Data cleaning is the backbone of healthy data analysis. When it comes to data, most people believe that the quality of your insights and analysis is only as good as the quality of your data. Garbage data equals garbage analysis out in this case.
If you want to establish a culture around good data decision-making, one of the most crucial phases is data cleaning, also known as data scrubbing.
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What is data cleaning, cleansing, and scrubbing?
Clean data is crucial for practical analysis. The first stage in data preparation is data cleansing, cleaning, or scrubbing. It’s the process of analyzing, recognizing, and correcting disorganized, raw data.
Data cleaning entails replacing missing values, detecting and correcting mistakes, and determining whether all data is in the correct rows and columns. A thorough data cleansing procedure is required when looking at organizational data to make strategic decisions.
Clean data is vital for data analysis. Data cleaning sets the foundation for successful, accurate, and efficient data analysis. Because the information in the dataset will be disorganized and scattered without first cleaning it, the analysis process won’t be clear or as precise. Clean data is required for effective analysis; it’s as simple as that.
Data cleaning aims to produce standard and uniform data sets that allow business intelligence and data analytics tools to access and find the relevant data for each query.
What are the benefits of data cleaning?
Data cleaning is beneficial to your career as a data specialist. Data cleaning helps other businesses, making your position as a data professional easier.
The longer you store insufficient data, the more it will cost your firm in both money and time. This also applies to quantitative (structured) and qualitative (unstructured) data.
It’s the 1-10-100 principle:
It is better to invest $1 in prevention than spend $10 on correction or $100 on fixing a problem after failure.
These are just a few of how it will assist you in your job:
Clean data allows you to conduct your study faster. Because having clean data avoids the creation of numerous mistakes, and your findings will be more accurate, you won’t have to repeat the entire operation because of incorrect results.
Even if you are highly eager for outcomes, the results will not be accurate if the data isn’t clean. As a result, the result may or may not be accurate when you present your work. As a consequence of adopting this practice, you must become accustomed to slowing down and correcting data before presenting it. There’s less room for errors as a result of this.
You’ll soon learn to be more exact with the data you put in at first since data cleaning takes up so much time. Data cleaning will still be required for various reasons, but doing it helps you get used to be more precise from the start.
Data cleaning challenges
Analysts may have difficulties with the data cleaning process since good analysis requires ample data cleaning. Organizations frequently lack the attention and resources to affect the study’s conclusion due to a lack of data scrubbing efficiency. Inadequate data cleansing and preparation are often a cause for inaccuracies slipping through the gaps.
The lack of data scrubbing, which allows for inaccuracies, is not the fault of the data analyst. It’s a symptom of a more significant problem: manual and siloed data cleaning and preparation. Traditional data cleansing and preparation also take too much time beyond the shoddy and faulty analysis.
Forrester Research claims that up to 80% of an analyst’s time is spent on data cleansing and preparation. So much time is spent cleaning data that it’s easy to overlook data cleaning processes. Most businesses require a data cleansing tool to help them analyze the data more efficiently while saving time and money on preparation.
The least enjoyable activity for data scientists is the cleaning and organizing their data, according to 57% of respondents.
Comparison: Data cleaning vs data transformation
Removing data that does not belong in your dataset is known as data cleaning. Data conversion from one form or structure to another is called data transformation.
Cleaning data is one of the most critical tasks for every business intelligence (BI) team. Data cleaning processes are sometimes known as data wrangling, data mongering, transforming, and mapping raw data from one form to another before storing it. This post focuses on the techniques of cleaning up your information.
How to clean data in 6 steps?
The first step in any data cleaning project is to take a step back and assess the overall picture. Consider, what are your objectives and expectations?
You’ll need to develop a data cleanup strategy next to reach those objectives. Focus on your top metrics is a fantastic starting point, but what questions should you ask?
- What is the most important measurement you want to achieve?
- What is your firm’s objective, and what do each of your employees hope to get out of it?
The first step is to gather the key stakeholders and get them to brainstorm.
Here are some best practices for developing a data cleaning procedure:
Keep track of trends where most of your mistakes originate from. This will make it easier to spot and correct incorrect or faulty data. Records are particularly significant if you’re incorporating multiple solutions into your fleet management system so that other teams don’t get bogged down.
Standardize your process
Make sure that the point of entry is standardized to help minimize duplication.
Validate data accuracy
When you’ve finished cleaning your current database, double-check the consistency of your data. Invest in real-time data management technologies so that you may clean your data regularly. Some tools even employ artificial intelligence (AI) or machine learning to improve testing for accuracy.
Scrub for duplicate data
To help save time when examining data, look for duplicates. Repeated data can be avoided by researching and purchasing various data cleaning tools that may process raw data in bulk and automate the procedure.
Analyze your data
Use third-party sources to integrate it after cleaning, validating, and scrubbing your data for duplicates. Third-party suppliers can obtain information directly from first-party sites and then clean and combine the data to provide more thorough business intelligence and analytics insights.
Communicate with your team
Share the new procedure for cleaning your data with your team to help promote its use. It’s critical to keep your data clean now that you’ve cleaned it. Keeping your teammates informed will assist you in generating and strengthening customer segmentation while also sending more relevant information to consumers and prospects.
Finally, check and review data regularly to discover any anomalies.
When you’re done with your data, make sure it’s clean. Whether you’re using simple numerical analysis or sophisticated machine learning on huge documents, open-ended survey responses, or consumer comments worldwide, cleaning up your data is crucial in any well-executed study.
7 best data cleaning tools
There is no debate about the value of big data these days. However, if you want the best data possible, it must be as accurate as possible. This implies that your data must be current, accurate, and clean. Using one of these top data cleaning tools might help guarantee this for you.
