Data is inherently dirty. If you look deeper at your CRM data, you’ll see dozens of fields with missing information, typos, noise in the data (commas, punctuation in the field), and much more. This data needs to be cleansed before it can be used for analytics and insights.
Data cleansing, therefore, is a critical function of a data quality framework that is used to remove duplicates, clean errors, and ensure the data is accurate, valid, and reliable.
In this article, we’ll cover the basics of data cleansing and see how it can help you save time and money.
What Is Data Cleansing?
Data cleansing is a process, or set of processes, that ensures data is consistent and accurate. It typically includes assessing data, detecting errors, removing duplicate records, correcting typos and other inaccuracies, filling in missing values, and verifying the accuracy of the data. The goal of data cleansing is to create reliable and useful data for further analysis.
How Is Data Cleansing Done?
Data cleansing begins with identifying bad records or outdated information. This includes ensuring that all attributes have values and that no fields are empty or incomplete.
It also ensures that all information matches the correct format; for example, names should not contain numerical values or special characters, phone numbers should not contain letters, etc.
Once all errors are identified and corrected, the data can be organized and categorized to maximize its usefulness for reporting purposes. Data cleansing is either done manually using scripts or via Excel.
Employees still spend a significant chunk of their time manually cleaning departmental data on Excel before it is used for analysis. For example, marketing teams can spend hours cleaning a single data set before it is used for an email campaign.
The other easy option is using data cleansing solutions like WinPure that allow business users to simply import data and perform a clean without having to code or undergo extensive training.
These solutions automate the cleaning process and use built-in algorithms and libraries to sort out complex dirty data problems. This can save companies hundreds of hours of manual effort, which can then be used for work that matters most.
How Does Data Cleaning Really Save Time And Money?
Data cleansing can help you save time by eliminating manual data entry tasks such as double-checking information for accuracy and manually correcting any errors within a dataset.
Automated processes make it much faster to identify and clean bad records and organize structured data into meaningful categories for analysis purposes.
Additionally, automated processes enable businesses to detect potential errors before they become major issues quickly – this helps reduce costs incurred from issuing corrections or reworking processes due to incorrect data inputs.
Examples of how businesses use automated processes for efficient data cleansing include:
- deduplication solutions that automatically detect duplicate records within datasets;
- address hygiene solutions that verify addresses against postal databases;
- email hygiene solutions which validate email addresses against mail provider’s databases;
- fuzzy matching algorithms which compare datasets across different sources;
- entity resolution which unites multiple related entities under one record;
- standardization algorithms that convert measurements into uniform formats (metric/imperial); among many more solutions available on the market today.
Data cleansing can be a time-consuming and tedious task but it can greatly benefit businesses when done correctly. Cleaning up your business’s data can help you make better decisions more quickly, spot trends sooner, and turn them into opportunities for growth.
For example, if you have customer records with inaccurate information like incorrect addresses or duplicated contact information, it will be difficult to target potential customers with relevant marketing messages or understand where sales are strongest.
Cleaning up this data will allow you to better segment customers by location and tailor your marketing efforts accordingly.The benefits of implementing an automated solution for data cleansing extend beyond time-saving and cost reduction – it also makes it easier to generate insights from collected data by reducing manual labor associated with cleaning up datasets.
Data cleansing isn’t just beneficial to businesses—it can also be used by individuals to organize their personal finances more efficiently and monitor spending patterns over time.
For example, if an individual keeps track of all their expenses in one spreadsheet but has multiple entries for the same purchase across different months due to typos or inaccuracies in the original record-keeping process, this could lead to miscalculations when budgeting for future purchases or estimating their taxes at the end of the year.
Data cleansing would remedy this issue by ensuring all transactions are accurately tracked within a single database over time which would save them time when crunching numbers later on down the road while also providing greater insight into spending trends that could be used to make more informed decisions about how they manage their finances going forward.
Automated solutions allow businesses to rapidly analyze large amounts of structured information in order to discover trends or relationships which could potentially lead them toward uncovering new opportunities or strategies for growth.
By keeping their stored information clean and accurate through efficient data cleansing methods, businesses can gain access to valuable insights about customer behavior in order to optimize their operations further down the line.
Overall, data cleansing is an important process for companies of any size looking to achieve maximum efficiency from their operations while still staying compliant with regulations—and it doesn’t have to break the bank either.
By investing time into establishing clean datasets now organizations can save themselves money in the long run by avoiding costly errors down the road and gaining valuable insights into customer buying habits and market trends that could prove invaluable for propelling growth in today’s competitive landscape.