Running a marathon is simple in concept: take one step, then repeat 26,993 times, give or take a few thousand based on your height. It is a daunting task, and as a former marathon coach, a fun one too. Unlike a sprint, running marathons follow an extended process that takes planning, patience, endurance, support, and even a bit of luck at times to complete. As I work with clients to bring order to their master data, e.g., suppliers, spend, catalogs, the underlying processes and the discipline required unfold to demonstrate that it is an ongoing process rather than a destination.
In the state and local government procurement sphere, Periscope Holdings is the custodian of the NIGP Code. In this role we manage, market, and license the Code on behalf of NIGP. I am lucky in that I get to work every day with states, cities, counties, and schools. I help them to bring order to their procurement data and also help them provide more value to their constituents. Time and again, it is the process, not the end state, that often trips up the data categorization project.
Susan Walsh, Founder and Managing Director at The Classification Guru and author of the upcoming book Between the Spreadsheets: Classifying and Fixing Dirty Data, developed a mnemonic to describe the approach to data management and classification. She refers to the C.O.A.T. approach which stands for: Consistent, Organized, Accurate, and Trustworthy (Walsh, 2020). As I work with clients, whether loading catalogs, diversifying suppliers, or classifying spend, these concepts resonate. In my case, the application of the NIGP Code taxonomy helps in this process.
The cornerstone of the process is consistency. Getting everyone on the same page on how suppliers, products, and services are described across the procurement chain is essential. Without it, the other goals are unreachable. The NIGP Code provides a standard taxonomy (classification) structure of descriptions that render clarity and promotes consistency. Consistency means de-duplicating suppliers, identifying the products/services they supply, classifying contracts/catalogs, and instilling a discipline to carry this forward in daily operations.
If you look at your spend data, does it resemble a storeroom neatly organized into rows and bins, or does it resemble an episode from A&E’s Hoarders? The NIGP Code provides an underlying organizational structure – from which layers of organization can be built. Questions can then be asked of the data like: “What MBE suppliers provide IT consulting?” or “What approval path should A&E services be routed?” or “How much did we spend on office supplies in Q3 2020?”
Thomas Redman, in his Harvard Business Review article, “Assess Whether You Have a Data Quality Problem” put data quality in this context: “To see how it impacts your business, go one step further. Bad data causes all sorts of trouble — good decision making is harder, customers are angered — and it adds costs. The so-called ‘rule of 10’ provides a simple means to estimate these costs. It is based on the observation that ’it costs 10 times as much to complete a unit of work when the input data are defective as it does when they are perfect.’" (Redman, 2016) If your baseline cost per PO is $25 and you process 1,000 PO's, the impact of even 10% bad data is staggering. Having a taxonomy underlying your item descriptions can lead to better accuracy in your processes, which can reduce unnecessary costs.
The first three steps lead to the final component, the trustworthiness of the data. If your data across platforms is consistent, organized, and accurate, then it leads to trust of the analyses and outputs from those processes. However, trust is not the end state, but an incremental affirmation of the process and a reminder to remain diligent in maintaining the policies, processes, and procedures for the implementation of data quality through a taxonomy like the NIGP Code.
As Susan often asks, “Does your data have on its COAT?” Like donning your coat, it does not magically appear around your shoulders. Data quality and the implementation of a taxonomy like the NIGP Code (or any other schema) requires intentional effort, discipline, and determination. As one step leads to another, over time, the goal can be reached.
Redman, T. C. (2016, July 28). Assess Whether You Have a Data Quality Problem. Retrieved from Harvard Business Review: https://hbr.org/2016/07/assess-whether-you-have-a-data-quality-problem
Walsh, S. (2020, July 3). Get your COAT, you’ve pulled (clean data). Retrieved from Linkedin.com: https://www.linkedin.com/pulse/get-your-coat-youve-pulled-clean-data-susan/