Development

Agile Software Development for data sharing solutions based on GS1 Data Standards.

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Data Quality

The Data Quality Framework (DQF) provides an industry-developed best practices guide for the improvement of data quality and allows companies to better leverage their data quality programmes and to ensure a continuously-improving cycle for the generation of master data. It details the crucial processes and capabilities that help organisations improve their data quality and maintain a sustainable good quality data output.

Benefits of Data Quality
Good data quality is a key ingredient of an efficient supply chain. Having the means to continuously maintain high quality data is not only vital to reducing errors in the supply chain, but it is also fundamental to increasing efficiency, reducing costs and positively impacting customer satisfaction. Data quality also allows for more sophisticated types of collaboration between trading partners.

Better quality data bring solutions to many problems

Better data quality processes can also help solve the following problems:

  • Point of Sale & New Item Introduction
  1. Incorrect ingredient information (for example, concerning the presence of allergens, or an item’s kosher status or Halal status…) could results in lawsuits or fines
  2. Wrong GTINs impact readability at point of sale resulting in delays and pricing errors
  3. Erroneous data can lead to ordering problems generating overstock or poor replenishment that affects presence on the shelf and creates lost sales opportunities for trading partners, as well as unsatisfied customers
  4. Retailer rejections of new items at the back door delay product launches and result in lost sales opportunities
  5. Erroneous descriptions, bad measurements and pricing errors can lead to shoppers walking away unsatisfied, which means lost sales and lost clients!
  • Distribution
  1. Poorly optimised transportation is not sustainable
  2. Wrong GTINs lead to an inability to receive merchandise at distribution centres or stores
  3. Poor handling information could result in the improper and potentially risky handling of dangerous goods
  4. Incorrect case and pallet information means merchandise can get damaged when it is restacked to fit into the slots
  5. Bad dimensions also translate into wasted space on trucks, making the distribution of a product much more expensive
  6. Overweight trucks are susceptible to fines and can even be diverted or stopped, making shipments late or undeliverable
  • Regulatory
  1. Improper ingredient information and incorrect measurements can result in fines or penalties
  2. Mandates for third-party product audits and item validations before accepting items generate costs for the service and delay the availability to sell

How to start

1. Understanding Data Quality

First steps

A data quality project should be started and turned into an ongoing business process. For this to happen, commitment from senior management is needed. This will only occur when senior management understand:

  • what benefits could be realized through synchronisation of quality data with trading partners
  • what “bad” data is costing the company
  • what other companies have done about it

Awareness of data quality and its relationship with data synchronisation is therefore of the utmost importance.