With a growing reliance on data analysis and insights, data cleaning is gaining increasing importance in all the fields that depend on quality data. As the inferior quality data directly hampers the effectiveness of the analysis, getting your data cleaning right has become the utmost priority of today’s businesses.
In this guide, you will learn what data cleaning is, its need and benefits, steps to achieve data cleaning, associated challenges, various tools to clean data effectively, and companies that facilitate data cleaning services.
Clean and quality data is vital for accurate decision-making. In the real-world, business data may be incomplete, inconsistent, or even contain missing values. Many times, data comes from different sources and may also have duplicate values. Using such data for any decision-making algorithms such as in analytics and Machine Learning can result in inaccurate output, leading to wrong business decisions and increased costs.
Data cleaning, data cleansing, or data scrubbing helps identify the incomplete, irrelevant, incorrect, or missing part of data and replace, modify, or delete it based on the need. It’s a foundational element essential for quality outputs and may take significant time for large volumes of data. According to various surveys, data cleaning can take up to 80% of the total project time.
Now that the answer to “what is data cleaning?” is clear, let’s discuss its need in the industry. Today, most businesses rely on data for their business growth. Further, error-free data is a prerequisite for data-intensive industries like retail, insurance, banking, telecom, and more. Imagine a sales representative failing to contact potential business prospects or a company presenting irrelevant offerings to its customers due to substandard data quality in its ad campaign. Low data quality can negatively impact a business’s revenue and reputation. Further, inferior quality operational data to production machines can create significant problems for a manufacturing company.
The data cleaning process enables businesses to increase efficiency, productivity, revenue, and reputation. It helps eliminate data errors and inconsistencies and maximizes the accuracy of insights derived from the data. Due to its ability to provide error-free and relevant data, data cleaning has become one of the must-haves in every domain and presents a lucrative career opportunity. One annual survey on Data Science recruitment in India shows that data cleaning is one of the in-demand skills for data scientists.
Though data cleaning and data transformation both modify data, they differ from each other. While data cleaning focuses on improving data consistency, data transformation emphasizes making data processing easier. Data cleaning is about identifying and removing inaccurate or corrupted records from a database, table, or recordset. On the other hand, data transformation encompasses converting data from one structure or format into another one for analysis or warehousing.
Following are some of the commonly used data cleaning methods.
One of the goals of the data cleaning process is to have data without unwanted observations. Unwanted observations consist of irrelevant observations and duplicates. The data cleaning techniques that include removing unwanted observations are essential to save time, space, and cost and significantly eases model building.
These observations consist of data that doesn’t fit the specific problem that you are aiming to solve. One example can be occupants’ data in a dataset used for building a model for apartment prices in an estate. You need to take care of irrelevant observations by removing them. These types of observations mainly occur in the data generated by scraping another data source.
When some data occurs repeatedly, it’s said to be a duplicate. Duplicate data can result from fetching the same information from multiple sources. It can also be due to data entry errors or numerous submissions by a respondent to a survey. This data cleaning method takes care of such records by deleting them from your data to maintain the data quality.
Structural errors arise due to human mistakes such as data-entry errors. It can also be due to other issues resulting from data transfer or poor data management. Such errors primarily occur in categorical data and consist of typographical errors, inconsistent punctuations, mislabeled classes, and others. To rectify these errors, you need to correct the misspelled words, update the case, and summarize long category headings.
Outliers are the data points that vary significantly from other observations. They appear because of an error in the experiment or the variability of the measurements. Outliers can be very tricky as they provide more insight into your model. You can consider removing an outlier once you ensure that it’s a mistake or is irrelevant for your analysis.
Non-response bias from respondents or data collection errors can result in missing values in the data. One way to avoid missing values in the first place is by adding validations in your data collection process. You can treat already existing missing values by either dropping them or inputting missing values.
If you drop the entire observation for a missing value, you may lose out on some vital information that may help you make better decisions. Some missing values may be genuine and not due to errors. For example, in a dataset containing students’ marks, a student may be absent for a particular paper (say English) because he/she was sick. In this case, by deleting the entire record for this missing value, you won’t detect that the student was ill on the exam day.
Alternatively, you may choose to input either a random value or a value based on certain criteria. However, this approach has another caveat. Consider a missing value in the same dataset mentioned above where the teacher has forgotten to mention another student’s marks. Filling up a random score in its place may result in incorrect data as the student may have scored higher or lower than the arbitrary score. The safe way is to display a missing value by creating a missing category for categorical data or filling up with 0 for numerical data.
In an untidy dataset, each column represents values but not variables. On the other hand, in a tidy dataset, each column represents a separate variable, and every row represents individual observations. Using this data cleaning method, you can fix common data problems.
Some data columns may have inconsistent data types, and with this data cleaning method, you can convert them into the appropriate ones.
In the real world, most of the data is unstructured data. You can clean the data by changing, matching, parsing, or analyzing the strings.
