Data cleaning can be a complex, time-consuming activity. Experts estimate that data scientists spend most of their time—between 50 and 80 percent—on collecting and preparing information rather than analyzing it for useful insights.
Nevertheless, the data cleaning process is essential for data-driven enterprises. Performing data cleaning regularly ensures that the final data analytics and reporting results are accurate, relevant, and trustworthy.
This article will explore four best practices for enterprises should follow to improve their data cleaning process.
#1. What is Data Cleaning Process?
Data cleaning removes low-quality information from a dataset, sometimes called data cleansing, data wrangling, or data scrubbing. Data cleaning is crucial to data preparation and transformation, ensuring that the data points are free of typographical errors, inconsistencies, and inaccuracies before they are stored and analyzed.
The types of information removed during the data cleansing process may include:
- Irrelevant data that is not necessary for reporting and analytics
- Duplicate data from multiple sources that has to be reconciled
- Corrupt data due to hardware or software failures or human error
- Incomplete data can paint a skewed picture or lead to incorrect conclusions
- Out-of-date data that needs to be updated or is no longer useful
According to a Harvard Business Review study, just 3 percent of datasets meet the minimum requirements for data quality standards.
However, cleaning data is an essential foundation for business-critical practices that require accurate data—such as data analytics, reporting, and emerging technologies such as artificial intelligence and machine learning.
Some of the benefits of data cleaning are:
- Better data quality: Data cleaning helps correct various issues with the input data set. The output of data cleansing is more reliable, leading to smarter data-driven decision-making.
- Lower costs: According to Gartner, poor data quality costs organizations an average of $12.9 million per year. Data cleansing helps reduce the costs of low-quality and faulty information, such as lost revenue opportunities or customer dissatisfaction.
- Stronger data security: Many organizations handle sensitive information during the data preparation process. During the data cleansing process, businesses can delete, mask, or encrypt confidential data to lower the risk of exposure during a data breach.
#2. 4 Best Practices to Consider in Your Data Cleaning Process
Data cleaning is a complex yet fundamental task, which makes it an excellent target for businesses looking to become more efficient and productive. Below, we’ll suggest four best practices for the data cleansing process.
2.1. Ensure Your Data Infrastructure is Sound
If your data cleansing needs are especially variable or unpredictable, your data cleaning infrastructure must be scalable and reliable. Your choice of hardware, software, and data cleaning tools must support various workflows and be flexible enough to adapt to changing requirements.
Upgrading to cloud-based services and future-proofing your data cleaning architecture can be challenging, costly, and time-consuming. However, putting in this effort ahead of time can reap dividends later on.
Recognize the gaps in your data cleaning infrastructure, consider whether the effort is worth it, and loop in stakeholders for the best chance of success.
In particular, consider both your current and future needs regarding data cleansing. If the intent of the data cleansing process is to handle large data sets for end users, it’s worth evaluating whether your existing data pipeline is strong enough to achieve the desired result.
2.2. Track and Log Changes
Version control is an extremely valuable best practice for many IT functions, including data cleaning. Leverage version control tools and systems to ensure that your data sets (and any machine learning models based on these data sets) are captured and recorded between changes. For example, this makes it much easier to reverse changes impacting other data pipeline areas.
Monitoring and observability are also essential IT best practices, especially in data cleansing.
For example, if an API (application programming interface) is a key source for your data pipeline, any changes in that API need to be monitored to ensure that the data flow is uninterrupted. Keep track of your data cleaning activities and log them regularly to track your progress, identify errors, and ensure repeatability.
Finally, maintain clear, extensive documentation about your data cleaning processes—whether to help refresh your memory or onboard new hires. This documentation should include the purpose, data quality rules, data validation tests, and code changes to help with troubleshooting and collaboration.
2.3. Employ Automation in Your Data Validation Framework
Data cleaning is time-intensive, so introducing as much automation as possible into the process is desirable. Use data validation processes and tests to validate the integrity of your data—both before data cleaning begins and after it is complete.
Automated tests should continuously occur across multiple stages of the data cleaning process. The resulting reports can help you quickly pinpoint any data quality issues and provide an overview of the workflow for key stakeholders. In addition, you should work to constantly improve your validation tests over time, especially as your enterprise data resources evolve.
2.4. Collaborate with Data Consumers
A “data consumer” is any person or IT system using the output of the data cleaning process. These data consumers are the ultimate beneficiaries of data cleansing, so their feedback should be prioritized when seeking to improve. Gather input from consumers within your organization, such as data analysts and business teams.
Collaboration with data consumers can help identify problems with your data quality and misalignment between the process and end users’ requirements. It can also elevate the effectiveness of the business as a whole.
Often, data engineers may be thinking in technical terms and advanced analytics, whereas consumers may need to answer simple questions before adopting more complex capabilities.
Below are just a few high-level questions to ask to generate good feedback from data consumers:
- Can you get answers to key questions easily and effectively? For example, stakeholders may want to answer questions about business performance, annual recurring revenue, and year-over-year growth.
- How many steps does it take you to answer these questions?
- Can you identify actionable insights from this data?
- How does the output influence your decision-making?
3. Book a Free Project Evaluation & Enhance Your Data Cleaning Process with KMS Technology
Data cleaning is a challenging yet fundamental task for organizations that are or want to be data-driven. If you’re struggling to establish an effective data cleaning process, you might wish to join forces with a data services provider like KMS Technology.
KMS is an AI-Native product engineering partner with a wealth of experience in developing data analytics solutions. We’ve helped our clients analyze customer and industry data to uncover new revenue streams, find key actionable insights, improve their efficiency and productivity, and create new features and innovations for their target audience. Our list of data engineering services includes:
Data Pipeline Assessment: Data engineering experts from KMS Technology will help your enterprise to turn data chaos into clarity—a focused three-week engagement that uncovers hidden bottlenecks, quantifies performance gaps, and provides a roadmap to build a faster, AI-ready data foundation.
Data Platform Assessment: KMS Technology build an AI-Ready Foundation to Scale with enterprise big data solutions: a focused, three-week engagement that evaluates your data platform, integration ecosystem, and overall architecture—identifying cost inefficiencies, complexity gaps, and AI readiness blockers—then delivering a clear, actionable modernization roadmap.
Cloud Data Migration Strategy: We will help businesses transition from legacy data warehouses to modern cloud platforms through a structured roadmap that avoids budget overruns, minimizes downtime risk, and ensures the right architecture is chosen for your workloads.
Data Analytics Accelerator: A six-week engagement leveraging enterprise big data solutions to create and implement customer-facing analytics—such as predictive insights, performance benchmarking, and quality analytics—integrated directly into your product instead of standalone BI tools.
Ready to learn how KMS’ data engineering services can help?
Get in touch with us today for a chat about your business goals and requirements.
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