Top 5 Data Cleaning Practices

Explore the top 5 essential practices for pristine data cleaning

Businesses must collect, store, and process information and data to improve the customer and employee experience. Providing an experience based on erroneous data might jeopardize the experience and employee efficiency.

According to a Gartner analysis, poor data costs firms around US$12.9 million annually.

Aside from the cost ramifications of poor data quality, it adds to the complexity of data ecosystems. It is one of the significant factors that contribute to bad decision-making.

Many businesses need help with gathering, storing, and analyzing high-quality data. To obtain the intended outcomes, businesses must overcome data quality issues. The key is a robust data management plan with data cleaning at its heart. Businesses should have the finest data cleaning practices to cleanse data and maximize its value.

1. Create Roadmap for Data Quality

DataOps teams must establish data expectations.Set good key performance indicators (KPIs) for data quality. It is critical to define what constitutes strong data KPIs. To satisfy data KPIs, businesses must implement strategic data management plans. To ensure data hygiene, decision-makers should check the health of the data.

2. Evaluate Data Accuracy

Organizations should assess the data to verify that it satisfies all statutory standards. Manual validation of tiny data sets is relatively simple. However, larger and more complex data sets might require more manual work. To address the issue, businesses might use a variety of data quality measures.

3. Correct Data at The Source

Businesses should have clean and organized data to keep a clean data repository. This method helps to verify that all data from the source is error-free. It significantly decreases the time and effort necessary for DataOps teams to clean data.

Decision-makers should develop a standard operating process for data collection. They must also enforce it across all data sources to guarantee clean data reaches the repository.

4. Delete Duplicate Data

Data duplication is a prevalent error in the vast majority of data sets. Duplicate data from several sources might be caused by human mistakes. Organizations that do not keep a close eye on duplicate data will have large databases full of useless material.

Duplicate data may substantially influence the organization, resulting in poor business decisions. Enterprises must use best practices to eliminate double data entry.

5. Facilitate Using Clean Data

Setting clear expectations with all resources is critical whenever a company decides to clean its data. Business executives should educate their resources on the necessity of clean data. It is critical to guarantee that all resources practice clean data regardless of their job description.