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The ‘Goldilocks Zone’ for Spend Analysis Success

Scientists have identified and articulated a similar dynamic in their study of planets. Dubbed the “Goldilocks Zone,” planets with the conditions required to sustain life are, to borrow from the fairy tale about Goldilocks and the Three Bears, neither too hot, nor too cold, but “just right.” Being able to define a prosperous middle ground on a series of continuums—whether they pertain to porridge or spend analysis—provides a good model for procurement organizations looking to deliver strategic, actionable spend visibility.


The three areas that procurement should focus on getting “just right” are timeliness, quality, and taxonomy.


Real-time reporting and analytics may be all the rage, but we should not assume they are called for in all cases. Procurement should always ensure that the timing of data refreshes is neither so fast that quality is called into question nor so slow that data is considered expired before it reaches the people who need to base decisions upon it. Data may become cleaner and more authoritative over time, but there are circumstances when it is useful in a raw state if it is made available very quickly.


Rather than allowing the functional or technical horizon to determine the right refresh frequency, procurement should look to how the data is used. For instance, the spend analysis requirements of strategic sourcing can be well met by a quarterly refresh schedule. Compliance efforts require much more frequent updates—monthly or even weekly. The schedule will be dictated by how often data is pulled and updated as well as how much processing it undergoes before being made available. The level of investment in normalization (layout, formats, fields, number of suppliers) and data aggregation (data from disparate systems with different formats) has to be considered in the context of what it contributes to clarity versus the delay it creates.



We would all agree that procurement needs to provide “quality” spend data to the organization, but just like data that is available “on time,” quality is a relative term. The more rigid procurement’s quality standards are, the more likely we are to become a bottleneck to availability, accessibility, and progress. We must understand our standards in terms of the value they provide as well as what they cost the organization.

Procurement’s challenges with data quality are not new. We are accustomed to the difficulty of pulling and aggregating data sets from disparate systems. But when we focus exclusively on quality, we need to accept the fact that some data sets will reach a higher quality than others. The question then becomes, what is the minimum threshold for inclusion in spend analysis?

Further complicating the debate is the fact that the quality of the spend data is a matter of perception as well as relativity. What is the organization’s overall view of the data? Is it viewed as trustworthy or dismissed as unusable? Procurement teams need to ensure that the data they provide to the organization is trusted and that it deserves the characterization. It does the company no good if they trust in spend data only to find out after the fact that quality issues led to misinformed decisions.

Achieving a “just right” balance of quality and availability is one of the top challenges facing procurement today. It is eased to an extent by the development of AI and smart machines that are able to automate some of the cleansing and classification that used to contribute to the delay between data pull and data publish.



If there is a third factor that affects the adoption of spend analysis, it is the taxonomy or categorization of the data. UNSPSC is a strong, trusted option, but it was designed for another purpose and therefore does not always make sense for procurement’s applications. The taxonomy should be tied directly to contracts to help demonstrate spend under management (or influenced spend).

Beyond the purposes of reporting and reconciliation, the categorization vocabulary must be correctly assigned and meaningful to users. Because of this, the preferred taxonomy is likely to be unique from company to company. There may even be the need for more than one taxonomy to meet varying business unit or category specific requirements.

But how much granularity is required? UNSPSC can accommodate as many as five levels. If procurement creates or designates one or more taxonomies, how many levels do you need? As with timeliness, procurement should consider how the data will be used and require each level of categorization to earn its inclusion. Additional factors include the ability to consistently and automatically assign categories across source systems.

The final lesson that procurement can take away from the definition of the Goldilocks Zone is that the objective is to sustain life. This doesn’t require optimal conditions, just acceptable ones. The same is true for spend analysis—working within an acceptable range places a great deal of flexibility and responsiveness at procurement’s disposal.  Data-driven though procurement may be, we cannot expect perfect or even perpetually stable conditions. Our processes and technology must be flexible enough to recognize that we have to work within the inevitable data chaos and enable us to be as successful as possible.

Not too hot, not too cold; just right.



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