Data cleaning (cleansing) is no longer restricted to data analysts. If you are dealing with a list of prospects, if you use scraped data in your processes, or if you consolidate multiple source of data, you know the importance of effective data cleaning.

Google Sheets and Excel are enough for simple operations but are limited when it comes to consolidation and deduplication.

In this guide, you will learn how to use Datablist, a free online tool to clean and normalize your data.

Here is a quick summary of the clean-up operations found in this article:

Import from CSV or copy-paste data

Datablist is a perfect tool for cleaning data. It's an online CSV editor with cleaning, bulk editing, and enrichment features. And it scales up to millions of items per collection.

Open Datablist, and load your data sources collections.

To create a new collection, click on the + button in the sidebar. And click "Import CSV/Excel" to load your file. Or click the shortcut from the getting started page to move directly to the file import step.

Create a collection
Create a collection

Auto detect format

Datablist import assistant detects automatically email addresses, Datetimes in ISO 8601, Booleans, Numbers, URLs, etc. when they are well formated.

Type auto detection
Type auto detection

If your data required more complex analysis (different datetime format, typos in URL or email address), import them as Text property. I'll show you in the next section how to convert your text properties to Datetime, Boolean, or Number.

Select data type
Select data type

Convert text to datetime, boolean, number

Marie Kondo says "Life truly begins after you have put your house in order". Same with your data: "Sales truly begins after you have put your data in order"! 😅

Filtering on a date (creation date, funding date, etc.), a number (price, number of employees), or a boolean is so much easier when they are native objects and not just text.

Open the "Text to Datetime, Number, Checkbox" tool from the "Clean" menu.

Convert Text to data types
Convert Text to data types

Convert any text to Datetime format

Datetime has an international format called ISO 8601 with a defined structure. If your data uses the ISO 8601 format, a Datetime property will be created automatically during import to store the data.

For Date and Datetime values in other formats, you have to specify the format used so Datablist can convert it to structured Datetime values.

Select the property to convert and select "Convert to Datetime".

Convert Text to Datetime
Convert Text to Datetime

Common formats are listed (datetime formats used by Google Sheets and Excel) or select "Custom format" to define your datetime format.

Custom Datetime format
Custom Datetime format
Datetime conversion preview
Datetime conversion preview

👉 Visit our documentation to learn more on custom datetime formats.

Create Checkboxes (Boolean) from text values

Datablist converts automatically columns with "Yes, No", "TRUE, FALSE", etc. to Checkbox properties on import. Use the converter for more complex conversions.

Define the values (separated with commas) that will be converted to a checked checkbox. Other values will be kept unchecked.

Checkbox conversion
Checkbox conversion
Checkbox conversion preview
Checkbox conversion preview

Extract number values from texts

Use the "Text to number" converter to:

  • Normalize numbers with custom decimal and thousand separators
  • Extract numbers from texts with letters
Number conversion
Number conversion
Number conversion preview
Number conversion preview

👉 Visit our documentation to learn more on number conversion.

Clean data

Convert HTML to text

Scraping tools parse HTML code and you may get HTML tags in your texts.

HTML codes have links, images, and lists with bullet points. And are written with paragraphs and multi-lines.

The goal is to keep some of the order HTML brings but transform a non-readable code into plaintext.

Datablist HTML to Text converter keeps newlines, and transforms bullet points into list prefixed with -.

To transform your text with HTML tags into plaintext, open the Bulk Edit tool in the Edit menu.

Bulk Edit Tool
Bulk Edit Tool

Select your property with HTML tags. And select "Convert HTML into plain text".

Bukl Edit Convert HTML
Bukl Edit Convert HTML
HTML to Text conversion
HTML to Text conversion
HTML to Text Results
HTML to Text Results

Remove extra spaces

Another common issue with messy data is extra spaces. Spaces come from new lines, from Tab, and other characters that represent a space in HTML.

Datablist comes with a cleaning tool to get rid of extra spaces.

  • It removes extra spaces between words
  • It removes empty lines
  • It removes leading and trailing spaces on each line

To remove extra spaces, go on the "Bulk Edit" tool from the "Edit" menu. Select your property and the "Remove extra spaces" action.

