E-commerce store owners know that product data quality is of paramount importance for their business. Poorly entered or structured data can lead to a number of negative consequences, including failed orders, customer dissatisfaction, and bad reviews.
That’s why it’s important to have a good understanding of what product data quality entails and how it can be achieved.
In this blog, we will be looking at what product data quality is and how you can ensure one for your eCommerce store (s).
Well then, let’s dig in and know more about product data quality.
What is Product Data Quality?
Product Data Quality (PDQ) is the degree to which data about products conforms to a standard that can be used in a consistent manner by all parties with an interest in that product.
It encompasses all data for a given product, including its name, description, characteristics, usage, attributes, and specifications.
In general terms, Product Data Quality is considered as the accuracy and consistency of product data as well as how it is presented.
Product data quality ultimately depends on how well a product’s story can be understood. For everyone involved in the entire grocery process. As a result, it requires a common language that everyone can understand.
Everyone who is part of grocery products must obtain product knowledge in a consistent manner. It could be manufacturers, vendors, transportation companies, retailers, or consumers.
The supermarket sector would not function as expected if this were not the case.
Achieving product data quality is more than just ensuring the accuracy of your products. To be sure that you are providing a good experience to your customers, you must also know that you can trust and rely on your product data.
Product data quality should include 5 key attributes – completeness, accuracy, relevancy, consistency, and timeliness.
Good quality is reliant on a good content strategy, Check out how Apimio can help your product content strategy.
The importance of product data quality
If you are looking to create an eCommerce business, then product data quality is the cornerstone of your entire business.
Product data quality can make or break your business. If you want to manage your product data effectively, you will need to make sure that it’s as accurate and informative as possible.
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E-commerce sites that sell physical products rely on product data to drive sales. Without a quality product feed, conversion rates are often low and search engine optimization efforts suffer.
In order to avoid these negative effects, companies need to focus on creating a product feed that includes accurate, relevant, and unique information.
Consumers are more likely to buy products from a company whose product feed has rich content. This increases the chances of conversions and improves SEO ranking.
How do you ensure product data quality?
Product information is one of the most valuable assets a company has. It’s also one of the most underutilized assets in the industry.
As a result, companies lose out on sales and revenue. Even worse, many companies are risking their reputation by providing customers with product data that are inaccurate or incomplete.
Leading companies know this and have taken action to improve their data quality. By taking a few simple steps, you too can be among the leading companies in your industry.
In this section, we will be discussing some steps that you can follow to improve your data quality.
1. Collect and prepare your current product data
The first step after implementing a PIM system is to gather your existing product data and prepare to enrich it for use in your channels.
This information is typically gathered from a variety of sources, including your ERP, marketing systems, and external suppliers.
PIM will assist you with cleaning this data and determining which sources provide the most accurate attribute data.
Furthermore, once you define these workflows, you can use them not only for deploying a PIM solution but also for onboarding new items.
2. Data Profiling and Controlling
In most cases, poor product data quality is the result of data reception. In most cases, data in an organization comes from sources outside of the firm or department’s control.
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It could be information given from another organization or information gathered by third-party software in many circumstances.
As a result, you can not guarantee its data quality. The most important part of all data quality control activities is a comprehensive data quality control of incoming data. It becomes required to employ a good data profiling tool; such a tool should be able to check the following data characteristics:
- Data patterns and data format
- Data constancy
- Anomalies and data value distributions
- The entirety of the data
It’s also important that you automate data profiling and data quality alerts so that incoming data quality is constantly checked and maintained; never assume incoming data is as good as expected without profiling and inspections.
Finally, you should manage all incoming data according to the same standards and best practices. You should have a centralized catalog and KPI dashboard in place to reliably record and monitor data quality.
3. Carefully design a data pipeline to avoid duplicating data
You may experience duplication of data when you create a whole or part of data from the same source and logic. However, various people and teams are dealing with it for distinct downstream goals. This leads to inconsistency in product data quality
When you mistakenly create duplicate data, it is very certain that it would be out of sync. Moreover, the data may result in various outcomes, with rapid effects across multiple systems or databases.
Finally, determining the root cause of a data problem, much alone resolving it, becomes difficult or time-consuming.
You must define and carefully develop your data pipeline. You should be extra careful in areas such as data assets, business rules, and architecture. By being aware, you will be able to avoid these issues for your organization.
You also need effective communication inside the company to promote and enforce data sharing. This will increase overall efficiency and eliminate any potential data quality issues caused by data duplications. This gets into the meat and potatoes of data management, which is beyond the scope of this paper.
On a high level, there are three areas that you must form to avoid the creation of duplicate data:
- To eliminate department silos, you should implement a data governance programme that clearly specifies dataset ownership and effectively communicates and supports dataset sharing.
- Centralize Data modelling and asset management. Review and Audited them on a regular basis.
- At the enterprise level, there is a clear logical architecture of data pipelines that is shared across the business.
Solid data management and enterprise-level data governance are critical for future effective platform migrations, given today’s rapid changes in technology platforms.
4. Arrange your products and product attributes for enhancement
With thousands of goods to manage, finding solutions to expand your product information procedures is crucial.
A product information management system (PIM) should make the process go smoothly and make it simple for marketers to find and enrich the necessary product data. Apimio, for example, makes the process easier by letting you form families and attribute groups.
A family of products can share a set of qualities, so when new products are added to the family, they inherit the shared attributes as well. These property values can be modified in a batch or sequentially.
An ‘attribute set’ is another feature of Apimio. This feature allows you to group attributes together to better organize similar qualities and provide those teammates who are responsible for delivering and validating those values more visibility, as well as to assist coordinate the team’s work across many products.
You can be group together name and description, for example, in a “marketing” attribute group, size, weight, and color in a “Technical specs” attribute group, and photos and files in a “media” attribute group.
To make editing easier, these attributes will be visible together on the product form. You can further restrict who can do what by granting rights to attribute groups, allowing you to designate who can change the values of certain attributes.
With all these features, you are able to ensure steady and up-to-mark product data quality.
5. Group your products
PIM allows you to organize your catalog into categories in addition to controlling attributes and attribute groups.
You should divide these categories into groups based on what clients are looking for. It’s a good idea to check with your eCommerce managers when deciding on your own product categories since they have a thorough understanding of how your clients search for products.
Conclusion
In our experience, we’ve seen that the most successful eCommerce companies are those who have invested in product data quality.
PIM solutions can ensure your products have a proper structure, making it easier to create rich content and marketing material.
With a PIM, you can make sure your product information is up-to-date and accurate so that customers will know they are purchasing a quality product.
Remember to consider your unique business needs when choosing the right solution for your team.
What to do next?
- Choose the best PIM solution, Apimio, to manage your product data and grow sales. Sign Up here.
- Read more about our Product Information related Guides here.
- Contact our support or comment your thoughts under the blog section if you have any further questions.
Frequently Asked Questions
PIM ensures product data quality by providing a centralized platform where you can consolidate and enrich your product information.
It helps to improve the accuracy and completeness of product information, enhances the consistency of data across different channels, and enables better customer experiences.
PIM allows you to have a single source of truth for your product data. It enables you to validate and standardize product information, ensuring that it is accurate and up-to-date.
