PIM • 7 min read

Product data quality: picking the right tool for the job

Joeri Moors | 26-11-2019

Product data is the oil of your company today. Without a certain level of quality your business processes will clog and erode. Especially if you are aiming to grow your e-commerce, you’ll need to improve the data quality of your products. But, like any motor mechanic looking at the engine block in front of him, you’ll have to use the right tools and approach.

Before you start taking the engine apart, you have to listen to the complaints. And for product data, typical management questions are:

  • Why do we have such limited product information in our webshop?
  • Where do we stand with our desired level of quality of our product data?
  • How do we enrich our product data efficiently?
  • How do we monitor its quality on crucial points such as details, material codes or food ingredients?

And if you dig a little deeper in your organization, you may uncover other issues. Low quality product data can also cause high return rate of certain products, increased service requests and complaints. Labeling a product with the wrong attribute can have a tremendous impact on a building project or someone’s health for example. To improve the quality, you’ll require tooling that can analyze, validate and improve the product data through your business processes.


First, you’ll need to define the scope of your product data quality. This depends on the kind of data you’re working with and what your company goals are. Selling B2B products online will bring different objectives than if you’re striving to deliver health care or produce dairy foods. Quality can be understood as the fitness for the intended purpose. Whether this is in the context of existing business operations, analytics or emerging digital business scenarios. For example, a short product description may be of high quality for a retailer selling in stores, but of poor quality for a brand selling online.

The scope is determined by the following data aspects:

  • Completeness, precision: How complete is the data entry? Are all the necessary fields for all the business processes filled in?
  • Conformity: Is your data following the set standards? How clean is the product data?
  • Consistency: How consistent is the data with other data after entry? How is this checked?
  • Accuracy: How valid is third party data? Are prices, dimensions and descriptions correct?
  • Duplications: Is more than one source used? How can doubles be filtered out?
  • Integrity: Is the process or the person who entered the data trustworthy?
  • Timeliness: Has the data deprecated over time? Does your product data has a time stamp?

When determining and discussing product data quality in your organization, always try to get specific. That helps you pinpoint the tangible problems and pick the right tooling.


The discipline of data quality ensures that your data is fit for purpose. But data quality tools cover more than just analyzing sets of information to identify incorrect or incomplete data. That’s only the technology part. Data quality also includes program management, roles, use cases the organizational structure and processes. These data quality processes help to organize, set targets, suggest data improvements, monitor data quality overall or specific sections or attributes, filter out exceptions, match data to legislation and validate before releasing the data.

The following six actions encompass product data quality and require tooling:

  1. Profiling: initially assess the data to understand its quality challenges;
  2. Standardization: ensure that data conforms to quality rules (industry standards, local standards, user-defined business rules);
  3. Enrichment: enhancing the value of the data by appending related attributes from external sources (for example, geographic descriptors, address validation and consumer demographic attributes);
  4. Matching or Linking: compare entries within or across sets of data so that similar, but slightly different records can be aligned, to lead to a master record;
  5. Monitoring: keep track of data quality over time and report variations in the quality of data;
  6. Quality Processing: use batch or real time processes to keep the data clean.


A reliable source for comparing the data quality tools on the market comes from Gartner. Its Magic Quadrant evaluates every year the vendors in the data quality tools market. Per vendor it describes the strengths and cautions, the ability to execute and the completeness of vision. By naming leaders, challengers, visionaries and niche players, Gartner helps to find the most suitable data quality tool for an organization’s needs.



For data quality to work effectively you must continuously profile, improve and monitor your data. Especially if you’re looking for a long lasting positive effect, you’ll need to keep on monitoring and adjusting your data quality rules to make sure that you follow the new requirements on data quality within your organization. A day-to-day business will be monitoring the overall data quality by creating insightful reports. By keeping an eye on your data you can constantly improve your quality and thereby enjoy the positive effect of good data quality.

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Joeri Moors

MDM Market Development Manager

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