Data Cleansing: The Backbone of Predictive Modeling

Many companies use data to make decisions. However, only a few of them stay at their best. Do you know why? Well, the answer is the poor quality of data being used for predictive modeling (analysis). The data used by companies for analysis is often repetitive, incorrect, and incomplete.

A study shows that poor data quality costs companies a loss of $15 million on average each year. Besides financial losses, poor data quality also impairs companies’ decision-making abilities. This is where the need for data cleansing services arises.

Data cleansing, also called data cleaning, is a crucial step in preparing data for predictive modeling. It involves finding and removing anomalies in data such as errors, gaps, or duplicates. Imagine data cleaning as arranging a messy room. Just as clutter makes it tough to find things, dirty data leads to bad choices, wrong conclusions, and failed projects. By cleaning data, companies ensure it is complete, correct, and ready to use. It helps them build a model that gives accurate and useful findings to make the right decisions. This post explores the popular ways for data cleansing and enrichment that help companies prepare their data for successful predictive modeling.

Step 1: Check for Data Quality Issues

Imagine making a choco lava cake for a party. If you have an incomplete recipe or use the wrong items, the cake won’t come out as it should. The same applies to data cleaning. If your data is missing important information or has some errors, it’s likely that you will end up making poor decisions. To avoid this, it is important to find out what’s wrong with the data. Check your data for issues like:

• Typos: Words or numbers that are spelled wrong.
• Missing information: Empty spaces where there should be data.
• Numbers that don’t make sense: Like someone’s age is 200 years.
• Duplicates: Remove any repeated/redundant records to avoid double-counting.
• Information that doesn’t match: Like two different addresses for the same person.
• Outliers: Identify unusual values that might skew your analysis.

Identifying these common issues helps companies set the foundation for building robust predictive models and make data-driven decisions.

Step 2: Remove Irrelevant and Duplicate Data

The next step for data cleaning is getting rid of unnecessary and repeated information. For a while, imagine you are cleaning your wardrobe on a weekend. While cleaning, you find clothes that you used to wear a couple of years ago. Now, what will you do? It’s likely that you are going to get rid of them. Aren’t you? The same goes for removing irrelevant and duplicate data.

Simply get rid of the data that is not required, such as wrong details, missing information, or duplicate entries. Wondering why this is so important? Having data that is not useful creates unnecessary confusion. It’s like trying to find something in a room that is untidy. If you put things in order, it’s easier to find what you are looking for. Not to mention, having relevant data keeps your models performing faster during the analysis process. By removing data that is not useful or redundant, companies enhance the performance of predictive models and make smart and effective decisions.

Step 3: Standardize Data

Companies collect data in different styles and formats. For example, datasets may have dates in different styles such as 01/10/2024 or 2024/10/01. Another example could be using $ in one place and USD in another. This makes it tricky for predictive models to analyze and compare. Standardizing data helps companies make sure that their data follows the same format or style. This simply avoids the confusion and mistakes that occur due to different formats. When data is consistent and easy to understand, predictive models analyze better and generate more accurate predictions.

Step 4: Clean and Enrich Data

Suppose you are making a choco lava cake, again. After checking all your ingredients, you might find some items are missing or not as good as they should be. So, you decide to replace the bad ones with good ones and add any missing ingredients. That’s like cleaning and enriching data for predictive modeling. This step involves two steps: the first one is getting rid of mistakes and the second is adding extra information to existing data to make it even better. For instance, if a customer name is misspelled, make sure it is corrected because any mistake in the data may lead to poor predictions when using the model. Similarly, you may also provide additional information about your customers (such as age group, buying habits, etc.) when using the predictive model. This extra information helps the model understand your customers better and provide more accurate predictions.

Step 5: Validate Data

Once again, let’s take the example of the cake. For preparing a choco lava cake, you may require some ingredients. To make sure you have all the items available and in the right amount, you should simply cross-check the recipe you have before you start baking. That’s more like validating data. This step involves checking data to make sure it is complete, correct, and consistent before it is used for predictive analysis. To validate data, consider answering these questions:

• Is the information complete? Are there any missing pieces?
• Does the information look right?
• Do the dates match up? Are they in the correct order?
• Are the numbers realistic? Do they make sense?
• Is the data up to date? Are there any unusual patterns in the data?

By simply cross-checking data, companies ensure correct and complete data for predictive models. This leads to more accurate predictions and better decision-making.

Summing Up

Data cleaning may require a lot of work, but it’s worth the effort. It allows companies to reduce the risk of errors that lead to poor choices. Besides, it also helps companies to improve the accuracy of their prediction models. If you are looking for ways to ensure good data in the system, seek help from a professional data cleansing outsourcing company.