In this digital world, data is everything, and you cannot neglect the importance of clean and quality data. Clean data provides better insights into the product or business area and helps users understand the scenario to make informed decisions. If you are wondering what Data Cleansing is, you’ve landed at the right place. This article will give you an in-depth understanding of what Data Cleansing is, why it’s important and how you can clean your data easily.
What is Data Cleansing?
Data Cleansing is a process of removing or fixing incorrect, malformed, incomplete, duplicate, or corrupted data within the dataset. Data coming from various sources may tend to contain false, duplicate, or mislabelled data, and if such data is fed to algorithms for analysis, it may produce incorrect results.
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There is no particular way for Data Cleansing, as every data is different, and one cannot have the same set of approaches for every data. But, it is important to establish a template for the Data Cleansing process.
Why Data Cleansing is Important?
The following points showcase why Data Cleansing is an essential process:
- Data Cleansing removes the errors, inconsistencies, and duplicates from the data, thereby making the data much more accurate.
- There are several tools available in the market that you can use to resolve data issues quickly.
- Data Cleansing improves data accuracy, which helps to get correct and proper insights from that data.
- It allows you to understand data and learn more about where it is coming from.
- Inaccurate or false results because of inconsistent data will result in poor decision-making and business strategies that might bring down an organization’s reputation.
What is the difference between data cleaning and data transformation?
Data Cleansing removes the data that does not belong to the dataset while Data Transformation converts the data from one format into another. The transformation of data is also referred to as Data Wrangling, Data Munging.
How often should you carry out data cleansing?
The frequency of the Data Cleansing process depends on the amount of data you have. Big Companies with large chunks of data should clean their data once every 3 to 6 months of period. Small companies with less data should clean data at least once a year.
What kind of errors does data cleansing fix?
Data Cleansing does not focus on deleting the information but on identifying the discrepancy and correcting it to make data more accurate. Data Cleansing activity can involve the following activities:
- Fixing spelling and syntax errors.
- Standardizing data sets.
- Correcting empty fields.
- Missing codes.
- Identifying duplicate data points and many more.
What are the Steps for Data Cleansing?
You can clean your data by implementing the following steps:
Step 1: Identify Critical Fields
Data Cleansing is a costly operation, and we know that not every field is equally important, some fields are added for auditing purposes. In contrast, some fields depict the system name and other parameters. Thus, it is crucial to analyze and identify the critical/essential fields among the data for a given project to ensure proper and effective Data Cleansing.
Step 2: Collect and Store Data
After analyzing critical fields, collect the entire data from sources and store them either in the database or in the storage system to perform Data Cleansing activities.
Step 3: Remove Duplicates or Filter Unwanted Data
When the data originates from multiple sources, there is a likelihood of duplication in the data. De-duplicating the data can help to free up the storage and removes redundancy from the data.
Unwanted information refers to the data records that are not relevant to the particular analysis. Suppose you are analyzing data for the current year, and your dataset contains data from older years. Removing this information will help to make a more efficient analysis and minimize the risk.
Step 4: Handle Missing Values
There may be some missing values in the data. Those need to be handled correctly; otherwise, it will throw an exception while performing analysis as algorithms will not accept any missing/blank data. You can either drop the records containing missing values, or input specific values based on historical observation, or create a system that can handle null values.
Step 5: Standardize and Clean the Data
Before Data Cleansing, standardization of data is necessary so that it can be replaced easily. For example, in a particular field, we are getting the following values as N.A., NA, Not Applicable, etc. This type of data has to be standardized so that we get one value across the rows.
Step 6: Set up the Process
Once you identify what data from the dataset has to be cleansed, next is to determine the process by which it can be applied across the data. To some extent, it can be a manual process (if the data size is small), or else there are several tools available in the market that can help you to set up a Data Cleansing process with ease.
Step 7: Set the Schedule
Now that you’ve identified the process, set up a schedule for how often you want to scrub your data. It can be hourly, daily, weekly, or monthly based on the incoming frequency of the data.
Step 8: Perform QA
At the end of the Data Cleansing activity, you need to perform QA on the clean data to answer the questions below.
- Does the cleaned data make sense as per the requirement?
- Is it ready to feed into the algorithm?
