What Is Data Cleaning? Why You Should Care About Dirty Data

Data is the currency of the 21st century.

It’s at the center of just about every decision you make. Data informs your strategies, lets you gauge progress and success, and is the hub of some of the world's most advanced and sophisticated technologies.

Businesses collect a lot of data about their operations, but not all of it is useful. Most of this data is dirty, out-of-date, or duplicated. Clean, current information gives you the power to make intelligent business decisions. With clear and accurate information, you can create targeted marketing campaigns, improve your website, and optimize your e-commerce strategy. But if your data is dirty, all of that time, money, and effort goes to waste.

It’s no secret that companies with access to high-quality datasets make the best decisions. They recognize the value of having reliable data at their fingertips.

Data cleaning is the first step to clean your data for your business intelligence (BI) or analytics applications. Using data cleaning services and solutions (such as data quality software) is necessary to ensure accurate and reliable data sets for analysis and maximum value.

Data cleaning is a vital part of the overall data management process and one of the core components of data preparation work that readies data sets for use in BI and data science applications. Data quality analysts, engineers, and data management professionals typically perform data cleaning. But data scientists, BI analysts, and business users can also clean data or participate in the process for their applications.

Data cleaning removes discrepancies, corrects syntax errors and typos, rectifies issues such as missing codes and empty fields, finds duplicate data points, and normalizes data sets. It helps create trustworthy answers and simplifies the analytical process as a fundamental feature of data science. 

Data cleaning provides consistent and high-quality datasets for data analysis and BI tools to easily access and perceive accurate data for any problem.

Most data cleaning is possible with software applications but is sometimes done manually. Although data cleansing can be daunting, it’s crucial to managing organizational data.

Why is data cleaning important?

Businesses often store a lot of information like business, employee, and, in some instances, customer or client info. Companies, unlike individuals, need to guarantee data privacy and security both internally and externally. Data cleaning protects this sensitive data from leaks and malicious threat actors.

Business practices and decision-making are more data-driven as companies seek to leverage data analytics to enhance business performance and gain a competitive advantage. Clean data is essential for BI and big data teams, business leaders, marketing managers, sales representatives, and operational employees, especially in retail, financial services, and other data-intensive businesses.

Inadequate cleaning of customer records and other company data leads to incorrect information. This can result in poor business judgments, improper strategies, lost opportunities, and operational issues, all of which can increase expenses and lower revenue and profits.

Components of quality data

Determining data quality necessitates evaluating its attributes, followed by weighting them in terms of what is most relevant to your business and application(s). High-quality data must meet various quality requirements. Some of these are:

• Validity refers to how well the data adheres to predefined business guidelines or constraints.
• Completeness is the extent to which all required data is accessible.
• Data consistency measures how consistent data is both within and across datasets.
• Uniformity is the degree to which information is represented using the same measurement system.
• Accuracy measures how close the business data matches the actual values.

Data management teams develop data quality metrics to measure these attributes, error rates, and the total number of flaws in data sets. Many experts evaluate the business impact of data quality issues and the potential value of addressing them using surveys and interviews with company leaders as part of the process.

What kind of errors does data cleaning fix?

Data cleaning handles many issues and difficulties in data sets, such as incomplete, invalid, inconsistent, and corrupt data values. Some of these errors occur due to human failure during the data entry process, while others result from varied data structures, formats, and languages in different systems.

Data cleaning vs. data transformation

Data warehouses help with data analysis, reporting, data visualization, and sound decision-making. Data transformation and data cleaning are two common data warehousing strategies. Data cleaning is the process of deleting data from your dataset that doesn’t belong. Data transformation is the process of converting data from one structure or format to another.

Data transformation techniques, often known as data munging or data wrangling, translate and map data from a more 'raw' data format to a format suitable for processing and storage.

Data cleaning is sometimes confused with data transformation. This is because data transformation involves changing data from one format to another to fit a given template. The difference is that data wrangling doesn’t delete data that’s not part of the target dataset, but data scrubbing does.

Data cleaning steps and techniques

While data cleaning strategies differ based on the type of data,you can use these basic steps to create a standardized framework for data cleaning.

Step 1: Inspect data sets

First, evaluate and audit the data to determine its quality and highlight problems for analysts to rectify. This stage includes data profiling, which identifies relationships between data components, examines data quality, and collects statistics on data sets to discover inaccuracies, inconsistencies, and other issues.

