it can be corrected.

III. The Philosophical Mapmaker: Understanding That a Map Is a Model, Not the World

One of the most difficult but essential truths in cartography is that a map can never be fully accurate in every dimension. A map is a model, not the thing itself. Understanding this transforms how we judge accuracy.

No map can include everything. The mapmaker must decide what to include and what to omit, what to emphasize and what to generalize. This selective process shapes meaning as much as measurement does. A map that focuses on roads sacrifices terrain; a map that shows landforms hides political boundaries; a nautical chart prioritizes depth, hazards, and tides while ignoring nearly everything inland.

Even more fundamentally, the Earth is round and a map is flat. Flattening a sphere introduces distortions in shape, area, distance, or direction. No projection solves all problems at once. The Mercator projection preserves direction for navigation but distorts the sizes of continents dramatically. Equal-area projections preserve proportional land area but contort shapes. Conic projections work beautifully for mid-latitude regions like the United States but fail near the equator and poles.

Scale introduces another layer of philosophical choice. A map of a neighborhood can show driveways, footpaths, and fire hydrants; a map of a nation must erase tens of thousands of such details. At global scale, even major rivers become thin suggestions rather than features.

Finally, maps inevitably carry bias. National borders are often political statements as much as geographic descriptions. Cultural assumptions guide what is considered important. The purpose of a map—a subway map, a floodplain map, a highway atlas—governs its priorities. Every map quietly expresses a worldview.

Thus, “accurate” does not mean “perfectly true.” It means “fit for the purpose.” A map is correct to the extent that it serves the need it was created for.

IV. The Modern Cartographer: Satellites, GIS, and the Era of Precision

The modern mapmaker operates in a world overflowing with spatial information. GPS satellites circle the earth, constantly broadcasting timing signals that allow any handheld receiver to determine position within a few meters—and survey-grade receivers to reach centimeter-level accuracy. High-resolution satellite imagery captures coastlines, forests, highways, and rooftops with astonishing clarity. LiDAR sensors measure elevation by firing millions of laser pulses per second, creating three-dimensional models of terrain. GIS (Geographic Information Systems) software organizes, analyzes, and visualizes enormous spatial datasets.

The work of the modern cartographer is less about drawing lines and more about managing data. A GIS analyst must understand spatial statistics, database schemas, metadata verification, remote sensing interpretation, coordinate transformations, and the difference between nominal, ordinal, interval, and ratio data. The skill set is analytical, computational, and scientific.

The motivations have expanded as well. Modern mapping supports transportation engineering, zoning, emergency response, flood mitigation, environmental policy, epidemiology, commercial logistics, climate science, and international security. Governments, companies, and researchers all rely on constantly updated maps to make daily decisions.

Yet the abundance of data introduces new complications. Errors no longer stem primarily from lack of information but from inconsistency among datasets, outdated imagery, automated misclassification, incorrect coordinate transformation, or the false sense of precision that digital numbers can give. Even in a world of satellites, the mapmaker must remain vigilant and skeptical. Accuracy must still be earned, not assumed.

V. Case Studies: How Real Maps Achieve Real Accuracy

The theory of mapmaking becomes clearer when examined through specific examples. Four case studies reveal how different contexts produce different solutions to the same universal problem.

Case Study 1: The USGS Topographic Map

The United States Geological Survey began producing standardized topographic maps in the late nineteenth century, combining triangulation, plane-table surveying, and field verification. Later editions incorporated aerial photography and eventually satellite data. These maps formed the spatial backbone of national development. Engineers relied on them to place highways, dams, airports, pipelines, and railroads. Hikers and outdoor enthusiasts still use them today.

Their accuracy was remarkable for their time: often within a few meters horizontally and within a meter vertically. They became the nation’s common spatial language, demonstrating how consistent methodology and repeated verification create reliability across vast geographic space.

Case Study 2: Nautical Charts and the Challenge of the Ocean

No mapping discipline demands more caution than nautical charting. Mariners depend on accurate depths, hazard markings, and tidal information. Early sailors used weighted ropes and visual triangulation to estimate depth. Today’s hydrographers use multibeam sonar, satellite altimetry, LiDAR bathymetry, and tide-corrected measurements to produce charts that can reveal underwater features with astonishing detail.

Yet the ocean floor is dynamic. Storms move sandbars. Currents reshape channels. Dredging alters harbor depths. For this reason, nautical charts are never fully “finished.” They require constant updating. The challenge is not simply measuring depth once, but sustaining accuracy in a world that changes.

Case Study 3: The London Underground Map and the Meaning of “Accuracy”

The London Tube Map, introduced by Harry Beck in 1933, revolutionized the concept of cartographic truth. Beck realized that subway riders did not need geographic precision. They needed simplicity, clarity, and relational accuracy—knowing how stations connected, not how far apart they were in miles.

