Data massaging – The most innovative way to redefine data quality and survey Insights.

Published on July 1, 2025 by Manish Kumar Agarwal

In today’s data-driven world, businesses rely heavily on substantial amounts of accurate, clean, and organised data to make informed decisions. However, raw data is often unorganised, inconsistent, and unstructured. This is where data massaging – also known as data cleaning – plays a crucial role, including in detecting fraudulent responses.

Data massaging refers to the process of transforming raw data into a structured format by rectifying inconsistencies, removing errors, and ensuring data reliability to achieve meaningful insights.

In this blog, we delve deeper into data massaging in the corporate world, its benefits, processes, automation opportunities and real-world impact.

The cost of poor data quality

Bad data is a major liability for organisations. It impacts finances, operations, and reputations. The following are some eye-opening statistics that highlight its real-world consequences:

Monetary impact:

• Poor data quality costs businesses an average of USD15m per year in losses, according to Gartner

• IBM estimates that bad data costs the US economy USD3.1tn annually due to inefficiencies, lost revenue, and compliance risks

Operational inefficiencies:

• Ninety-one percent of businesses suffer from data errors that negatively impact operations, according to research by Experian.

• Data scientists spend 80% of their time cleaning and preparing data instead of analysing it, according to Harvard Business Review.

Customer and reputational impact:

• Eighty-four percent of CEOs are concerned about the quality of the data they rely on for decision-making, according to a survey by KPMG.

• Nineteen percent of businesses have lost customers due to incorrect or incomplete data, according to a study by Dun and Bradstreet.

These statistics underscore why data massaging is critical for business success. Companies that fail to prioritise data cleaning and detection of bad respondent’s risk making misinformed decisions, wasting resources, and losing customer trust.

How data massaging improves business operations

Real-world case studies

Amazon’s AI-powered data cleaning

• Amazon processes over 1.5n gigabytes of data daily and uses AI-driven data-cleaning algorithms to ensure accurate customer recommendations and fraud detection.

• Poor data quality can increase logistics errors by up to 30%, leading to delayed shipments and customer dissatisfaction.

Financial sector – preventing fraud with clean data.

• The financial sector loses over USD42bn annually due to fraud caused by poor data quality.

• Banks such as JP Morgan and Citibank have implemented automated fraud-detection algorithms to flag suspicious transactions in real time.

Healthcare – The cost of dirty data

• Bad data contributes to over 250,000 deaths annually in the US, according to a study by Johns Hopkins, making medical errors the third-leading cause of death.

• Hospitals using AI-driven data massaging have reduced administrative errors by 60%, leading to better patient outcomes and cost savings.

Key benefits of data massaging

• Enhances Decision-Making: With clean, structured data, businesses make accurate and data-driven decisions.

• Boosts Operational Efficiency: Eliminates the need for manual data validation, saving time and resources.

• Improves Customer Satisfaction: Accurate data allows for personalised interactions, boosting customer retention by up to 40%.

• Ensures Regulatory Compliance: Clean data helps organisations avoid fines and legal risks associated with data-privacy laws (e.g., GDPR, CCPA).

Application of data massaging for business:

The following are key areas in which we use data massaging:

The role of automation in data massaging

• AI-powered data cleaning: AI and machine-learning models can detect anomalies and clean data in real time, reducing errors by up to 90%.

• Automated fraud detection: AI-driven fraud detection in surveys and financial transactions has cut fraudulent activities by 70% in major companies.

• Big data processing: Automated tools enable businesses to process petabytes of data within seconds, enabling faster decision-making.

Tools and technologies for data massaging

Some of the top tools used to automate survey data massaging and detection of bad respondents:

• Forsta (ConfirmIT and Decipher) – Real-time survey data validation.

• Qualtrics – AI-driven response filtering to remove fraudulent or inconsistent data.

• Python and R – Automates data cleaning using libraries such as pandas.

• BI tools (Tableau, Power BI, DisplayR) – Provides real-time visualisation of clean data.

How Acuity Knowledge Partners can help

We are a global leader in market research solutions, providing businesses with high-quality, actionable insights.

Real-time solution integration

We integrate real-time data cleaning and detection of bad respondents with the following:

• Survey platforms – Forsta, Qualtrics

• Programming languages – Python, R

• BI tools – Tableau, Power BI

This enables real-time tracking of bad respondents and on-the-go final reporting.

What Sets Us Apart?

• Tailored Solutions: Customised survey design, data cleaning and reporting

• AI-Driven detection of bad respondents: Prevents contamination by bad data in research.

• Global expertise: We provide data solutions across regions and industries.

• Fast and reliable execution: Quick turnaround times without compromising accuracy.

• End-to-end expertise: We offer seamless execution – from project inception to actionable insights.

The following are some of the innovative real-time bad-detection techniques we use in data massaging. These ensure the authenticity of survey data.

Conclusion

Data massaging is a critical process for transforming raw, unorganised data into valuable insights. By ensuring data quality, consistency and usability, companies can do the following:

• Enhance operational efficiency.

• Improve decision-making.

• Increase customer satisfaction.

• Maintain regulatory compliance.

In a world where bad data can cost businesses billions, investing in automated data massaging solutions is no longer optional; it is essential.

Sources:

• https://www.forbes.com/councils/forbestechcouncil/2021/10/14/flying-blind-how-bad-data-undermines-business/

• https://www.greenbook.org/insights/data-quality-privacy-and-ethics/the-rising-issue-of-bad-data-in-online-surveys-causes-and-contributing-factors

• https://www.cognitivemarketresearch.com/blog/market-research-missteps-how-bad-data-can-cost-you-big

• https://www.mrbuddies.online/fraud-cop/

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