Research Topics:

Data Cleaning

Content Type:

Glossary

Data Cleaning Definition

The process of improving the quality of data by modifying its form or content including the removal and/or correction of erroneous data introduced by data entry errors, expired validity of data, improper sequence of questions.

Data cleaning identifies and corrects errors, inconsistencies, inaccuracies and incomplete information within a dataset. The process involves modifying the data’s form or content, removal and correction of erroneous numbers introduced by data entry errors, expired validity of data and improper sequence of questions to ensure that the data is accurate, reliable and ready for analysis. Data cleaning is crucial in marketing research for several reasons, including accuracy, credibility, efficiency, insightful analysis and effective strategy. Simply put, clean data is useful data.

Who relies on data cleaning?

Marketing researchers and managers, data analysts and decision makers within businesses and other organizations rely on data cleaning so they possess reliable and accurate data for making informed strategic decisions, targeting the right audience and deriving meaningful insights.

Why should I care about data cleaning?

Data cleaning impacts the quality of marketing research outcomes because clean and accurate data leads to reliable insights. Neglecting data cleaning can result in flawed analyses, misinterpretations of findings ,and misguided decisions. Unreliable data can lead to wasted resources and missed opportunities.