By replacing geographic realism with abstract geometry, he created a map that was technically inaccurate but functionally brilliant. Nearly all subway maps worldwide now follow the same principle. This case study illustrates that the “right” map is the map that serves the user’s need, not the map that most faithfully represents ground truth.

Case Study 4: Google Maps and the Algorithmic Cartographer

Google Maps represents an entirely new form of mapping. Unlike paper maps, it is not a static depiction of geography. It is a constantly shifting model created from satellite images, aerial photos, street-level observations, user reports, and complex routing algorithms. It recalculates itself continuously, adjusting for traffic, construction, business changes, and political variations in border representation.

Its power is extraordinary, but its limitations remind us that automation cannot eliminate human judgment. The platform reflects commercial incentives, political boundaries, and the imperfections of crowdsourced information. Accuracy is high but uneven, and like the ocean charts, the system must be updated constantly to remain trustworthy.

VI. A Unified Theory of Mapmaking

Across all eras and technologies, the mapmaker’s challenge remains the same. The world is too large and too complex to be perceived directly, so the mapmaker must choose which aspects of reality to capture. Those choices—shaped by purpose, tools, knowledge, and bias—determine whether the resulting map will be useful or misleading. Measurement introduces error; projection introduces distortion; interpretation introduces judgment. Accuracy is always relative to context, intention, and method.

The mapmaker succeeds not by eliminating error altogether, but by understanding its sources, managing its influence, and balancing the competing truths that every map must negotiate.

VII. Technical Appendices

Appendix A: Coordinate Systems and Projections

Modern mapping rests on systems that allow the entire Earth to be described mathematically. Latitude and longitude divide the globe into degrees, providing a universal reference easy to conceptualize but difficult to measure perfectly at large scales. The Universal Transverse Mercator (UTM) system divides the Earth into narrow vertical zones, each of which minimizes distortion for engineering purposes. The North American Datum (NAD83) and the World Geodetic System (WGS84) provide precise mathematical models of the Earth’s shape, enabling GPS receivers to calculate location with remarkable accuracy.

Map projections translate the curved surface of the Earth to a flat plane. Each projection sacrifices something: the Mercator preserves direction but exaggerates the size of high-latitude regions; equal-area projections maintain proportional land area at the cost of distorting continents; the Robinson projection compromises carefully to create a visually balanced world. The choice of projection reflects the map’s purpose more than the mapmaker’s preference.

Appendix B: Surveying Instruments Through Time

The tools of mapping have evolved dramatically. Ancient civilizations used gnomons to measure shadows, ropes to mark distances, and rudimentary cross-staffs to gauge angles. Renaissance innovations introduced compasses, astrolabes, sextants, and the plane table, bringing scientific precision to exploration. By the eighteenth century, the theodolite allowed surveyors to measure angles with unprecedented accuracy.

Modern surveyors rely on total stations, which combine angle measurement with laser-based distance calculation; GNSS receivers capable of centimeter-level precision; LiDAR instruments that generate three-dimensional point clouds of terrain; and drones that capture aerial photographs suitable for photogrammetric reconstruction. Although the instruments have changed, the underlying goal has remained constant: to measure the Earth in a way that minimizes error and maximizes reliability.

Appendix C: Sources of Error and How Mapmakers Correct Them

Cartographic errors emerge from several sources. Positional error occurs when instrument readings or GPS signals are distorted by environmental conditions, equipment limitations, or signal reflections from buildings or terrain. Projection error arises because any flat map must distort some combination of shape, area, direction, or distance. Human interpretation error appears during the classification of aerial images or the delineation of ambiguous features. Temporal error affects maps that have not been updated to reflect natural or man-made changes.

Mapmakers mitigate these errors by using redundant measurements, cross-checking data from multiple sources, incorporating ground-truth verification, applying statistical corrections, and selecting projections tailored to the region being mapped. Accuracy is achieved not through perfection but through a disciplined process of detecting, bounding, and correcting inevitable imperfections.

Conclusion: The Eternal Mind Behind the Map

From a Babylonian surveyor tying knots in a rope, to a Polynesian navigator reading waves in the dark, to Lewis and Clark marking compass bearings along unknown rivers, to a modern GIS analyst adjusting satellite layers on a computer screen, the mapmaker’s mind has never changed in its essential character. The world is too vast, varied, and dynamic to be seen directly, so we create representations—models that reveal structure, meaning, and relationship.

