How to Evaluate Product Data Quality in 1 Hour with PIM

Data is eating the world.

According to The Digitization of the World From Edge to Core report by IDC and Seagate, the summation of all data created and stored in the cloud, enterprise infrastructure, and endpoints (PCs, smartphones, IoT devices) is rapidly growing. 

IDC experts call this huge data tank the Global Datasphere, which is predicted to grow to 175 Zettabytes (ZB) by 2025 (from 33 ZB in 2018)

So we talk about the jump of 142 x trillion gigabytes in only seven years.

Challenges in datasphere

The IDC report indicates that data growth will be the fastest in enterprises’ data centers.

Such a rapid data stream forces enterprises to manage and maintain their data (master, business, product data) effectively and keep their finger on the quality pulse.  

Businesses are looking to centralize data management and delivery (e.g., online video streaming, data analytics, data security, and privacy) and leverage data to control their businesses and the user experience (e.g., machine-to-machine communication, IoT, persistent personalization profiling).

IDC, The Digitization of the World From Edge to Core report

In digital marketing, any customer shouldn't see lorem ipsum copy or pixelated images in your marketing or sales messages (nobody appreciates sophisticated copy in Latin or lo-fi design effects).

Being deadly serious, the consequences of poor data quality in the front of shoppers’ eyes can be very costly.

In this article, I will show you some key metrics used in data quality assessment, and some cool features in PIM software that help users evaluate product data quality seamlessly.

What is product data quality?

According to the definition, data quality (DQ) is the degree to which a given dataset meets a user's needs.

Quality of data is a critical criterion for making data-driven decisions.

Product data quality can measure how well a data set serves the user and provides information about the product.

The quality of product information is essential for customers to make considered purchase decisions.

What metrics use to measure up product data quality?

To measure data quality, you need to learn more about essential metrics.

There are several data quality dimensions (some sources refer even twelve), but these six dimensions: accuracy, completeness, consistency, validity, uniqueness, and timeliness are considered the most crucial.

Let’s focus on each one and refer to product data.

6 key dimensions of data quality

Accuracy

This dimension means that data reflects the actual real-world state (of physical or digital products) and can be confirmed by reliable sources.

It greatly matters because, for example, even a tiny mistake in the product description in an online store can change the accuracy, which can cause customers disappointment and have consequences in a number of returns. 

Accuracy errors might often arise when manually entering and transferring data. Automation tools used in data management eliminate this risk through fast syncs, checking data, and automatic updates.

Completeness

The completeness metric determines whether a given data set contains all the required necessary information.

The state of completeness also applies to the media and the provided documents such as photos, specification sheets, or video files.

Consistency

The consistency dimension means that data should be uniform and synchronized in all locations (channels, applications) they appear.

In omnichannel retail, product data consistency is crucial to building a seamless shopping experience at every sales and marketing touchpoint. 

For example, when a shopper switches from social media ad to a product page, encountering conflicting data that can confuse, which, sadly for the brand, may end with an abandoned cart.

Keeping product data consistent can take a lot of work to do manually.

Still, it can be much easier and faster with data management tools like Product Information Management (PIM) software which automatically correlates data in multiple channels and checks if data is saved in proper formats.

Validity

This data quality metric means data sets should comply with generally accepted standards and defined business requirements.

These rules should be monitored on an ongoing basis to keep the data up-to-date.

For this purpose, API-based data management is used, which, thanks to protocols, checks compliance with specific standards.

Timeliness

Timeless refers to whether the information is available when expected and needed.

The metric depends on the specific time and the user’s expectations.

Keeping data up-to-date is essential and manual updates of large datasets can be tedious, but automatic synchronization in data management software ensures that data are always up-to-date and accessible.

Uniqueness

The uniqueness dimension means that a given data particle is the only one in the data set.

Cleaning and reducing duplicates is an integral part of data management, as it improves the overall data quality index and makes it easier to organize.

Costly consequences of poor data quality

Following the key metrics, high-quality data is accurate, complete, consistent, validated, up-to-date, and unique.

