What is Data Cleaning?

Data cleaning is the process of modifying data to assure that is correct, accurate, and relevant. The definition might be simple, but data cleaning is used in many scenarios. Also, data cleaning refers to a multitude of activities. These activities aim to improve the quality of your data. Often times, these tasks are accomplished by joining many other operations. Today’s blog posts will discuss the most important data cleaning tasks.

Schema Matching and Data Standardization

Most often, schema matching is the first task you need to perform. Its aim is to align the attributes coming from new datasets with the ones in your existing database

Existing Customer Schema (Name, Country, Address, Phone)
Incoming Customer Schema (Country, City, Street, Apt, Phone)

To match these schemas and move forward with your data matching initiative, you need to devise a process that converts each tuple in the Incoming Customer Schema to Existing Customer Schema.

Another scenario we will discuss here refers to the same two schemas but assumes that the data records about your customers do not contain zip codes. If you need to understand how many customers are there for a specific code, it is key to have the correct zip values.

However, the same rules apply when you need to maintain your product catalog database. You must make sure that all dimensions of a product are both expressed in the same units and that these values are not missing. If not, search queries will return incorrect results. The task that makes sure all values are using the same convention is called data standardization. This is the task you should perform before other data cleaning activities such as data matching and data deduplication. These are by no means trivial activities and, most often, it is not feasible for you to perform them manually.

For example, WinPure Clean & Match is engineered to process all types of addresses such as City/State/Zip and Suite Numbers. To make it easier for you, the software returns an Address Quality flag to help you spot questionable addresses. Furthermore, our solution will assist you to standardize addresses for faster processing and easier record matching. Also, WinPure Clean & Match makes sure that addresses are always standardized to match USPS or PAF recommendations.

Record Matching

The aim of record matching is to match each and every record from a dataset with the records from another dataset. Usually, you need to perform this activity when you import new data. By doing so, you will make sure the new datasets do not introduce duplicate entities.

Think about a scenario when you need to import a new set of customer records into your sales database. Obviously, you must check if the same customer is represented in both incoming batch or existing databases. Of course, you should keep only one record. Unfortunately, due to typing errors or representational errors, the same record in both data could seem different. Hence, it might not match the relevant attributes such as phone, address, and name.

The difficulty is often increased in the case of entries where the product description is a concatenation of more than one attribute. Thus, the goal of record matching is to find pairs of records in each of the two data sets which correspond to the same entity.

The most important challenges you need to address in this tasks are:

identify the criteria that assure two records are indeed corresponding to the same real-world entity
with the large datasets available today, you have to find the most efficient computation method. This method should be able to determine the aforementioned pairs over large sets of data.

Fortunately, WinPure Clean & Match can help you overcome this hurdles. By using its intelligent fuzzy matching engine, our product is engineered to find the most true matches and the least false matches. Furthermore, you can combine these results with the customizable knowledge base library. Also, WinPure Clean & Match is engineered to automatically across many processors. This makes it an efficient solution for processing large volumes of data.

Related: Data Accuracy Guide For 2021

Data Deduplication

Data deduplication aims to group records in a dataset. By doing so, it makes sure that each group is representing the same real-world entity. For best results, you should perform this process both when you populate the database for the first time and also when you add new records. When compared to data matching, deduplication is usually involving additional grouping of matching records. This approach allows the groups to collectively partition the input datasets.

Consider an example where your database stores different records, such as:

a Nikon D750 Camera
Nikon D750 SLR
Nikon D750 Digital SLR

This set has multiple records that represent the same entity. Thus, you must be able not only to match two of them but match all three records to the same real-world entity

WinPure Clean & Match removes the need to manually check for duplicates and allows you to quickly and easily correct duplicate data in your database.

Data Profiling

Since data cleaning is an interactive process, it is essential for you to be able to evaluate the quality of your data. You should be able to do this both before and after the data cleaning process. By doing so, you will be able to gauge its effectiveness. We call this process data profiling. Its most important goals are to ensure that your values match with your expectations.

Consider that you might expect customer name and address to uniquely identify each customer in your database. Thus, the number of unique tuples must be as closest as possible to the total number of entries in your database.

However, even you might obtain subsets of elements through several SQL queries, this approach is inefficient and time-consuming. Our Data Profiling / Statistics is easy to use and a powerful data profiling tool crafted to help you discover patterns in your data sets. Furthermore, the module can check the quality of your data by analyzing value counts, types, formats, and completeness. The module provides a complete set of statistical data aimed to help clean your data.