4 Steps to Perfecting Product Data Quality

Well, it’s true. If you don’t build your product experience with accurate, consistent, complete product information, that foundation will crumble over time, leaving you to deal with the rubble of a bad product experience. That’s why it’s crucial to ensure that your product data is correct, consistent, and complete, and leaves your customers with a perfect understanding of who you are and what you sell.

But while enriching products with correct, consistent, and complete product data may sound relatively simple, it’s often a daunting task that can seem impossible on its face. That’s why we put our heads together to come up with this easy-to-follow, three-step guide to perfecting your product data quality and building a strong product experience.

Step 1: Collect and standardize product data

If you want to improve your product data quality, the first step is gathering and standardizing your existing product data.

Raw product data will exist in a wide range of systems, including your ERP, suppliers, existing spreadsheets, and other source systems. So, start by identifying your best and most trusted source of information and gathering all of your product data in one place. Be sure to keep an eye out for varying file formats, which often slows down the collection process. Once your product information has been gathered into one single source of truth, it’s time to standardize and structure your product information for future enrichment.

This includes the varying file formats we mentioned above, along with any inconsistencies in the format of the data itself and the attribute values associated with this product data. In order to accelerate this process without introducing errors, find a solution that can use capabilities like automation, a business rules engine, and bulk actions that allow you to edit many fields in one step.

Step 2: Identify and correct sources of incorrect and inconsistent product data

You’ll also need to identify what information is missing or incorrect — and where that inconsistent or inaccurate information is coming from.

This can seem easy or even obvious but is often easier said than done, so lean on the members of your team with the most intimate knowledge of each product and product attribute. Usually, these people are “business users” – those with deep knowledge of the data and can quickly spot inconsistencies. They can provide a starting point that your technical resources can then supplement. Once you’ve identified the inconsistent and incorrect information that plague your product experience and found the source of your product data problems, it’s time to clean up inaccurate product data.

Start by trying to correct the source of your problems to ensure that your product data is accurate and consistent going forward, then use your PIM to update individual pieces of information. It’s also crucial to make sure your customers aren’t left with more questions than answers when browsing your sales channels. So, ensure that your product data isn’t just correct and consistent, but complete as well, by adding any data that may be missing from your sales channels.

Step 3: Iterate to great

Rome wasn’t built in a day, and your PIM system won’t solve all of your problems with product data quality overnight.

That’s why it’s crucial to remember that offering a great product experience built on a foundation of high-quality product data is not a one-time project. It’s an ongoing effort that must be updated and maintained over time. Product information is dynamic — customer preferences and trends are always changing, products and technologies are continually being phased in and out.

This means your product data and your governance policy alike will need to be updated in order to accommodate and reflect those changes so it remains accurate and consistent. It’s also vital to remove data that has become irrelevant. Once your PIM is live, you can progressively work towards improving your data by assigning your product team small side projects around certain attributes or parts of the product catalog.

Step 4: Monitor your product data quality

Now that you’ve taken the important first steps to get your product data quality to the level it needs to be, you need to monitor its current state and how it evolves over time.

Ideally, your PIM solution will give you insights into not only the state of your entire catalog’s data quality but also for each individual product. That’s why Akeneo offers a Data Quality Insights feature that evaluates and scores product data quality in terms of consistency and enrichment. With the distribution of scores at the catalog level (and as with most things in Akeneo PIM, by channel and locale) you can see at a glance how data quality rates overall and how it evolves by different time increments – daily, weekly, and monthly – along with key indicators for important enrichment topics. An additional benefit of this feature is that it helps your teams easily identify where additional work on quality needs to be done so they can efficiently focus on the most important areas.

So, find a PIM solution that helps you identify low-quality product data and gives you ideas to fix it. This includes giving you a quick overview of your product enrichment and consistency and measure your data quality, helping you more rapidly detect poor data and quickly improve it to reduce your time-to-market. That way, you can be sure you’re providing a compelling product experience to your customers and see increased conversion rates as a result!

Perfect your product data quality with Akeneo PIM

Improving your product data quality is no small feat. It takes a knowledgeable and agile team of product information experts and enthusiasts working together to gather product data, identify and correct problematic information, and maintain high standards for product data into the future.

It also requires a high-quality solution that is purpose-built to help you collect, standardize, and enrich product information. That’s where Akeneo PIM comes in. It can help you quickly and efficiently upgrade your product data quality without introducing inaccuracies or inconsistencies, leading to a top-notch product experience. And, with our Data Quality Insights feature, you can measure and follow up on your product data quality with a simple, color-coded grading system that helps you identify bad product information and improve your product data quality.