- Is the data free from errors/unwanted rows, and contains standardized fields?
Step 9: Review, Adapt and Repeat
Now that you’ve validated the data, you can incorporate the review comments (if any) and adapt the changes and repeat the process until the data is free from any errors/inconsistencies.
What are the Characteristics of Quality Data?
The following represent the key characteristics of quality data:
- Validity: The degree to which the data belongs to defined business rules or constraints.
- Accuracy: It ensures that the data contains a value close to the true value.
- Completeness: Measures the degree which defines the completeness of data.
- Consistency: An important aspect of any analysis. Data should be consistent within the dataset or across datasets.
- Uniformity: The data should be uniform across the datasets.
What are the Challenges involved in data cleansing?
Data cleansing is not without its difficulties. The number of issues that need to be handled in many data sets, as well as the difficulty of pinpointing the causes of some inaccuracies, are two of the most significant drawbacks. The following are some more prevalent difficulties:
- deciding how to resolve missing data values so they don’t affect analytics applications;
- fixing inconsistent data in systems controlled by different business units;
- cleaning up data quality issues in big data systems that contain a mix of structured, semi-structured, and unstructured data;
- obtaining sufficient resources and organizational support;
- and dealing with data silos that make the data cleansing process more difficult.
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Informatica Data Quality is an excellent tool that manages data quality across the organization, and users can easily transform and clean the data. It has two major components Data Quality Workbench and Data Quality Server.
Informatica Data Quality Pricing
Informatica Data Quality offers a 30-day free trial following which the user has to pay based on the consumption. More details on pricing will only be revealed by the sales team at Informatica based on the user’s business and data requirements.
2) Talend Data Quality
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Talend Data Quality is an open-source data management tool that can handle data parsing, standardization, matching, and data profiling.
Talend Data Quality Pricing
Talend offers its “Data Quality Management” software as a part of its open-source Talend Studio which is free to download. Along with Talend Studio, Talend offers a Pipeline Designer, its own Cloud Data Integration with a Data Fabric.
Pricing for Talend Pipeline Designer following the 14-day free trial and Talend Data Fabric can be finalised upon discussion with the sales team at Talend.
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Informatica MDM is an enterprise master data management solution with excellent data handling capabilities and can connect with multiple sources and targets.
Informatica MDM Pricing
Informatica MDM offers the same pricing model as Informatica Data Quality in which the price has to be discussed with the sales team at Informatica following the 30-day free trial.
4) DataLoader.io
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Dataloader.io delivers a cloud-based solution to import and export data from Salesforce.
DataLoader.io Pricing
DataLoader.io offers 3 tiers as follows:
- Free Tier with basic functionality.
- Professional Tier which allows the user to load more records per month, has a higher maximum size limit for file loading as compared to the Free Tier along with many other features.
- Enterprise Tier not only offers higher functionality as compared to the Free and Professional Tier, it also offers unlimited task scheduling, up to 10 Salesforce connections, and a 30-day free trial.
An in-depth analysis of all tiers offered by DataLoader.io can be seen below.
5) Apache Spark
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Apache Spark is an open-source lightning-fast cluster computing technology, developed by Apache and designed for fast computation and analysis. It allows you to develop custom codes to perform Data Cleansing operations.
Apache Spark Pricing
Apache Spark is a free-to-use open-source tool. Hence, all the user has to do is download it from the official website and install it in their systems.
Learn More About:
A Guide to Effective Data Cleaning Tools in Python
Conclusion
This article provided you with a simple and in-depth understanding of what Data Cleansing is and what steps are involved in ensuring your data is clean before any analysis is performed on it. You can either clean your data manually if the dataset is small or use existing tools like Hevo for large datasets.
Hevo provides a No-Code Data Pipeline that allows accurate and real-time replication of data from 100+ sources of data.
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Vishal Agarwal is a Data Engineer with 10+ years of experience in the data field. He has designed scalable and efficient data solutions, and his expertise lies in AWS, Azure, Spark, GCP, SQL, Python, and other related technologies. By combining his passion for writing and the knowledge he has acquired over the years, he wishes to help data practitioners solve the day-to-day challenges they face in data engineering. In his article, Vishal applies his analytical thinking and problem-solving approaches to untangle the intricacies of data integration and analysis.