A map is not merely a depiction of space. It is a human judgment about what matters. Every accurate map represents a triumph of curiosity over ignorance, order over chaos, and understanding over confusion. The tools are part of the story, but the deeper story is the capability of the person wielding them: the patience to measure carefully, the discipline to verify and correct, the imagination to translate complexity into clarity, and the humility to know that no map is final, complete, or perfect.

Mapmaking is the oldest form of reasoning about the world, and perhaps the most enduring. To draw a map is to make the world legible. To understand a map is to understand the choices of the person who created it. And to appreciate accuracy is to recognize that behind every line lies a mind trying to grasp the infinite.

The Mind of an Inventor: The Common Thread of Creation

.Reading time 14 minutes.

A collaboration between Lewis McLain & AI

I. Introduction — The Spark That Changes the World

Every great invention begins not in a laboratory but in a restless mind that refuses to accept things as they are. The inventor lives in the thin air between wonder and frustration: the wonder of seeing what might be, and the frustration that it does not yet exist.

To invent is to cross the border between imagination and matter—between “why not?” and “now it works.” Across centuries, the world’s greatest inventors have built in different mediums—stone, steam, circuits, code—yet share the same mental wiring: curiosity that won’t rest, courage that won’t quit, and a faith that imagination can serve humanity.

II. The Inventive Mindset

The inventor’s mind is a paradox. It thrives on both chaos and order, fantasy and formula.

  • Curiosity is its compass—an ache to understand how things work and how they could work better.
  • Observation is its lens—seeing patterns others overlook.
  • Playfulness is its fuel—testing ideas without fear of failure.
  • Persistence is its backbone—enduring the thousand prototypes that don’t succeed.

Failure doesn’t frighten the inventor; indifference does. To stop asking “why” is a far greater tragedy than a circuit that burns or a model that breaks.

III. Ten Inventors, Ten Windows into the Mind of Creation

Leonardo da Vinci — Sketching the Sky Before It Existed

Leonardo filled his notebooks with wings, gears, and impossible dreams. He studied the curve of a bird’s feather as if decoding a sacred language.

“Once you have tasted flight,” he wrote, “you will forever walk the earth with your eyes turned skyward.”
He painted with one hand and designed with the other, proving that art and engineering are not rivals but reflections. His flying machines never left the ground, yet every modern aircraft carries a trace of his ink.

Benjamin Franklin — Harnessing Heaven for Humanity

Franklin saw storms not as terrors but as teachers. He tied a key to a kite and coaxed lightning to reveal its secret kinship with electricity.

“Electric fire,” he marveled, “is of the same kind with that which is in the clouds.”
The lightning rod followed—a humble spike that saved countless roofs. His bifocals, his stove, his civic inventions all arose from empathy: an elder’s eyes, a neighbor’s cold house, a printer’s smoky air. He turned curiosity into charity.

Eli Whitney — The Engineer Who Made Things Fit

Whitney watched field hands comb seeds from cotton and thought, There must be a better way. His wire-toothed drum and brush—the cotton gin—sped production a hundredfold.

“It was a small thing,” he later said, “but small things change empires.”
The gin enriched the South and, tragically, deepened slavery. Seeking redemption through precision, Whitney built the first system of interchangeable parts, proving that uniformity could multiply freedom of production. He changed not just a crop but the logic of industry.

Thomas Edison — The Factory of Light

At Menlo Park, light spilled from the windows while others slept. Inside, hundreds of filaments burned and failed.

“I haven’t failed,” Edison smiled. “I’ve found ten thousand ways that won’t work.”
When carbonized bamboo finally glowed for 1,200 hours, he built an entire electric ecosystem—power plants, wiring, meters, sockets. His true invention was not the bulb but the process of systematic innovation itself.

Nikola Tesla — The Dream That Outran Its Century

Tesla lived amid lightning of his own making. To him, the universe pulsed with invisible currents waiting to be tamed.

“The moment I imagine a device,” he claimed, “I can make it run in my mind.”
His AC induction motor and polyphase system powered cities from Niagara Falls. His dream of wireless energy bankrupted him but electrified the future. In him, imagination was not daydreaming—it was blueprinting.

Marie Curie — The Glow of the Invisible

In a shed that smelled of acid and hope, Curie boiled tons of pitchblende until a speck of radium glowed.

“Nothing in life is to be feared,” she said, “it is only to be understood.”
Her discovery of radioactivity opened new worlds of medicine and physics. During World War I she outfitted trucks with X-rays, saving thousands of soldiers. Science for her was not ambition—it was service illuminated.

The Wright Brothers — Learning the Language of Air

In their Dayton workshop, the Wrights balanced on wings of wood and faith. They built a wind tunnel, measured lift with bicycle parts, and studied every gust as if air itself were a textbook.

