Dealing with Noisy Data Using Gigasheet

In the realm of data analysis, one of the most formidable challenges analysts face is dealing with noisy data.

Noisy data, often rife with errors, inconsistencies, and missing values, can significantly impede the accuracy and reliability of analytical outcomes. No wonder analysts spend 60% of their time cleaning datasets, meticulously organizing datasets to ensure they're fit for analysis.

The consequences of neglecting this crucial step are profound: unclean data not only skews results, leading to inaccurate insights, but also obscures underlying patterns and trends, making it arduous to extract meaningful information.

Recognizing the critical importance of data cleanliness, this blog delves into the essential techniques and strategies for cleaning spreadsheet data effectively using Gigasheet, while also exploring tools to expedite the process and streamline analysis.

Let's embark on a journey to unravel the intricacies of data cleaning and unleash the full potential of your datasets.

7 Steps for Dealing with Noisy Data

These are the basic steps involved in cleaning data:

1. Identify Missing Data:

To identify missing data, you look for empty cells or inconsistencies in data patterns. You can use filtering or conditional formatting to filter out populated cells to quickly identify missing values. Highlight empty cells with conditional formatting to make them visually distinct.

For example, in a sales dataset, you might find missing values in the "Customer Email" column, indicating missing contact information.

2. Handle Missing Data:

By now, you’d have found missing values. Now, choose how to address missing values based on their impact on your analysis goals.

You may want to remove rows/columns that have multiple missing values if they significantly impact your analysis. Or, you can fill in missing values using statistical methods like mean/median or specific formulas depending on the data type.

Domain knowledge can also be used to fill in missing information accurately.

For example, you might decide to remove rows with missing "Customer Email" as you cannot perform email outreach without contact information.

3. Identify and Remove Duplicates

To identify duplicates, sort data by relevant columns and look for repeated entries. Use filters to identify rows with identical values in key fields. Then, you can use conditional formatting to highlight duplicate rows to make them easily distinguishable.

For example, you may find duplicate entries in a customer list with the same names and addresses.

You can either remove them manually by selecting and deleting duplicate entries or, use the remove duplicates using "Remove duplicates" functions to automate the process.

4. Correct Inconsistencies

Inconsistencies are common in large datasets. Scan the data for typos, inconsistent capitalization, punctuation, or units of measurement. You can use the "Find and replace" function to replace common errors or inconsistencies with desired formats.

For example, you may find product names spelled inconsistently, like "Headphones" and "headphones." Use "Find and replace" to ensure all product names are capitalized consistently.

5. Standardize Data Formats

Sometimes, data values such a dates, times or units of measurements are not in the same format. For example, some dates may be in the DD-MM-YYYY format while some may be in the YYYY-MM-DD format.

Or, some weights may be in kilograms while some will be pounds.

Such discrepancies will hamper your analysis. So, in this step, standardize all data values in the same format for easy analysis.

6. Deal with Outliers

Outliers are basically weird, out-of-place numbers. You first need to check if they're mistakes or if they actually belong there. If it's a mistake, you can remove it. If it's real, you might keep it but still, you need to explain why it's different.

For example, if you see a super high sales number, you'd investigate to see if it's a big sale or just a typo. The goal is to keep your data accurate and tell the true story.

7. Validate Data

You can set data validation rules to regulate the input of data. Validation ensures that the data adheres to predetermined criteria such as format or range.

For example, in a spreadsheet or database, you can specify that only numeric values can be entered into a designated "Price" column.

Clean Data Using Gigasheet

56% of data analysts find cleaning and organizing data, the least enjoyable part of their job. And nobody can blame them. It is a tedious task.

That’s why Gigasheet came up with super easy cleaning features so analysts can finish cleaning and organizing data in minutes. Let’s see how Gigasheet empowers you to transform messy spreadsheets into analysis-ready data:

Remove duplicates:

Removing duplicates in Gigasheet takes 30 seconds. You have to:

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Click on the "Data Cleanup" feature within the Gigasheet taskbar.
Choose the column containing the duplicate entries you want to eliminate.
Click "Remove" and watch as Gigasheet swiftly removes all duplicates, leaving you with a streamlined list of unique lead sources.

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cleaning noisy data using Data Cleanup tools in Gigasheet

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Removing duplicates helps deal with noisy data

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Eliminate unnecessary spaces

Remove leading or trailing spaces with a single click to ensure consistent formatting throughout your data.

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Transform case

Easily convert text to all lowercase, uppercase, or proper case, depending on your needs.

Changing case removes noise from a dataset

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Combine or split columns

Merge multiple columns into one for simplified analysis or separate a single column into distinct elements for clearer organization.

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Split columns to help dealing with noisy data

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Combining columns as part of cleaning noisy data

Use IF-Then Builder:

For complex data cleaning challenges, you can use Gigasheet's "IF-Then Builder". With this no-code solution, you can define custom logic and automatically create a new, clean version of your data based on specific conditions.

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Create standardized columns using the If Then Builder

Convert Columns

With Gigasheet, you can convert the data type of any column easily. This comes in handy when a column is incorrectly identified as one data type on import, but you need to transform it to another format to work with it in a different way.

For example, changing a column, “product response code” from text to numerical format for calculations.

Change data types to standardize noisy data

Find & Replace

Find and Replace in Gigasheet's lets you search for specific words, numbers, or even symbols in your worksheet and change them throughout your entire document with just a few clicks.