Data Cleaning Tools: A Market Research GuideData cleaning tools have been a massive time saver for market researchers, streamlining tedious tasks like detecting outliers and checking for missing responses. Most of the tools market researchers use to clean and tidy survey data exist as capabilities within wider data analysis software. This can make it hard to compare them side-by-side in terms of data cleaning - but no more.
This is your guide to data cleaning tools for market researchers. It goes over the pros and cons of some of the most commonly used data cleaning tools, plus it gives you some important data cleaning tips to help power more accurate analysis.
Why Do Market Researchers Clean Data?
Before diving into all things data cleaning tools, it’s important to look at why data cleaning is so important. To put it simply, data cleaning is a way to improve the accuracy and reliability of our survey analysis. It's a way to remove errors, inconsistencies, or outliers that could skew results.
We sometimes talk about data cleaning versus data tidying. These are not interchangeable terms. Data cleaning is all about removing what’s dirty, while tidying is what we do to make it presentable.
- We clean data so that we can get better insights.
- We tidy to find these insights faster.
Data Cleaning Workflow
Before you even begin using data cleaning tools, it's crucial to examine the raw data file itself—whether it’s an Excel spreadsheet, a .sav file from SPSS, or another format. Many researchers jump straight into software without first checking if the structure of the data is even suitable for analysis.
You won’t be able to run proper significance tests or clean the data reliably unless the file is structured correctly.
A better approach is to ensure that your raw data file is already in a clean, analysis-ready format. For Excel files, this means:
- One row for variable names
- A unique ID column
- One row per survey respondent
- Data stored in numeric form
- Multi-response questions are represented across multiple columns (one per option)
Once you have a well-structured raw data file, you can then move into your data cleaning tool of choice and begin the real work of tidying, validating, and transforming the data for deeper analysis.
Want to learn more about how to clean your survey data? Check out Displayr's latest ebook for a comprehensive guide to data cleaning.
Automated Data Cleaning Tools Vs Excel, SPSS, or Text Editors
The biggest benefit of using any of the data cleaning tools on this list, is that they automate time-consuming tasks. Tools like Excel, SPSS, and text editors, on the other hand, are very manual. Even when using macros or syntax files, users must continuously modify their workflows for each new dataset. This leads to:
- High time cost: Every data file requires manual intervention. When managing multiple waves of data or iterative research projects, this quickly becomes inefficient.
- Poor repeatability: Cleaning steps often need to be recreated from scratch or carefully adapted, increasing the risk of inconsistency.
- Limited documentation: Any records of what was changed must be manually written and maintained. If someone forgets, cuts corners, or leaves the team, that context may be lost.
- Lack of transparency: Changes are made directly to the data, making it difficult to trace what was done without comparing back to the original file. This complicates quality control and collaboration.
As a result, relying solely on these tools for cleaning often makes the process slower, harder to audit, and more error-prone.
In contrast, automated data cleaning platforms streamline this process. This creates a more efficient and reliable workflow.
Key benefits include:
- Time savings: Automated tools apply predefined cleaning operations—such as fixing labels, identifying question types, standardizing missing values, or flagging outliers—instantly and at scale.
- Reusability: Cleaning steps can be saved, shared, and reapplied across multiple projects or waves of data, reducing setup time and minimizing inconsistencies.
- Built-in documentation: Many modern tools log all changes by default, making it easy to audit, review, or reverse edits as needed.
- Separation of raw and cleaned data: Changes are often stored separately from the original file, allowing for cleaner workflows and more confident decision-making.
- Ease of updates: When new data is added (e.g., additional survey responses), the cleaning process can be automatically re-run, ensuring consistency across time.
For market researchers handling increasingly complex survey datasets, investing in a robust data cleaning tool is more than just a convenience—it’s a safeguard against inefficiency, errors, and lost insight.
Cleaning Across The Data Value Chain
Before we talk about data cleaning tools, we need to first think about something called the data value chain, which describes the distinct stages of turning data into value. Stages one and two of the data value chain involve the capture and collection of unstructured data (surveys, focus groups, interviews, etc). Stage three is normalization of data - i.e., getting it ready for analysis. This is where data cleaning usually takes place.
However, the evolution of survey platforms such as Qualtrics and SurveyMonkey has made it possible to perform basic data cleaning tasks at stages one and two of the data value chain. This is not going to be as comprehensive as data cleaning in the normalization stage, but it can be an effective way to identify and remove obvious errors before they can even be saved into a data file.