“The bird doesn’t just rise,” Wilbur observed, “it balances.”
Their 1903 flight at Kitty Hawk lasted only seconds, yet the world’s horizon shifted forever. They proved that methodical curiosity could conquer gravity itself.

Albert Einstein — Thought as an Instrument

Einstein’s laboratory was his imagination. He pictured himself chasing a beam of light and realized time might bend to keep pace.

“Imagination,” he said, “is more important than knowledge.”
From that image grew relativity, which remade physics. Yet his most practical insight—the photoelectric effect—became the foundation of solar power. Einstein invented with ideas instead of tools, showing that creativity can re-engineer reality.

Steve Jobs — The Art of Simplicity

Jobs demanded elegance as fiercely as others demanded speed. He fused hardware and software into harmony.

“It just works,” he’d say, though it took a thousand revisions to reach that ease.
The Mac, the iPod, the iPhone—each was less a gadget than a philosophy: that design is love made visible. Jobs reinvented the personal device by stripping it down until only meaning remained.

Tim Berners-Lee — The Architect of the Digital Commons

In a corridor at CERN, Berners-Lee envisioned scientists everywhere linking their work with one simple syntax.

“I just wanted a way for people to share what they knew.”
He built HTTP, HTML, and the first web server—then released them freely. No patents, no gatekeepers. His generosity made the World Wide Web the shared library of humankind.

Together they form a single conversation across centuries. Leonardo sketched the dream of flight; the Wrights gave it wings. Franklin tamed electricity; Tesla made it sing; Edison wired it into homes. Curie revealed invisible forces; Einstein explained them. Jobs and Berners-Lee re-channeled that same human spark into light made of code. Each voice answers the one before it, echoing: The world can be improved, and I will try.

IV. The Invisible Thread — Purpose and Pattern

Behind every experiment lies a conviction: that the universe is intelligible and worth improving.
Their shared geometry is imagination → iteration → illumination.
They teach that invention is not chaos but a form of hope—faith that our designs, however imperfect, can serve life itself. The true legacy of invention is not a patent portfolio; it is a pattern of thinking that turns wonder into welfare.

V. Conclusion — Love, Made Useful

The mind of an inventor is not born whole. It is forged in curiosity, hammered by failure, and tempered by empathy. These ten lives remind us that progress is a moral act, rooted in patience and compassion.

To think like an inventor is to love the world enough to fix it—to build not merely for profit or prestige but for people yet unborn. Invention, at its purest, is love that learned to use its hands.

Appendix — Biographical Notes and Key Inventions

Leonardo da Vinci — Italian polymath; foresaw helicopters, tanks, and canal locks through meticulous study of anatomy and motion.
Key: flight sketches, helical air screw, gear systems.

Benjamin Franklin — Printer, scientist, diplomat; proved lightning’s electrical nature; invented lightning rod, bifocals, Franklin stove.
Key: electrical experiments, civic innovations.

Eli Whitney — American engineer; built the cotton gin and standardized interchangeable parts for firearms, shaping mass production.
Key: cotton gin, precision tooling.

Thomas Edison — Inventor-entrepreneur; created the practical light system, phonograph, and motion picture camera; pioneered industrial R&D.
Key: incandescent lamp, phonograph, Kinetoscope.

Nikola Tesla — Serbian-American engineer; developed AC motors, polyphase power, radio principles, and the Tesla coil.
Key: alternating-current system, wireless power concepts.

Marie Curie — Physicist-chemist; discovered radium and polonium; founded radiology; first double Nobel laureate.
Key: radioactivity research, mobile X-rays.

Orville & Wilbur Wright — American aviation pioneers; invented three-axis control, conducted first powered flight.
Key: controlled flight, wind-tunnel data.

Albert Einstein — Theoretical physicist; formulated relativity, explained photoelectric effect, father of modern physics.
Key: relativity, photoelectric effect.

Steve Jobs — Apple co-founder; integrated technology and design into consumer art; drove personal computing and mobile revolutions.
Key: Macintosh, iPod/iTunes, iPhone, iPad.

Tim Berners-Lee — British computer scientist; created the World Wide Web’s foundational architecture and kept it open.
Key: URL, HTTP, HTML, first web server/browser.

🎨 Painting Concept: “The Council of Inventors”

Setting:
A softly lit Renaissance-style hall that feels timeless — stone arches overhead, candlelight mingling with the faint glow of electricity. At the center, a great oak table curves like an infinity symbol, symbolizing endless human curiosity. Around it, the ten inventors gather in dialogue — not chronological, but thematic, their inventions subtly illuminating the room.

