Data quality is largely a factor of what rules have been defined for creating new records and what systems have been put in place for adherence to those rules. Most companies struggle due to a deficiency in one or both of these, which leads to data elements like item descriptions or classifications become subjective and prone to inconsistencies or errors.
With distributed plants and locations, material data is thereby subject to individual or organizational preferences, creating a fractured dataset that may be inconsistent, incomplete, duplicated or inadequate.
Over the years, we have developed a rich and comprehensive MRO data taxonomy (aka data dictionary), which covers over 5000 commodities, and is suitable for multiple industries. Based on our own internal taxonomy, we also develop custom data taxonomy for clients if needed.
Once the taxonomy is established for a client, that forms the foundation for an initial data cleansing and enrichment program, as well as serves as the basis for an ongoing data governance system .
The taxonomy typically has three main elements:
Here’s an example of how 3 different inventory managers have created 3 different descriptions for the exact same item
|Inventory Manager||Purchase description|
|Peter (Site 1)||SWITCH, PRESSURE 3 BAR 250 VAC: -25 C TO + 70°C SIEMENS XMLB002A2S11|
|Sam (Site 2)||MONITOR, PRESSURE XMLB002A2S11 3 BAR 250 VAC -25 C TO + 70°C SIEMENS|
|Rob (Site 3)||CONTACT, PRESSURE (SIEMENS); PRESSURE: 3 BAR 250 VAC XMLB002A2S11 -25 C TO + 70°C|
Enventure has our own proprietary readymade MRO taxonomy, as well as deep expertise in industry-standard taxonomies such as UNSPSC, eCl@ss, etc. Based on your industry and business purpose, our MDM Specialists can suggest the most suitable taxonomy for your company, or customize a standard taxonomy based on your specific needs.Read More
A well-structured MRO Data Taxonomy is crucial to provide a reliable and scalable structure for the MRO materials database. Implementing a good MRO taxonomy enables accurate classification of materials into different commodity groups, so intelligent decisions can be supported by analytical data.Read More