Why is This So Hard? Identifying and Solving Procurement’s Spend Data Challenges
If “data is the new oil,” spend data is “black gold.” Like oil, spend data is inherently valuable, but must be extracted, cleansed, categorized, refined, and distilled into a useful commodity.
Spend analysis gives procurement teams the intelligence they need to drive value – to make more informed sourcing and purchasing decisions with lower potential risk. It’s at the heart of the SpendHQ platform and should be integral to procurement operations. Ardent Partners’ Founder and Chief Research Officer, Andrew Bartolini, says that Chief Procurement Officers (CPOs) that fail to conduct spend analysis are guilty of committing “procurement malpractice.” It’s that important.
But conducting spend analysis is fraught with challenges and complexities. Why?
Manual Processes are Unsustainable
It’s neither reasonable nor sustainable to manually manage spend data at scale. It can be a resource- and time-intensive process that’s complicated, error-prone, and hard to replicate.
For starters, companies may need to pull raw spend and supplier data from 10, 20, 30 or even more internal systems and sources. Even when companies do automate their data collection and management processes, they may become overly reliant on automated systems or lack sufficient domain expertise to properly code or recode data.
In-house data models, such as spend cubes built with tribal knowledge, may not be easily repeatable. Without knowledge transfers, departing staff would deprive their teams of the knowledge or ability to run or refresh models for new business cycles. And analyses can carry high degrees of uncertainty (i.e., Did we collect everything? Where else do we have spend data?).
Consolidating data is tricky
Spend data can reside across dozens of business units (e.g., accounts payable/finance, and banking systems) and databases (e.g., ERP systems). Spend data can also reside within the company’s vendor master sheets, with the vendors themselves, and with third-party sources, such as financial institutions.
Consolidating spend data in multiple and often incompatible data formats from dozens of disparate data sources is complicated. Without APIs and data connectors, and common or compatible data formats, procurement teams will have to manually pull the data from each of these sources and clean it, a process that can take months. While in reality, CPOs often need to make decisions in minutes.
Normalizing data at scale is hard
Another time- and resource-intensive process: normalizing naming conventions. There can be a hundred variations of the same supplier, service, product, part, or commodity name across an organization – depending on data coding and preferred taxonomies. Multiply that by thousands of suppliers, tens of thousands of categories, and hundreds of thousands of parts and products.
Without automated spend management solutions that employ machine learning algorithms to intelligently recode disparate category names into standardized taxonomies, procurement teams will be in the weeds cleaning up their spend data when they could be at the surface level steering the ship.
Procurement teams that oversee the purchase of thousands of components, materials, products, and services have their hands full when they try to categorize them. They can still leave more than 70% of their spend uncategorized, even using 200-1,000 sub-categories. With so many spend categories, it can be difficult to align on a workable naming taxonomy with other stakeholders across the organization.
Data enrichment is hard, too
If you’ve pulled, consolidated, standardized, and categorized your spend data, you probably need to enrich it into actionable spend intelligence to make more informed sourcing and purchasing decisions. This can involve integrating transactional data from AP/finance databases (e.g., payment terms) and supplier information management systems (e.g., supplier addresses, diversity, and sustainability data). There may not be established processes, which could complicate matters further.
Procurement may also need to enrich its spend data with supplier performance, risk, and sustainability data as they focus more on ESG initiatives, laws, and regulations. Many of these laws, such as the German Supply Chain Due Diligence Act and the US Uyghur Forced Labor Prevention Act, require companies to gain visibility into their suppliers’ environmental impact, human rights records, and corporate governance practices. But procurement teams are struggling to solve their data problems in time to match their enterprise spend behaviors with their suppliers’ ESG impact, which will ultimately hinder their compliance to ESG laws and regulations. Additional drivers for data enrichment could be corporate compliance related to corporate goals, or goals related to investors and employee targets.
Distillation depends on the audience
Providing stakeholders with the most relevant spend data and intelligence to inform their decision making is critical – and easy to get wrong. High-level data may underwhelm stakeholders, while granular data may overwhelm. Procurement teams need flexible spend intelligence solutions that provide header-level views of critical spend data with drill-down capability for more detailed views. They provide the best of both worlds for procurement teams that have multiple audiences and data priorities.
The Solutions to Your Spend Data Challenges
Procurement teams don’t have to consign themselves to manual, time- and energy-intensive processes to conduct regular spend analysis. They don’t have to recreate the wheel to gain current, actionable spend intelligence to make the most informed sourcing and purchasing decisions. Intelligent spend analysis solutions can make CPOs’ and procurement teams’ lives easier and drive more value. And SpendHQ has been perfecting these solutions for more than a decade.
We provide an automated, AI-driven spend intelligence solution that lets you overcome all these common data management challenges. We match our deep domain expertise with our industry-leading spend intelligence solution to help you wrangle 100% of your enterprise spend data, extract critical intelligence, and use that intelligence to identify and realize cost-savings and value creation opportunities. We also can help you drive non-financial performance, such as sustainability, risk management, and legal and regulatory compliance, via more informed sourcing and purchasing decisions.