Foreground Figures

  • Leonardo da Vinci stands near the left, sketchbook open, gesturing midair with a quill as though explaining the curvature of wings. His gaze meets the Wright Brothers, who are bent over a small model glider resting on the table.
  • Benjamin Franklin leans in nearby, one hand on a metal key, the other holding a faintly glowing lightning rod that arcs softly — the light blending into the candle glow.
  • Across from him, Edison adjusts a glowing bulb, its light reflecting in Franklin’s spectacles. Behind him, Nikola Tesla gazes upward, a tiny arc of blue current jumping between his fingertips, illuminating the diagram behind them.

Middle Figures

  • Eli Whitney sits near the table’s midpoint, hands on precision tools and calipers, his musket parts laid out like a puzzle. The Wright Brothers’ propeller model rests beside his gear molds, symbolizing the bridge between ground and air.
  • Marie Curie stands slightly apart, her face serene but determined, holding a small vial that emits a gentle ethereal light — a faint halo of pale blue radiance, illuminating her lab notes.
  • Albert Einstein leans over her shoulder, pipe in hand, scribbling light equations on a parchment that glow faintly, as if chalked by photons.

Background Figures

  • Steve Jobs is seated farther right, dressed in his signature black turtleneck — timeless among them — explaining the first iPhone to Tim Berners-Lee, who nods thoughtfully while holding a glowing string of code shaped like a thread of light. Between them, a subtle digital aura rises — a lattice of glowing lines suggesting the web connecting every mind in the room.

Drones as a Core Municipal Utility: Policy, Training, and Future Directions for Texas Cities

.Reading time 19 minutes.

A collaboration between Lewis McLain and AI

Executive Summary

Municipal drone programs have rapidly evolved from experimental projects to dependable service tools. Today, Texas cities are beginning to treat drones not as gadgets but as core municipal utilities—shared resources as essential as fleet management, radios, or GIS. Properly implemented, drones can provide faster response times, safer job conditions, and higher-quality data, all while saving taxpayer money.

This paper explains how cities can build and sustain a municipal drone program. It examines current and emerging use cases, outlines staffing impacts, surveys training options and costs in Texas, explores fleet models and procurement, and considers the legal, policy, and community dimensions that must be addressed. It concludes with recommendations, case studies of failures, and appendices on payload regulation and FAA sample exam questions.

Handled wisely, drones will make cities safer, smarter, and more responsive. Mishandled, they risk creating public backlash, wasting funds, or even eroding trust.

The Case for Treating Drones as a Utility

Cities that succeed with drones do so by thinking of them as utilities, not toys. A drone program should be centrally governed, jointly funded, and transparently managed. Just like a municipal fleet or IT department, a citywide drone service must be reliable, equitable across departments, compliant with law, interoperable with other systems, and transparent to the public.

This approach ensures that drones are available where needed, that policies are consistent across departments, and that costs are shared fairly. Most importantly, it signals to residents that the city treats drone use seriously, with strong safeguards and clear accountability.

Current and Growing Uses

Across Texas and the country, municipal drones already serve a wide range of functions.

Public Safety: Police and fire agencies use drones as “first responders,” launching them from stations or rooftops to 911 calls. They provide live video of car crashes, fires, or hazardous scenes, often arriving before officers. Firefighters use drones with thermal cameras to locate victims or track hotspots in burning buildings.

Infrastructure and Public Works: Drones inspect bridges, culverts, roofs, and water towers. Instead of sending workers onto scaffolds or into confined spaces, crews now fly drones that capture detailed photos and 3D models. Landfills are surveyed from the air, methane leaks identified, and storm damage mapped quickly after major events.

Transportation and Planning: Drones monitor traffic flow, study queue lengths, and document work zones. City planners use them to create up-to-date maps, support zoning decisions, and maintain digital twins of urban areas.

Environmental and Health: From checking stormwater outfalls to mapping tree canopies, drones help environmental staff monitor city assets. In some regions, drones are used to identify standing water and apply larvicides for mosquito control.

Emergency Management: After floods, hurricanes, or tornadoes, drones provide rapid situational awareness, helping cities prioritize response and document damage for FEMA claims.

As automation improves, “drone-in-a-box” systems—drones that launch on schedule or in response to sensors—will soon become common municipal tools.

Staffing Impacts

A common fear is that drones will replace jobs. In practice, they save lives and money while creating new roles.

Jobs Saved: By reducing risky tasks like climbing scaffolds or entering confined spaces, drones make existing jobs safer. They also reduce overtime by finishing inspections or surveys in hours instead of days.

Jobs Added: Cities now employ drone program coordinators, FAA Part 107-certified pilots, data analysts, and compliance officers. A medium-sized Texas city might add ten to twenty such roles over the next five years.

Jobs Shifted: Inspectors, police officers, and firefighters increasingly become “drone-enabled” workers, adding aerial operations to their responsibilities. Over time, 5–10% of municipal staff in critical departments may be retrained in drone use.

