Learn the data cleaning process, see the top data cleaning tools and techniques, and even discover how to clean your data.
A crucial part of data analysis, data cleaning (sometimes called data cleansing) is the process of checking a dataset for accuracy, consistency and completeness. Because modern organizations rely on data for decision-making, they must ensure that any data they use accurately reflects whatever it is measuring and that it is in a usable and consistent format.
The reliability of analytics, reporting and machine learning tools depends on the accuracy of the data that those tools ingest and utilize, which makes this process essential. Inaccurate data can lead to poor decisions, inefficient data storage and can cause performance issues for systems at the other end of the data pipeline.
In this piece, we’ll look at the fundamentals of data cleaning, the techniques involved and discuss why it’s a critical part of the data analytics field.
Data cleaning works to improve the accuracy and trustworthiness of data by addressing any observable inconsistencies, outliers, data formatting issues or discrepancies with other data sources. Because many of these elements depend on how the system is collecting and using the data, data cleaning processes can vary greatly from use case to use case.
Other common data quality issues are duplicates, in which a datapoint is repeated multiple times, and missing values, wherein a lost datapoint is replaced with an estimated stand-in value. Before this data is pushed into storage or ingested by a system it needs to be cleaned, as this allows organizations to address any issues early on and determine the accuracy and reliability of a dataset.
The terms data cleaning and data transformation are sometimes used interchangeably, but they describe fundamentally different processes.
Data cleaning is primarily about correcting any mistakes in a raw dataset. The data cleaning process works to remediate missing datapoints, identify values that seem wildly out of scope and remove any irrelevant data. The data cleaning step is solely focused on correcting any mistakes and making the raw data more accurate.
Data transformation is a more involved process which transforms the data structure and format for a particular use. For example, a data visualization tool, machine learning algorithm or BI workflow will need datasets to match a specific structure and format before it can be successfully processed. This process, which takes place after data has been cleaned and checked for accuracy, is what transforms raw data into a usable format.
It’s similar to a professional kitchen where ingredients are cleaned and sorted before cooking. Once they’ve been cleaned, they’re prepped for a particular dish — a carrot may be diced for soup or shredded for a salad, a transformation which depends on the dish it is meant for.
Data-driven decisions help organizations avoid incorrect assumptions or biased interpretation. If data is not accurate, contains flaws or is missing key components, it will not offer these advantages. This makes data cleaning a key function at any data-driven organization, as it prevents flawed data from impacting any downstream data-driven tools or processes.
Data cleaning will only become more important as more organizations capture and analyze increasingly larger datasets and train sophisticated AI and ML models on that data. Performing these tasks without a robust data cleaning operation can put an organization at risk of serious missteps, such as misallocating resources and losing customers. This can be particularly damaging in sectors like medicine and finance, where inaccurate data can lead to negative health or financial outcomes.
These are some of the primary benefits that data cleaning can provide:
Clean, accurate data can provide major advantages, as it allows organizations to allocate resources efficiently, build better products and meet the needs of the market.
Inaccurate data can cause a variety of decision-making issues throughout the organization. Organizations that have a successful data cleaning program avoid problems related to poor quality analytics and rework associated with bad data.
Data cleaning prevents costlier issues from occurring later on in the data lifecycle. Broken analytics or predictive algorithms can lead to expensive mistakes and lost customer trust. Additionally, deduplication can save money spent on data storage resources, particularly when dealing with larger datasets.
A number of standards govern data quality, and data cleaning can be fine-tuned to ensure that all data is in compliance. Cleaning sensitive data is also important for improving security, ensuring that the proper permissions and access rights are applied.
Ready to get started with data cleaning? Here are five key steps organizations take to clean their datasets:
Duplicate data is a common issue that can be tackled first. It is often the result of system errors, manual data entry mistakes or the integration of multiple datasets. Identifying any instances of repeated or irrelevant data and removing it makes the dataset more efficient and accurate.
Different sensors or data collection tools may use different classes or data formats, while manual data entry often leads to typos or mistakes like inconsistent capitalization. These errors can cause structural compatibility issues downstream.
Identify outliers and noise — irrelevant data points which should be ignored during analysis — and remove them from the dataset to prevent them from negatively impacting the decision-making process.
Connectivity issues, hardware failures or other problems can lead to missing points in the dataset, which can reduce its usability and value. Addressing these gaps by recreating the data or adjusting the range of the dataset can help maintain its continuity and value.
