9 data analytics biases and how executives can address them
Analytics can exhibit biases that affect the bottom line or cause reputational damage through discrimination. It's important to address those biases before problems arise.
Undetected bias in analytics is an enterprise risk that quietly 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 governance 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 starts by recognizing that data bias exists in the data itself, the people analyzing or using it and the analytics process. There are many adverse impacts of bias in data analysis, ranging from making bad decisions that directly affect the bottom line to adversely affecting certain groups of people.
To understand where these biases most commonly surface and how organizations can address them, we asked data and analytics leaders across industries to share the patterns they see most often in practice.
1. Training data misalignment
Organizations often default to large, available data sets rather than targeted, granular data. For example, a team might gather data on all stores in a retail chain's daily sales by week for a particular analysis. Inna Kuznetsova, former CEO of ToolsGroup, a supply chain planning and optimization firm, said this can sometimes take more time and expense, but is far less useful for planning promotions than a smaller set of much more granular data.
Sales in a small cluster of stores with similar demographics, tracked by operating hours, would allow for planning promotions targeted to the needs of a particular customer set. "Bigger data is not useful for that store, but more granular data is," said Kuznetsova.
Start with the type of analysis and consider the best way to identify patterns across related data sets. Identify when certain data sets might not be relevant to a given analysis. For example, a standalone store of an upscale brand on a summer vacation island might not follow the regular pattern of large sales at Christmas. It makes most of its sales during the summer and sells almost nothing once the big-city crowd leaves 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 often found in evaluations and is most likely to go undetected when results look 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 company. "However, if the results don't confirm our hypotheses, we go out of our way to reevaluate the process, the data or the algorithms, thinking we must have made a mistake."
Organizations should develop a process to test for bias before any model reaches end users. Ideally, it is run by a separate team that can evaluate the data, model and results with a fresh set of eyes to identify problems the original team might have missed.
3. Availability bias
Matt McGivern, managing director and enterprise data governance lead at Protiviti, said he is increasingly seeing a new kind of bias: High-value data sets previously in the public domain are being locked behind paywalls or are no longer available. Depending on the modelers' financial backing and the types of data involved, future model results might be biased toward data sets still available for free in the public domain.
Organizations should direct their teams to evaluate high-quality synthetic data sets as mitigation when data becomes inaccessible. Additionally, there is an advantage in the future, as more data sets previously available only to individual organizations are now made publicly available, even if they carry charges.
4. Temporal bias
Temporal bias arises when data from specific time windows is used to make predictions or draw conclusions without accounting for seasonality, cyclical patterns or other time-dependent variables. It's important to consider how a specific prediction might change over different time windows, such as weekdays/weekends, end of month, seasons or holidays.
Patrick Vientos, principal advisory at Consilio, an eDiscovery platform, said organizations should direct their teams toward time-series analysis techniques, rolling windows for model training and evaluation and regularly updating models with new data.
5. AI infallibility bias
Generative AI (GenAI) models can craft authoritative-sounding prose that obscures factual errors. This problem is well documented in legal cases involving hallucinated citations but is equally present in business analytics. Nick Kramer, vice president of applied solutions at SSA & Company, a global consulting firm, said he has 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 with no experience. Analytics teams adopting GenAI tools to help interpret analytics need thorough training on the strengths and weaknesses of GenAI and large language models (LLMs). It's also important to retain healthy skepticism toward the results models produce.
6. Optimist bias
Analyst teams often default to generating insights that are positive, hopeful and supportive of enterprise objectives, sometimes at the expense of accurate risk identification and a complete picture of the most likely outcomes. Left unaddressed, this bias can leave leadership making decisions without visibility into the risks they entail.
Donncha Carroll, partner and chief data scientist at corporate advisory and business transformation firm Lotis Blue Consulting, recommended organizations normalize, recognize and reward accuracy and early identification of risks that the business must manage. This requires asking the right questions to elicit the right information and to understand the value of a balanced perspective. Organizations should also review the underpinnings of past business decisions to determine which insights and methodologies delivered the best results.
7. Ghost in the machine bias
Carroll is also seeing 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 answer could be a cobbling together of information from different sources, which makes it more difficult to understand whether each component thread or source is represented accurately and appropriately weighted in the final result.
Carroll recommended that organizations begin by honestly assessing the level of impact associated with making bad decisions based on system-generated answers, then identify 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 easy to overlook. Allie DeLonay, a senior data scientist for the data ethics practice at SAS, said decisions on 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 various processes. Data from home health monitoring devices collected by patients might 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, particularly in high-stakes domains such as healthcare, where these decisions have been shown in some studies to increase unfairness. When evaluating how the pandemic affected blood pressure values for patients with hypertension, for example, a data scientist must decide whether to impute missing vital signs rather than exclude them.
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 running analytics against unique enterprise data. "What ends up happening is that generative AI does not understand company-specific terminology," said Arijit Sengupta, founder and CEO of the AI platform Aible. For example, one company might refer to a "sales zone," but the AI model might not interpret that as "sales territory."
Organizations must consider how representative their enterprise data is relative to the data their LLM is trained on. Sengupta said prompt augmentation can help in simple cases by translating company-specific terms into terms the LLM recognizes, while more substantial differences might 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 last 30 years he has written more than 3,000 stories about computers, communications, knowledge management, business, health and other areas that interest him.