The net result is redistribution rather than reduction. Drones are not eliminating jobs; they are elevating them.

Training in Texas

FAA rules require every commercial or government drone operator to hold a Part 107 Remote Pilot Certificate. Fortunately, Texas offers many affordable training options.

Community colleges such as Midland College and South Plains College provide Part 107 prep and hands-on flight training, typically costing $350 to $450 per course. Private providers like Dronegenuity and From Above Droneworks offer in-person and hybrid courses ranging from $99 online modules to $1,200 full academies. San Jacinto College and other universities run short workshops and certification tracks.

Online exam prep courses are widely available for $150–$400, making it feasible to train multiple staff at once. When departments train together, cities often negotiate group discounts and host joint scenario days at municipal training grounds.

Fleet Models and Costs

Municipal needs vary, but most cities benefit from a tiered fleet.

  • Micro drones (under 250g) for training and quick checks: $500–$1,200.
  • Utility quads for mapping and inspection: $2,500–$6,500.
  • Enterprise drones with thermal sensors for public safety: $7,500–$16,000.
  • Heavy-lift or VTOL systems for long corridors or specialized sensors: $18,000–$45,000+.

Each drone has a three- to five-year lifespan, with batteries refreshed every 200–300 cycles. Cities must also budget for accessories, insurance, and management software.

Policy and Legal Landscape

Federally, the FAA regulates drone operations under Part 107. Rules limit altitude to 400 feet, require flights within visual line of sight, and mandate Remote ID for most aircraft. Waivers can allow for advanced operations, such as flying beyond visual line of sight (BVLOS).

In Texas, additional laws restrict image capture in certain contexts and impose rules around critical infrastructure. Local governments cannot regulate airspace, but they can and should regulate employee conduct, data use, privacy, and procurement.

Transparency is crucial. Cities must publish clear retention policies, flight logs, and citizen FAQs.

Privacy, Labor, and Community Trust

For communities to embrace drones, cities must be proactive.

Privacy: Drones should collect only what is necessary, with cameras pointed at mission targets rather than private backyards. Non-evidentiary footage should be deleted within 30–90 days.

Labor: Cities should emphasize that drones augment rather than replace workers. They shift dangerous tasks to machines while providing staff new certifications and career paths.

Equity: Larger cities may advance faster than small towns, but shared services, inter-local agreements, and regional training programs can close the gap.

Community Trust: Transparency builds legitimacy. Cities should publish quarterly metrics, log complaints, host public demos, and maintain a clear point of contact for concerns.

Lessons from Failures

Not every program has succeeded. Across the country, drone initiatives have stumbled in predictable ways:

  • Community Pushback: Chula Vista’s pioneering drone-as-first-responder program drew criticism for surveillance concerns, while New York City’s holiday monitoring drones sparked public backlash. Lesson: transparency and engagement must come first.
  • Operational Incidents: A Charlotte police drone crashed into a house, and some agencies lost FAA waivers due to compliance lapses. Lesson: one mistake can jeopardize an entire program; training and discipline are essential.
  • Budget Failures: Dallas and other cities saw expansions stall over hidden costs for software and maintenance. Smaller towns wasted funds buying consumer drones that quickly wore out. Lesson: plan for lifecycle costs, not just hardware.
  • Legal Overreach: Connecticut’s proposal to arm police drones with “less-lethal” weapons collapsed amid backlash, while San Diego faced court challenges over warrant requirements. Lesson: pushing boundaries invites restrictions.
  • Scaling Gaps: Rural Texas counties bought drones with grants but lacked certified pilots or insurance. Small towns gathered imagery but had no analysts to use it. Lesson: drones without people and integration are wasted purchases.

Recommendations

  1. Invest in training through Texas colleges and private providers.
  2. Procure wisely, choosing modular, upgradeable hardware.
  3. Adopt clear policies on payloads, privacy, and data retention.
  4. Prioritize non-kinetic payloads such as cameras, sensors, and lighting.
  5. Prepare for BVLOS, which will transform municipal use once authorized.
  6. Ensure equity, supporting smaller cities through regional cooperation.

Conclusion

Drones are no longer experimental novelties. They are rapidly becoming a core municipal utility—a shared service as essential as public works fleets or GIS. Their greatest promise lies not in flashy technology but in the steady, practical benefits they bring: safer workers, faster response, better data, and more transparent government.

But the promise depends on choices. Cities must prohibit weaponized payloads, publish clear policies, train and retrain staff, and engage openly with their communities. Done right, drones can strengthen both city effectiveness and public trust.

