In the age of information, data could be seen as the “new oil”. It fuels decision-making processes in market research. However, just as crude oil needs refining to be useful, raw data must undergo a process known as data cleaning or data cleansing. It is the process of identifying and correcting errors, inconsistencies, inaccuracies, and irrelevance within datasets.

In the realm of market research where data-driven decisions are important, the need for clean and reliable data cannot be overstated. Having clean data prevents researchers from wasting resources on faulty information and reduces the need for expensive rework later in the process. Moreover, reliable data is the foundation of effective decision-making. Data cleaning ensures that decision-makers have access to trustworthy information, enabling them to make informed choices. Therefore, AcuityHub always performs data cleaning as a mandatory in processing data, in order to provide high-quality data.

The best practice of data cleaning starts with removing unwanted data, which involves identifying and eliminating data that is irrelevant, erroneous, or duplicated. The process then followed by correcting data to make it uniform and usable. Data errors aren’t limited to inaccurate information, but also from inconsistent formatting. Afterward, validating data comes in place, which involves verifying the data quality (i.e. accuracy, relevancy, completeness, and consistency). This process is critical, particularly when dealing with external data source or customer information (research.aimultiple.com, sagacitysolution.co.uk).

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