What is Product Data Integrity?
Product data integrity refers to how accurate and consistent product data is throughout the lifecycle. The product data (color, dimensions, technical specifications, inventory status, shipping information) remains valid throughout the product life cycle.
In eCommerce, inconsistent and inaccurate product data delivers the wrong product information to shoppers. As a result, it contributes to rising product returns, declining brand loyalty, low returning visitors, and eventual substantial revenue losses for the store.
Here are a few statistics from a report from Experian to prove my point:
- 83% report that poor data quality impacts their business initiatives.
- 79% say data ties directly to business objectives, while only 2% trust their data entirely.
- Finally, 56% of organizations lose sales opportunities to bad data quality.
Now we know how badly wrong data can impact an eCommerce business. But managing product data in an eCommerce environment comes with its own set of challenges.
Business Challenges of Preserving Product Data Integrity
When it comes to challenges managing data integrity, the threats are more from internal resources than external sources. Here is why.
Take Amazon US, for example. The store has 100 million products. Each product has a data set like color, dimensions, technical specifications, vendor details, category, and sub-category. Vendors, suppliers, merchandisers, eCommerce teams, and many internally and externally associated with amazon touch the data. Every time the product data comes into human contact, it increases the probability of data error and data integrity compromise.
Here are a few more challenges the eCommerce industry faces while preserving data integrity.
- Multiple Sources of Data
- Multiple Interacting Applications
- Manual Data Pulls
- Inconsistently Built Reports
- Dependencies on spreadsheets
- Lack of Best Practices
Multiple Sources of Data
An eCommerce product data repository receives information from various places, from the company intranet to external suppliers and vendors. According to Gartner, by 2019, three-fourths of all analytics solutions relied on ten or more external data sources. Therefore, businesses need to check the separate pieces against each other to ensure data consistency and then integrate them into a single centralized hub.
Multiple Interacting Applications
ECommerce stores pull data from numerous sources through APIs to enrich the product data. They also have multiple tools to analyze the product data in their possession. Business and IT teams often do not align with each other. They do not communicate well or understand what the other half is doing—which results in software purchases with overlying capabilities. Using multiple applications squanders money and duplicates effort.
Manual Data Pulls
Pulling data is tedious and wastes valuable time, knowledge, and experience. The worst possible scenario is that it puts the performance of your business in danger. If you remember the 2012 London Whale incident, JP Morgan Chase Bank lost $2 billion due to errors in manually copying and pasting Excel data. So unless you’ve entirely automated your data flow, manual data functions probably create more issues than solving them.
Inconsistently Built Reports
Inconsistent data result in erratic reporting. Manually built reports may create conflict in various ways, like their authors, the intended audience, the release schedule mismatch, and the disjointed information. If separate departments have several reporting standards, this can create issues for cross-functional team collaboration.
Dependencies on Spreadsheets
Microsoft Excel has been the go-to for productivity software in the past. Unfortunately, it is still useful for more purposes than its creators’ intention, including complex process management tasks. Excel fails at managing enormous data sets that go up to billions and performing complex data analyses. It also does not allow for effective cross-team collaboration. A report by IDC found that advanced spreadsheet users waste eight hours on repeatable work every week.
Lack of Best Practices
Isolated business functions with disconnected product data silos can negatively affect business performance. Businesses need to manage the interdepartmental consistency of best data collection and analysis practices to make the best out of current data and ensure data integrity. Without established workflow standards for data processing, each department will retreat instead of exchanging valuable information with each other.
When product data circulates from manufacturers to suppliers to brands and retailers to third-party sellers, it’s too easy for various functions to introduce errors that threaten data integrity. That damage only accumulates and multiplies as information businesses use erroneous product data. As the data changes hands, it culminates in more inaccuracies, little more than for product content managers and consumers alike to blow their lids off.
However, there is a solution to all of this. Unbxd is a product information management platform that can solve your workflow challenges and bring your product data integrity back on track. Unbxd enables eCommerce marketers, managers, and business owners to centralize, optimize, disseminate, and investigate any amount of product data.
With clean data and fortified integrity, eCommerce companies are ready to grow their business and provide excellent shopping experiences that begin with the product team and radiate to consumers.
Are you looking to fix data integrity and credibility challenges for your eCommerce store?
You can request a PIM Demo today, and our PIM expert will contact you shortly.