Step 2: Remove irrelevant observations

The first step in data cleaning is eliminating undesirable observations (or data points), including unrelated and irrelevant data. For example, when examining data on millennial clients, if your dataset includes observations from previous generations, you need to eliminate such observations. This improves analysis efficiency, reduces distraction from your core goal, and provides a more accessible and highly functional dataset.

You can also remove duplicate data at this stage. Duplicate data is generated via merged data sets from numerous sources, scrape data, or data from different clients or departments.

Step 3: Fix structural errors

Structural errors occur due to inadequate data management, such as irregular capitalization, common during manual data entry. These discrepancies can incorrectly classify groups or classes. 

Suppose you have a dataset with information on the characteristics of various metals. 'Iron' and 'iron' can be two distinct classes. Ensuring correct and consistent capitalization across data sources cleans up the data and makes it easier to use.

Also, check for mislabeled categories. For example, 'Iron' and 'Fe' (iron's molecular symbol) can be classified as different classes, despite being the same. Other red flags are the usage of underscores, dashes, and other erroneous punctuations.

Step 4: Standardize the data

Fixing structural mistakes helps normalize your data, but it goes further. Correcting errors is crucial, but you must also verify that all cell types adhere to the same system of rules. For example, you should decide whether your values are all lowercase or all uppercase and stick to that throughout your dataset.

Standardization also entails using the same measurement system for things like numerical data. For example, using both miles and kilometers in the same dataset will produce issues.

Step 5: Remove any undesired outliers

Outliers are data points that significantly deviate from the rest of the record. They can create issues with certain data models and evaluations. While outliers can impact the outcomes of a study, they should always be removed with discretion. 

If you have a valid cause to eliminate an outlier, such as incorrect data input, doing so will improve the performance of the data you’re working with. However, the presence of an outlier might occasionally confirm a hypothesis.

Remember that the existence of an outlier does not imply that it’s erroneous. This step is required to determine the accuracy of the data points. Consider deleting an outlier if it appears to be unimportant for analysis or is a mistake.

Step 6: Address contradictory data errors

Another typical issue to watch out for is contradictory or cross-set data errors. Contradictory errors happen when a whole record has conflicting or incompatible data, such as a log of athletes’ race times. 

A cross-set issue occurs when the column displaying the overall amount of time spent running does not equal the sum of each race time. Other examples include a student's grade coupled with a field that only offers 'pass' or 'fail' alternatives or an employee's taxes being higher than their total compensation.

Step 7: Fix errors in type conversion and syntax

After you resolve any remaining errors, the contents of your spreadsheet or dataset may appear to be good to go. However, you must also ensure that everything is in line behind the scenes. 

Type conversion, or typecasting, refers to transferring data from one data type to another. For example, numbers are numerical data, but currency employs a currency value. You must guarantee that numbers are recorded as numerical data, text is stored as text input, dates are stored as objects, and so on.

Step 8: Deal with missing data

You can't overlook missing data because many machine learning algorithms won't acknowledge it. There are several approaches to dealing with missing data. The first option is to delete the entries related to the missing data. The second option is to estimate the missing data based on other comparable data. However, in most circumstances, both of these solutions have a detrimental influence on your dataset in different ways. 

Data removal frequently results in the loss of other critical information. Data guessing may strengthen established patterns, which could be incorrect. There is also a risk of losing data integrity since you act on assumptions rather than facts.

The third (and often best) option is to mark the data as missing. To do this, make sure that all empty fields have the same value, such as 'missing' or '0' (if it's a numerical field).

Step 9: Verify your dataset

The final step is to validate your dataset once it’s been cleansed. Validating data means ensuring that processes like rectifying, deduplication, and standardizing have been completed. This frequently involves employing scripts to determine whether or not the dataset conforms with established validation criteria or 'check procedures'. Data teams can also perform validation against existing 'gold standard' databases.

Step 10: Report the results

The findings from the data cleansing process should be conveyed to IT and business administration to highlight data quality trends and progress. The report can include the number of issues detected and resolved and updated information on data quality levels.

The cleansed data can then be pushed into the other data preparation steps, beginning with data structure and data transformation, to further prepare it for analytics usage.

Data cleaning tools

A good data cleansing tool is a must-have for anyone who works with data. So, which tools could be helpful? The answer depends on factors such as the data you work with and the systems you employ. However, here are some essential tools to get started with.