Data Cleaning Tools for Market Researchers
This list is not so much a definitive ranking as an overview of some of the different data cleaning solutions available.
Displayr
Data cleaning in Displayr caters to all different types of survey data, allowing you to manually clean the data file variable by variable or automate the entire process.
Automated data cleaning in Displayr is not about simply ticking off a workflow - it's about looking at your data file from the perspective of a market researcher and quickly addressing common issues.
- Automatic data checks on import: Displayr inspects your data file as soon as it’s uploaded. It detects which data collection tool was used and automatically fixes common issues—like missing labels, wrong question types, and missing values.
- Built-in cleaning automation: Automations are available to handle common cleaning tasks such as removing outliers, flagging “don’t know” responses, reversing scale directions, capping extreme values, and identifying flatlining behavior.
- Custom cleaning scripts (Enterprise feature): With an Enterprise license, you can build your own data cleaning automations using QScript—either from scratch or by tweaking existing ones to fit your specific workflow.
- Automatic re-cleaning on updates: When new data is added, all previous cleaning steps are automatically reapplied—saving time and ensuring consistency across waves.
- Easy auditing and reversibility: Every change made in Displayr is stored in the document, making it simple to audit your work or roll back to the original data if needed.
- Optional locked-down workflows: If you don’t want end-users to see or modify the cleaning steps, you can create a cleaned version of your data in one Displayr document and export it (as an SPSS file or to the Displayr Cloud Drive) for use in a separate reporting document.
Tableau Prep
Tableau is Salesforce's business intelligence platform, which aims to connect sources and turn unstructured data into insights. In terms of market research, Tableau has gained popularity for its visualization capabilities and analytics tools. To make it easier for users to prepare data for analysis, Tableau launched Tableau Prep (previously Project Maestro) in 2018 to help speed up the data cleaning process.
Tableau Prep helps market researchers with:
- A drag-and-drop visual interface that allows researchers to see the data cleaning process as it happens.
- Connections to a wide range of data sources facilitate the consolidation of disparate datasets into a single, cohesive view.
- Ability to address common data issues, including missing values, duplicates, and inconsistent formatting.
- Scheduled and automated data preparation workflows, ensuring that datasets are refreshed regularly without manual intervention.
KNIME
KNIME is an open-source tool that streamlines data cleaning and preparation, making it particularly beneficial for market research applications. It is another example of how different the data cleaning process can be across various tools. KNIME focuses on building data cleaning workflows with automated flows that can run daily, weekly, monthly, etc.
KNIME can certainly be used to clean survey data, but it will require a certain level of knowledge on the fundamentals of data cleaning in market research to maximize effectiveness. Key features include:
- Handling missing values, detecting duplicates, and correcting inconsistent formats. These tools are essential for ensuring the quality of market research data.
- Easy checking on the state of data after each data cleaning step.
- Once a data cleaning workflow is created, it can be saved and reused, automating repetitive tasks and ensuring consistency across multiple market research projects.
- With over 300 connectors, KNIME can integrate data from various sources such as spreadsheets, databases, and cloud services.
SightX
Consumer research platform SightX offers data cleaning capabilities, designed to improve the accuracy of survey analysis. The automated data cleaning feature in SightX helps researchers identify:
- Duplicate answers from the same IP address
- Incomplete responses
- Non-sensical answers to open-ended questions
SightX also adds a respondent code to the raw file, meaning that if you review the data in Excel or similar and find responses to be removed, you can search the code back in SightX and remove it in the app.
SurveyMonkey
As mentioned, the data cleaning capabilities of these different tools vary—and this is certainly the case when you look at SurveyMonkey. Paid users can access Response Quality, the AI-powered data cleaning tool that scans open-ended responses and multiple-choice survey questions to flag poor-quality responses.
SurveyMonkey's Response Quality feature can:
- Identify if a respondent has answered the same option on every question or followed a matrix pattern (e.g., the respondent chose option A for row 1, option B for row 2, option A for row 3, etc.)
- Highlight if the survey was completed in a questionable amount of time
- Flag gibberish open-ended responses
- Show answers that have been copied and pasted from the question text.
It also makes it straightforward to remove any flags that you deem unnecessary, which in turn helps improve the quality of the algorithm.
SurveyMonkey's Response Quality may not be the most comprehensive data cleaning tool available, however, it is a quick and effective way to create a better quality data file for analysis. This improved data file could then be imported into a more robust solution like Displayr for deeper analysis.
Ready to get your survey data clean and ready for analysis? Try Displayr today and see why market researchers can't work without it.
