A Step-by-Step Guide to Data Cleansing Services – aMarketForce

Introduction:

No CEOs, CMOs, or team leads love dealing with an unstructured, duplicate, and irrelevant dataset. It’s the least of their favorite things to avoid under any circumstances. Random typos and outdated information lead to unwanted business and client mismanagement, which causes costly blunders. To avoid this in the first place, many startups and entrepreneurs give priority to data cleansing.

Data cleansing service methods are highly adopted by companies of all sizes worldwide. The process involves numerous steps that are resourceful in fixing errors and inconsistencies in your dataset. Defined and expert data cleansing enables you to focus on quality analysis, giving you access to quality data to accelerate your business growth.

Let’s explore data cleaning and its methods of improving messy datasets in this informative guide.

What is Data Cleansing?

Data cleansing or cleaning is similar to cleaning your bedroom. While cleaning your room, you pick up the important stuff and remove the unwanted ones from the trash. Similarly, in data cleansing services, you deal with information. During the cleaning, you clean the messy ones, keep the verified ones, and recycle the half-informative ones to make them usable.

Data cleaning service providers combine multiple resources to identify duplicate and mislabeled data. There is no fixed method for data cleansing Services, as it varies from one dataset to another. However, the ultimate goal for professionals is to help businesses access streamlined and result-driven data.  

How Can You Clean Data?

Different companies follow different data cleaning processes based on their requirements, budgets, and relevancy. The steps vary for all! However, a fundamental process works best to initiate the process and get access to accurate data ready for analysis.

Remove Irrelevant Values: Irrelevant observations are pieces of information that don’t relate to the analysis you are doing. For example, if you’re analyzing the impact of social media on teenagers’ purchasing habits, but your data includes information about adults, the data on adults would be irrelevant to your specific research question. Taking out this kind of data would make your analysis simpler, avoid confusion, and give you a clearer and more useful set of information that focuses on the group you are studying.

Eliminate Duplicates: When people mention datasets, the issue of duplication always comes up, especially in the case of integrating datasets obtained from different sources or collecting data from multiple websites, clients, departments, and so forth. This repetition can cause incorrect analysis, slow data processing, and wasted storage space. By finding and deleting duplicate entries, organizations can make their data better, improve analysis, save storage space, and speed up data p[processing with accurate information.

Fix Structural/Typographical Errors: Structural mistakes happen when you look at or move data and see unusual names, spelling errors, unusual categories, or wrong letters. These problems can lead to categories and/or groups being labeled incorrectly.

For example, you are gathering data about car ownership for your clients. It is highly possible that you encounter responses like “No car,” “Don’t own a car,” and “N/A.” While these responses are phrased differently, they all indicate the same thing: the respondent does not own a car.

Update Missing Contact Information: Missing information is a big problem in any type of data analysis because you are clueless about analyzing data that has gaps. You can handle it in three different ways:

• You can remove the parts with missing data, but this can mean losing important information.
• Another way is to fill in the missing parts using statistics or expert knowledge, which might be inaccurate and could change the original data.
• A third way is to adjust how you analyze the data to handle the missing parts better, like using special methods that keep the data accurate or techniques that work with missing data.

Validate and Convert Data Types: Validating and changing data types is a crucial final step in data cleaning. The task in this particular step is intended to ensure that the data is formatted in the expected manner useful, e.g., as texts, numbers, or dates. This step also enables the following:

• Improvement of the accuracy of the data
• Enhances analyses
• Increases the usability of the data

Benefits of Data Cleansing

Having clean data will help improve overall productivity and provide the best information for making decisions. Here’s a list of a few notable benefits of data cleaning services:

• Boosts Productivity: Clean data helps you find potential customers more quickly, saving you a lot of time in the sales process.

• Minimizes Mistakes: If your data isn’t accurate, you might accidentally contact the wrong person.

• Keeps You Updated: Clean data allows you to stay informed about changes, such as new employees, job roles, or potential customers’ locations.

• Speeds Up Sales: Using clean data simplifies and reduces the duration required for sales teams to work with appropriate leads.

• Increases Lead Generation: Better data allows your campaigns to target more people likely to become prospective customers.

Improve Your Data Quality with aMarketForce

Cleaning up your data is an important part of any business plan that uses data to make decisions. By improving the quality of your data, you can make the most of it and help your business grow. It’s good practice to maintain and regularly update your data in a constantly evolving digital space. If you don’t have the time or skill to clean your data yourself, consider using our b2b data cleansing service. We have a specialist with proven expertise in the latest data-cleaning software and tools to help you get clean, verified, and useful data. Contact us today to learn more about our data cleansing services and management services!