How to Reduce Noise Using the FINESSE Fishbone Diagram

A concise, balanced, and well-structured communication reduces noise.

Noise is anything that interferes with the receiver’s understanding. The FINESSE Fishbone Diagram® includes Noise with Frame, Illustrate, Empathy, Structure, Synergy, and Ethics because if you don’t reduce noise, nothing else works.

A Real-World Example

The agency director looked at me across the conference table and asked what I thought about the latest five‑year management assessment. It was a loaded question. If the executive team had been aligned with the report, the COO would not have asked me to review it before it went to the board.

“I had some issues with the report,” I said. “They lost me with that multicolored, busy graph that had unnecessary information. They’ll lose your board, too.”

He nodded. The graph had a dozen colors, stacked bars, trend lines, and two vertical axes. The information was not necessary for the decision at hand. It was a perfect example of what the N in FINESSE (Noise reduction) warns us about. When complexity and uncertainty are already high, noise is the last thing decision makers need.

Three Practical Ways to Reduce Noise

Below are three practical ways to use the FINESSE Fishbone Diagram® to reduce noise and communicate more effectively.

1. Balance the Three Forms of Communication

One of the most overlooked sources of noise is the imbalance between perceptual, interpretive‑verbal, and interpretive‑symbolic communication.

• Perceptual & sensory: what people see, hear, or feel.

• Interpretive & verbal: narrative, explanation, story

• Interpretive & symbolic: numbers, charts, formulas

Technical professionals often overemphasize symbolic communication—tables, charts, and calculations—because that is where we are most comfortable. But too much of any one form creates noise for the others.

Project Managers Reducing Noise

The FINESSE Fishbone Diagram® reminds us that concise, balanced, and well-structured communication reduces noise. A simple narrative paired with a clean visual often outperforms a dense graphic or a long explanation. When in doubt, ask yourself: Does this help the receiver understand, or does it help me feel like I’ve explained enough?

Those are not the same thing.

2. Match the Message to the Forum

Noise is not only about content. It is also about context.

There are five common communication forums:

• Senior management (decision makers)

• Internal work teams

• Public speaking

• Media

• Elected officials

Align Data with Meeting Types

Each forum has its own noise profile. Internal teams expect data; senior management expects clarity. Public audiences expect entertainment; elected officials expect stories and sound bites.

The FINESSE Fishbone Diagram® helps you align your message with the forum. For senior management, “less is more” is not a slogan—it is a survival strategy. For internal teams, noise reduction may mean structuring the data rather than reducing it. For public speaking, noise reduction may mean simplifying visuals and focusing on a single memorable takeaway.

Noise is not universal. It is situational.

A Classic Public Speaking Example

3. Let the Data Speak—But Don’t Let It Shout

In the story from Facilitating with FINESSE, the original report claimed problems in seven of ten areas. The data supported maybe three. The rest was noise—overinterpretation, overcoloring, and overconfidence.

Noise reduction does not mean hiding data. It means presenting data and information in a balanced, ethical manner and resisting the urge to oversell complexity. A concise conclusion at the beginning, followed by only the layers of detail the receiver asks for, is often the most respectful and effective approach.

Knowing Your Message Is Clear

The FINESSE Fishbone Diagram® reinforces this discipline. It reminds us that the burden of communication is on the sender, not the receiver. When we reduce noise, we increase understanding. When we increase understanding, we increase trust.

Reducing Noise with FINESSE

Noise is present wherever information is shared. The FINESSE Fishbone Diagram® provides technical professionals with a structured way to reduce it. Whether preparing a board briefing, facilitating a workshop, or crafting a public presentation, the N in FINESSE reminds you to keep the message clear and concise.

Complexity and uncertainty will always introduce noise. Our job is to make sure we don’t add more. Are you Communicating with FINESSE?

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JD Solomon Inc. provides solutions for program development, asset management, and facilitation at the nexus of facilities, infrastructure, and the environment.

