The quality product data is:

• Accurate: from the most relevant and reliable sources;
• Complete: provides all the necessary parameters for a full profile;
• Relevant: not packed with every detail and cleared of unimportant clutter;
• Updated: real time revision of all product data to keep it live and working;
• Consistent: prepared for all platforms and easy to use in any situation;
• Timely: updated promptly to meet market dynamics.

So, what are the best practices to improve product data quality?

1. Eliminate product data silos

A data silo is a data collection held by certain employees or departments in an organization that other employees or departments cannot easily access. Data silos make it impossible to get a complete view of the company's product data or define a single point of truth for everyone who collaborates on product data creation or needs access to it.

The main purpose of eliminating product data silos is to share product data, without copying it. The aim is to avoid inconsistency, and data loss, and reduce mistakes that result from suboptimal processes and from the use of outdated or incomplete data.

2. Enhance product data accessibility

Giving your customer easy access to product data can make it easier for them to find and purchase the products they need. You can make your product data more accessible by adding it to search engines, online catalogs, and other online resources. Additionally, you can provide product data in a variety of formats (e.g., text, images, videos) to make it easier for customers to find and understand the products you sell.

3. Verify your product data

After you gather your product data, you will want to verify that it is accurate and complete. This includes checking for duplicate entries, ensuring that all required fields are populated, and verifying that the data is formatted correctly. Confirm that you have used the correct format for your product titles, descriptions, and other data and that they accurately represent your products. If you find any errors, correct them before you move on to the next step.

Here, the combination of manual and automated data procedures is essential. You can enhance your product data importation and input with automatically applied rules, which enable you to import everything with little manual effort while safeguarding the data quality.

You can also include manual verification in the form of data approval processes, which you can automate.

4. Apply control rules for data quality of products

These rules guarantee that the data complies with the required quality standards and are applied during the data entry and data management processes.

You must continuously investigate product data quality problems and apply appropriate steps to avoid them in the future. For this, you can define and measure different metrics, e.g. define.

• a minimal product description length
• required attributes for a certain product group, family, or category.
• data types for each used attribute and its format for data comparison

Establish a process to measure data completeness, which defines the percentage of completed mandatory attributes depending on the product type. Adjust your data entry processes to exclude data duplicates and inconsistencies. By setting data modification dates you can assure that your product data is up-to-date and audited.

5. Introduce and use own or existing product classification standards

Define your own rules to describe your products or use other popular classifications systems like GS1,ETIM, ECLASS, UNSPSC. Such classification standards improve the efficiency and accuracy of communication throughout the global supply chain. Classification standards are important because they help to ensure that all items in each category are similar in use, purpose, or function.

Additionally, classification standards ensure that items are organized in a way that makes most sense for the context in which they are used.

6. Establish internal guidelines for data governance

Data governance includes setting standards for data collection and management and establishing procedures for verifying the accuracy and reliability of that data. It also includes creating and enforcing policies for those who can access data, and establishing procedures for handling data breaches and other incidents that could compromise the security or integrity of the data. Thus, data may be used by different teams in different ways, your data governance policy should make this possible.

Omnichannel data management can also lead to a growing avalanche of iterations that could influence the product data quality. This is why it is important to create a system of data governance.

7. Assign a product data manager

Giving someone else access to your data can help you manage it as someone always monitors your product data and verifies that it is correctly used and that no one is tampering with it.

A person in charge helps to develop and manage data-collection processes or systems and ensures that data is consistently and properly formatted across different systems and is a single point of contact for data-related questions or issues.

8. Adopt an effective update plan for quality data of products

Critical product data updates should be timely, but it is also essential that you have real time data. It is a fact that stale data inevitably transforms into low-quality data.

After gathering the first customer feedback (e.g. from product reviews or questions about the product) product data can be further improved. This should be an iterative process.

Regular updates about promo offers, special prices, or any other changes will appeal to your customers and increase their loyalty levels.

9. Adapt your product data to your communication channels

Product data quality is greatly affected by the channels for interaction and communication. They are destination points for your product information as well as a source for fast data updates and improvements based on feedback, and for compliance.

Support as many distribution channels as possible to take your products closer to your customers. Adapt your data to appropriate customer target groups. The product data for websites, social media, e-commerce platforms, online shops, print catalogs, etc. may have different scope, volume, and style.

10. Create a "single source of truth" for all of your product data

Organize all your product data so that it is all stored and managed in a single place. Ensure that everybody involved has access, so that they can create, improve or simply find and use the stored product information.

A "single source of truth" can help to ensure data accuracy and prevent data fragmentation and duplication. This will improve data quality and ensure that data is consistent across systems. Additionally, a "single source of truth" will make it easier to properly use and manage data.

11. Implement a Product Information Management (PIM) System

A Product Information Management (PIM) System is a software application that enables businesses to manage and control product data and information. PIM systems allow businesses to consolidate product data from multiple sources, including product descriptions, images, specifications, and other related data and to distribute them through all the communication and marketing channels. Often a PIM system already includes DAM functionality.

A PIM system can help a company to:

• Store product information in a central location
• Manage product variants and revisions
• Create product catalogs and price lists
• Monitor product data quality
• and much more.

When selecting a PIM system, it is important to consider the needs of your business. Some factors to consider include:

• The number of products you have
• The number of product variants you have
• The complexity of your product data
• The level of automation you need
• The level of integration you need

A good PIM system like AtroPIM will enable you to implement all the best practices as mentioned above.

12. Use data quality metrics to understand progress

Data quality metrics track the progress of your data quality improvement efforts. These metrics can also help you to identify areas where further improvement is needed.

The most important data quality metric is completeness. This is the percentage of data elements that are included in the data set. Accuracy is also essential, especially if you use the data set to make decisions. Other factors you can track include timeliness, consistency, and accessibility.