Remove Extra Space Configuration
Remove Extra Space Configuration
Remove Extra Space
Remove Extra Space
Remove Extra Space Results
Remove Extra Space Results

Clean text case

Changing the case of your text is simple. Open the "Bulk Edit" tool in the "Edit" menu.

Select the property to process and use the "Change text case" action.

Change Text Case
Change Text Case

3 modes are available:

  • Uppercase - All letters will be converted to their uppercase version. Ex: john => JOHN
  • Lowercase - All letters will be converted to their lowercase version. Ex: API => api
  • Capitalize - The first letter of all words will be capitalized. Ex: john is a good man => John Is A Good Man
  • Capitalize only the first word - Only the first letter of the first word will be capitalized. Ex: john is a good man => John is a good man

Remove symbols from texts

Texts scrapped from HTML pages, or with user inputs (for example LinkedIn profile titles) can contain text symbols: smiley, and other characters that will impact the processing of your data. A simple smiley at the end of a name can prevent it from being spotted by a deduplication algorithm.

Datablist has a built-in processor to remove any non-text symbols from your data.

Click on "Bulk Edit" from the "Edit" menu, select a text property and, pick the "Remove symbols" transformation.

Remove symbols
Remove symbols

If the preview is good, run the transformation to process your items.

Remove symbols results
Remove symbols results

Normalization with Find and Replace

To build segments on your prospect lists, you must normalize your data.

  • Normalize job titles
  • Normalize countries, cities
  • Normalize URL
  • Etc.

Your goal is to reduce a property with free text into a property with limited choices. Or to transform your texts into a more basic version (URL with paths into a simple URL domain).

Datablist comes with a powerful Find and Replace tool. It works with simple text and with regular expressions.

Regular Expressions are both complex and very powerful.

Here are some examples of how to use RegEx to clean your data.

Remove query params from an URL

Scraped URLs have useless query parameters for tracking or marketing reasons. Removing them from your URLs will give you clean URLs. And it will help you deal with deduplication by using the URL to find duplicate items.

To remove query parameters from your URLs, check the "Match using regular expression" option. And use the following regular expression with an empty replacement text:

\?.*$
Regular Expression to remove query parameters
Regular Expression to remove query parameters

And apply it on your URL property.

Preview without query params
Preview without query params

Get domain from email addresses

Another use of Find and Replace with regular expressions is to get website domains from email addresses.

Duplicate your email property to preserve your source data. And use the following regular expression with an empty replacement text:

^(\w)*@
Regular Expression to get domain from email address
Regular Expression to get domain from email address
Domains from email addresses preview
Domains from email addresses preview

👉 To learn more, visit our Find and Replace documentation.

Split Full Name into First Name and Last Name

When scraping lead lists, you get contact with "Full Name" that you have to split into "First Name" and "Last Name". Being able to accurately parse a person's name into its constituent parts is a crucial step.

Separating the first name and last name is useful to address individuals more personally in your cold emailing campaigns, to find your contact's gender, and to get the academic title.

The task of splitting names can be complicated. Fortunately, Datablist provides an easy-to-use tool to split "Name" into two values using the space as a delimiter.

To start, open the "Split Property" tool in the "Edit" menu.

Split Property tool
Split Property tool

Then, select your property with the names to parse. Select Space for the delimiter. And set the max number of parts to 2.

Configure Split Property
Configure Split Property

Run the preview. Datablist will parse your first 10 items to generate a preview. If the results are good, click "Split Property" to run the algorithm on all your current items.

Run preview
Run preview

After running the splitting algorithm, rename the two created properties with "First Name" and "Last Name".

First Name and Last Name results
First Name and Last Name results

This example focuses on parsing names in the Western naming convention, which typically includes a first name and a last name. It can become more complex when dealing with non-Western names, including those with multiple given names or surnames. Or when names have titles or suffixes.

Data Deduplication

Datablist has a powerful deduplication algorithm to dedupe records. It finds similar items using one or several properties, and it has an automatic algorithm to merge them without losing data.

To run the deduplication algorithm, click "Duplicate Finder" in the "Clean" menu.

Run Duplicate Finder
Run Duplicate Finder

Select the properties to use for the matching process.

