The role of data in today’s business model is becoming more important than ever before. Data is produced by the companies at an exponential rate and needs to be well managed for further use for making informed and strategic business needs.
Just collecting data isn’t the endgame for agencies or businesses. Instead, the next step is to focus on what that data can do to improve your company’s performance. To get the most accurate insights, data cleaning and enrichment should be prioritized just as much as data collection.
You might have heard the terms data cleaning and data enriching many times. But what do they mean exactly, and how do they interrelate with each other? In this article, we will go through the difference between these two concepts and know how you can deploy both to improve your business.
Before discussing the distinction between data cleaning and data enrichment, let us know what do they really mean?
What Is Data Cleaning?
Data cleaning is often taken as the first step, and the purpose of this method is to identify any missing and inaccurate records so that you can eliminate all the invalid data points.
Unreliable data can lead to time wasted chasing contacts that aren’t related to your business and can even jeopardize your further plans. That is why data cleaning allows you to fix incorrect or misplaced data to validate the present information for better marketing campaigns.
For instance: You might have prepared an email list to launch marketing campaigns. In this case, data cleaning would help erase all the fake and duplicate email addresses from your list.
When you’re done, you’re left with an organized list of your customer’s information and can now move on to the next step: data enriching.
What Is Data Enriching?
Data enrichment is the process of extracting external data from third-party data sources and adding it to your existing database. This overall process improves the data you already have and makes it more useful. This can be done in numerous ways. One of the most basic ways is by combining data from different sources.
For instance: Now that you have got your email list ready, the next step would be to enrich it. How will you do that? Just improve your list with things like full name, their role, and the type of industries they serve.
It can be done either through a tool or through a third-party service provider. This will undoubtedly make your email list more valuable. Enriching data turns your raw data into valuable insights for better decision-making.
How Are Data Cleaning And Enrichment Different?
Now that you have got a better understanding of data cleaning and enrichment, it’s time to explore the differences between these two terms. Let us look at the following differences between data enrichment and data cleaning:
1. Generic Differences
Data enrichment is the process of getting data from other third-party data sources into a database to enrich the existing data in the database. Whereas, data cleaning involves eliminating irrelevant and inconsistent data and keeping your database up to date.
Data enrichment is a continuous process that needs to be monitored frequently because customer data is temporary, it can become old and hence needs to be updated. In order to organize the data in a more systematic way, companies should often enrich their data using data enrichment tools or any other third-party software.
The data cleaning process varies on the basis of dataset and company requirements. The process usually starts with analyzing data cleaning and ends by reporting the data.
The method for the data cleaning process is mainly divided into 4 steps:
- Profiling: Here, users are responsible for identifying all the issues in the data so that they can be fixed in the further cleaning process.
- Cleaning: This is the main stage of the data cleaning process where the errors are fixed and corrected.
- Verifying: After the cleaning process, companies verify their data to make sure that their data cleaning process has taken place successfully.
- Reporting: The reports of cleaned data are then given to business executives to deal with data quality trends.
Benefits of data enrichment:
- Offers you valuable data: Data enrichment allows companies to find relevant data that meets their requirements and solves problems.
- Improve data validity: Improving data accuracy is possible because data enrichment inspects and verifies the data to ensure that the information in the database is up-to-date.
- Saves time: Users can smartly manage their database due to the automation of data enrichment. This in turn reduces their efforts and saves an ample amount of time.
Benefits of data cleaning:
- Better decision-making: Cleaned and accurate data allow users to make better decisions for business operations and strategies.
- Reduces data costs: Eliminating unnecessary data can save time as well as cost because you don’t have to fix the same errors again and again in the datasets.
- Better operational performance: High-quality data helps businesses stay up-to-date with the requirements for performing better operations.
Setting up an organized database may be too much for you to do independently. Gathering information is the most challenging component of sales prospecting. However, FunnL handles this for you! We help you to find your ideal prospects to build your list within minutes.