“Do Machine Learning Models Dream of Rules…?”

Rule-Based Matching or Machine Learning. Is there a difference?

How does this impact my company’s Strategy & Tactics?

Life—and database matching—is full of false choices. Do you pick rules or machine learning? This is one of those questions that trips people up because they’re more interested in looking modern than being right. You want to solve a problem—use a method that works, and no fancier. The great algorithm of life is to keep it simple. The more you overthink it, the stupider you get. 

In the beginning, we relied on brittle rule-based systems: human experts painstakingly encoding their own reasoning as a nest of “if-then” statements. These setups were static and fragile—effective for narrowly defined tasks but quick to fail when confronted with any deviation from their scripted instructions. It was like a chess player who knew how to respond to only one highly specific opening, left helpless the moment an opponent tried something new. 

Over time, the field realized that trying to manually anticipate every contingency was futile. The world is just too complicated. That insight led us toward data-driven methods. Instead of telling the computer exactly what to do, we let it learn from examples. This was the dawn of machine learning. You present a model with heaps of data, and it infers patterns on its own patterns that no human would ever think to articulate. This shift liberated us from rigid rules and allowed systems to become more adaptive and nuanced. 

The transition from fixed rules to learned models was a fundamental conceptual breakthrough. Under the old paradigm, human experts tried to be omniscient architects of logic, building delicate towers that collapsed at the slightest tremor. Under the new paradigm, we accept uncertainty and complexity, providing examples and letting the machine figure out its own strategies. This yields more robust, flexible solutions that continue to improve as they absorb new data, though definitely not without errors, biases and risks. 

So, we went from “Do what I say, computer,” to “Here’s a bunch of examples—figure it out.

“Though, “Is all that glitters, Gold?”. How do we figure out which to use. Is it a binary choice? 

Let’s dig a little deeper.

Rule-Based Matching & The Power of Simplicity 

Let’s start with rule-based matching—the tried-and-true method. It works when you have clean, structured, and somewhat predictable data. It doesn’t take a genius to say:

Data Matching in Databases

Rule: Match customer records based on Name, Address, and Phone Number.

Example:

IF Customer_Name = “John Smith”

AND Address = “123 Main St”

AND Phone_Number = “555-1234”

THEN it’s a match.

Fraud Detection (Banking)

Rule: Flag transactions over $10,000 coming from outside the country.

Example:

IF Transaction_Amount > 10,000

AND Origin_Country ≠ “Home_Country”

THEN mark transaction as Suspicious.

Supply Chain Vendor Matching

Rule: Match vendors based on their registered name and tax ID.

Example:

IF Vendor_Name = “ABC Supplies Ltd.”

AND Tax_ID = “12345678”

THEN it’s the same vendor.

E-commerce Product Matching

Rule: Match products based on Brand, Model, and Specifications.

Example:

IF Product_Brand = “Samsung”

AND Model = “Galaxy S21”

AND RAM = “8GB”

THEN items are the same product.

It’s simple. It’s transparent. It’s fast. And more importantly, it’s deterministic. Given the same inputs, you’ll always get the same output. In database matching, this is invaluable because certainty matters. If you can apply a set of logical rules that humans can understand and audit, you’ll save yourself a lot of grief. 

Use rule-based matching when the data is structured, clean, and transparent. If IDs, emails, or standardized fields exist, rely on them—there’s no need to overthink it. When data isn’t full of typos or inconsistencies, simple rules work perfectly. Rules are easy to explain and debug: “Here’s the rule, here’s the match.” Unlike black-box models, they provide full transparency, making it easier to justify decisions in the boardroom. 

For example: Matching customers across two databases using unique identifiers like Social Security Numbers or emails. You don’t need machine learning for this. You just need basic logic and discipline. 

Machine Learning? 

If rule-based systems fail—whether due to chaotic data, complex patterns, or excessive human bias and entropy—machine learning can be a valuable alternative. However, users must proceed with caution. Machine learning is not magic; it is a probabilistic tool designed to identify patterns that cannot be easily encoded through rules. It shines when problems are inconsistent, messy, or simply too complex for static, rule-based approaches. 

Machine learning is particularly effective when data lacks clear rules or consistency. For example, in tasks like matching free-text descriptions—such as “Red Nike Sneaker, Size 10” and “Scarlet Running Shoe, Nike 10”—rule-based systems often fall short. Machine learning can infer underlying patterns from training data and handle challenges like misspellings, abbreviations, and synonyms without requiring exact matches. Unlike static rules, machine learning systems are also flexible: they can adapt and improve over time as more examples are provided. 

Despite its strengths, machine learning solutions are often presented as black boxes, where the inner workings of the model remain opaque. This lack of transparency can pose significant challenges, particularly when biases or assumptions embedded in the model are not fully understood. Since machine learning models are shaped by their training data and design assumptions, it is essential to comprehend how these factors influence the model’s decisions and outcomes. 

