You Have (Data Quality) Issues

PIM Data Quality

The Importance of Being Earnest (About the Quality of Your Content)

This two-part series examines the challenges of enterprise data management, the most common content pitfalls, and how Magnitude Agility PIM can help you overcome them.

Part One: You Have (Data Quality) Issues

You are an eCommerce or Marcomm manager in charge of your company’s ocean of product data —tens of thousands, perhaps millions of SKUs and their attributes and descriptions. Whether you realize it or not, you likely have data quality issues today that are impacting your customers’ ability to easily interact with you.

The short list of probable trouble spots includes:

  • Incomplete attribution
  • Inconsistent attribute values
  • Inconsistent date/number formatting
  • Uncontrolled choices
  • Missing images
  • Missing relations
  • Missing translations/updates
  • Need to localize units of measure

Too Many Hands in the Pie

Why is your data less than optimal? The answer could be lax data governance, with too many people having unfettered access rights to create content, and not enough structure, limits and guidelines to ensure consistency and accuracy. Or you have a consolidation of data from different acquisitions over time, with information added as-is without any standardization. Maybe data quality has never been a priority. Or perhaps your data quality has been reasonably good for print, but it’s not in step with the evolving demands of eCommerce across mobile platforms.

The Visible Impact: Judged by Appearance

Data quality issues impact customer and product experience and may affect sales and customer confidence. Perception matters. Sloppy content stands out.

At the start of this millennium, eCommerce was in its infancy. Relatively few companies had dynamic websites, and even rudimentary eCommerce capabilities were rare. Before refinement filters, product comparisons and shopping carts, a customer typically placed a telephone or fax order after viewing a static electronic catalog page. Data errors were inevitable and could be explained as the byproduct of manual data entry or technological limitations. And nobody was perfect.

Fast forward to today. Every B2B or B2C marketer who is serious about eCommerce has a transactional website with all the functionality customers have come to expect. In today’s uber competitive markets, dirty data will cripple the effectiveness of the best-looking website.

Attributes that are incomplete or inconsistently populated make search refinements clunky or ineffective. Data that can be entered as free text runs the risk of inconsistent formatting (i.e., 20 mL, 20mL, 20ml or 20 milliliters). And a product without an image makes a web page look unfinished.

The Verbal Impact: Precision Matters

According to Gartner, by 2020, 5% of digital commerce transactions will come from a smart machine. According to ComScore, 50% of all searches will be voice searches by 2020. Your data must be precise, clear and unerring to support these new smart channels.

Verbal devices must be supported by a repository of clean data that can easily maps to human commands. There is no visual presentation of options for the user to refine, so the data must be robust enough to do the refinement without intervention.

With more and more customers engaged with the Internet of Things (IoT), missing and inconsistent data has a far greater impact than it did in traditional print publications and eCommerce sites. In fact, the margin for error shrinks with every new device introduced. As eCommerce matures and expands to new platforms, the question won’t be simply what products you sell, but how fast do you them to market, and how effortless the customer shopping experience is. The importance of data quality and consistency will only accelerate. Data optimization is crucial.

Next: In Part Two (coming soon), we’ll discuss how Agility helps you to lock down the quality of your data with an array of tools for creation, transfer, governance and deployment across omni-channel platforms.