9 data analysis biases and how managers can address them

Undetected bias in analytics is an enterprise risk that silently distorts forecasts, undermines model-driven decisions, and exposes organizations to regulatory scrutiny before anyone in leadership realizes the underlying data was flawed. Understanding where bias enters the analytics lifecycle, and what controls prevent it, is now a managerial responsibility, not just a technical one.

Organizations can never completely eliminate bias in data analysis, but they can take measures to detect and mitigate issues in practice. Avoiding bias begins by recognizing that data bias exists in the data itself, the people who analyze or use it, and the analysis process. There are many adverse effects of bias in data analysis, ranging from making bad decisions that directly affect the bottom line to disadvantaging certain groups of people.

To understand where these biases most commonly emerge and how organizations can address them, we asked data and analytics leaders across industries to share the patterns they see most in practice.

1. Training data misalignment

Organizations often use large, available data sets rather than targeted, granular data. For example, a team might collect data on all stores in a retail chain’s daily sales per week for a specific analysis. Inna Kuznetsova, former CEO of ToolsGroup, a supply chain planning and optimization firm, said it can sometimes take more time and expense, but is much less useful for planning promotions than a smaller set of much more granular data.

Sales in a small group of stores with similar demographics, tracked by hours of operation, will allow for the planning of promotions targeted at the needs of a specific customer set. “Bigger data is not useful for that store, but more granular data is,” Kuznetsova said.

Start with the type of analysis and consider the best way to identify patterns across related data sets. Identify when certain data sets may not be relevant to a given analysis. For example, a standalone store of a luxury brand on a summer holiday island might not follow the regular pattern of big sales at Christmas. It makes most of its sales during the summer and sells almost nothing once the big city crowds leave at the end of the season.

2. Confirmation bias

Confirmation bias occurs when analytics teams select only the data that supports their existing hypothesis. Confirmation bias is most commonly found in evaluations and is most likely to go unnoticed when results appear favorable.

“If the results tend to confirm our hypotheses, we don’t question them any further,” said Theresa Kushner, partner at Business Data Leadership, a data consulting firm. “However, if the results do not confirm our hypotheses, we go out of our way to reevaluate the process, the data or the algorithms, and think we must have made a mistake.”

Organizations must develop a process to test for bias before any model reaches end users. Ideally, this is run by a separate team that can evaluate the data, model and results with a fresh set of eyes to identify problems that the original team may have missed.

3. Availability bias

Matt McGivern, managing director and leader of enterprise data management at Protiviti, said he’s increasingly seeing a new kind of bias: High-value data sets that were previously in the public domain are being locked behind paywalls or are no longer available. Depending on the modelers’ financial support and the type of data involved, future model results may be biased towards data sets that are still freely available in the public domain.

Organizations should instruct their teams to evaluate high-quality synthetic datasets as mitigation when data becomes inaccessible. In addition, there is an advantage in the future, as more data sets that were previously only available to individual organizations are now being made publicly available, even if this entails costs.

4. Temporary bias

Temporal bias occurs when data from specific time windows are used to make predictions or draw conclusions without accounting for seasonality, cyclical patterns, or other time-dependent variables. It is important to consider how a particular forecast may change over different time windows, such as weekdays/weekends, end of the month, seasons or holidays.

Patrick Vientos, principal advisor at Consilio, an eDiscovery platform, said organizations should focus their teams on time series analysis techniques, the role of windows for model training and evaluation, and regularly updating models with new data.

5. AI infallibility bias

Generative AI (GenAI) models can create authoritative-sounding prose that hides factual errors. This problem is well documented in legal cases involving hallucinatory quotations, but is equally present in business analysis. Nick Kramer, vice president of applied solutions at SSA & Company, a global consulting firm, said he’s also seen the same problem in business analytics cases, where users rely on GenAI to do the math, then trust the numbers or rush emails with incorrect facts.

Kramer recommended approaching AI as you would approach new hires without experience. Analysis teams using GenAI tools to help interpret analytics need thorough training on the strengths and weaknesses of GenAI and large language models (LLMs). It is also important to maintain healthy skepticism towards the results that models produce.

6. Optimistic bias

Analytics teams often fail to generate insights that are positive, hopeful and supportive of business goals, sometimes at the expense of accurate risk identification and a complete picture of the most likely outcomes. If not addressed, this bias can leave decision-making leadership without insight into the risks involved.

Donncha Carroll, partner and chief data scientist at corporate advisory and business transformation firm Lotis Blue Consulting, recommended that organizations normalize, recognize and reward accuracy and early identification of risks that the business needs to manage. This requires asking the right questions to elicit the right information and understanding the value of a balanced perspective. Organizations should also review the underpinnings of past business decisions to determine which insights and methodologies produced the best results.

7. Ghost in the machine bias

Carroll also sees cases where AI tools integrated into traditional analytics obscure how insights are actually generated. While these sophisticated models can provide important, high-value insights, they also introduce complexity under the hood. For example, each response may be a composite of information from different sources, making it more difficult to understand whether each component thread or source is accurately represented and appropriately weighted in the final result.

Carroll recommended that organizations start by honestly evaluating the level of impact associated with making bad decisions based on system-generated responses, and then identifying where the insight pipeline is most machine-driven. From there, organizations should build one or more human-in-the-loop review steps to audit the information and the methodology before acting on the results.

8. Preprocessing bias

How data is staged and prepared before analysis can introduce bias in ways that are easily overlooked. Allie DeLonay, a senior data scientist for the data ethics practice at SAS, said decisions about variable transformations, handling missing values, categorization, sampling and other processes can skew results before any model is run.

For example, when telehealth expanded rapidly during the pandemic, it introduced systemic changes in the data available to healthcare professionals. As a result, data scientists had to consider how to process different data sets across multiple processes. Data from home health monitoring devices collected by patients may require different processing steps than similar data collected by nurses in a hospital.

DeLonay said organizations need clear protocols for how teams handle missing or inconsistently collected data, especially in high-stakes domains like health care, where these decisions have been shown in some studies to increase inequity. For example, when evaluating how the pandemic affected blood pressure values ​​for patients with hypertension, a data scientist must decide to impute missing vital signs rather than rule them out.

9. Terminology bias

GenAI models trained on public data can introduce bias when the data uses terminology that differs from an organization’s own language, creating problems when performing analytics against unique enterprise data. “What ends up happening is that generative AI doesn’t understand company-specific terminology,” said Arijit Sengupta, founder and CEO of AI platform Aible. For example, one company may refer to a “sales zone”, but the AI ​​model may not interpret this as “sales territory”.

Organizations should consider how representative their enterprise data is relative to the data their LLM was trained on. Sengupta said quick augmentation can help in simple cases by translating company-specific terms into terms that the LLM recognizes, while more substantial differences may require fine-tuning the model itself.

Editor’s note: This article was republished in March 2026 to improve the reader experience.

George Lawton is a journalist based in London. Over the past 30 years, he has written more than 3,000 stories about computers, communications, knowledge management, business, health and other areas that interest him.