Understanding bias is critical because it directly impacts the fairness and accuracy of a model’s results. Without transparency, machine learning systems risk perpetuating existing biases or introducing new ones, leading to incorrect or unfair decisions. A clear understanding of the algorithms and their design enables better troubleshooting, evaluation, and improvements, ensuring the model aligns with desired goals and ethical standards. 

One common example of bias in machine learning can be found in facial recognition systems trained primarily on images of light-skinned males. Because these systems have seen fewer examples of darker-skinned faces or female faces during training, they may be less accurate at recognizing or correctly identifying individuals in those groups. As a result, these models tend to have significantly higher error rates when classifying the faces of people with darker skin or women, reflecting the bias that came from imbalanced or non-representative training data. 

If a machine learning system operates as a black box and direct access to its algorithms or biases is unavailable, reverse-engineering/auditing the outputs can offer insights. By systematically analyzing outputs against a variety of inputs, patterns and potential biases can be uncovered. This process allows users to validate the model’s predictions and assess whether it is the appropriate solution for the problem at hand. 

Transparency and understanding of machine learning systems are not just technical concerns—they are ethical imperatives. To use these tools responsibly and effectively, it is vital to ensure they align with principles of fairness, accuracy, and trust. Machine learning can be a powerful solution for complex problems, but its implementation must be approached with clarity, diligence, and a commitment to ethical decision-making. 

The following are different types of Machine Learning methodologies and use cases: 

Supervised Learning 

Supervised learning involves training an algorithm on a labeled dataset to make predictions or classifications. 

Customer Churn Prediction

Use Case: Predict which customers are likely to leave a subscription service.

Features: Usage patterns, customer complaints, last login date.

Algorithm: Support Vector Machines (SVM), Neural Networks.

Unsupervised Learning

Unsupervised learning involves finding patterns in unlabeled data. 

Customer Segmentation

Use Case: Group customers into segments for targeted marketing.

Method: Analyze purchasing behavior, demographics, and website activity.

Algorithm: K-Means Clustering, Hierarchical Clustering.

Reinforcement Learning

Reinforcement learning (RL) uses agents that learn by interacting with their environment and receiving rewards or penalties. 

Dynamic Pricing Systems

Use Case: Adjust prices dynamically based on demand and competition.

Example: Ride-sharing apps like Uber use RL to optimize pricing.

Algorithm: Q-Learning.

Semi-Supervised Learning 

Semi-supervised learning works with a combination of labeled and unlabeled data. 

Image Classification

Use Case: Label a small number of medical images to detect tumors.

Example: Train a model with few labeled CT scans and many unlabeled ones.

Algorithm: Self-training, Graph-Based Models.

Deep Learning (Neural Networks) 

Deep learning models use artificial neural networks for complex tasks. 

Natural Language Processing (NLP)

Use Case: Translate text, summarize articles, or perform sentiment analysis.

Example: Google Translate, ChatGPT, and Siri.

Algorithm: Recurrent Neural Networks (RNNs), Transformers (like GPT). 

But don’t get carried away. It will sometimes be wrong, and you need to be comfortable with that. Worse yet, it’s harder to explain. If the CEO asks, “Why did it match these two records?” and you mumble something about a neural network, you’re in trouble. 

Start with rules when deciding which methodology to use—simplicity beats complexity. Rules are cheaper, faster, and easier to trust, and they help you better understand the problem by clarifying your thinking and revealing patterns, outliers, and data gaps. Machine learning, on the other hand, is expensive and requires significant time, data, and computational power. If a problem can be solved with a few lines of code, there’s no need to bring in a bulldozer. Use machine learning only when rules fail. 

How to Decide: Rules vs. Machine Learning 

Are the patterns clear and simple?

·       Yes → Use rules.

·       No → Consider machine learning. 

Is the data clean and structured?

·       Yes → Use rules.

·       No → Machine learning might help clean up the mess. 

Do you need transparency?

·       Yes → Rules are king.

·       No → Machine learning can work but proceed with caution. 

How big is the problem?

·       Small and specific → Use rules.

·       Large and messy → Machine learning might scale better. 

Next Steps and Strategic Questions for a CEO 

The first question I’d ask is: What problem are we solving, and what method solves it best with the least hassle?Life, and business, works best when you keep it simple. Simplicity makes execution easier, decisions clearer, and outcomes more predictable. Complexity introduces friction, expense, and uncertainty.

“What does the problem and the data tell me?” 

If the problem is straightforward—matching structured and clean data like customer records or financial transactions—then rule-based systems win. Rules are deterministic, transparent, and easy to audit. They allow me to explain decisions to stakeholders with confidence: “This is the rule, here’s the result.” If something goes wrong, I can pinpoint the error and fix it quickly. With rules, I get control and reliability. Why would I waste time and money on something unnecessarily complex? 