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JD Solomon writes and consults on decision-making, reliability, risk, and communication for leaders and technical professionals. His work connects technical disciplines with human understanding to help people make better decisions and build stronger systems. Learn more at www.jdsolomonsolutions.com and www.communicatingwithfinesse.com

Risk and Uncertainty: Understanding Small Worlds, Large Worlds

Large worlds are environments where measurement is difficult and uncertainty rules. Small worlds are environments where measurement is feasible and risk rules.

Real decisions rarely unfold in the tidy models we prefer. Most of the time, we operate in “small worlds,” where the boundaries are known, the variables are manageable, and experience gives us confidence. The trouble comes when we unknowingly step into “large worlds,” where assumptions break down, data thins out, and the consequences of being wrong grow quickly. Understanding the difference is essential for anyone charged with making decisions that stand up to scrutiny.

Large Worlds

Large worlds are environments where measurement is difficult and key input variables are interdependent, making accurate prediction challenging. Their defining feature is complexity, leading to persistent uncertainty.

Small Worlds

Small worlds are environments where measurement is feasible. Inputs are separable and often treated as independent, allowing for accurate estimation of input-output relationships and calculable risks. Their hallmark is predictability.

We Try to Decompose into Small Worlds

From systems engineering to business analytics, we tend to decompose large worlds into small worlds. Creating small worlds, or measurable states, is an appropriate analytical approach for making complexity more understandable. Decomposition of larger states into more discrete components can be powerful.

Decomposition Misses Larger Uncertainties

The one big issue with decomposition is that re-aggregating the smaller states and understanding their risks does not usually explain the uncertainties of the larger state. In many cases, the larger world may have natural or constructed “layers of protection” that protect the system from risk in the smaller, decomposed states.

Specialists often waste much time and money trying to fully resolve their small states when a decision maker really needs a better understanding of the larger one. And, of course, many of the inter-relationships and dependencies that create uncertainty in the larger state cannot be measured or may not be totally understood.

Example: The Manhattan Project

Jimmie Savage, the statistician of the Manhattan Project, understood a lot about small worlds, large worlds, risk, and uncertainty. When you are working on a team to create the world’s first nuclear bomb, you tend to understand much about large worlds and uncertainty.

Savage understood that in small worlds, debates related to math and statistics were more justified. In large worlds, the debate is largely irrelevant because much is not understood or cannot be measured.

In large worlds, the best we can do is probably more akin to the Bayesian, who views probability as a "degree of belief" that changes as new evidence becomes available. In other words, they learn as they go.

Most Big Decisions Are In Large Worlds

Most of us work in small worlds and are not the decision makers in the large world. We often lose sight of that as we banter about who has a better approach or a better methodology. In small worlds, where we can quantify risk and there is limited uncertainty, these things matter. In large worlds, full of less measurable uncertainty, the bantering just creates unhelpful noise.

Applying Small Worlds and Large Worlds

Context matters. Effective decision making depends on knowing when we’ve moved beyond the comfort of small worlds and into the uncertainty of larger ones. Models and decomposition help only to a point; after that, judgment and awareness matter more than precision. Staying alert to that shift is what keeps our decisions grounded and credible in the real world.

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Credit for the concept of Small Worlds and Large Worlds to Leonard “Jimmie” Savage in his 1954 classic book, The Foundations of Statistics. And don’t let the title fool you – the book is more about decision making than about statistics. I prefer the 1972 Second Edition.

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This article was first published by JD Solomon on LinkedIn.

Solomon, J. D. (2018, September 26). Risk and uncertainty: Understanding small worlds, large worlds. LinkedIn. https://www.linkedin.com/pulse/risk-uncertainty-understanding-small-worlds-large-jd-solomon 

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JD Solomon writes and consults on decision-making, reliability, risk, and communication for leaders and technical professionals. His work connects technical disciplines with human understanding to help people make better decisions and build stronger systems. Learn more at www.jdsolomonsolutions.com and www.communicatingwithfinesse.com.

Three Ways to Look at Asset Life…and why it matters more than you think

Engineers, accountants, and operations leaders may use the same terms for asset life, yet they often mean entirely different things

How long will an asset last? It sounds straightforward, but anyone who has spent time in infrastructure planning knows it’s one of the most deceptively complex questions we face.