Microsoft Excel

Since its introduction in 1985, Microsoft Excel has been a mainstay of the computing world. Whether you like it or not, Excel is still a popular data-cleaning tool. 

Data cleaning in Excel is achievable using many built-in methods to automate data cleaning, ranging from deduplication to substituting numbers and text, shaping columns and rows, and integrating data from different cells. It's also reasonably simple to understand, making it most novice data analysts' first port of call.

Programming languages

Performing specialized batch processing on massive, complicated datasets often necessitates the creation of your own scripts. This is accomplished using computer languages such as Python, Ruby, SQL, or R. 

While more experienced data analysts may write these scripts from the ground up, several ready-made libraries are available. Pandas and NumPy are only two of Python's many data cleaning modules.

Visualizations

Data visualizations help you quickly find inaccuracies in your dataset. A bar plot, for example, shows unique values and can aid in identifying a category that has been named in several ways. Similarly, scatter graphs can identify outliers so that you can study them further (and remove them if needed).

Data cleaning software

Data cleaning software is an essential part of data quality software. These software applications improve your data's integrity, relevance, and value by removing errors, reducing inconsistencies, and deduplicating data. This enables businesses to trust their data, make well-informed business choices, and provide better customer experiences.

Benefits of data cleaning

Data analysis needs thoroughly cleansed data to offer precise and trustworthy results. However, clean data provides several other advantages:

• Better decision-making: Analytics applications deliver better outcomes with more accurate data. This helps businesses make better-informed decisions about business strategy, operations, medical care, and government initiatives.
• Improved mapping: Organizations are increasingly striving to upgrade their internal data infrastructures. They engage data analysts to perform data modeling and design new apps for this purpose. A robust data hygiene plan is a logical approach because having clean data from the outset makes it significantly easier to compile and map.
• Improved operational performance: Clean, high-quality data helps businesses avoid inventory deficits, delivery mishaps, and other business issues resulting in greater expenses, decreased profits and strained customer relationships.
• Decreased data costs: Data cleaning prevents data inaccuracies and problems propagating further in systems and analytics applications. This saves time and money in the long run since IT, and data management teams don't have to keep repairing the same data set issues.

Challenges of data cleaning

 There are always challenges to face when you work with data. Data cleaning is one of the most time-consuming and tedious processes to tackle due to the many errors in many data sets and the difficulty in determining the sources of inconsistencies. Other typical challenges include the following:

• Issues handling big data: Resolving data quality challenges in large data systems, including a mix of structured, semistructured, and unstructured data, is tedious and expensive.
• Incomplete data: Analysts can miss out on valuable insights due to inadequate data. This is pretty typical when missing observations and outliers are discarded.

Data cleaning best practices

Data cleaning is an essential part of any analytics implementation. Your data cleaning strategy must address delivery, quality, and structure requirements and yield a culture of data ownership and control that nurtures data stewardship. Below are some best practices to follow.

• Create a good approach and stick to it. Establish a data cleaning process that is appropriate for your data, your goals, and the tools you use for analysis. This is an iterative process, so you must adhere to them carefully for all subsequent data and analysis after establishing your appropriate methods and methodologies.
• Make use of tools. There are a variety of data cleaning solutions available that assist with the process, ranging from free and basic to complex and machine learning enhanced. Conduct some research to assess which data cleaning tools are ideal for you.
• Pay attention to mistakes and note where dirty data originates. Monitor and label common challenges and patterns in your dataset, so you know what sorts of data cleaning techniques to employ on data from various sources. This will save you a lot of time and make your data even cleaner - especially when combined with analytical tools you use frequently.
• Remove unnecessary data silos. Carefully disposing data at the end of its lifecycle is important to comply with data regulations. Businesses that have obsolete hardware should take the correct elimination processes before disposing and selling the device. However, if this isn't followed, data from such devices can end up in the hands of unauthorized individuals. Use data destruction software to completely and irreversibly remove data from computing equipment.

Show me the data!

Acting on instinct is excellent. However, companies that make decisions based on clean data sets perform better than their competitors. When you know what your customers want and when they want it, you can meet their needs better. 

Businesses can’t underestimate the importance of data cleansing. Data quality is crucial for organizations, particularly in risk mitigation, compliance, and cost reduction. Seeing where potential profits and savings are will help you grow faster, reduce your risks, and maximize your returns.

Data, data everywhere and not a byte to eat. Learn how data destruction can help you eliminate data that has out-served its purpose.