On the results page, run the "Auto Merge" algorithm once with only the option "Merge non-conflicting duplicates". It will merge duplicate items that are easily mergeable and list the properties with conflicts.

The dedupe algorithm has two options to deal with conflicting data. You can decide to "Combine conflicting properties" using a delimiter. Or to drop conflicting values to keep only one master item.

Automatic Merging
Automatic Merging

👉 Visit our guide on how to merge duplicates on CSV files.

Write custom transformations with JavaScript code

Datablist lets you run custom JavaScript code on your data. With this ability to write custom code, you can address unique data challenges, handle specialized data formats, perform intricate calculations, and apply sophisticated data transformations.

This powerful feature allows you to unleash your creativity and expertise in data manipulation and transformation. You have the flexibility to apply custom logic, create loops, data conditions, and utilize a wide range of JavaScript functions to handle even the most intricate data-cleaning tasks.

Open the JavaScript editor by clicking on "Run JavaScript" from the "Edit" menu.

Data cleaning with JavaScript code
Data cleaning with JavaScript code

👉 Visit our documentation to learn more on how to write JavaScript code.

Validate Email Addresses

Data from scraping can be old, can have typos, or it can be invalid. This is especially true for email addresses that you get from scraping.

When the data is user generated, you will get fake email addresses in your database. Or email addresses from a disposable provider.

Datablist has a built-in email validation tool that let you validate thousands of email addresses.

Click on "Enrich"
Click on "Enrich"

The email validation service provides:

  • Email syntax analysis - The first check is to ensure the email conforms to the IEFT standard and does a complete syntactical analysis. This analysis will flag addresses without the at sign (@), with invalid domains, etc.
  • Disposable providers check - The second check is to detect temporary emails. The service looks for domains belonging to Disposable Email Address (DEA) providers such as Mailinator, Temp-Mail, YopMail, etc.
  • Domain MX records check - A valid email address must have a corresponding domain name with configured MX records. Those MX records specify the mail server accepting the email messages for the domain. Missing MX records indicate an invalid email address. For every email address domain, the service checks the DNS records and looks for the MX ones. If the domain doesn't exist, the email will be flagged as invalid. If the domain exists and doesn't have a valid MX record, it will also be flagged as invalid.
  • Business and Personal Email addresses Segmentation - With prospects from lead magnets or to segment your user base, you might want to segment your contacts between business emails and personal ones. The email validation service gives you this information to enrich your contacts' data.
Email verification results
Email verification results

👉 Visit our guide on how to clean an email list.

FAQ

What is data cleaning and why it's important?

Data cleaning, also known as data cleansing or data scrubbing, refers to the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It involves detecting and rectifying problems such as missing values, duplicate records, formatting errors, outliers, and inconsistencies in data representation.

Data cleaning is a crucial step in data processing, as it ensures that the data is accurate, reliable, and suitable for analysis or use in various applications.

What are the other free tools for Data Cleaning?

The data-cleaning landscape goes from generic tools such as spreadsheet tools to specialized applications. Here is a list of recommended free tools other than Datablist you can use for your data-cleaning operations.

OpenRefine

OpenRefine (formerly known as Google Refine) is an open-source tool focusing on exploring, cleaning, and transforming messy and inconsistent data.

OpenRefine is a standalone desktop application compatible with tabular files (CSV, TSV), Microsoft Excel files, and other structured files such as JSON and XML files.

OpenRefine is very useful to deal with invalid CSV files:

  • It manages CSV encoding issues very well
  • It offers options to solve CSV format errors

On the weak points, OpenRefine has a steep learning curve and lacks some business-related features. It doesn't have deduplication capability or simple workflows to join a dataset with another list to update or consolidate data. It also lacks collaboration features and business-related enrichments and integrations.

Microsoft Excel and Google Sheets

Microsoft Excel and Google Sheets are powerful spreadsheet applications that can be utilized for data cleaning and preparation tasks. Although they have some differences, both tools offer a range of features and functionalities that make them useful for cleaning and transforming data.

You can use formulas to do data transformation and manipulation. And with conditional formatting, you can highlight invalid values that require manual handling.

Need help with your data cleaning?

I'm always looking for feedback and data cleaning issues to fix. Please contact me to share your use case.