Businesses that accept credit card information are beholden to the Payment Card Industry (PCI) Data Security Standard (DSS) regulations; if any customer names, addresses or credit card information are leaked, the company has a PCI violation. Being able to secure that PCI data is absolutely critical.
For organizations that focus on retail, we have customized our products to be "aware" of :
These industry-specific versions of our products address multiple industry challenges related to
Organizations in the retail industry need to ensure that their master data (for items, customers, suppliers, warehouses and employees) is of high quality. Retail organizations that sell online must be particularly careful about data accuracy and timeliness.
Typically, most organizations do not have a "systemic" understanding of their master data. Master data within a few silos is understood, but the flow of master data across the data ecosystem is not. Consequently, a "blind spot" exists on data quality, and no guarantees can be made about data accuracy or integrity.
The lack of data quality metrics at the data ecosystem level creates various problems:
The Global IDs machine-centric approach to master data management creates a foundation for firms to manage their data assets. Through automated discovery, data profiling, quality analysis and metadata documentation we allow companies to create transparency, enhance accuracy and reduce the resources required to manage data assets.
Global IDs software attempts to systematically measure the quality of each data asset found in the data ecosystem through automated means. While many aspects of data quality can be computed, additional user input is required for construction and execution of rules.
1) Item Data Governance
A large Fortune 100 retailer used the Global IDs Data Quality Product Suite to create data quality metrics for item master data in multiple countries around the globe. These operational data quality metrics were shared across a global community of product managers and item data stewards.
2) Metadata Aggregation using Data Landscape Profiling
One retail organization had many thousands of databases spread out on a global data ecosystem. The Global IDs Data Profiling Product Suite was used to auto-generated a metadata repository through data landscape profiling and semantic analysis.
The resultant metadata repository, which documented a large fraction of structured data assets for the organization, supported semantic searches of enterprise metadata.
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