Therefore data components of low quality may have various forms; for example, a product description contains missing and outdated information misspelled words, or typos.

These are little things, but they might hugely affect your sales and marketing efforts.  Just look at these numbers.

Bad data quality affects customer experience

The latest survey on data visibility conducted by Acceldata shows that 45% of surveyed data executives have experienced data pipeline failure (11-25 times in the past two years) due to data quality or errors that were discovered too late.

Nearly half of those executives (43%) indicate that the customer experience suffered from these failures.

According to Gartner, poor data quality costs organizations an average of $12.9 million annually. 

Low-quality data has vast business consequences. For example, inaccurate, out-of-date, and incomplete product information leads to bad decisions, lost revenue opportunities (such higher rate of abandoned carts or e-mail churn), missed sales opportunities, and overall, spoils customer experience. 

Poor data quality can ruin the customer shopping experience

In global companies with extensive product portfolios, product data is shared and processed by many people.

Evaluating and maintaining product data manually into many spreadsheets files can be tedious and error-prone, predominantly when a company operates over 1000 SKUs. 

However, there are ways to do it quickly and efficiently (Bluestone PIM!) and hacks to improve product data quality and maintain it.

But first things first, you have to evaluate the quality of your product data and identify which data needs to be completed or corrected. 

How can PIM help you in evaluating product data quality?

PIM solutions allow gathering product data in one central repository and, thanks to the advanced analytics module, enable fast identification of which information requires quality improvement.

Bluestone PIM provides intelligent data analysis capabilities to draw inferences from product data and improve data quality.

In the example of Bluestone PIM features, I will show you how to quickly check the quality of your product data based on each data quality metric.

Centralize and clean

Identifying errors is much easier when you put them all together than relying on scattered data.

A PIM system allows you to centralize all product information and create a single source of truth for the entire organization.

It helps maintain consistency and uniqueness of data quality by removing duplicates and mistakes.

Use automatic sync

Automatic updates will fix the data quality problem with timeless.

By activating fast syncs, you can always keep your content up to date across all channels.

Import reliable product information

Bluestone PIM enables to import and sync of large volumes of product data from manufacturers, suppliers, industry databases, or business partners.

The system can also exchange live product information with ERP and e-commerce platforms on the fly.

These actions help you detect the validity and accuracy of your data and improve it.

What’s more, it maximizes product information quality with validation rules and increases efficiency with compound attributes.

Check the state of completeness

One of the useful features of Bluestone PIM is the completeness score, which highlights issues needed to be fixed, like missing attributes or wrong image sizes.

Thanks to these insights, you can monitor the data quality and completeness of product information for a given product on an ongoing basis and publish it on the website when everything is ready.

The Completeness Score tells you what other information your product is missing and how you can maximize its visibility on the marketplaces.

Pro-tip: Troubleshooting 

In Bluestone PIM, you can automate import jobs by status to quickly identify those completed with issues.

The failed jobs report provides detailed troubleshooting information — it identifies the reasons for errors and offers helpful suggestions for fixing them.

What else can PIM do? 

Working with the Product Information Management platform significantly accelerates the evaluation of product data quality, but this is not the only function of this solution.

With PIM, you can also organize data in a smart way, working on improving product data quality with your teams and partners and distributing product information through channels.

For retailers, all these activities at the base can help to maximize sales opportunities and increase revenues.

If you are curious about all features of Bluestone PIM powered by MACH architecture (Microservices, API-first, Cloud-native, and Headless), look into Bluestone PIM Features Overview 2022. 

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Product information management is the cornerstone of customer-centricity and a successful retail business. With PIM in place, you can focus on creating a compelling and engaging shopping experience for your customers with speed and efficiency, and eliminate the frustration of manually managing an inventory catalog with over 1000+ SKUs.

Contact our advisors to learn more about PIM and how it can help your business sell more, better, and faster. Or book a demo for a free guided product tour of Bluestone PIM.