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GUIDE
Inaccurate data creates massive inefficiencies to the global commerce ecosystem. Without quality data at the beginning of a process, it is extremely time consuming and costly to manage later.
4K+
Hours spent annually manually managing product content for a company with 10,000 SKUs
76%
Online grocery shoppers expect more production information
42%
Reduction in returns from detailed, accurate product descriptions (Source: (1) A.T. Kearney (2) Food Manufacturing (3) Global Web Index (4) GS1)
1/4”
Error in case height = – 1,000 fewer cases per truckload
– 20 fewer cases per pallet
– 6 more trucks than necessary
Without guidelines to your data entry process, you risk errors in your data, redundant work, and items sitting idle that could be selling.
Inaccurate data creates massive inefficiencies to the global commerce ecosystem. Without quality data at the beginning of a process, it is extremely time consuming and costly to manage later.
Without selecting the attributes or assets an employee works on, they will have full visibility to proprietary data.
Items get lost in the mix without a responsible party.
Data can trickle in from multiple working groups and current data could accidentally be overwritten.
There is no way to ensure data accuracy.
Companies often struggle to stay updated with changing requirements and efficiently manage content across multiple channels and siloed data sources.
Suppliers have multiple systems that house product content for individual departments
Product content requirements change quickly and often.
Disparities between ecom, space management, advertising, other needs can lead to damaging consumer experiences.
Establishing a data culture and improving data quality is not a one-time project. It is an ongoing discipline that drives breakthrough results and critical competitive advantages. A simple approach involves the following steps:
There are many elements that determine data quality, and each can be prioritized differently by different organizations based on their goals.
Syndigo’s robust Data Quality Engine performs thousands of validations to ensure not only the completeness, but also the readiness and accuracy of your data.
Here are some basic ways to take charge of your data quality and give your customers the best shopping experience online and in-store:
Product Information Management System: Accessible to all with workflows and protections consistent with governance.
Photographer: Hire a professional who understands GS1 Standards and your governance procedures.
Connections: The GS1 Network is important, but also consider other places that your data needs to go.
When the right data is delivered to the right place at the right time, your business wins. Your data should be:
Custom Dictionary: Spell Check, Grammar
Real-Time Readiness Scores: All Products
Custom Audit Rules: Supplier Quality Management
Comprehensive Validation Rule-set: Recipient/Industry Specific
It is difficult to distribute data in multiple formats across retailers and internal departments. Managing it all is a drain on internal resources, and a challenge if using multiple solution providers. And if data quality is the driver, then it starts with a single source of truth.
A recent Forrester Consulting study, commissioned by Syndigo, revealed that 94% of participants believe that having an end-to-end solution to create, manage, syndicate, enrich, and optimize product data would be valuable for retailer integration.
Syndigo is your single end-to-end solution for all content management and distribution, across GDSN, nutrition, core and enhanced content, with analytics & reporting…enabling our clients to provide a single source of truth to their consumers.
Our Content Experience Hub saves time and resources by integrating all product content management in a single place, away from existing process of managing multiple data sources manually and independently.