But if the data is messy, the patterns are ambiguous, or human biases are too strong to encode clean rules, machine learning could offer value. It can identify relationships that aren’t obvious, handle inconsistencies like typos or synonyms, and improve as more data becomes available. However, I’d have to ask: Do I fully understand the risks, costs, and trade-offs of introducing machine learning?

“How does this impact strategy and tactics?” 

On the strategic level, rule-based systems reflect discipline and clarity—values I care about as a business leader. They encourage a culture of focused, logical problem-solving. Machine learning, on the other hand, adds capability but also complexity. It requires investments in people, tools, and ongoing maintenance. If used recklessly, it can muddy decision-making and create black-box problems that no one can explain. Transparency is critical when you’re making decisions that affect customers, regulators, or shareholders. If I can’t explain why something happened, I lose trust and credibility. 

At a tactical level, the decision is about cost and scale. Rules are faster to implement, cheaper to maintain, and effective for specific, well-defined problems. Machine learning scales better for larger, chaotic datasets, but it demands time, talent, and computational resources. If I’m matching 10,000 records in a predictable format, rules are enough. If I’m matching millions of unstructured data points across complex patterns, then machine learning becomes worth the trade-off. 

“How will I keep the business accountable?” 

Here’s where judgment matters. If I choose machine learning, I must ensure the model’s outputs align with business goals and are continuously monitored for fairness, accuracy, and bias. Blindly trusting a model is no better than flipping a coin. Worse, if a machine learning system gives me a result I can’t explain, I’ll look like a fool when the board or a customer asks, “Why did this happen?” Transparency and accountability are non-negotiable 

“Is there diminishing Returns with machine learning? 

The law of diminishing returns is obvious in farming—dumping fertilizer on a field will boost yields at first, but beyond a certain point, each additional bag produces less benefit. In machine learning, we see a similar pattern: after the initial leaps, every incremental improvement in a machine learning model requires substantially more data, more computing power, and more engineers. The low-hanging fruit is plucked early, and getting that last percentage point of performance becomes ever more expensive. Not only do costs rise, but so do the energy bills, as these massive training runs devour electricity. At some point, the added complexity and resource expenditure isn’t worth the marginal gain. 

Anyone who thinks rationally about this should ask: “Does a slight uptick in model accuracy justify a colossal jump in cost and energy usage?” The answer is often no. Just as a sensible investor will avoid chasing a stock that’s overpriced relative to its potential return, a sensible machine learning team will refuse to pile on capital and effort for meager payoffs. Instead, the wise approach is to hunt for more elegant solutions that don’t require brute force—things like model compression, improved training strategies, or theoretical insights that stretch existing resources further. The smart player knows when to walk away from diminishing returns and redeploy resources where they’ll produce more value. 

“Does ethics come into play?” 

When it comes to developing and using rule-based matching and machine learning, an unwavering commitment to ethical decision-making is fundamental. In today’s world, you can’t just rely on your internal moral compass; you’ve got to stack it against what the law requires and what credible authorities recommend. There’s a whole ecosystem of established laws, regulations, and best practices out there—take them seriously. Keep an eye on the ones that are still evolving, too. Don’t be caught flat-footed when some new privacy rule or anti-bias standard lands on your doorstep. 

At the same time, understand that what passes for ethical behavior in one part of the globe might trigger outrage or legal trouble elsewhere. Cultural norms vary widely, and doing business in multiple countries means you’ll face a patchwork quilt of expectations. You might find yourself perfectly compliant in one jurisdiction but off the moral rails in another. That complexity doesn’t give you a free pass to ignore standards; it means you must be thoughtful, do your homework, and sometimes aim higher than the bare minimum. The smartest approach is to get out in front of the problem—know the laws and best practices, anticipate the changes, and be nimble enough to adapt. That’s not only how you stay on the right side of the regulators, but also how you build the kind of long-term reputation that money can’t buy. 

“Simplicity first, complexity when necessary” 

Start with rules. They solve 80% of problems effectively, and they clarify thinking. Use machine learning only when rules fail—when the data is messy, the patterns are too complex, or the scale demands it. Be skeptical of over-engineering; as the saying goes, “When you have a hammer, everything looks like a nail.” Know when to keep it simple and when to bring out the bulldozer—but don’t confuse tools with solutions. 

“Don’t let the tail wag the dog.” Strategy and tactics must remain aligned with solving real business problems efficiently. Start simple and escalate complexity only when necessary. When the time comes to use machine learning, do it with eyes wide open, understanding the additional complexity, cost, and scrutiny required. Ultimately, this decision affects not only the company’s bottom line but its reputation for sound judgment, prudence, and clear thinking.

The real solution is always good judgment, and good judgment begins with asking the right questions.