5 Best Practices for Data Cleaning

Before implementing data cleaning, it’s important to look at the big picture, otherwise you may drown in a mess of old, inaccurate contact data. What are your goals and expectations? How do you plan to execute it successfully? As you implement data cleaning, keep the following tips in mind.


Tip 1: Develop a Data Quality Plan

Knowing where most data quality errors occur and identifying incorrect data [link to What is Data Cleansing] will help your team better assess the root problem and develop a project plan. A comprehensive data quality plan will impact many departments so keep communication open and emphasize that better intelligence will save everyone money

Tip 2: Standardize Contact Data at the Point of Entry

Check important data at the point of entry. This ensures that all information is standardized when it enters your database and will make it easier to catch duplicates.

Tip 3: Validate the Accuracy of Your Data

Validate the accuracy of your data in real time or by cleaning your existing database. Research and invest in data tools which can clean information like list imports or provide address verification software. Effective marketing occurs when high-quality data and tools are used to seamlessly merge various data sets.

Tip 4: Identify Duplicates

Save your team time and incorporate tools that effectively identify duplicates. The less manual work, the better.

Tip 5: Append Data

After your data has been standardized, validated, and scrubbed for duplicates, use third party sources to append it. Reliable third-party sources can capture information directly from first-party sites, then clean and compile the data to provide more complete information for business intelligence and analytics. This will help you develop and strengthen your customer segmentation and send more targeted information to customers and prospects.

Successful Data Cleaning

When done correctly, successful data cleaning implements three key practices. Successful data cleaning:

Detects and removes major errors and inconsistencies in single data sources and when combining multiple sources

Utilizes tools to reduce manual inspection and programming efforts

Works in conjunction with schema-related data transformations and specific mapping functions, not solo

Article From: synthio.com