Appendix A: Administrative Regulation on Payloads

Title: Drone Payloads and Weapons Prohibition; Data & Safety Controls
Number: AR-UAS-01
Effective Date: Upon issuance
Applies To: All city employees, contractors, volunteers, or agents operating drones (UAS) on behalf of the City

1. Purpose

This regulation ensures that all municipal drone operations are conducted lawfully, ethically, and safely. It establishes clear prohibitions on weaponized or harmful payloads and sets minimum standards for data use, transparency, and accountability.

2. Definitions

  • UAS (Drone): An uncrewed aircraft and associated equipment used for flight.
  • Payload: Any item attached to or carried by a UAS, including cameras, sensors, lights, speakers, or drop mechanisms.
  • Weaponized or Prohibited Payload: Any device or substance intended to incapacitate, injure, damage, or deliver kinetic, chemical, electrical, or incendiary effects.
  • Authorized Payload: Sensors or devices explicitly approved by the UAS Program Manager for municipal purposes.

3. Policy Statement

  • The City strictly prohibits the use of weaponized or prohibited payloads on all drones.
  • Drones may only be used for documented municipal purposes, consistent with law, FAA rules, and City policy.
  • All payloads must be inventoried and approved by the UAS Program Manager.

4. Prohibited Payloads

The following are expressly prohibited:

  • Firearms, ammunition, or explosive devices.
  • Pyrotechnic, incendiary, or chemical agents (including tear gas, pepper spray, smoke bombs).
  • Conducted electrical weapons (e.g., TASER-type devices).
  • Projectiles, hard object drop devices, or kinetic impact payloads intended for crowd control.
  • Covert audio or visual recording devices in violation of state or federal law.

Exception: Non-weaponized lifesaving payloads (e.g., flotation devices, first aid kits, rescue lines) may be deployed only with prior written approval of the Program Manager and after a documented risk assessment.

5. Authorized Payloads

Authorized payloads include, but are not limited to:

  • Imaging sensors (visual, thermal, multispectral, LiDAR).
  • Environmental sensors (methane detectors, gas analyzers, air quality monitors).
  • Lighting systems (searchlights, strobes).
  • Loudspeakers for announcements or evacuation instructions.
  • Non-weaponized emergency supply drops (medical kits, flotation devices).
  • Tethered systems for persistent observation or communications relay.

6. Oversight and Accountability

  • The UAS Program Manager must approve all payload configurations before deployment.
  • Departments must maintain an updated inventory of drones and payloads.
  • Quarterly inspections will be conducted to verify compliance.
  • An annual public report will summarize drone use, payload types, and incidents.

7. Data Controls

  • Minimization: Only record what is necessary for the mission.
  • Retention:
    • Non-evidentiary footage: 30–90 days.
    • Evidentiary footage: retained per case law.
    • Mapping/orthomosaics: retained per project records schedule.
  • Access: Role-based permissions, with audit logs.
  • Public Release: Media released under public records law must be reviewed for privacy and redaction (faces, license plates, sensitive sites).

8. Training Requirements

  • All operators must hold an FAA Part 107 Remote Pilot Certificate.
  • Annual city-approved training on:
    • This regulation (AR-UAS-01).
    • Privacy and data retention.
    • Citizen engagement and de-escalation.
  • Scenario-based training must be conducted at least once per year.

9. Enforcement

  • Violations of this regulation may result in disciplinary action up to and including termination of employment or contract.
  • Prohibited payloads will be confiscated, logged, and removed from service.
  • Cases involving unlawful weaponization will be referred for criminal investigation.

10. Effective Date

This regulation is effective immediately upon approval by the City Manager and shall remain in force until amended or rescinded.

Appendix B: FAA Part 107 Sample Questions (Representative, 25 Items)

Note: These questions are drawn from FAA study materials and training resources. They are not live exam questions but are representative of the knowledge areas tested.