Reliability engineers, accountants, and operations leaders may use the same terms for asset life, yet they’re often talking about entirely different things—different assumptions, different incentives, different definitions of “truth.” When those perspectives stay siloed, organizations make decisions that look defensible on paper but fall apart in practice. When we reconcile them, we create clarity, strengthen our systems, and make investment choices that actually hold up in the real world.

An Insightful (and Typical) Meeting

The discussion started like many others. The engineer, Jeff, slid some data onto the screen and confidently stated that the pump in question had a mean time to failure of 27 years. The CFO, Karen, glanced down at her depreciation schedule and shook her head. “Our financial system shows a useful life of seven years. After that, the asset is fully depreciated.” Mike, the COO, sat back in his chair. He added his own experience: “These pumps don’t make it past 18 years in the field. Not with this duty cycle.”

Three professionals. Three facts. Three different “lifetimes” for the same physical asset.

I thought, “27, 7, and 18 years? For the same asset?”

The room fell quiet for a few minutes. The contradictions hung in the air. Then the questions began. Which number should we plan on? Who is right? And how do we make a decision that works across engineering, finance, and operations?

It's a scene we've witnessed countless times. The answer remains the same: all three are right, but for their own discipline.

1. Reliability Perspective: Calculating Mean Life

Reliability engineers calculate how long an asset should last based on failure data, statistical distributions, and population-level models.

Their core metric is mean life, whether expressed as:

·     MTTF (Mean Time to Failure) for non-repairable items

·     MTBF (Mean Time Between Failures) for repairable ones

Mean life is calculated by analyzing failure records across similar assets. It involves fitting statistical curves, often using Weibull analysis or exponential models. The result is a probabilistic average, not a prediction for any single asset.

A common planning heuristic is that mean life is roughly two‑thirds of service life. This holds for many mechanical assets with typical Weibull shape parameters, but it is not a universal reliability engineering rule.

The reliability engineering question is, “What is the inherent life of this asset, assuming ideal conditions?”

Mean life is powerful for modeling risk, setting critical spares strategies, and evaluating design options. But it’s rarely the number you should use for budgeting or replacement planning.

2. Accounting Perspective: Calculating Useful Life

Accounting focuses on economic usefulness, not physical longevity. The key measure is useful life, typically set by:

·     IRS depreciation schedules

·     GASB or FASB guidance

·     Industry norms

·     Organizational financial policy

Useful life calculations are grounded in capital recovery. It shows how quickly the organization should reclaim the investment on its balance sheet. For many assets, this period is much shorter than the physical life.

The accounting question is, “How long should we recognize value from this asset for financial reporting and capital budgeting?”

Useful life steers depreciation, replacement funding, and rate-setting. It is not intended to reflect how long the asset physically performs.

3. Operations Perspective: Calculating Service Life

Operations leaders calculate service life, the actual observed performance in the field.

This calculation is based on:

·     Duty cycles

·     Operating environment

·     Maintenance history

·     Performance degradation

·     Real failure dates

A common method is simple: track when assets were installed and when they were retired. More advanced approaches use condition assessments and predictive models to estimate remaining service life (RSL).

Condition assessments are usually performed to provide an indicator of remaining useful life.

The question that frontline operation and maintenance professionals ask is, “How long will this asset actually last in our real-world conditions?”

This number often sits between the accounting useful life and the reliability mean life. It’s also the most relevant for planning.

Bringing the Three Together

The goal isn't choosing which life is "right." The goal is to understand why each life exists and how to integrate them into decisions.

·     Mean life shows inherent reliability.

·     Useful life guides financial recovery.

·     Service life reflects operational reality.

Understanding Asset Life Makes You More Effective

High-reliability organizations (HROs) build bridges between these viewpoints. They align engineering, finance, and operations so that asset decisions are defensible, practical, and focused on long-term value.

It’s more than just knowing what you are doing or being "right." Understanding asset life is the foundation for clear communication. That is how you make better decisions.

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Need help getting started? JD Solomon Inc. provides practical solutions to align asset useful life and strengthen your asset management program.