Data cleaning: Steps and techniques for better analysis

It’s not enough to just have data; you also have to perform the occasional “spring cleaning” to improve data quality. Data cleaning, also known as data scrubbing, is the process of removing inaccuracies. Why is this important? Bad data is rampant in most industries, meaning most companies operate from faulty information. In one Deloitte study, respondents examined their personal data from a company they did business with. 71% of the respondents claimed that the information was less than 50% accurate.

To avoid faulty data that can lead to misguided campaigns, learn the latest data cleaning steps and techniques.

5 key steps for data cleaning

Here is a basic primer in an easy-to-follow step-by-step format for cleaning data.

1. Handling missing values

Missing data, also known as missing data values, simply refers to data that are absent where needed. For example, a customer’s user profile may be incomplete if it’s missing fields like phone number, address, etc. Create a process for completing these fields. This may include reaching out to customers to remind them to complete their profiles or acquiring the information from existing data sets.

2. Handling duplicates

Duplicate data, or redundant data, may occur when merging data sets from separate silos. Duplicates consume storage, and AI may also misinterpret the duplicate as being separate information. Use an automated data tool for deduplicating data. This is an important data preprocessing step before the information can be converted for analytic purposes.

3. Handling outliers

Set your data analytics to remove outliers, or data from the extreme ends of a dataset. For example, a customer aged 65 or above is an outlier if your demographic is between ages 18 and 30. Outliers may throw data off by altering the data mean and median. However, recognizing outliers may also help you spot patterns and cater precisely to a much smaller demographic with highly personalized campaigns. Make the judgment call about whether outliers have a place in your analysis.

4. Data normalization

Normalization means creating a standard format for structuring your data across all repositories. For example, addresses with “avenue” or “street” should be written as such instead of the abbreviated “Ave” or St.” Likewise, determine that numbers like “3” should be written in that way rather than “three.”

5. Data transformation

Data transformation is a follow-up to normalization where you’re simply making changes to the data so it follows a uniform structure. Using the above address example, all addresses written as “Ave” or “St” will be updated and be written as “Avenue” and “Street.” Systems may also be set so that addresses written using abbreviations will automatically rewrite using full spelling.

The top techniques for data cleaning

There are also techniques employed by data scientists for cleaning data. Consider these methodologies.

Text cleaning

Text cleaning is a fundamental element of data preprocessing for normalization. Most CRMs have a built-in text cleaner for removing redundant words and affixes that can lead to incorrect structuring and AI misinterpretation.

Remove irrelevant data

Just as missing data is a problem, having too much data, or irrelevant data to be more precise, is equally problematic. On the dashboard of your data management tool, use the filters setting to remove irrelevant data from the analytics. Before initiating a project, determine the data that doesn’t have bearing on the campaign. For example, geolocation data may not be relevant if selling a product or service that has a national or global-wide audience.

Handle inaccurate data

Data can also be flat-out incorrect. For example, a customer’s birth year is listed as 1985 when it’s 1995. Unfortunately, this is hard to detect because this is often due to human error on the customer’s end. The best way to correct these errors is by occasionally sending a copy profile to the customer’s email for review and requesting edits if any information requires correction or updating.

Handle inconsistent data types

As mentioned earlier, data normalization is integral for removing inconsistent formatting and structuring. Fortunately, through advancements in artificial intelligence and machine learning, data management systems nowadays can be trained to detect inconsistent data and automatically reformat data to the correct setting, spelling, filing, etc.

Understanding data cleaning tools

Much of the techniques and steps can be automated with the right data-cleaning tools. Some of these include data warehouses, data modeling, and master data management systems. The good news is that most modern SaaS systems feature all-in-one functions for filtering, preprocessing, and normalizing your data – all to improve its accuracy and reliability, and prep it for analysis.