The Cost of Bad Data: Why Data Cleaning Is Essential for Business Success

What makes Data Cleansing or Data Integrity essential for a company's success? Allow me to tell you a story!

This was one of those major projects that sometimes just lands on your lap. Back in Brazil, I was part of a large team tasked with replacing the operational systems (front-office and back-office) across several hotels. Brazilian government had introduced new fiscal procedures that required additional layers of compliance, and our previous software couldn’t keep up with the new requirements. As part of the implementation team, I had to ensure that all tables, historical data, customer profiles, and system logic transferred seamlessly, not only to keep our guests’ experience intact but also to meet the government’s updated data standards.

Getting into the “guts” of that database taught me an incredible lesson. While preparing for the transfer, I found myself studying the raw data patterns created by years of check-ins for that particular table. In the process, I witnessed how hurried human behavior impacts data entry. A recent visit to a conference hotel in Las Vegas reminded me of that experience: the long lines and the receptionists juggling task after task with no time for a breath. In such situations, do you think the customer profile data is accurate? Often, the receptionists rush through fields, selecting default values or typing the bare minimum just to manage the next guest. Back in Brazil, we discovered fields filled with random, default data from years of hurried interactions. Imagine trying to run localized marketing campaigns or creating customer personalization experiences on that data! This is just one example of how inaccurate data, accumulated over time, erodes business value in ways often unseen until it’s too late.

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Why Bad Data Costs So Much

In today’s data-driven world, bad data has become a quiet but costly epidemic across industries. According to IBM (2023), 80% of the time spent by data scientists and data analysts goes toward cleaning data rather than analyzing it for actionable insights. With these experts spending the bulk of their hours preparing data, the potential cost impact on productivity and project timelines is enormous. Just imagine the lost opportunities if teams could dedicate even half of that time to innovation or customer experience improvements.

Let’s break it down. If the average data scientist handles about four projects per year, with each project requiring 200 hours (or five weeks) of work, that’s 800 hours annually per scientist. Using IBM’s 80% estimate, 640 of those hours are spent just cleaning data. Considering the average salary of a data scientist in the U.S. is around $120,000 per year, we can estimate that $96,000 of a single data scientist’s salary goes purely toward data cleaning. In an organization employing 10 data scientists, that’s close to $1 million spent annually on data cleaning alone—a stark financial reminder of the cost of bad data.

Hidden Costs Across Industries

In the hotel industry, data accuracy is critical. As I observed during the hotel data overhaul, years of inaccurate, incomplete, or hastily-entered customer profiles make it impossible to leverage data for targeted marketing or personalized guest experiences. This isn’t unique to hospitality; According to HFS Research (2022), 75% of executives don’t trust their data.

McKinsey (2020) notes that organizations can reduce costs by up to 30% by maintaining data integrity throughout the data pipeline. When data is clean, accurate, and reliable, businesses can confidently execute strategies that lead to real-world impacts—whether that’s localized marketing for hotels, predicting inventory for retail, or identifying fraud in finance.

The Process of Cleaning Data and Why It Matters

The data-cleaning process isn’t just about deleting duplicates or correcting typos. According to IBM’s InfoSphere QualityStage (2024), data cleaning is a multi-stage process that involves understanding organizational goals, analyzing and preparing data, designing transformation jobs, and iteratively evaluating results. This process is crucial for creating a solid data foundation. Without clean data, any advanced analytics or machine learning algorithms are only as good as the “garbage in” they receive.

For businesses, cleaning data means having access to consistent and relevant information that drives real value. McKinsey (2018) has identified key areas where clean data directly contributes to business growth, from customer retention strategies to supply chain optimization. When accurate data is paired with insights, companies are equipped to make prompt, informed decisions that drive down costs and elevate customer satisfaction.

Bad Data Is Everyone’s Problem

What’s clear is that data cleaning isn’t just a technical issue; it’s a business priority. Companies relying on poor-quality data lose out on efficiency, see higher operational costs, and often struggle to compete in today’s fast-paced digital economy. The hidden costs accumulate, whether through unstructured customer profiles, inaccurate inventory counts, or misdirected marketing dollars.

Effective data management is essential, especially considering the time and costs associated with poor data quality. To minimize errors and ensure that teams have access to accurate and reliable information, organizations should implement automated data cleansing tools and invest in data governance strategies. In an era where data is arguably a company’s most valuable asset, ensuring that asset is accurate and ready for action is nothing less than essential.

Investing in Clean Data for Future Gains

From my experience in Brazil to the data dilemmas in every industry today, one thing is clear: clean data is a critical foundation for success. The resources and time allocated to data cleaning are crucial. Although it may appear expensive in the short term, the long-term advantages of making informed decisions based on accurate information are invaluable. By focusing on data cleaning and accuracy, businesses can streamline their operations and strategically position themselves in the market to capitalize on authentic, data-driven opportunities.

For organizations seeking efficiency, revenue growth, and competitive advantage, clean data isn’t optional—it’s mandatory.

Reference

Bryan Petzold, Matthias Roggendorf, Kayvaun Rowshankish, & Christoph Sporleder. (2020, June). Designing data governance that delivers value. Global management consulting | McKinsey & Company. https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/Designing%20data%20governance%20that%20delivers%20value/Designing-data-governance-that-delivers-value-NEW.pdf

Curry, D. (2022, June 23). 75% of executives don't trust their data. RTInsights. https://www.rtinsights.com/executives-dont-trust-data/

Holger Hürtgen, & Niko Mohr. (2018, April 27). Achieving business impact with data. McKinsey & Company. https://www.mckinsey.com/capabilities/quantumblack/our-insights/achieving-business-impact-with-data

IBM. (2024). InfoSphere QualityStage methodology. IBM - United States. https://www.ibm.com/docs/en/iis/11.7?topic=cleansing-infosphere-qualitystage-methodology

Mathur, G. (2023, July 6). Data science vs. machine learning: What's the difference? IBM - United States. https://www.ibm.com/think/topics/data-science-vs-machine-learning