Several variables determine the specifics of the program you pick. This includes your data source, administration procedures, programs you use, and more. Remember that low-quality data can cause a slew of problems in your company. You could waste money on duplicate records while also missing out on sales. Incorrect addresses may lead to dissatisfied customers or lost income.
Data cleansing tools help you maintain high data quality. These are the some of the best ones:
IBM Infosphere Information Server
The IBM Infosphere Information Server is a data integration platform. It has many of the best data cleaning tools available. IBM’s deal may use end-to-end solutions for a variety of services. This package deal includes standardizing information, classifying and validating data, removing duplicate records, and researching source data. Ongoing monitoring ensures that your data stays clean by catching insufficient information before reaching your applications and services. You can use USAC and AVI to clean your mailing addresses.
This platform offers several additional features, including data monitoring, data transformation, data governance, near-real-time integration, digital transformation, and scalable data quality operations.
Key benefits of IBM Infosphere Information Server
- The project’s goal is to build a comprehensive end-to-end data integration platform.
- It protects against poor-quality data from being exported to other systems.
Oracle Enterprise Data Quality
Oracle Enterprise Data Quality is an excellent data quality management solution. It’s made to supply reliable master data for integrating with your company applications. Address verification, standardization, real-time and batch comparison, and profiling are available data cleaning tools.
The following software is designed for more experienced technical users. It does, however, provide several capabilities that even non-technical persons may utilize right out of the box. Governance, integration, migration, master data management, and business intelligence are all supported by Oracle Enterprise Data Quality.
Key benefits of Oracle Enterprise Data Quality
- Data quality management software with a complete feature set.
- For commercial applications, it provides reliable master data.
SAS Data Quality
Data cleaning software from SAS, known as the SAS Data Quality Tool, is a data quality solution that works to clean data rather than moving it from its origin. Businesses may use this platform for on-premises and hybrid solutions. SAS Data Quality Tool can also utilize it with cloud-based data, relational databases, and data lakes. Deduping, correction, entity identification, and data cleanup are just a few data cleansing tools available.
With this broad range of features, SAS Data Quality is one of the most effective data cleanup solutions. That isn’t all, though. Data quality monitoring, master data management, data visualization, business glossary, and integration are all included in SAS Data Quality.
Key benefits of SAS Data Quality
- This tool works with a lot of different data sources.
- Cleans data at the source
Integrate.io is a data pipeline platform that includes ETL, ELT, and replication functionality. With a no-code graphic user interface, you can set up these features in minutes. Before moving it to a data lake, data warehouse, or Salesforce, the transformation layer may clean your data and change it into something different. Integrate.io is one of the best data cleaning solutions because of its wide range of services.
You also have access to several other helpful data integration features in addition to those offered by ETL. The easy-to-use design allows anyone in your company to establish a data pipeline. You may thus free up IT and data team time for other activities. The cloud-based platform also relieves you of routine maintenance and management duties, allowing you to integrate as much or as little as you need. This ensures that you don’t add new technology on top of what you already have. With this adaptable ETL software, you can quickly increase or decrease your usage.
Key benefits of Integrate.io
- User-friendly interface with no programming necessary.
- Data sent to data warehouses are cleaned and masked before it reaches them.
Informatica Cloud Data Quality
In Informatica Cloud Data Quality, data quality and data governance are addressed. It does so through a self-service approach that makes it one of the top data cleaning solutions. As a result, it gives everyone in your company the tools they need to access high-quality information for their apps.
Prebuilt data quality rules may be used to quickly deploy numerous services, including deduplication, data enrichment, and standardization procedures. This software package includes data discovery, transformation, address verification, reusable rules, accelerators, and AI. Artificial intelligence is essential since it will allow you to automate many aspects of the data cleaning process.
Key benefits of Informatica Cloud Data Quality
- Data cleansing, transformation, discovery, and governance platform for self-service
- Built-in data quality rules
Tibco Clarity is a one-stop-shop for data cleaning that utilizes a visual interface to simplify data quality improvements, discovery, and conversion. Businesses may use this tool to transform any raw data into usable information for their apps.
You may use deduplication techniques and check addresses before shipping data to the target. While data is being processed, Tibco Clarity provides several graphical representations that you can utilize. This allows you to have a deeper understanding of the data set. For another layer of data quality control, define rules-based validation. After its setup, you may reuse the cleaning procedure configuration for future raw data. Thanks to this unique configuration, Tibco has earned a place on our top data cleansing tools list.
Key benefits of Tibco Clarity
- Visual data cleansing interface
- Data visualizations
- Rules-based validation
Melissa Clean Suite
Melissa Clean Suite is a data cleaning software that improves data quality in many major CRM and ERP systems. It works with Salesforce, Oracle CRM, Oracle ERP, and Microsoft Dynamics CRM. Indeed, one of the most prominent data cleaning programs because of its extensive integration with other applications.
The Melissa Clean Suite has a lot of functions. These include data reduction, contact autocompletion, data verification, data enrichment, up-to-date contact information, real-time and batch processing, and data appendage are just a few examples. Using the supplied plugins, you may integrate this solution with your CRM in minutes.
Key benefits of Melissa Clean Suite
- It works with a wide range of CRM and ERP solutions.
- Cleaning application dedicated to data
Regardless of what type of company you run, you undoubtedly deal with a lot of data. That is why you must do all possible to improve the quality of your data. This implies using one of the top data cleansing tools on the market. The services offered here provide unique advantages and have different pricing plans based on your needs.
You may also tailor your program to suit the needs of particular businesses. Depending on the software you require, you may select from various permission settings, integration choices, and administrative capabilities.
Your objective in business is to produce money, not time. This implies you’ll need to spend less time and resources dealing with duplicated records, managing an unmanageable number of records, and correcting false information.