Validation is the final step in the cleaning process, and it can involve both manual and automated checks to ensure that each of the above potential issues has been caught and addressed.
These are a few of the most common processes that help prepare data for analysis, visualization or ML training.
Data standardization removes any formatting differences, typos and other errors, making the data consistent and easier to structure accurately. For example, distance data may be standardized on meters rather than feet, and dates standardized in a MM-DD-YYYY format.
Removing duplicates based on specific data characteristics, such as identical data collected by multiple tools at the same time, ensures data points are not counted multiple times, skewing results. This can also involve pruning irrelevant data, for example, any columns which contain a value that isn’t useful to a particular use case.
Addressing missing data points might mean re-running a process to regenerate the data or filling gaps with estimated values based on the overall trend. Missing data might also prompt administrators to uncover and address whatever problem led to the gaps in the first place. In extreme cases, it might lead administrators to discard a dataset, as the volume of missing data may make the remaining data too risky to use.
There are many different types of validation, including checking that all other data cleaning processes have completed, comparing the cleaned dataset to another, known-good dataset and, in the cases of smaller datasets, visual inspection to ensure that there are no obvious outliers, missing values or duplicates.
The exact data cleaning techniques used will depend on the data and the use case, but these are some of the most common problems organizations deal with related to data cleaning.
Addresses often vary in ways that can lead to processing issues or other mistakes. For example, a dataset which includes both single-family homes and apartment addresses will need to handle the addition of an apartment number. Similarly, some addresses may include the +4 digits on the end of a zip code while others do not. This can become even more complex when integrating international datasets which have different address formats, spelling or language conventions.
Both manual and automated data entry can create errors, as typos, edge cases or formatting issues can lead to data being misspelled, capitalized incorrectly or labeled inconsistently. A database of user information with typos in the email column might mean that some users do not receive crucial information about an important software update, which can lead to major security risks.
Some missing values, particularly those which belong to a data series, can be handled by estimating the most likely value based on the trend and the nearest values in the series. Other missing values, like a user’s surname, cannot be estimated, but might be inferred from their email address or some other means.
Here are some of the best practices organizations use to prevent low-quality or inaccurate data from tainting their decision-making, analytics and ML tools.
Data cleaning should be an iterative process that becomes more efficient as it identifies and corrects issues with the data. Organizations which document their data cleaning work allow everyone to benefit from the process, as they can more easily replicate successes and improve procedures over time.
Saving pre-cleaning data can be useful in a number of contexts, such as using it as a “before and after” reference point for data validation and for helping to investigate the reason for unclean data points. What may initially seem like random noise could indicate an undiscovered trend or a bigger problem with data collection.
Data cleaning is about more than the accuracy of a single dataset — it can also point to the root cause of any quality issues. For example, a data collection tool or sensor that routinely misses data points or collects aberrant data may need recalibration or replacement.
Manual data cleaning is an important element of the process, but most valuable datasets are too large and complex for organizations to rely solely on human analysis. Incorporating automation and AI tools can also greatly increase the efficiency and speed of the data cleaning process.
A data cleaning program is not a black box and should be subject to continual assessment and improvement. Data cleaning can be made more efficient by proactively addressing frequent issues, for example, working to ensure data is formatted correctly at the time it is captured rather than relying on a tool to fix it after the fact.
Data, like any raw material, requires processing to unleash its full value. Data cleaning is an essential part of this process, as it addresses any mistakes or gaps in the data and allows organizations to utilize each datapoint, trusting that they accurately reflect what they are supposed to measure. It can also have long-term benefits, as it helps analysts and administrators identify and fix any issues with the way data is collected. Although setting up an effective data cleaning process can be a challenge, it’s also an essential way to improve data-driven decision-making, analytics and AI and ML performance.
There are many ways to clean a database, depending on the data structure and what it is used for. Depending on the database size, it might be possible to start with manual cleaning, but most large databases will need to be analyzed with automated tools which may be included with your database package. These tools are designed to remove irrelevant or redundant data, to enforce a uniform data structure and to identify any missing data points.
There are a number of data cleaning solutions, many of which are geared toward specific use cases. Some of the more popular tools include dbt (data build tool) and OpenRefine, which are designed to assist with both data cleaning and data transformation. Some language libraries, such as Python’s Pandas, include data cleaning functionality as well.
Snowflake’s collaboration tools are designed to make data cleaning easier. These features include dynamic tables, which can help troubleshoot issues in the data pipeline, and data clean rooms, which can help promote data analysis and collaboration while preserving data security and improving compliance.
