• Accuracy: For your product data to be high quality, it has to be accurate.

• Relevancy: The data has to bear relevance to the particular need for which it intended.

• Completeness: A high-quality product data has to be complete, with no missing values or records.

• Timeliness: Product data has to be updated as frequently as there is an update for it to be considered high quality. Once data can be updated at the time of new data receipt or changes, the data quality is easy to maintain.

• Consistency: Consistency is a big deal. Product data has to be consistent across all possible forms and platforms.

1. Each Data collected must be from verified sources.

Once your body of product data consists of a few falsehoods, then the entire data cannot be said to be accurate, and this few incorrect data will stall the quality of the whole data body.

Invest time in verifying the facts, images, videos, and texts attached to each product data. To do this, check up some Free Data Quality Audit systems to run through your data and ensure that it is adequately formatted and validated, ready for public use.

2. Do Regular Data Update.

Run regular updates on your data. Data cleaning is not once-and-for-all-task. As soon as there is a change in any product detail, change in price, available promo offers, or whatever the case may be, then there is a call for an update. Stale data is low-quality data.

3. Utilize An Automated Database Management Platform.

This is one of the most effective ways of improving quality product data. Stay away from using spreadsheets for data management. Automated database management platforms keep track of several details that most likely get ignored on manual platforms.

Get advanced product categorization, data collection, update, and distribution with automated management platforms like PIM software.

4. Centralized Data Assets Management and Data Modeling.

Avoid duplication of data. Ensure that your data gets centralized into a single document that will serve as a reference document for users to refer.

Once a part of data or the entire data is created, worked on, adjusted by different people, then this gives room for inconsistencies and errors. And with time, it becomes difficult even to track the source of the variance when data issues begin to arise.

5. Effective Communication chain.

Effective communication is very relevant in the maintenance or improvement of product data quality. What it does is to help the organization understand the flow of verified data, so data do not get obtained from every available source.

In a situation where data is decentralized, each unit of a department should know where to and where not to get verified product data. It is a method of managing data duplication and aims at promoting a trackable and accountable data sharing structure within the organization.

6. Engage A Seasoned Data Quality Control Team.

Having high-quality product data will require that you have the best hands on your data quality control team. Your quality assurance team that ensures the continuity of quality by running data quality checks on new data to a considerable extent determines the quality of your product data.

7. A data governance program,

Put up a data governance program that dictates who is in charge of data management and updates. This tip will ensure that there is clear cut ownership of the product dataset within the company to avoid any department silos.