For anyone starting a machine learning project, it’s clear that the most important step is to properly define business goals.
We can compare goals to the foundation of a building. What mistakes can be made when laying a foundation? The foundation might not align with the building’s layout. Imagine a situation where part of the building doesn’t rest on the foundation. Additionally, the density and reinforcement of the foundation material could be insufficient, risking future collapse. One thing is certain: once the foundation is laid and the house built upon it, changing that foundation without completely dismantling the house is impossible.
The same applies to projects. If we adopt incorrect goals and requirements and begin the project based on them, we won’t be able to change them mid-project. Faulty assumptions can lead to complete project failure. Only the client can provide the goals and needs, yet they may not always be competent or ready to define them.
The Client May Not Understand Their Business
It’s not ideal to assume that clients don’t understand their own processes. We remember the old adage: „the customer is always right.” However, the typical client might not always know where the problem lies. We often expect them to have a high-level understanding of everything happening in the company, along with a deep understanding of processes and all aspects of their business.
But the reality can be quite different. A particular client might be excellent at the core part of their business and only that part. For example, the owner may be „the best in the world” at winning construction bids, which is the primary source of the company’s success. Thanks to this market advantage, the owner can hire specialists to handle all essential supporting processes. This situation may lead to a complete lack of knowledge about other business areas.
It’s likely that the client contacted us not about the part of their business they excel in, but the part they don’t understand. They might be struggling in this area and feel helpless. So, we may be wrong in our expectations regarding the current awareness of the clients seeking our help. Naturally, clients feel obligated to show us their present problem. And in this case, the client is the one who should present the issue. But is this person the right one for that?
When a patient visits a doctor, they must share their symptoms and health issues, but no one expects them to make a medical diagnosis. When a customer brings a car to a mechanic, they describe the problem, but they don’t diagnose the cause. Unfortunately, in the business world, owners often point to the causes of their problems, leaving analysts hesitant to question their views.
Sometimes I feel data analysts or other experts in business problem-solving should also be psychologists. At the start of a project, we need to gather all the symptoms and allow the owner to share their hypothesis about the cause. This hypothesis can be valuable to us. However, like a doctor or mechanic, we must gather all available data and use our professional tools to identify the true problem. It’s great if our findings align with the owner’s hypotheses.
Sometimes the Owner is Subconsciously Embarrassed by Their Problem
In my work, I try to avoid delving into business psychology whenever possible. Unfortunately, this is rarely achievable, as this realm permeates everything in business. Businesses are run by people with their dilemmas and issues, often too proud or plagued by certain fears. Business is created by people; office furniture, machines, and vehicles don’t generate income on their own. Embarrassment and anxiety among business owners are more common than one might think, often tied to low self-esteem, shame from personal insecurities or incompetence, or even a subconscious need to impress.
As I mentioned, psychology isn’t my strong suit, but avoiding this aspect is a recipe for failure in gathering project requirements. The risk isn’t just about wasting time collecting irrelevant requirements or setting false goals. It’s also about maintaining good relations with the owner, which greatly impacts future collaboration.
Consider the story of a man who runs a business because he’s excellent at winning construction bids. Some residents and employees see his success and regard him as a business genius, overlooking his flaws. Despite his evident issues, his erratic behavior, his long working hours, and his drivers cheating on fuel, they still hold him in high esteem. This owner doesn’t know how to solve certain problems and is embarrassed by them. So, when external analysts arrive, he discusses a problem he wishes he had, not the things he finds shameful.
It May Sound Complex, But It’s Very Simple
The owner realizes that the problem may be simple for others, but it’s not for him. So, he creates a sort of mystery around it. It’s like visiting a psychiatrist due to an irrational fear of the neighbor’s cat but not knowing how to explain it without sounding ridiculous. This analogy doesn’t fully capture the complexity of the problem, but it gives some insight.
The Difference in Worldviews
This essay doesn’t aim to highlight the limitations or ignorance of business owners. Instead, it provides guidelines for building a solid foundation for a project. External analysts aim to address all potential problems. Their profession is to identify possible causes and solutions to issues that trouble companies. For this, developing effective situational awareness is essential.
Proper situational insight is crucial to understanding processes. Imagine someone struggling with logistics costs. Their business model shows that logistics expenses consume a significant portion of the production profit, resulting in company losses. Unfortunately, humans have inherent limitations shaped by evolution. Humans can’t consider more than six or seven factors simultaneously. In contrast, machine learning algorithms can handle large data dimensions, often comprising thousands of features and enriched by inter-temporal shifts. Humans can’t interpret thousands of interconnected business factor correlations. Naturally, human cognition is limited, but those who use AI’s potential start to think outside the box, forming visions that may confuse or even annoy others.
Sadly, the lack of abstract and unconventional thinking is common among managers focused solely on their own business. When I worked in large corporations, consultants or people with overly abstract thinking were often sidelined or ignored. In our previous example, transport expenses consumed a large portion of production profit. The typical management response is to reduce overall costs and impose strict fuel monitoring, but the impact is limited. Fuel consumption can’t fall below technical norms; truck insurance or driver layoffs are unavoidable.
Significant change can come from outsiders who aren’t entangled in daily operations. They may suggest optimal truck load capacity for this specific logistics process, drawing insights from operational research algorithms. They might even reveal that the current transport model isn’t economically justified, and alternative transportation solutions could be identified, new transport networks established, warehouse locations optimized, and optimal transport times and load sizes determined. It may turn out that transport in this company isn’t necessary at all. Perhaps focusing on profitable core activities and leaving logistics to clients would be more effective.
What is Optimization?
We are accustomed to using the term „optimization,” but I’m not sure if everyone fully understands it.
A process can be described as a configuration of two value streams: a set of input resources entering the process and the outputs at the end. Before explaining optimization, I want to clarify efficiency. Efficiency is the ratio between input and output values in a process.
Efficiency should align with intended goals. Suppose our goal is to reduce transport costs. To achieve this, we decide to buy new trucks. However, these new trucks need to be paid off. In the end, we have the same revenue from transport, but our costs have increased. The difference between old and new trucks isn’t significant, and the debt service cost has reduced logistics profitability. Despite the investments, the goal of increased efficiency and improved financial results wasn’t achieved. If the management had set a goal of environmental protection or accident risk reduction, acquiring new trucks might have been a good decision despite the higher maintenance costs.
So, we conclude that the initiative’s goal is crucial. Goal information is the most important insight we must obtain from the business owner during project initiation.
Summary
When building a house, creating a proper foundation is essential. To optimize and find the best business solution, it’s vital to understand the source of the problem and identify the true goal of the initiative. Failing at this stage will likely result in project failure.
Wojciech Moszczyński
Graduate of the Quantitative Methods Department at Nicolaus Copernicus University in Toruń, specializing in econometrics, data science, and management accounting. He focuses on optimizing production and logistics processes and conducts research in AI development and application. He has been dedicated for years to promoting machine learning and data science in business environments.