When using data, most people agree that the insights are only as good as the data they’re using. This is why data cleaning is an important step that your business or organization should be taking. ‘Clean data’ helps marketing managers, business executives, sales reps, operational workers, data science teams, etc. make smart, data-driven decisions. If the data isn’t clean, your reports may not be accurate.
Let’s learn more about data cleaning, why it’s important and what kind of data errors you can fix. If you need help ‘scrubbing’ your data, contact Arkware for a consultation. We will be happy to assess your database and how data cleansing can help you.
What is Data Cleaning?
Data cleaning, also referred to as data cleansing or data scrubbing, is the process of fixing incorrect, inaccurate or incomplete information in a data set. It involves identifying data errors and then fixing these errors by changing, updating or removing them. The purpose of data cleaning is to provide more accurate and reliable information that can help your business or organization make data-driven decisions.
Data cleansing is an important part of the data management process. It’s typically done by data quality analysts, engineers and other data management professionals. Without data cleansing, you could experience faulty information, flawed business decisions, missed opportunities, operational problems and misguided strategies.
What Types of Issues Does Data Scrubbing Fix?
Data cleansing addresses a wide range of problems that can happen in data sets, such as inaccurate or corrupt data. Some of the problems can happen from human error during the data entry process, while other times it’s from different data structures.
Here are some examples of the types of issues that data cleaning can fix:
- Invalid or missing data. Data cleaning corrects structural errors in data sets like misspellings, missing values or wrong numerical entries.
- Duplicate data. Duplicate records in data sets can also be fixed with data cleaning. The process is referred to as data deduplication, which removes or merges duplicate records.
- Inconsistent data. Names, addresses and other attributes may be formatted differently. For example, some customers have middle initials and others do not. Data cleaning will fix this data so you can run consistent reports.
- Irrelevant data. Some data may not be relevant and can skew your results. Data cleansing removes redundant data, reducing data processing and storage resources.
What Steps are Involved in the Data Cleaning Process?
The amount of work that will be required to clean your data depends on your data set and its complexity. While there may be some differences in how the data scrubbing process works, it generally requires the following steps:
- Inspection and profiling. The first step is to inspect and audit data quality and identify the issues that need to be fixed.
- Cleaning. The cleaning process corrects missing, duplicate, redundant and inconsistent information.
- Verification. Once the cleaning is complete, the data needs to be inspected again to make sure that it’s clean.
- Reporting. The results of the data cleaning are reported to IT and business executives. This report may include the issues that were found and corrected.
‘Dirty’ data can cause a lot of problems because you won’t be able to make the best decisions for your business or organization. If you believe that dirty data is affecting your business, contact Arkware for a consultation. We’ll make sure that your data is clean, accurate, complete, uniform and valid – everything you need to run a successful, efficient business.