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Our Content Engineers focus on four key areas:

Data Completeness

Data Consistency

Data Conformity

Data Duplication

Customer Name
Company Name
Purchased Item
Purchased Item Number
Purchased Quantity
Date of Purchase
Warranty Till

abbreviations and

Check against internal data

Exact matches

Noun / Modifier pairs

Naming convention

Compare data dictionary to

Exact substitutes

Attribute fill rate



Functional equivalent

The Data Transformation process commences with Taxonomy Development.

Taxonomy generally refers to the organizational structure of categories and attributes that define how you classify, describe and manage your data. It is also commonly referred to as Data Dictionary or Data Schema. A good taxonomy is like an architectural drawing and is the basic building block for your master data. A good taxonomy will assist with sourcing metrics, enable easy searches in your parts catalog, and allow quick identification of obsolete and duplicate parts.

Once taxonomy issues are covered, the process of building better data by correcting errors, standardizing information across tables and validating information that is inconsistent and inaccurate can begin. We often refer to these as the 4C´s of Data Transformation namely Cleanse, Consolidate, create and classify.

Data Transformation solutions encompass a number of features which allows:
  • Normalization & Standardization - ensuring that attribute values have the same name for the same attribute and abbreviation expansion
  • NMA Pairing - identification of correct pairings of nouns, modifiers and attributes
  • Data Dictionary Adherence - follow set of defined attribute names and attribute values
  • De-duplication - removal or identification of duplicate records
  • History Transfer
  • Enrichment - passing the electronic item master through a master library of parts or a manual collection of information
  • Physical Verification - leveraging "crib crawls" as required
  • Classification & Coding - assigning like products to common industry standard class groupings (UNSPSC, eCl@ss, eOTD)
Cleanse & Consolidate
At the start of the transformation process we compile industry-standardized nomenclature for each part’s description using manufacturer’s specifications, information available on the web or information available on the part itself. The item descriptions are then normalized, and duplicate records are identified.

Item normalization includes assignment of a noun and noun modifier and standard spelling and syntax for item description details. The elements of normalized data include:
  • Noun - identifies a product category
  • Modifier - identifies a product within a category
  • Attribute Name - discrete title for descriptive information about a product
  • Attribute Value - descriptive detail that distinguishes a product within a category
Create & Classify
Frequently, the information driving the organization's business processes is incomplete, unspecific or outdated, resulting in lack of recognizing the needs of clients, customers, partners or vendors. Or it may be difficult to link some information about one piece of data, such as a person or a business, to other information in the system. As a result, the business is left with gaps in their understanding about top customers, top selling products and more productive business partners. Our data management methodology includes an enrichment phase. Data enrichment, sometimes known as data enhancement, allows generating or appending additional bits of data from other internal or external data sources to information already used in the organization. These additional data sources can extrapolate meaningful details from sketchy bits of information within the existing data sources.

Data enhancement procedures can include postal address enrichment, geocoding and demographic data additions. This additional data enables a more accurate picture about the individuals or companies in the corporate data stores and how they relate to each other. Enhancing inventory data significantly improves day-to-day operations by ensuring more accurate purchasing and better control over inventory levels. It also means customers have the right inventory available to keep operations running smoothly. With enhancement, parts are electronically matched using manufacturer name and part numbers and then reviewed and enhanced in accordance with industry standards.

After transformation, items are organized by commodity groups using client customized or standard categorization codes such as NATO, UNSPSC, eCl@ss or EOTD. Coding items provides a framework for classifying goods and services by commodity. By developing the corporate item master list, we help companies consolidate and organize their database to identify savings opportunities, create a multi-site database, and initiate demand-driven purchasing.
Data Analysis
Taking a company's cleansed database a step further, we identify opportunities to reduce costs and maximize efficiency. We focus on inventory and vendor rationalization, competitive insights, spend management, and sourcing.
Data Integration
The last step in our methodology is to upload and integrate the cleansed data back into our client's systems. We work with our clients to provide transformed data back to them in a format that is easy to load and integrate with their databases and applications.
  Related Information
Data Quality, Governance and Health Check
Our Approach
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