Dave Sheppard for Procurement Foundry
It goes without saying that ‘digital transformation’ and ‘automation’ are hot topics and popular catchphrases for procurement professionals, but the stark reality is an organization must learn to crawl before it can walk.
Unfortunately, and in their enthusiasm to jump to the digital promised land, most tend to overlook the necessary journey that they must take before leaping into the deep end with ‘digital transformation’ and ‘automation.’ Specifically, overlooking the spend categorization process.
Having clean and well-structured, i.e., properly categorized data to be able to analyze spend is the crawling stage followed by digital transformation (walking), and then automation, which is relatively speaking, sprinting. It is worth noting at this point that effective spend analytics is heavily (~75% or more) dependent on proper categorization.
So, with the above in mind, how do you “clean and categorize” your data?
While this may be somewhat different for every organization, similarities exist in some of the challenges. In discussions and meetings, it sounds like it should be a straightforward exercise, whereas, in practice, it always proves to be far more arduous. You may be wondering what makes cleansing and categorization of spend data so difficult.
Without a proper governance model, factors such as semantics, context, perspective, poor communication, and a lack of stakeholder collaboration all contribute to an individual and usually conflicting interpretation of what constitutes the categorization of spend to a particular category.
Therefore, a key contributing factor to reaching the point of having clean spend data revolves around the data governance practices within your organization.
Accurately capturing and recording data from a multitude of potential entry points in which there is an apple is an apple consistency is essential for not only having clean data but having data that can be analyzed appropriately to improve business decisions.
In other words, an important part of the data cleansing process is that it is structured and able to be categorized in the context and level of granularity required to generate valuable business insights.
Opposing Internal Perspectives
A Common Scenario:
ACME Energy Co. hired the services of ABCXYZ Trucking Co. to haul steel wall panels from a fabrication plant to a construction site where ACME Energy Co. is constructing a new facility. The invoiced value of these services was $150,000.
How should ACME Energy Co. classify and categorize this spend?
The Accountant: The provision of services was in the context of constructing a new facility that will be “capitalized” as an asset, and therefore we categorize this $150,000 of spend as “Construction Services – Transportation.”
The Engineer/Project Manager: The provision of services concerned a facility construction project, which is very different than other types of construction (i.e., road construction, site construction) and is “charged” to my construction budget. To be able to manage project budgets and have visibility to the breakdown of costs within a facility’s construction to reference next time we build a facility, I need to have the $125,000 hauling component of this service categorized as “Facilities Construction – Trucking and Hauling,” and the $25,000 labor component of this service categorized as “Construction Labour.”
The Procurement Category Manager: The services were provided using equipment capable of servicing many different aspects of the business. Furthermore, this supplier is part of an extensive network of companies that we rely on for services to meet our varying and shifting demands. When determining the most appropriate procurement strategy, with an objective of obtaining the most economically advantageous services for our requirements, we may want to approach the entire supply base that can provide us with what we need. For this reason, we categorize this entire $150,000 of spend as “Trucking and Hauling – General Heavy Hauling,” and will manage the separation of heavy equipment services and labor during commercial evaluations of proposals. Another aspect of this approach to categorization is to see where this type of service has been provided throughout our company across multiple business units, and be able to leverage that aggregated purchasing power to approach the market with a bigger scope of work in hopes of encouraging higher levels of competition, which ideally can result in higher performance at a lower cost and risk to our business.
It is important to note that none of these perspectives are “wrong.” Each of these individuals within the organization sees’ the company through their own ‘data lens,’ and these perspectives involve very specific contexts that are critical in their ability to manage their area of the company.
Getting to One Source of Truth
The level of detail within a financial statement is insufficient for somebody in operations to run their part of the company.
Similarly, spend data that is clean and well structured for operations may not be the required structure or provide the level of detail necessary for procurement to aggregate and consolidate its spend to approach the market from a commercially aware and high leverage position.
Therefore, getting to a “one source of truth” position is critical to organization-wide efficiency and success. The quality of your outcome depends on it!
To arrive at that, one source of truth begins with the recognition that multiple stakeholders view data through their “own” lenses. The mere understanding of this paradox is the first step to fostering the cross-functional collaboration to determine what opportunities exist to develop a solution that fits their needs individually as well as the enterprise collectively.