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Data Refinement
InQuera's data refinement process aggregates product data from different languages,
sources, applications, and formats; cleans and standardizes the data; classifies it into
categories according to a given taxonomy; extracts product attributes; enriches the data;
and identifies duplicate or similar products, to create consolidated, tabulated product data.
Taxonomy
InQuera enables you to gain control over your product data. Our taxonomy experts can help you
categorize and attributize your data, making it easy to find and simple to use. Taxonomy allows
you to classify your product data into clearly defined categories, providing a dynamic,
updateable, and referenceable structure that accommodates real-time changes — providing fast
access to product information across the organization.
SAP® MDM
Optimize your MDM implementation through custom catalog design, taxonomy creation, and content
management. InQuera's experts can custom design an MDM catalog to your specifications and help
you to categorize your data, making it easy to locate and use. In addition, our team will assist
you to aggregate, clean, normalize, classify, extract attributes, enrich data, and identify
duplicates before loading it to the MDM system.
Master Data Management (MDM)
Master data — an organization's "single view of the truth" — is key to effective control of company assets.
But while MDM is an excellent media for organizing master data, it takes special skills and tools to maximize its potential.
InQuera's seven years' experience in MDM, coupled with our proprietary tools, facilitate data rationalization in the MDM environment.
InQuera is listed in The MDM Institute's top 50 CDI-MDM integrators.
Product Data Quality Check-up
InQuera's Product Data Quality Check-up methodology provides a rapid, effective analysis of the current health of customers' product data. Within weeks, customers are empowered with sufficient understanding of their product data challenges to enable informed decision-making. The results provide a clear picture of key risk factors and the steps needed to effectively address product data quality issues.
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