  1. Under Part 107, what is the maximum allowable altitude for a small UAS?
     A. 200 feet AGL
     B. 400 feet AGL ✅
     C. 500 feet AGL
  2. What is the maximum ground speed allowed?
     A. 87 knots (100 mph) ✅
     B. 100 knots (115 mph)
     C. 87 mph
  3. To operate a small UAS for commercial purposes, which certification is required?
     A. Private Pilot Certificate
     B. Remote Pilot Certificate with a small UAS rating ✅
     C. Student Pilot Certificate
  4. Which airspace requires ATC authorization for UAS operations?
     A. Class G
     B. Class C ✅
     C. Class E below 400 ft
  5. How is controlled airspace authorization obtained?
     A. Verbal ATC request
     B. Filing a VFR flight plan
     C. Through LAANC or DroneZone ✅
  6. Minimum visibility requirement for Part 107 operations?
     A. 1 statute mile
     B. 3 statute miles ✅
     C. 5 statute miles
  7. Required distance from clouds?
     A. 500 feet below, 2,000 feet horizontally ✅
     B. 1,000 feet below, 1,000 feet horizontally
     C. No minimum distance
  8. A METAR states: KDAL 151853Z 14004KT 10SM FEW040 30/22 A2992. What is the ceiling?
     A. Clear skies
     B. 4,000 feet few clouds ✅
     C. 4,000 feet broken clouds
  9. A TAF includes BKN020. What does this mean?
     A. Broken clouds at 200 feet
     B. Broken clouds at 2,000 feet ✅
     C. Overcast at 20,000 feet
  10. High humidity combined with high temperature generally results in:
     A. Increased performance
     B. Reduced performance ✅
     C. No effect
  11. If a drone’s center of gravity is too far aft, what happens?
     A. Faster than normal flight
     B. Instability, difficult recovery ✅
     C. Less battery use
  12. High density altitude (hot, high, humid) causes:
     A. Increased battery life
     B. Decreased propeller efficiency, shorter flights ✅
     C. No effect
  13. A drone at max gross weight of 55 lbs carries a 10 lb payload. Payload percent?
     A. 18% ✅
     B. 10%
     C. 20%
  14. At maximum gross weight, performance is:
     A. Improved stability
     B. Reduced maneuverability and endurance ✅
     C. No change
  15. The purpose of Crew Resource Management is:
     A. To reduce paperwork
     B. To use teamwork and communication to improve safety ✅
     C. To reduce training costs
  16. GPS signal lost and drone drifts — first action?
     A. Immediate Return-to-Home
     B. Switch to ATTI/manual mode, maintain control, land ✅
     C. Climb higher for GPS
  17. If a drone causes $500+ in property damage, what is required?
     A. Report only to local police
     B. FAA report within 10 days ✅
     C. No report required
  18. If the remote PIC is incapacitated, the visual observer should:
     A. Land the drone ✅
     B. Call ATC
     C. Wait until PIC recovers
  19. On a sectional chart, a magenta vignette indicates:
     A. Class E starting at surface ✅
     B. Class C boundary
     C. Restricted airspace
  20. A dashed blue line on a sectional chart indicates:
     A. Class B airspace
     B. Class D airspace ✅
     C. Class G airspace
  21. A magenta dashed circle indicates:
     A. Class E starting at surface ✅
     B. Class G airspace
     C. No restrictions
  22. Floor of Class E when sectional shows fuzzy side of a blue vignette?
     A. Surface
     B. 700 feet AGL ✅
     C. 1,200 feet AGL
  23. Main concern with fatigue while flying?
     A. Reduced battery performance
     B. Slower reaction and poor decision-making ✅
     C. Increased radio interference
  24. Alcohol is prohibited within how many hours of UAS operation?
     A. 4 hours
     B. 8 hours ✅
     C. 12 hours
  25. Maximum allowable BAC for remote pilots?
     A. 0.08%
     B. 0.04% ✅
     C. 0.02%

I’m Back!

.Reading time 2 minutes.

I see that my last post was in 2019. So, why start posting again? There are several reasons.

  1. I’ve actually been writing quite a bit – just not posting since most of my writings have been about personal matters.
  2. I now write in collaboration with AI, mostly ChatGPT. By the time I have had AI add, rewrite, and let me be the content guide and editor, is it Lewis or AI? Like I said, it is a collaboration in the truest since of the word.
  3. I’m heavily influenced by a Bible Study I am in as well as the ages and stages of life. Our oldest granddaughter, Lindsey, has now graduated from college, is teaching 4-year-old autistic children and living in her own apartment in downtown McKinney. Lily is a junior architectural student at Texas Tech. Anderson just left last Friday for Texas Tech as a freshman. He is planning to study business and computers. Kenneth & DeAnne are downsizing their home and plan to live in the historic district in Downtown McKinney. Linda & I are both 78, in so-so health, and are celebrating our 57th anniversary today. God is good, and all is well. Just happy to be alive!
  4. After years of being politically neutral as much as possible, with conservative leanings, I am full bore conservative/anti-woke and a Trump supporter now. My disdain for liberalism is greater than my support of conservativism.
  5. I still write about governmental finance topics even though my preferred subject stream is wherever my mind and heart are at any given moment. I still work close to 40 hours a week with my expertise being narrowed to Sales Tax Analyses as well as Multi-Year Financial Planning (MYFP). I love every minute of my consulting and will probably continue as long as I can use the keyboard.

    What this means is that if you are not interested in the type of topics I mostly write about these days, then I think there is a way you can unsubscribe on your own.

    If you think I have anything interesting to say, please forward to any of your friends, colleagues and family.

    Thank you!
    Lewis