Different ways of Data Cleaning by Market Research Companies

Data cleaning techniques serve to raise the quality of the collected data and make it simpler to analyze. After all the data has been gathered and cleaned, analysis is a key component of market research. When done properly, it makes sure that all the important material is distinguished from the unimportant.  Additionally, avoiding irrelevant data will make it simpler to comprehend the findings after the research study is over.

Companies vary slightly in their data-cleaning approaches though every market research firm focuses mainly on getting reliable data from them. Discussed in the blog are various approaches to data cleaning by marketing research companies.

Different Approaches of Data Cleaning by Market Research Companies
2 step approach to Data Cleaning

Step 1: Check and make necessary modifications to the data format and structure

Must inspect and correct the following :

  • Data structure
  • Metadata
  • Data format
  • Labels (survey data, digital data, etc.)
  • File names

Step 1.1: Validation

Validation is a key process that helps to maintain consistency in the data, get rid of data redundancy, and find information that is missing.

Step 2: Review and update the correct data

Numerous automatic and manual checks should be performed during a closer examination of the data, including:

Benchmarks for comparison

Examine to see if you have all the data you intended and search for deviations from historical data such as observation of typical seasonal peaks and troughs.

Anomaly and Outlier Detection

Find data that seems out of place and evaluate whether it is accurate or if it is an error caused by incorrect tags, missing data, or another problem.

Fuzzy Matching Algorithms

Used to make sure that information is consistent amongst data sets by finding matches for records that are most likely to be ignored by a conventional lookup and identifying possible matches for records.


Ensure that the data is consistent throughout in terms of language and formatting. (e.g., “New Jersey” vs. “ NewJersey” vs. “NJ”)

Look for missing values

For example, do you have information for each of the 50 states?

When these procedures are taken, the process of data enrichment becomes largely automatic (including automatic data re-ingestion when necessary), and you can usually enrich data in a few hours as opposed to days or even weeks without them.

Data Massaging Approach

The practice of extracting data to eliminate extraneous information and cleaning up a dataset to make it usable is known as “data massaging,” sometimes known as “data cleansing” or “data scrubbing.”

The Approach of data massage :
  • Change the date format from m/d/y to d/m/y to reflect the target system requirements rather than the typical source system emissions.
  • When a value is missing, use default values, such as “0,”.
  • Remove records from the target system that are not required.
  • Check the accuracy of the data and discard or report any rows that could lead to an error.
  • Normalize data to eliminate variances that should be the same, for example changing “01” to “1” and upper case letters with lower case letters.

Data Massaging also includes basic exploratory analysis and data crunching, which can be used to investigate the narratives hidden in data.

The above-mentioned approaches of data cleaning are followed by many along with the topmost giants in market research.


Business value is destroyed by the quality of data which is not good. According to a 2018 Gartner study, businesses estimate that poor data quality causes losses of $15 million on average annually. One efficient way to avoid such great losses is for market researchers to give prime importance to data cleaning rather than merely focusing on getting insights out of data.