Businesses store similar types of data in multiple files to control the data volume and facilitate ease of access. You can concatenate these files to get the final massive volume of data.
Before commencing data cleaning, you must set your organizational expectations and goals and then work towards achieving them. It’s best to get all the key stakeholders together and brainstorm for reaping maximum results. Following are the 6 data cleaning steps that can come in handy.
It’s the first and one of the most primary data cleaning steps. Analyzing and recording the pattern of errors and their sources make it easy to identify and fix corrupt or incorrect data and quickly achieve your data cleaning goals.
Standardizing your data sources helps reduce data duplication risk significantly. Also, standardize your complete data cleaning process based on the data error trend.
After cleaning the existing database, validate the data accuracy. There are tools available in the market that support data cleaning in real-time, and some of them leverage machine learning and AI to offer better accuracy. Research and invest in such tools based on your business requirements.
Many data cleaning tools can automatically analyze the raw data in bulk and speed up the data analysis process by removing duplicate data. You can research and invest in these tools.
You can use reliable third-party sources to append your standardized and validated data. Also, you can use them to capture information directly from their origins and then clean and compile data to provide complete information for analytics and business intelligence.
Promote the new data cleaning protocol adoption by communicating your new standardized data cleaning process with your team. It will help you clean-up your data regularly and develop and strengthen customer segmentation to send more targeted information to your prospects and customers.
To determine your data quality, you need to examine its characteristics and weigh them against your enterprise’s priorities and applications intended to use this data. Following are the crucial components of quality data.
The ‘complete data’ is the data that contains the entire set of mandatory data items needed by the organization. For example, if you want your customers to provide their names, you need their first and last names. In the absence of any of these, the data will be incomplete.
The data you are collecting should be relevant to your goals. No matter how much aligned to other data quality features, any irrelevant data won’t help you.
Accuracy is a measure that shows whether your data is free from significant errors and is critical for making the right business decisions. Incorrect data can lead to erroneous results, and hence, it’s imperative to have accurate data.
Validity tells you whether your data follows the defined business rules. Invalid data will lead to invalid results. You can take the help of various software as a tool to gather data in a valid format.
It’s imperative to record data as soon as possible after the real-life event as time passage can make statistics less accurate or less valuable.
Data consistency is another vital component of quality data. Data consistency ensures that the data carries the same value irrespective of its source and version. It helps all the concerned teams and departments in the organization to work towards a common goal.
Data cleaning offers a range of benefits and helps businesses stay competitive.
Data cleaning eliminates data inconsistencies and errors that can result in inaccurate business decisions. Data cleaning removes errors in the data irrespective of its volume and number of sources. Enhanced data accuracy enables more efficient business practices and helps to make quicker business decisions. Further, error monitoring and reporting make it easier to rectify corrupt or incorrect data for future applications.
Accurate information for marketing campaigns ensures high engagement rates. It not only offers value for money but also saves costs incurred on ineffective marketing practices.
With the correct information, businesses can better target their audience using the right marketing strategies and earn higher revenue by generating more customers and sales.
In the absence of updated information, such as support tickets, the staff may waste time calling the wrong customers. Accurate and updated information saves employees spending their time and efforts on unnecessary tasks. It helps them focus on the essential priorities.
Whether to provide relevant offers to your customers or sharing data with the public, clean and error-free data helps boost reputation and trust. It also leads to more happy and satisfied customers.
Here are some of the challenges associated with the data cleaning process.
The data cleaning process is time-intensive and takes up to 80% of an analyst’s time. Most organizations require a data cleaning solution with reduced time and resources spent on data preparation.
Scaling data cleaning techniques to the rapidly growing large datasets is one of the primary challenges.
A significant portion of data resides in an unstructured or semi-structured format. However, data quality issues for unstructured and semi-structured data pose a significant challenge as they remain relatively unexplored.
The data cleaning process needs to search through and examine raw data. However, certain industries, including medicine and finance, follow strict data regulations. Reconciling the need for unaggregated data access, data provenance, and privacy will continue to be the primary challenges in such domains.
With a renewed need and urge to collect data from mobile devices and sensors, there are challenges regarding how qualitative data cleaning approaches can manage distributed data streams.
Here are top data cleaning tools for 2021 that will ensure high quality for your data.
Following are some of the leading companies that offer data cleaning services.
This guide acquainted you with the ‘what,’ ‘why,’ and ‘how’ of data cleaning with various techniques and steps. It also described the data cleaning benefits, associated challenges, and tools and companies that can help you achieve data cleaning.
Your data’s characteristics will primarily drive the actual steps and methods of data cleaning. The techniques and roadmap mentioned above will come in handy to devise and implement your organizational data cleaning strategy.
Data cleaning is a must-have skill and an essential process for Data Science, Analytics, and building models for Machine Learning and Artificial Intelligence that are some of the go-to career options in the present day. In collaboration with the leading educators, Jigsaw Academy offers a range of courses in this arena for individuals and corporates. Some of our courses are –
For more information, visit our website.