Bias in business analysis

Bias can be usefully defined as ‘a systematic deviation from rational judgement that affects decision making and problem-solving’ (Thinking, Fast and Slow). Bias exists in all professions, quietly influencing how we think, make decisions, and interact, — but it can have particularly significant consequences in business analysis, where the goal is to guide change and design solutions to real, complex problems. This article examines the types of biases commonly encountered in business analysis, their impact, and practical strategies for recognising and mitigating them.

Why bias matters in business analysis

Consider a business analyst (BA) who focuses all their energy on refining an inefficient process, only to  discover later that outdated technology was the real issue. confirmation bias from previous experiences has led them towards familiar solutions instead of conducting an objective analysis. In another scenario, a BA spends too much time with a solution architect they already trust, and unintentionally sidelines business end users. This bias towards familiarity creates a subtle distortion in perspective.

These are just two examples of how  bias can distort understanding, undermine trust and lead to ineffective outcomes. For BAs, whose role is to challenge assumptions and guide change, this isn’t just a nuisance; it represents a genuine risk. 

In today’s fast-paced and complex business environment, recognising and addressing bias is essential. With digital transformation and artificial intelligence accelerating, and a diverse stakeholder landscape, unchecked biases can lead to costly misjudgements, misaligned solutions and diminished business value.

Common types of bias in business analysis:

• Bias in requirements elicitation: bias can easily influence requirements elicitation, particularly in high pressure environments. For example, the loudest voice in the room or the highest-paid person’s option (HiPPO) often dominates. Alternatively, when a stakeholder holds power but shows limited interest, BAs may unconsciously prioritise their needs, excluding quieter voices that could offer critical insight.
• Bias from stakeholder relationships: bias also emerges during workshops. A BA may favour technical stakeholders they know well, giving their views more weight. In one such case, a senior software developer dictated requirements, excluding business end users. Influenced by familiarity and rapport, the BA prioritised this technical input over a balanced, business first perspective. Business needs should drive technology, not the other way around.
• Bias from past experience: when working in a familiar industry, a BA might assume they already understand the problem. Instead of conducting fresh discovery sessions, they rely on previous solutions. This overconfidence bias can result in copy-and-paste solutions that don’t suit the current context.
• Bias in choosing tools and methods: Sometimes BAs commit to tools or techniques prematurely, before fully understanding the problem. They may promise specific deliverables in a contract, only to find their chosen  tools aren’t suitable. Rather than adapt, they reshape the problem to fit the technique. With hundreds of tools available, choosing one based on familiarity alone can significantly limit effectiveness.

Case study: bias in enterprise system implementation

When a new enterprise system entered the market, there was fierce competition among the leading organisations of one industry to be the first to adopt it and to migrate historic data. 

In one high profile case, stakeholders rushed to purchase the software and roll it out company-wide without fully considering broader implications. BAs assigned to the project were responsible for managing the end-to-end business change lifecycle, but their efficacy was impacted by bias throughout the process.
 
Firstly, their familiarity with past successes led to overconfidence and they skipped essential discovery work, instead relying on assumptions over fresh analysis.

Additionally,  BAs deferred to dominant stakeholders, whose opinions then shaped requirements, throughout the project. Influenced by authority and existing relationships, the BAs prioritised these voices while ignoring middle management and business end users. This relational bias narrowed engagement and excluded critical perspectives.

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Early assumptions about the solution’s fit had also become anchored in the team’s thinking. Confirmation bias then reinforced these assumptions.

The team also relied heavily on the ‘Five Whys’ technique,  a root cause analysis method in which we ask ‘why?’ repeatedly to drill down to an underlying cause. While useful in simple contexts, this proved insufficient for complex enterprise-wide transformations. Alternative tools such as fishbone diagrams, which organise potential causes into categories like people, processes and environment; impact mapping, a strategic planning technique that visually aligns teams around business goals by mapping objectives, actors, impacts and deliverables; or collaborative workshops where stakeholders explore issues, generate ideas and prioritise challenges, would have been more effective.
Selecting the right tool for the problem is more important than defaulting to a familiar technique.

Ultimately, when the solution went live, months of remediation were required to fix overlooked issues. The lack of holistic analysis and critical challenge led to inefficiencies, stakeholder dissatisfaction and costly rework, demonstrating how unchecked bias can derail even well-resourced initiatives.

This case highlights not only the risks of bias but also the limitations of over-relying on simple techniques. 

Key cognitive biases to watch for

The case study above displays a range of common biases which had a negative impact:

• Confirmation bias: favouring evidence that supports existing beliefs
• Anchoring bias: over-relying on the first pieces of information received
• Availability bias: giving undue weight to recent or memorable data
• Groupthink: prioritising consensus over critical thinking

By recognising these tendencies in ourselves and others, BAs can implement safeguards to strengthen decision making.
How to mitigate bias in practice

BAs can reduce bias by:

Using structured frameworks to test assumptions

Involving diverse stakeholders from the outset

Applying critical thinking and peer review

Revisiting solutions post-implementation to reflect and adjust

Staying curious and open to learning  

Reflecting on bias in business analysis

Bias is a natural part of how humans think — but in business analysis, our role is to challenge assumptions, not reinforce them. That means staying objective, facilitating honestly,  making decisions based on context rather than comfort, and remaining open to reflection.

Ask yourself: when was the last time you questioned a well worn assumption? How do you ensure your solutions reflect real business needs? These questions elevate a competent BA into a true change agent.