The Spend Visibility Curve: Start Here

spend visibility

A few weeks back, I participated in a Spend HQ webinar, The Spend Visibility Curve: Where Do You Stand. During the discussion, I introduced a spend visibility maturity model that I’ve been thinking about for some time – and which I’ll introduce today and cover in more detail in subsequent posts in this series. At the core of the model is more of a definitional question itself: What is the difference between spend analytics and spend visibility?

To answer this, I started out with the argument, “First there was spend.” Spend of both the managed and unmanaged sort happens every minute inside large companies. In many cases, procurement organizations cannot make heads or tails of it on a continuous basis without proper systems or specialized toolsets. As companies begin to make sense of spend, they progress to looking at spend data – information that is cataloged to make it valuable – in a more complete manner, based on a means of acquiring it, cleaning it, normalizing it and classifying it.

With this is complete, we have true spend visibility, as we might call it, and the ability to run basic reports such as on-contract versus off-contract spend or purchase price variation (PPV).

But true spend analytics is different than basic spend visibility. Analytics involves the art and science of drilling into data, which originate from multiple financial systems, in different ways, building spend cubes – and tossing them out – combining new datasets, getting category-specific in approaches and more. Finally, you can leverage spend analytics approaches to reaching what my colleague Pierre Mitchell terms “supply analytics,” or as he calls it, moving away from “spent analytics.”

This progression roughly maps to the maturity curve I spoke to in the rest of the webinar. In it, I took the group through 4 stages of overall spend analytics possibilities, starting with programs designed around sourcing and measurement and progressing through spend and supply to continuous improvement and finally to predictive and proactive analytics.
I’ll share my thinking behind each of these 4 stages in more detail in the coming days. Stay tuned!

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First Voice

  1. Bill Kohnen:

    The thought of moving beyond “spent analysis” is powerful. Many still argue and practice that excel spreadsheets created from legacy system downloads are good enough and is a big project to tackle however, really only tells what happened in the past. Even if gathering and presenting the “spent data” is automated it by itself does not say anything relative to the future without professional review and market expertise – which many organization don’t really have.

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