Key takeaways from the PASA Connect Round table: “Shaping your eProcurement systems’ TAXONOMY … including using UNSPSC coding”, 4th May 2020
The key to great spend analysis is good DATA. The key to good DATA is cleanliness, accuracy and structure. Structuring your data correctly offers easy analysis of spend patterns by category, by cost-centre, by transaction volume, and by both transaction value and aggregate value.
Choosing your data structure and depth is the key part of your taxonomy, or classification scheme (naming convention) for your spend data. Spend taxonomy is the classification of an organization’s spend in a hierarchical order that usually ranges from 3 to 5 levels depending on the complexity of their spend. They are usually classified as:
- Level 1 (Group),
- Level 2 (Family),
- Level 3 (Category),
- Level 4 (Commodity)
4 levels are usual, 6 possible, even 9 levels, in an open source 8 digit numerical code.
Roughly 41% of the world’s ERP and P2P systems, it is estimated by Gartner, use the UNSPSC approach – but one size does not always fit all. So, 75% do not use UNSPSC; in fact most (53%) use their own taxonomy – BYOT then.
But which is best for you and why?
This PASA CONNECT Roundtable revisited the need for a consistent taxonomy in your systems, and the pros and cons of different approaches with expert Gordon Donovan FCIPS – one of the country’s most experienced CPO’s and procurement trainers now working with SAP.
The key to it all is one simple rule – align your spend data structure to your product needs to better serve your procurement strategy, more than your systems strategy.
Gordon used a case study from Epworth Healthcare which illustrated the importance of a technology roadmap and categorisation plan and the vitality of good data.
The Deloitte CPO report suggests 54% of CPOs believe data analytics is the most important tool moving forward. Data classification is usually the root cause of inaccurate data stories. Therefore your internal “data dictionary” or taxonomy, is a necessary way to manage an internal agreement on the way we classify data. It is imperative to using supply side data.
The subsequent discussion drilled down on to exact issues some face in defining their ‘data dictionary’ – these included; selling a taxonomy choice to stakeholders, explaining “why” taxonomy change is needed – as part of any change management plan, getting descriptions consistent too, recommended coding levels (4), benefits of UNSPSC membership, integration questions, managing compromises on data definitions, harmonising internal jargon with data classification choices, contradictions on data level quality for some categories.
Gordon’s other case study – red wine – illustrated the coding structures of business taxonomy very well; country/region/grape/variety/style/vintage/bottled/Vineyard/brand